Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges.We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions.By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction identified using FRGM, RPKM and DESeq were 16,2361, 260 and 51, respectively, from the 350 European samples.The proposed FRGM for epistasis analysis of RNA-seq can capture isoform and position-level information and will have a broad application. Both simulations and real data analysis highlight the potential for the FRGM to be a good choice of the epistatic analysis with sequencing data.The online version of this article (doi:10.1186/s12864-017-3777-4) contains supplementary material, which is available to authorized users. Epistatic effect in gene expression, defined as the departure from additive effects in a linear model of eQTL analysis , plays aGene expression is an intermediate phenotype that bridges the genotype and higher level phenotypes such as diseases , 9. StudApplication of next generation sequencing (NGS) techniques to the genetic analysis of gene expression involves (1) generating millions of short reads of mRNA or cDNA which are mapped to the genome and lead to a sequence of read counts at the hundreds of millions of genomic positions –16 and , FRGM with scalar response and functional predictors (SFGM) and regression on principal component analysis (PCA). For the PCA method, the PCA was performed on the RNA-seq data and the number of PCs were selected to explain 80% variance of number of reads at different genomic positions. The multiple functional regression was performed to analyze the data . In the α = 0.05. We observed that under all four interaction models the BFGM had the highest power, followed by the regression on PCA. Power of the SFGM was the lowest. The results demonstrated that summary statistics such as RPKM for measuring gene expression could not capture the expression variations across the gene and almost had no power to detect the interaction between two genes with rare variants.Figure The BFGM can also be applied to the presence of both common and rare variants. Figure P value < 10−9 for declaring HW disequilibrium), 2,566,261 SNPs in 18,986 genes were included in the epistasis analysis. In the RNA-seq data pre-processing, we removed the genes whose expressing rates were less than 30% and the genes that did not contain any SNPs. Finally, RNA-seq data of the 15,656 genes were included in the analysis. We used DESeq  be the read counts of the ith individual at the genomic position τ. Consider two genomic regions (or genes)  and . Let xi(t) and zi(s) be genotypic functions of the ith individual defined in the regions  and , respectively. Let t and s be a genomic position in the first and second genomic regions, respectively. The genotype functions xi(t) and zi(s) are defined ast and s, PM(t) and Pm(t), and PM(s), Pm(s) are the frequencies of the alleles M and m at the genomic positions t and s, respectively. Consider a functional regression model with functional response and functional predictors (BFGM):μ(τ) is an overall mean function at the genomic position τ, Wi is a vector of covariates for ith individual, ω(τ) is a vector of effects associated with the covariates, α is a genetic additive effect function at genomic position t of the first gene and genomic position τ of the RNA-seq profile, β is a genetic additive effect function at genomic positions s of the second gene and the genomic position τ, γ is an interaction effect function between two putative quantitative trait loci (QTLs) located at the genomic positions t and s influencing the read counts at the genomic position τ, and εi(τ) is a residual function of the unexplained effect for the ith individual at the genomic position τ. The interaction function is measured by double integrals of the genotype function in two genes.For the convenience of discussion, position level read counts are taken as the RNA-seq profile and is referred to as a function-valued trait. Let xi(t) and zi(s) are expanded in terms of the orthonormal basis function as:ψl(s) are sequences of the orthonormal basis functions. The expansion coefficients ξij and ηil are estimated byWe assume that both position level read count function and genotype functions are centered. The genotype functions In practice, numerical methods for the integral will be used to calculate the expansion coefficients. Substituting Eq.  into Eq.αj(τ), βl(τ) and γjl(τ) are referred to as genetic additive effect and additive x additive effect score functions. These score functions can also be viewed as the expansion coefficients of the genetic effect functions with respect to orthonormal basis functions:The parameters Y(τ), μ(τ), ω(τ), α(τ), β(τ) and γ(τ) are vectors, W, ξ, η and Γ are matrices.Equation  can be wY(τ), μ(τ), ω(τ), α(τ), β(τ), γ(τ) and ε(τ) in terms of the orthogonal basis functions yieldExpanding Define expansion coefficient vectors and matrices as follows.Thus, substituting the above expansion into Eq.  gives6\dτ, the coefficients on both sides of Eq. . Formally, we investigate the problem of testing the following hypothesis:vec denote the vector operation. To develop test statistics, we begin with calculating the covariance matrix of the Let Recall thatTherefore, we haveΛ be a matrix consisting of the last JLK columns and JLK rows of the covariance matrix JL rows of the estimators of the matrix a1, b1] and  asLet H0 : γ = 0, TI is asymptotically distributed as a central χJLK):μ(τ) is the overall mean at the genomic position τ, yi(τ) is the normalized number of reads at the genomic position τ of the ith individual and εi(τ) is an error stochastic process. The errors should be correlated stochastic process. The theoretic models for the errors are unclear. They were estimated from the data. The procedures for generating mean μ(τ) and errors εi(τ) consisted of the following steps.k index genes, j index the genomic positions and i index the samples. Assume that the gene k is located in the interval . Let xikj ,  be the observed count of reads of the gene k in the genomic position j of the ith individual where the length of gene k is denoted sk. For each genomic position, we define an n dimensional vector:Step 1: We randomly sampled 100 genes from the whole real RNA-seq dataset. Let xkj = T.m be the median length of 100 genes. In our dataset, m = 2, 456.Step 2. Let k in the genomic position j of the ith individual at the equally distributed new positions yikj. Define vectorStep 3. Re-map the original RNA-seq data of 100 genes to the interval  using transformation n samples: Step 4. Compute the means of the re-mapped the RNA-seq data over 100 genes and over Define the mean vector of the re-mapped counts of reads: μ(τ). Pooling all the re-mapped data:Step 5. Compute the mean function βl(τ) are the functional principal components.Use the pooled data to perform FPCA, which leads to functional principal component expansion:μ(τ) as follows:Calculate Step 6. Define the centralized RNA-seq data matrix:Z where means of the RNA-seq data at each genomic position over 100 genes is removed and obtain a set of functional principal components (eigenfunctions) {φ1(τ), …, φT(τ)} and functional principal component scores ηit, i = 1, …, n, t = 1, …, T. Define T random variables η =  with vectors of their sampling values:ηt = T, t = 1, …, T.We perform FPCA on the centralized dataset n sample of vectors εi = . The residuals εi(τ) will be defined asWe then calculate the sampling covariance matrix Model 2 :μ(τ) is the overall mean at the genomic position τ, εi(τ) is an error stochastic process, xij is an indicator variable for the genotype of ith individual at the jth SNP of the first gene, yi(τ) is defined as that in model 1. The coefficient αj(τ) = rj ⋅ α(τ), where rj is randomly selected from 0.5 to 1.5, is the additive effect function of the jth SNP of the first gene, μ(τ) is obtained by randomly sampling 100 genes from the real RNA-seq and WGS genotype data without interactions and α(τ) is obtained by randomly sampling 100 genes from the real RNA-seq and WGS genotype data under the condition that one gene have significant main effect, the other gene do not have significant main effect, and these gene pairs are not in the list of significantly interacted gene pairs in our results. The overall mean μ(τ), effect function α(τ) and the residuals εi(τ) were similarly simulated as that in Model 1.Model 3 :zik is an indicator variable for the genotype of ith individual at the kth SNP of the second gene. The genetic additive effect function βk(τl) is assumed to be equal to βk(τ) = skβ(τ), where sk is randomly selected from 0.5 to 1.5, other parameters are defined in Model 2. A total of 100 pairs of genes were randomly selected under the condition that both genes have significant main effect and these gene pairs are not in the list of significant interacted gene pairs in our results. The overall mean function μ(τ), main effect functions α(τ) and β(τ), and residual term εi(τ) were similarly generated as that in Models 1 and 2.k1 SNPs in the first gene and k2 SNPs in the second gene. Thus, there were totally k1k2 SNP pairs between these two genomic regions. For the hth pair of SNPs, let uijklh denote her/his genotypes of the hth pair of SNPs, where hth pair of SNPs at genomic position τ influencing gene expressions. Then we can use the following multiple regression model to generate the function-valued trait (RNA-seq) of the uth individual of the hth pair of SNPs at the genomic position τ.To evaluate the performance of the functional regression model for testing epistatic effects on gene expression, we estimated the power through simulations. We assumed that there were r varies from 0 to 1, and g(τ) is a common genotype coefficient function fitted by the real RNA-seq data and εu(τ) is the error stochastic process and estimated from the null model as that in null distribution in the test statistics section.Additional file 1: Table S1.Summary statistics of genetic variants in the five genes. (XLS 25 kb)Additional file 2: Table S3.A list of significant cis-trans interactions in the MAPK pathway. (XLS 114 kb)Additional file 3: Table S4.Ten communities and their significantly enrished pathways. (XLSX 12 kb)Additional file 4: Figure S1.LMNB2 and the data simulated by the negative distribution of the gene LMNB2. B. The real RNA-seq curve of the gene LMNB2 and the curve estimated by the FPCA of the RNA-seq data. (ZIP 120 kb)A. The real RNA-seq curve of the gene Additional file 5: Table S2.The value of risk parameter in different interaction models. (XLS 30 kb)


Our comparison of the two precatalysts [IrCl(COD)(IMes)] and [IrCl(COD)(SIMes)], coupled with 2H labelling of the N‐heterocyclic carbene and associated relaxation and polarisation field variation studies, demonstrates the critical and collective role these parameters play in controlling the efficiency of signal amplification by reversible exchange. Ultimately, with imidazole, a 700‐fold1H signal gain per proton is produced at 400 MHz, whilst for indazole, a 90‐fold increase per proton is achieved. The co‐ligand acetonitrile proved to optimally exhibit a 190‐fold signal gain per proton in these measurements, with the associated studies revealing the importance the substrate plays in controlling this value. Copyright © 2017 The Authors. Magnetic Resonance in Chemistry published by John Wiley & Sons Ltd.The signal amplification by reversible exchange (SABRE) approach has been used to hyperpolarise the substrates indazole and imidazole in the presence of the co‐ligand acetonitrile through the action of the precataysts [IrCl(COD)(IMes)] and [IrCl(COD)(SIMes)].  The process of SABRE is also affected by the magnetic field that is experienced by the catalyst during this process that is often called the polarisation transfer field (PTF).1a.N‐heterocyclic carbene (NHC) iridium complexes that contain 1,3‐bisimidazol‐2‐ylidene (IMes) or 1,3‐bis‐4,5‐dihydroimidazol‐2‐ylidene (SIMes) ligands are used in this study,pKa of the binding site, is important, as too strong an interaction can lead to low success by reducing overall exposure to p‐H2.Hyperpolarisation methods are being used widely to improve the sensitivity of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) to substrate detection.in vivo data on a hyperpolarised agent that may ultimately prove to be diagnostic of health.One of the drivers for the development of hyperpolarisation methods is the potential to collect We have selected indazole (ind) and imidazole (im), for this study because this family of nitrogen‐containing heterocycles plays a role in a wide variety of biological processes.et al. using the first generation of phosphine‐based SABRE catalystset al.et al. has also reported on the 15N hyperpolarisation of imidazole, with the supplementary information suggesting that a 100‐fold 1H signal enhancement is produced in H‐2 and 50‐fold gain in H‐4 and H‐5. Here, we describe a series of high‐field studies on polarisation transfer to both of these substrates where we detect their 1H and 13C NMR signals. We seek to improve on the degree of SABRE response by exploring the effect of catalyst structure and the use of a co‐ligand, in addition to varying the pH of the methanol solutions that are employed. It has recently been established that pH effects during SABRE can be substantial.Cl (2) and [Ir(H)2(ind)3(IMes)]Cl (3). These four complexes have been characterised by multinuclear NMR spectroscopy, the details of which can be found in the Supporting Information. In the case of 1a, when the initial ligand ratio of indazole–acetonitrile is 10:3, products 2a and 3a exist in a 1:34 ratio, and hence, 3a dominates. These complexes are diagnostically identified by the chemical shifts of their hydride ligands, which appear at δH −20.91 and −21.32 in 2a and at δH −21.26 in 3a respectively in MeOD at 298 K. The associated product ratio indicates a weaker Ir–NCMe bond, when compared with Ir–Nindazole, and thus, 3a is thermodynamically more stable than 2a. In contrast, the analogous reaction with 1b yields 2b and 3b in the ratio 1:50 under the same initial conditions. This indicates that the relative bond energies of the Ir–NC and Ir–Nindazole lie further apart when SIMes is the ancillary ligand as there is an even higher preference for 3b. The corresponding hydride chemical shifts for 2b are δH −20.95 and −21.28, whilst that for 3b is δH −21.23. The hydride chemical shifts in the related complexes 2a and 3a, and 2b and 3b, are therefore very similar to one another and hence not strongly dependent on the identity of the NHC. Figure 1H‐NMR spectra in methanol‐d4 that were obtained with indazole. The NMR trace in panel (a) corresponds to one that was obtained before the addition of H2, and signals for H‐3 through H‐8 of indazole are indicated. The addition of H2 changes this response as 2a and 3a form with the new resonances for the coordinated indazole ligands being indicated with the * and ● labels ). The hydride region of this NMR spectrum (right) reveals that 3a dominates.[IrCl(COD)(IMes)] a and Cl (2) exhibit antiphase character due to the parahydrogen‐induced polarisation effect in the corresponding 1H‐NMR spectra 3(NHC)]Cl (3) both exhibit a weak in‐phase signal gain when they are first observed after sample transfer from low field, but this gain lies just 1.6‐fold in favour of 3a over 2a. The difference in 2a:2b signal enhancement level is consistent with the fact that the observed rate of H2 loss at 298 K, as determined by exchange spectroscopy methods, from 2a is 0.65 s−1, and just 0.08 s−1 for 3a. Hence, there is more rapid p‐H2 introduction into 2a and a larger signal gain results. These enhancement data therefore indicate that the presence of the acetonitrile facilitates more rapid IrH/H2 exchange in agreement with observations reported previously for the related products that form with pyridine.When this reaction is completed with 2a and 3a, and free indazole, was subsequently probed for SABRE. These measurements were made in an automated flow apparatus that has been described previously.p‐H2 within a mixing chamber (MC) that is located in low field. The MC is surrounded by coil that can be used to generate a precise local magnetic field in the range −140 to +140 G and is located within a μ‐metal shield to screen the effect of the earth's field. For the flow measurements conducted in this study, a 3 ml volume of methanol‐d4 solution is typically employed that contains the iridium catalyst at a concentration of between 5 and 7 mM. It also contains 2–3 molar equivalents of the co‐ligand acetonitrile and 10 molar equivalents of the target substrate. Once a substrate is hyperpolarised, after bubbling p‐H2 through the solution for a predefined period, bubbling is stopped, and a 3‐s N2 purge activated. The solution then flows under nitrogen pressure into the NMR probe head for measurement in a process that takes 0.6 s, although a further delay of 0.1 s is added to allow the solution to settle before the NMR measurement is started. NMR measurement then proceeds in the usual way, although a receiver gain of 1 is typically employed to deal with the strongly enhanced signals we expect to detect. It takes therefore a total of 4.8 s to make a measurement after the SABRE step has been completed. During this time, the sample moves from low to high field and therefore experiences a range of magnetic environments and hence relaxation effects. These effects will act to reduce the level of detected SABRE and must play a larger role in shrinking the measured response of any rapidly relaxing signals. By employing this flow approach, the solution can ultimately be returned to the MC in order for it to be repolarised and the process started again. In this way, signal averaging, signal reproducibility and variable PTF plots can be constructed by repeated analysis of the same sample.A solution containing such a mixture of 1a in methanol‐d4 was 6.5 mM, and 49.4 mM of indazole and 17.0 mM of acetonitrile were introduced, strong SABRE enhancements were visible in the NMR signals of both these reagents. The maximum proton signal enhancement for the five non‐exchangeable protons of indazole proved to total 115‐fold that equates to a signal gain of ~20 per proton and resulted when the sample had been exposed to a 70 G magnetic field in conjunction with 20 s of exposure to p‐H2 . The corresponding acetonitrile proton signal was observed to achieve a maximum enhancement of 145‐fold after transfer at 80 G that equates to an ~50‐fold gain per proton . Previously, such a high level of polarisation transfer into acetonitrile has only been seen when a deuterated ligand scaffold is employed in conjunction with the co‐ligand pyridine.By using this equipment, we are able to precisely vary a number of parameters that control the SABRE effect in order to optimise them. When the initial concentration of 1b was examined in the presence of similar 10 and 3‐fold ligand excesses respectively, SABRE was again observed. Now, however, the total indazole proton signal enhancement within the five sites increased to 234‐fold (~47 per proton) after transfer at a PTF of 70 G and a 20‐s p‐H2 exposure time. The corresponding acetonitrile signal gain was 266‐fold at 80 G. Hence, the catalyst derived from 1b exhibits superior performance to that derived from 1a. This observation is in agreement with the fact that the effective rate of build‐up of indazole in solution, via dissociation from dominant 3a and 3b, at 298 K is 0.26 and 0.64 s−1 respectively. Scheme 3 into the corresponding signal for free indazole and has units of per second (refer to Supporting Information).When a 6.6 mM solution of 3a is the catalyst after transfer at 60 G.We also undertook a series of control measurements under analogous conditions without acetonitrile. The corresponding H‐3 signal of indazole was observed to yield a 330‐fold signal again under these conditions where 3 and acetonitrile from 2 will lead to a common 16‐electron intermediate, [Ir(H)2(ind)2(NHC)]Cl, alongside hyperpolarised indazole and acetonitrile respectively 2(ind)2(NHC)]Cl forms [Ir(H)2(ind)2(CD3OD)(NHC)]Cl that will be unproductive for methanol‐SABRE. Furthermore, the concentration of [Ir(H)2(ind)2(methanol)(NHC)]Cl must also be low as this species is not detected in these NMR spectra.Mechanistically, the loss of indazole from p‐H2 increases prior to reaching a relaxation‐controlled maximum.2 enhancement allows this effect to be visualised, as detailed in Fig. Under SABRE, the level of signal enhancement builds as the exposure time to 3OH and H2O to these CD3OD solutions in order to increase the proportion of CD3OH in solution. These results are detailed in the Supporting Information and reveal complex behaviour. When H2O is added, the indazole, acetonitrile, methanol and HOD signal gains all first increase in size before falling as the level of doping is increased. The origin of this fall with added H2O is likely to result from the reduced solubility of H2 in water and the associated decrease in 1H relaxation times.et al. who saw an increased level of polarisation transfer into the solvent molecules in acid solution at low field.We have also probed the effect of adding CDd22‐IMesd22‐SIMes, the levels of indazole and acetonitrile proton signal enhancements under SABRE changed as detailed in Table d22‐IMes, the maximum indazole proton signal enhancement was now obtained at 90 G rather than 70 G, and after 35 s of contact with p‐H2, rather than the 20 s used earlier. Its final value is, however, reduced from 115‐fold to just 82‐fold, and hence, it can be concluded that in this case, 2H‐labelling of the catalyst has a negative effect on the level of indazole polarisation that is achieved when compared with the IMes system. In contrast, the acetonitrile proton signal enhancement level increased very dramatically from the original 145‐fold value to 572‐fold, with transfer now taking place at 90 G rather than the original 80 G field value (190‐fold per proton). Hence, polarisation transfer into acetonitrile has become more facile, and as a consequence, it now receives the largest SABRE benefit.Upon replacing the protio form of these NHC ligands with their deuterated variants d22‐SIMes, however, resulted in an indazole signal gain that averaged to ~91‐fold per proton, and a 372‐fold CH3CN signal gain at the same transfer field values as used earlier, which remained optimal. Deuterating the carbene ligand of 1a therefore leads to a 41% fall in indazole polarisation, but for 1b, it leads to a 60% increase. We note that increases in temperature result in further increases in these signal gains as the ligand exchange rates are slow relative to what might be expected to be their optimum values.2a leads to a 300% gain in acetonitrile polarisation, whilst for 2b, it leads to a 17% fall. This difference will be rationalised later.The corresponding change to 3, there is one unique hydride coupling into the bound indazole ligand that will lead to transfer to proton H‐3 of Scheme via relayed transfer through H‐3. This behaviour accounts for the fact that when the SABRE efficiency of 3 is examined, as a function of PTF, a signal maximum is evident for all of these protons  with transfer into quaternary CB being observed between 50 (58‐fold) and 60 G, and 80 and 90 G, although there is a minimum at 70 G. The effect of the PTF on 13C transfer is therefore complex and reflects the number of different coupling pathways in operation. The size of the SABRE effect is PTF dependant with the 13C quaternary signal of free acetonitrile readily appearing at 116 ppm after transfer a ~0G in a μ‐metal shield . Upon increasing the transfer field beyond this value, the acetonitrile signal gain reduces, but at points beyond 100 G, it is again observed through an enhanced signal. We note that we do not observe polarisation transfer into the 13CH3 group of acetonitrile nor into the 13C atoms of methanol‐d4 under these conditions. This is consistent with earlier reports that detail how optimal 13C transfer proceeds via small 3JCH couplings rather than the larger 2JCH couplings.SABRE is also evident in the 13C signal gain achieved with 1b proved smaller than those with 1a as detailed in the Supporting Information, and the replacement of the SIMes or IMes ligands with d22‐SIMes and d22‐IMes respectively also led to worse SABRE performance with the maximum 13C signal enhancement seen for site A (Scheme The levels of 1a) and [IrCl(COD)(SIMes)] (1b) were then used to hyperpolarise imidazole (im). Similar 5 mM methanol‐d4 solutions of 1 or 2 were employed, with a 10‐fold excess of imidazole and a 3‐fold excess of acetonitrile. For 1a, 97% of the products were now in the form [Ir(H)2(im)3(IMes)]Cl (5a) with the remaining 3% corresponding to [Ir(H)2(NCMe)(im)2(IMes)]Cl (4a) as detailed in Scheme 1b, the reaction proceeded cleanly to form 5b as the sole product. Characterisation data for these products are detailed in the Supporting Information. When the SABRE effect was explored for imidazole, p‐H2 contact times of 60 s were used, rather than the 25–30‐s time for indazole, as this resulted in the detection of larger signal enhancements.[IrCl(COD)(IMes)]  and the imidazole protons in the SABRE transfer catalysts of Scheme δH 6.82, 6.86 (H‐4 and H‐5) and 6.9 (H‐2) in 5a and will all receive polarisation under SABRE with different efficiencies according to the PTF and couplings they experiences at the point of transfer. Consequently, upon the dissociation of hyperpolarised imidazole from 5, all three of its proton signals should appear enhanced, but only two signals are actually seen, at δH 7.70 and 7.07, due to the fact the resonances for H‐4 and H‐5 have effectively identical chemical shifts. As expected, the SABRE results, with imidazole, therefore show a strong PTF dependence as detailed in Fig. Figure d4, the 1a‐derived catalyst system proved to deliver signal enhancements of 606‐fold and 830‐fold for these two signals respectively per proton, which equates to a total signal enhancement value of 2042‐fold within im. It would therefore appear that the NCHN motif, rather than the CHCH arrangement, receives the highest level of SABRE transfer. The overall signal gain clearly exceeds the level seen for indazole where the corresponding value was just 115‐fold, as detailed in Table 1b, the overall signal enhancement falls to 1028‐fold that contrasts with the indazole return of 146‐fold, and the CHCH arrangement receives the most benefit. We can therefore conclude that imidazole is better suited to SABRE than indazole.In methanol‐We also undertook a series of control measurements under analogous conditions without acetonitrile. The corresponding H‐2 signal of imidazole was now observed to yield a 551‐fold signal again under these conditions after transfer at 60 G, whilst the signals for H‐4/5 yielded a 410‐fold gain after transfer at 70 G per proton. Hence, imidazole polarises better in the presence of acetonitrile.1a in the presence of HCl(aq) as summarised in Table 4. The level of SABRE exhibited by the solvent CHD2OD is also worse than that seen with indazole in accordance with 4 that plays a role in this process, but can be improved 20‐fold by 2H labelling the IMes ligand.These results also proved to be pH sensitive, with the overall proton signal enhancement seen for imidazole dropping to 1475‐fold  with 5a, without HCl, proved to be 3.39 s−1 and its H2 loss rate 3.68 s−1. However, in the presence of 0.8 equivalent HCl, the imidazole build‐up rate in solution fell to 2.52 s−1, whilst the apparent H2 loss rate became 4.75 s−1. For 5b, without HCl, the ligand build‐up rate was 1.04 s−1 and the H2 loss rate 1.75 s−1, but with HCl , they became 1.11 and 1.85 s−1 respectively. These ligand build‐up rates are therefore faster than those seen for indazole and that must account for the improved SABRE performance seen with imidazole. However, it is interesting to see that the IMes system now exchanges more rapidly than the SIMes form which that suggests the buried volume2 loss in acid solution suggests that the complex is susceptible to protonation that would be detrimental to SABRE.The free imidazole build‐up rate in solution of d22‐IMes‐1a and d22‐SIMes‐1b are deployed with imidazole, the signal intensities seen under SABRE in indazole and acetonitrile fall dramatically.When the catalysts 4 solution are 51.1 s for H‐2 and 26.6 s for the H‐4 and H‐5 response of imidazole, whilst the value for CH3CN was 16.3 s at 298 K . It is notable that the relaxation times for free imidazole are larger than those of indazole, with the N‐isolated proton H‐2 having a value of 51 s. At 298 K, this value falls by 67% to 16.9 s in the presence of 5a, whilst the corresponding fall in indazole H‐2 T1 value was 39% under analogous conditions and is consistent with the higher ligand exchange rates for imidazole.The relaxation times of the protons in imidazole were also studied in a similar way to those of indazole and are presented in Tables 5a of the three magnetisation receptors are 3.1 s (H‐2), 1.6 and 1.0 s (H‐4 and H‐5), whilst for 5b, they are 3.5, 1.8 and 1.9 s respectively, and hence, all are larger than the 0.9‐s value determined for H‐3 in 3a. This behaviour is therefore consistent with the improvement in SABRE efficiency that is seen on moving to imidazole. In the 2H‐labelled form of 5a, the corresponding T1 values increase to 4.3, 2.4 and 1.2 s respectively, whilst in 5b, they become 3.7, 1.4 and 1.9 s. Both of these sets of values at 9.4 T are therefore larger than those resulting from the corresponding 1H‐labelled catalysts. It is therefore clear that these high‐field values are not a good indicator of the role relaxation plays during SABRE transfer in low field.For IMes, at 263 K, the relaxation times within 13C‐NMR spectra were also recorded using a methanol‐d4 solution that initially contained a 7‐fold imidazole and 3‐fold acetonitrile excess relative to 1a, with and without HCl. These NMR spectra are detailed in Fig. 13C signals of free imidazole also proved to exhibit far greater signal gains than those found for indazole. For the signal due to CA, a strong response was seen, whilst the averaged signal, observed for the two remaining CH sites, was weaker. The intensities exhibited by these signals are again field dependant with both showing maxima after transfer at ca. 40 and 90 G. In contrast, upon adding acid, the signals for CB appear strongly (IMes)] and [IrCl(COD)(SIMes)]. These studies reveal that SABRE delivers a 830‐fold signal enhancement for the H‐3 resonance of imidazole and a 178‐fold improvement for the H‐2 resonance of indazole at 9.4 T in the presence of acetonitrile. These signal gains compare with values of 515 and 334‐fold that were observed here in the absence of acetonitrile and indicate that in the case of imidazole, the presence of a co‐ligand is beneficial. The co‐ligand acetonitrile was observed to exhibit a maximum 572‐fold signal gain (190‐fold per proton) during this study that reveals how this value is controlled by the identity of the substrate. If high acetonitrile polarisation is targeted, working in the presence of 2(sub)2(NCMe)(NHC)]Cl and [Ir(H)2(sub)3(NHC)]Cl. When the NHC is IMes or SIMes, the tris‐substituted complex is favoured over the bis‐substituted complex. Furthermore, both of these complexes undergo the necessary substrate and H2 loss processes that allow them to drive the hyperpolarisation of these agents. The H2 loss rates for the complexes with indazole were 0.65 s−1 for IMes and 0.08 s−1 for SIMes, whilst for imidazole, they were 3.68 and 1.75 s−1. Given that p‐H2 is the source of hyperpolarisation, these values suggest that indazole should show weaker polarisation than imidazole, and that the IMes form of the catalyst is superior. However, substrate loss rates also play a role in controlling the level of SABRE. In the case of 2a, this is reflected in the bound acetonitrile and indazole ligands, whilst in 3a, iwhilst in 3a, it is just indazole ligand loss that is important. The effective ligand build‐up rates in solution for indazole were 0.24 s−1 in 3a and compare with that for imidazole of 3.39 s−1 in 5a. Hence, the higher substrate loss rate matches with the observation of better SABRE performance in the hyperpolarisation of imidazole through 5a. However, all three of these free ligand build‐up rates are substantially smaller than that exhibited for pyridine by [Ir(H)2(IMes)(pyridine)3]Cl, which is 22 s−1. The hyperpolarisation of indazole and imidazole reported here is therefore less efficient than that achieved for pyridine.These results have been rationalised by studies on the catalyst, which in this case revealed two complexes result, [Ir(H)p‐H2 contact time, which is referred to here as the bubbling time when the automated polariser is used. Whilst polarisation transfer proceeds under J‐coupling, the catalyst gains its polarisation for transfer through the addition of fresh p‐H2. Hence, if the complex lifetime is short, p‐H2 addition and transfer must proceed on a relatively rapid timescale, and a short bubbling time can be employed. If the catalyst lifetime is longer, p‐H2 addition and transfer will proceed on a slower timescale, and a longer bubbling time can be employed to reach the point where relaxation acts to limit the ultimate level of polarisation that is exhibited by the free substrate. The recent paper by Barskiy et al. develops these points.1H response for indazole, extending to 35 s when looking at the corresponding 13C signals, and 60 s for imidazole. Furthermore, upon changing the NHC to SIMes, the corresponding indazole build‐up rates increase to 0.64 s−1 for 3b but fall to 1.04 s−1 for 5b. This suggests that for indazole, SIMes works better due to higher exchange, but for imidazole, the reverse is found experimentally, and hence, the H2 exchange rate must be more critical.One further impact of these ligand exchange values is reflected in the optimum 2H‐labelled forms of these catalysts with a view to improve on the level of SABRE by harnessing potentially longer catalyst relaxation times. An earlier report by Fekete et al.2H labelling of the catalyst led to a dramatic increase in the level of SABRE. Barskiy et al. have built on these, and other results, to produce a simple analytical model to describe this behaviour that suggests that relaxation and not ligand dissociation is critical to achieving optimal enhancement.d22‐SIMes exceeded the performance of the 1H‐labelled form by 150%. In contrast, the better performing 1H form of IMes yielded a 680‐fold signal gain per proton in imidazole, but upon changing to its d22‐IMes counterpart, SABRE efficiency fell by 60%. Hence 2H labelling does not automatically lead to improved SABRE.We also utilised a series of 13C and 15N responses of these agents, which in the case of imidazole were improved by the addition of trace amounts of HCl to promote proton transfer, resulting in a sharp coalesced response for its two nominally inequivalent CH‐CH centres (Supporting Information).SABRE derived signals gains were also revealed in the p‐H2‐derived hyperpolarisation during polarisation transfer. The use of 2H‐labelled NHCs in the form of d22‐IMes‐1a and d22‐SIMes‐1b, however, resulted in a slight reduction in these relaxation times. It is noteworthy, however, that the level of acetonitrile hyperpolarisation was dramatically improved by using d22‐IMes rather than its 1H‐containing version by over 390%. These results serve therefore to illustrate the complexity of this process and suggest that a series of rigorous experimental studies are needed to produce a truly optimised catalyst for a specific substrate.Results were presented here that detail the relaxation effects of the active form of the catalyst. All of these catalysts proved to contain bound substrate molecules whose protons had relaxation times that were between 25 and 5 times smaller than those of the free material. For imidazole, the longer proton relaxation times that are exhibited by the free material were found to translate into longer relaxation times in the corresponding protons on the catalyst at field. If this situation is mimicked at low field where SABRE transfer takes place, this will act to minimise the loss of Data S1. Supporting info itemClick here for additional data file.


TWCCSDN2→He). Upon reduction of BPTI, irrespective of precursor charge state, the TWCCSN2→He decreases to a similar distribution as found for the nESI generated ion of identical charge. The behavior of beta casein upon charge reduction is more complex. It presents over a wide charge state range (9–28), and intermediate charge states (13–18) have broad TWCCSDN2→He with multiple conformations, where both compaction and rearrangement are seen. Further, we see that the TWCCSDN2→He of the latter charge states are even affected by the presence of radical anions. Overall, we conclude that the flexible nature of some proteins result in broad conformational distributions comprised of many families, even for single charge states, and the barrier between different states can be easily overcome by an alteration of the net charge.Charge reduction in the gas phase provides a direct means of manipulating protein charge state, and when coupled to ion mobility mass spectrometry (IM-MS), it is possible to monitor the effect of charge on protein conformation in the absence of solution. Use of the electron transfer reagent 1,3-dicyanobenzene, coupled with IM-MS, allows us to monitor the effect of charge reduction on the conformation of two proteins deliberately chosen from opposite sides of the order to disorder continuum: bovine pancreatic trypsin inhibitor (BPTI) and beta casein. The ordered BPTI presents compact conformers for each of three charge states accompanied by narrow collision cross-section distributions (The online version of this article (doi:10.1007/s13361-017-1692-1) contains supplementary material, which is available to authorized users. It has become common practice to infer the populations of protein conformations in the gas phase from the charge state distribution (CSD) it presents following electrospray ionization (ESI) , 2. It iIon mobility spectrometry (IMS) is a separation technique that is increasingly used in combination with mass spectrometry (IM-MS) to measure gas-phase conformation of desolvated molecules –17. The Proteins in solution maintain structural integrity with noncovalent intramolecular interactions  and covalent disulfide bridges, all of which add rigidity to a molecule, limiting the conformations it can adopt. These interactions can be preserved in the gas phase –29 but sc, causes a decrease in the CCS compared with that seen for an nESI ion of that charge state we term this compaction 5+ and [M + 6H]6+ ions of BPTI were generated as a function of collision energy with a collision voltage range of 2–55 V.To assess the gas-phase stability of the structured BPTI, ions were 4+, [M + 5H]5+, and [M + 6H]6+ , 790 Å2 (5+), 860 Å2 (6+), and 960 Å2 (7+), and the crystal structure, 5PTI 4+· with a TWCCSDN2→He centered at 800 Å2  undergoes two reductions to the [M + 6H]5+· and [M + 6H]4+·· products . This is somewhat surprising, since disulfide bond cleavage upon exposure to electrons and radical anions in the gas phase is well reported re, 5PTI  gave a tn method , 63 of 7e Figure . The TWC2 Figure , whereass Figure  with TWCreported –66.Figur4+ ion. This fits to a theoretical distribution shown in solid black dots. The first charge-reduced product originating from the [M + 5H]5+ precursor fits a theoretical isotopic distribution for the ETnoD product, [M + 5H]4+  there is a broadening of the ATD and the appearance of another species with a later arrival time indicative of an unfolded form. [M + 6H]6+ is activated to a single unfolded form at the maximum collision voltage (55 V) used in these experiments  energy input , intermediate (13+ to 18+), and high (19+ to 28+), and compare the conformational change in each region as the charge is systematically reduced on individual ions. Figure TWCCSDN2→He previously mentioned are shown in Supplementary Table We investigate here the effect of charge on the structure of beta casein as we reduce the net charge via electron transfer from radical anions. In order to best report on the conformational change, each charge state of beta casein is individually ow 9+ to 2+, intercf. of the nESI form in the presence of radical anions. As an example, the 12+ ion before and after exposure to radical anions is compared along with its charge-reduced form originating from the 13+  no reduction to the 12+ ion from any higher charged precursor ion  has a TWCCSDN2→He centered at 3358 ± 2 Å2. Of note is the TWCCSDN2→He of the 13+ precursor with two overlapping conformers, which are converted to one on single reduction to the 12+ ion; noteworthy is that the reduced 12+ has a larger TWCCSDN2→He than the nESI generated 12+. Overall, the lower charge states, (12+ and 11+) undergo only up to two reductions compared with higher charge states where we are able to see up to four reductions under the same experimental conditions  is shown in Figure TWCCSDN2→He centered at 5375 ± 48 Å2 and 5341 ± 1 Å2, respectively. The charge-reduced products with a charge of 21+, originating from the 22+ (5455 ± 1 Å2), 23+ (5554 ± 102 Å2), and 24+ (5528 ± 55 Å2), have TWCCSDN2→He centered at 5341 ± 1 Å2, 5409 ± 1 Å2, and 5409 ± 95 Å2, respectively. The distributions for 24+, 23+, and 22+ precursors slightly shift to lower TWCCSN2→He upon reduction to the 21+. The same is true for other higher charge states representing extended structures  as that reported for intermediate charge states, vide infra, indicative that there are not enough noncovalent interactions in the unfolded forms to facilitate any collisional compaction and that Coulombic repulsion still dominates the conformations  is small in magnitude relative to the number of charge reductions seen Figures  and 6b. TWCCSN2→He and rearrangement (but no charge-reduced product with a larger TWCCSN2→He than its precursor); we attribute this to the conformational flexibility of these states, which appear as an intermediate between the Coulombic forces that dictate the structures of these ions in the gas phase, as indicated by the presence of multiple conformers  peaks at 3993 ± 21 Å2 with a shoulder to its right. Once again the reduced 14+ shows a more narrow distribution than the nESI 14+.Intermediate charge states show the most interesting behavior compared with the low and high charge states. We see both reduction in s Figure . The nESs Figure  show a bractions . The nESTWCCSDN2→He centered at 4230 ± 1 Å2 and 5097 ± 1 Å2. Upon exposure to radical anions, we see significant compaction and rearrangement with a broad TWCCSDN2→He and multiple conformers. The 19+, 20+, 21+, and 22+ precursors  undergoes three reductions, it is significantly altered to a TWCCSDN2→He with maxima at 3276 ± 123 Å2, 5213 ± 18 Å2  to produce a final structure resembling that of the nESI ion of same charge. Interestingly, when we collisionally activate BPTI, dehydrated forms of the gas-phase molecule extend more readily, suggesting that the water loss has disrupted a stabilizing contact  interacting with a proton.In this study, we monitor the change in gas-phase structure of the rigid BPTI and the conformationally dynamic beta casein as a function of charge where different charge states are representative of different degrees of compactness or disorder. We report the change in conformation as each charge state of each protein undergoes reduction upon exposure to radical anions of 1,3-dicyanobenzene. For the conformationally restricted BPTI, regardless of the precursor ion, reduction results in a smaller unimodal TWCCSN2→He is accompanied by significant conformational rearrangement. More extended structures (higher charge states) show compaction upon charge reduction to TWCCSN2→He comparable to those measured from ions generated from nESI, whereas low charge states reduce in TWCCSN2→He but sometimes with TWCCSN2→He larger than those measured for the nESI forms and with the formation of multiple conformers.In contrast, the intrinsically disordered protein beta casein shows charge state-dependent conformational change upon exposure to radical anions and reduction in charge. Partially folded conformers (intermediate charge states) of beta casein exhibit high conformational flexibility upon reduction in charge, whereby reduction of the mean The ease with which the conformation of both proteins can alter upon reaction strikes a cautionary note in that such processes readily occur in electrospray. Overall, this study illustrates the ease with which conformations can be manipulated for beta casein, an intrinsically dynamic protein, by altering its net charge, and most interestingly that a number of very different conformations can be found for a given charge state for beta casein, versus a much narrower landscape for the structured BPTI.Future studies will demonstrate whether these effects are inherent to intrinsically disordered proteins or whether the protein’s sequence is the primary cause for such effects. Investigations will include effects of protein size, isoelectric point (pI), post-translational modifications and charged groups, solution conditions that affect structural stability, and the effect of different charge-reducing reagents (both proton and electron transfer reagents) on conformation.ESM 1(DOCX 633 kb)Below is the link to the electronic supplementary material.


Colloidal suspensions of ferroelectric BaTiO3 nanoparticles act as a dispersible polarized interface that can influence the selectivity of non-faradaic reactions. 3 nanoparticles to the reaction solution changed the product ratio in the same direction and by a similar magnitude as performing the reaction at an electrode–electrolyte interface polarized by a voltage. The results demonstrate that colloidal suspensions of BaTiO3 nanoparticles act as a dispersible polarized interface that can influence the selectivity of non-faradaic reactions.The local environment at polarized solid–liquid interfaces provides a unique medium for chemical reactions that could be exploited to control the selectivity of non-faradaic reactions. Polarized interfaces are commonly prepared by applying a voltage to an electrode in an electrolyte solution, but it is challenging to achieve high surface charge densities while suppressing faradaic reactions. Ferroelectric materials have permanent surface charge densities that arise from the dipole moments of ferroelectric domains and can be used to create polarized solid–liquid interfaces without applying a voltage. We studied the effects of ferroelectric oxides on the selectivity of a Rh porphyrin-catalyzed carbene rearrangement. The addition of ferroelectric BaTiO Polarization of the oxide–electrolyte interface by applying a voltage across the Si electrodes was found to change the selectivity of reactions confined to the interface, including an epoxide rearrangement catalyzed by Al2O3 and an intramolecular carbene rearrangement catalysed by Rh porphyrins. The selectivity changes were dependent on the charge density at the oxide–electrolyte interface, which was determined from the applied voltage and the interfacial capacitance.The double layer region at a polarized solid–liquid interface has electrostatic and chemical properties that differ significantly from a bulk solution.9 The surfaces of ferroelectric particles have regions of high charge density where the dipole moment of the domain at the surface has a normal component, leading to the formation of double layers.10 Previous studies of chemical reactivity at ferroelectric surfaces have utilized the polarization to localize and enhance photoredox reactions.14 We hypothesized that ferroelectric materials would provide polarized solid–liquid interfaces that affect the selectivity of non-faradaic reactions in the same manner as an electrode–electrolyte interface polarized by a voltage  is a readily accessible ferroelectric metal oxide with a Curie temperature of 120 °C.15 The polarization of domains in BaTiO3 arises from a small displacement of the Ti4+ cation from the centre of the tetragonal unit cell. The polarization of BaTiO3 is stable at room temperature.18Barium titanate  of BaTiO3 nanoparticles. The Ps of a BaTiO3 nanoparticle composed of a single ferroelectric domain corresponds to the charge densities on the opposing surfaces of the domain that are normal to the dipole moment .22 Notably, surface charge densities of 20–120 μC cm–2 are considerably larger than what is accessible in the parallel plate cell at voltages below the dielectric breakdown limit (at most ∼14 μC cm–2).8 Larger BaTiO3 particles that have not been subjected to ball milling have little or no measurable Ps because they are composed of many ferroelectric domains that are randomly oriented and therefore cancel each other out. Nevertheless, the average magnitude of surface charge density on large BaTiO3 particles is expected to be similar to BaTiO3 nanoparticles because the charge density at any point on the surface is dominated by the orientation of the closest domain.The charge density on a BaTiOe moment . Ball mi1 reacts at ambient temperature with Rh porphyrin catalysts to form a mixture of products 2 and 3  and non-ferroelectric  nanoparticles. The nanoparticle samples were produced as colloidal suspensions by ball milling the corresponding bulk powders in the presence of heptane and oleic acid.19 Transmission electron microscopy (TEM) analysis indicated average particle sizes in the range of 7–10 nm for all the milled materials except for LiNbO3 .–1 of nanoparticles, similar changes in the product ratio were also seen with other ferroelectric materials like PbTiO3 (2.1 : 1.0) and LiNbO3 (4.5 : 1.0). In contrast, very little effect on the product ratio was seen with chemically similar non-ferroelectric nanoparticles such as CaTiO3 (11.1 : 1.0), SrTiO3 (11.0 : 1.0), or TiO2 (9.5 : 1.0).Under the same conditions with 1 mg mL1 was combined with BaTiO3 nanoparticles in the absence of catalyst 4a showed no conversion, which shows that the BaTiO3 is not itself a catalyst for the diazo decomposition. In addition, reactions performed in the presence of heptane and oleic acid without any nanoparticles gave a 2 : 3 ratio of 11.3 : 1.0, indicating that these additives themselves do not impact the product ratio.Control experiments in which 3–solution interface to affect the selectivity of 4a requires that a substantial portion of the catalyst partitions at or near the BaTiO3 surface during the reaction. The concentration dependence of the BaTiO3 effect in the results above is consistent with an interface-dependent effect: as the ratio of BaTiO3 surface area to 4a is increased, a larger change in product ratio is observed. Notably, the surface coverage of long aliphatic capping ligands at colloidal nanoparticles is much lower than one monolayer,23 which may be critical to allow the reactant and catalyst molecules to localize near the surface.The ability of the polarized BaTiO4a for localizing to the surface. When 100 μM TPP was added to a reaction with 2 mM 1, 2 μM 4a and 1 mg mL–1 of BaTiO3 nanoparticles, the product ratio was 10.6 : 1.0 compared to 6.3 : 1.0 when TPP was omitted and 11.0 : 1.0 when both TPP and BaTiO3 were absent. This result shows that the BaTiO3 effect is lost in the presence of a species that can block the surface localization of the catalyst. Furthermore, when 2 μM 4a was first stirred with 1 mg mL–1 of BaTiO3 nanoparticles for 24 h before the introduction of 100 μM TPP and 2 mM 1, the ratio was 10.2 : 1.0 after a further 19 h of reaction time. This experiment demonstrates that simply exposing the catalyst to BaTiO3 nanoparticles does not result in an irreversible change to a species with different selectivity. Notably, if the anion of 4a is replaced by chloride, a smaller change in product ratio from 8.1 : 1.0 (no BaTiO3) to 5.1 : 1.0 (1 mg mL–1 BaTiO3) is obtained, which may reflect reduced localization of the Cl-bound Rh porphyrin at the BaTiO3 surface.To further test this model, a reaction was performed in the presence of an excess of un-metalated 5,10,15,20-tetraphenylporphyrin (TPP), which could compete with 1, 2 μM 4a, and 1 mg mL–1 of ball milled oxide  in approximately 10 mL of CH2Cl2 were prepared, as well as an identical reaction with no oxide. Ball milled oxides were used within 60 minutes after ball milling had concluded. Over the course of 96 h, aliquots from each of the reaction mixtures were quenched with acetonitrile and immediately analysed by HPLC to determine the conversion and ratio of products 2 and 3. In each case, the product ratio stayed roughly constant throughout the course of 96 h, which suggests that catalyst molecules localize to the polarized ferroelectric surface within, at most, several hours. The slight increase in product ratio after the 6 h mark is attributed to some aggregation of the ball milled nanoparticles over the course of several hours, which resulted in a lowered surface area that was accessible to the catalyst.In order to understand the timescale on which ferroelectric nanoparticles affect the catalyst, the reaction kinetics were studied in the presence or absence of the various ball milled ferroelectric oxides . Solutio3 nanoparticles was also examined in solvents other than CH2Cl2. Reactions were performed with 2 mM 1 and 2 μM 4a in PhCF3, THF, EtOAc, and PhCH3. In the absence of BaTiO3 nanoparticles, the 2 : 3 ratio ranged from 9.4 : 1.0 to 17.9 : 1.0, demonstrating that the strong preference for cyclopropanation is maintained across diverse solvents. The addition of 1 mg mL–1 BaTiO3 nanoparticles caused a reduction in the 2 : 3 ratio in all cases, with a substantial variation in the magnitude of the effect (3 could not be used because they caused immediate flocculation of the nanoparticles.The effect of BaTiOe effect . The lar3 particles have no measurable Ps because they are composed of randomly oriented domains (see above), but they still have regions of high surface charge densities. Adding 1 mg mL–1 of unmilled BaTiO3 particles to a reaction with 2 mM 1 and 2 μM 4a caused only a 1.2-fold reduction in the 2 : 3 ratio, but the surface area of these relatively large particles (∼900 nm) is too low for effective physisorption of 4a in solution. To test for an interfacial polarization effect on selectivity with unmilled BaTiO3, we prepared Rh porphyrin 4b with four phosphonate groups to enable covalent surface attachment. The results of reactions performed with 4b are summarized in 4b reacted with 2 mM 1 in CH2Cl2 to give a 2 : 3 product ratio of 8.8 : 1.0. As with catalyst 4a, the introduction of 1 mg mL–1 ball milled BaTiO3 resulted in a 2.0-fold decrease of the product ratio to 4.5 : 1.0. Next, 4b was attached to various ferroelectric and non-ferroelectric oxides studied. Reactions performed with 2 mM 1 and 1 mg mL–1 unmilled BaTiO3 functionalized with 4b gave a 2 : 3 product ratio of 2.2 : 1.0, corresponding to a 4.0-fold reduction. The 2 : 3 ratio was further reduced to 1.8 : 1.0 if the BaTiO3 functionalized with 4b was first subjected to ball milling and then used at the same oxide concentration.Unmilled BaTiO4b was attached to the non-ferroelectric oxides TiO2, CaTiO3, and SrTiO3, a small (1.4-fold) effect on the reaction to approximately 6.3 : 1 was observed. Measurements of zeta potentials for TiO2 particles in hexane in the presence of surfactant indicate surface charge densities on the order of –0.1 μC cm–2, which is much lower than the surface charge density for ferroelectric nanoparticles.24 The small change in selectivity observed upon attachment of 4b to nonferroelectric nanoparticles may arise from the interaction of the catalyst with the oxide surface or a subtle conformational biasing for attached catalysts.26 When 4b was attached to ferroelectric PbTiO3, the ratio was 5.3 : 1.0, which was only slightly lower than the other non-ferroelectric oxides. This result may arise from decreased robustness of the phosphonate linkage to the PbTiO3 surface and increased leaching of catalyst molecules into the bulk solution. Nevertheless, attachment to ferroelectric oxides, especially BaTiO3, effected a substantially larger change in selectivity than attachment to other non-ferroelectric oxides.In contrast, when 3 surfaces or adding BaTiO3 nanoparticles to the reaction solution, the 2 : 3 ratio is reduced by up to a factor of 4.9. Similar effects are seen with ball milled ferroelectric PbTiO3 and LiNbO3; chemically similar but non-ferroelectric CaTiO3, SrTiO3, or TiO2 nanoparticles had essentially no effect, which indicates that the selectivity change requires the permanently polarized domains in BaTiO3.The results above demonstrate that the polarized interface between a ferroelectric oxide and an organic solvent can change the selectivity of catalytic reactions in the same way as an electrically polarized electrode–electrolyte interface. By attaching the catalyst to BaTiO3, a polarized interface can now be introduced to a reaction medium without the need for physical electrodes. The use of ferroelectric nanoparticles provides an accessible way to investigate and exploit the effects of interfacial polarization on catalysis.By using ball milled ferroelectric nanoparticles such as BaTiO


SPATA31  gene family belongs to the core duplicon families that are thought to have contributed significantly to hominoid evolution. It is also among the gene families with the strongest signal of positive selection in hominoids. It has acquired new protein domains in the primate lineage and a previous study has suggested that the gene family has expanded its function into UV response and DNA repair. Here we show that over-expression of SPATA31A1 in fibroblast cells leads to premature senescence due to interference with aging-related transcription pathways. We show that there are considerable copy number differences for this gene family in human populations and we ask whether this could influence mutation rates and longevity in humans. We find no evidence for an influence on germline mutation rates, but an analysis of long-lived individuals (> 96 years) shows that they carry significantly fewer SPATA31 copies in their genomes than younger individuals in a control group. We propose that the evolution of SPATA31 copy number is an example for antagonistic pleiotropy by providing a fitness benefit during the reproductive phase of life, but negatively influencing the overall life span.The  NPIP and LRRC37A)  an anBEX1 SPATA31 cSPATA31 primers, 5μM probe against SPATA31A mixed with 1.25μL of 20X primer-probe mix: 18μM PCR primers (each Albumin primers), 5μM probe against SPATA31 and finally 0.1μL BamH1HF (NEB) added to the reaction. In total 15μL of reaction mixture was prepared in an Eppendorf 96-well twin.tec PCR plate. 10μL of diluted Genomic DNA  was mixed with 15μL of the reaction mixture. The total 25μL reaction was mixed well by pipetting up and down and loaded into the Automated Droplet Generator  to generate oil droplets in each well of the plate containing 20μL of the reaction mixture. After droplets were generated, the plate was sealed with a pierceable foil heat seal using PX1™ PCR Plate Sealer  and then placed on a thermal cycler for amplification. Thermal cycling conditions were as follows: 95°C for 10 min (1 cycle), 94°C for 30 sec (ramp rate 2.5°C/sec) and 56°C for 60 sec (ramp rate 2.5°C/sec) (40 cycles), 98°C for 10 min (1 cycle), and 12°C hold. After PCR, the 96-well PCR plate was loaded on the QX100™ Droplet Reader  which reads the droplets from each well of the plate. The data obtained were analyzed using QuantaSoft™ analysis software linked with the QX100™ Droplet Reader. We scored the copy numbers by measuring the concentration of the target, SPATA31_All or SPATA31A relative to the concentration of the reference ALBUMIN.All digital PCRs between control and LLI were run using the same master mix and same plate . To reduce the sampling error, genomic DNA was first diluted in 99μL by taking 1μL from 50ng/ μL stock concentration and mixed well by pipetting. Primer and probe sequences are provided in We retrieved genome read data for 295 individuals (bam files) from the Simons Genome Project . Reads wRNASeq sequencing reads are available under SRA accession number PRJEB21178.Supplementary FiguresSupplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5


This figure is in agreement with a recent value quoted by the NBS.The NBS Thermal Neutron Flux has been calibrated in terms of the gold thermal neutron capture cross section. The effective thermal neutron (below cadmium cutoff energy) flux density is estimated to be 4307 ± 2 percent  B cross section 1/2 is less than ±1 percent. In addition, there may be a systematic error not exceeding ±1 percent associated with the absolute measurement of the radioactivity. The combined error is thus ±2 percent.The sum [Σ(It is concluded that NBS and NPL measurements of thermal neutron flux density are in agreement within the limits of experimental error.


RNA aptamer that shows high affinity to the Runt domain (RD) of the AML1 protein, a transcription factor with roles in haematopoiesis and immune function. From kinetic and thermodynamic studies, it was suggested that the aptamer recognises a large surface area of the RD, using numerous weak interactions. In this study, we identified the secondary structure by nuclear magnetic resonance spectroscopy and performed a mutational study to reveal the residue critical for binding to the RD. It was suggested that the large contact area was formed by a DNA‐mimicking motif and a multibranched loop, which confers the high affinity and specificity of binding.Since the invention of systematic evolution of ligands by exponential enrichment, many short oligonucleotides (or aptamers) have been reported that can bind to a wide range of target molecules with high affinity and specificity. Previously, we reported an  AML1acute myeloid leukaemia 1HMQCheteronuclear multiple quantum coherenceITCisothermal titration calorimetryNF‐κBnuclear factor‐κBNMRnuclear magnetic resonanceNOESYnuclear Overhauser effect spectroscopyRDERunt‐binding double‐stranded DNA elementRDRunt domainSELEXsystematic evolution of ligands by exponential enrichmentSPRsurface plasmon resonancein vitro technique known as systematic evolution of ligands by exponential enrichment (SELEX) Kd in the nanomolar to picomolar range), moderate molecular mass and ease of chemical production Aptamers are short oligonucleotides that can bind with high affinity and specificity to a wide range of target molecules, which can be generated by an AML1 (RUNX1) is a transcription factor that plays important roles in maintaining haematopoiesis and immune function in adults Kd = 0.99 ± 0.02 nm and 0.034 ± 0.004 nm, respectively) than the Runt‐binding double‐stranded DNA element  AML1 was originally isolated from a chromosomal break point in a case of human acute leukaemia m sodium phosphate (pH 6.5), 2 mm magnesium acetate, 300 mm potassium acetate, 50% glycerol and 1 mm DTT] and stored at −25 °C. The concentrations of proteins were determined based on the molecular absorption coefficient at 280 nm.AML1 N‐terminal fragment  and its mutants were prepared as described previously in vitro transcription as described previously S4‐SS and its mutants were synthesised by kon and the dissociation rate constant koff. The dissociation constant Kd was also determined as the ratio of koff to kon, and is presented as the mean ± standard error of three independent measurements.SPR assays were performed as described previously 15N‐labelled S4‐SS were dissolved in 20 mm sodium phosphate (pH 6.5). Concentrations of S4‐SS and 15N‐labelled S4‐SS were 0.4 and 0.02 mm, respectively. NMR spectra were measured using an Avance600 spectrometer . Spectra were recorded at a probe temperature of 10 °C. The imino proton resonances of G and U residues were distinguished by the 1H‐15N heteronuclear multiple quantum coherence (HMQC) spectrum measured with 15N‐labelled S4‐SS 2O with a mixing time of 150 ms using the jump‐and‐return scheme for water suppression The RNA samples were annealed by heating at 95 °C for 5 min followed by snap cooling on ice. Purified S4‐SS and We had already constructed S4‐S, which comprises two stem loops (stem II and III) and one multibranched loop from the 17th to 61st nucleotide of S4, the 5′‐GGA for effective transcription initiation by T7 RNA polymerase, and a 3′‐UCCA for stabilising the stem I structure, resulting in a length of 52 nucleotides  of S4‐SS for RD binding, calculated from the SPR profiles, was 0.289 ± 0.006 nm, whereas those of S4 and S4‐S were 0.044 ± 0.002 and 0.034 ± 0.004 nm, respectively , two hairpin loops and one multibranched loop. Mutational analysis of the aptamer revealed that the three CG base pairs  in stem II are important for RD binding, although the mutation at the C12–G17 base pair had moderate effects on binding affinity Table . TherefoAlthough S4‐SS seems to have the DNA‐mimicking motif, the degree of importance of the motif in S4‐SS differs from those in Apt1‐S and RDE. Mutations at C12–G17 in S4‐SS  and R80, which contact each other directly, showed complete loss of binding activity in the case of Apt1‐S and RDE 2 revealed that the aptamer binds to the DNA‐binding site of a transcription factor by mimicking DNA 2 had no resemblance to the target DNA in terms of sequence and secondary structure. However, comparison of crystal structures revealed that a DNA guanine recognised by NF‐κB p50 is replaced by two uracils in the NF‐κB p50–aptamer complex. Thus, the aptamer mimics the target DNA elements with its tertiary structure, but the base specificity is different from that of DNA. The knowledge obtained from studies of NF‐κB p50–aptamer and our current results that base specificity differs slightly from that of Apt1‐S and RDE suggested to us that we can obtain aptamers that bind to RD without a DNA‐mimicking sequence by carrying out SELEX using different conditions, primers and so on.The intensive analyses of an RNA aptamer against the NF‐κB p50 homodimer (p50)et al. 2) than those between antibodies and proteins (560–1300 Å2). However, binding affinity is not necessarily correlated with the size of the binding surface. They analysed the shape complementarity index (SC) Gelinas In summary, NMR and mutational analyses have shown the binding properties of S4‐SS binding to RD. We revealed that a high affinity of S4‐SS to RD is achieved by the multiple contacts of DNA‐mimicking motif and multibranched loop. Our results including those described in previous reports may be useful for the rational design of aptamers against many other proteins.RA, KT and TS conceived and designed the experiments; KT and RA performed the experiments and analysed the data; Y Nomura, YT, SS, TN, MK, Y Nakamura and TK contributed reagents/materials/analysis tools; TS, RA and TN wrote the manuscript.Fig. S1. SPR analysis of S4‐SS mutants binding to RD.Fig. S2. SPR analysis of S4‐SS binding to RD mutants.Click here for additional data file.


Herein, it is found that tetramethylene sulfone (TMS) and lithium nitrate (LiNO3) form the optimum electrolyte, which greatly reduces the overpotential at charge, exhibits superior oxygen efficiency, and allows stable cycling for 100 cycles. Linear sweep voltammetry (LSV) and differential electrochemical mass spectrometry (DEMS) analyses reveal that neat TMS is stable to oxidative decomposition and exhibit good compatibility with a lithium metal. But, when TMS is combined with typical lithium salts, its performance is far from satisfactory. However, the TMS electrolyte containing LiNO3 exhibits a very low overpotential, which minimizes the side reactions and shows high oxygen efficiency. LSV‐DEMS study confirms that the TMS‐LiNO3 electrolyte efficiently produces NO2−, which initiates a redox shuttle reaction. Interestingly, this NO2−/NO2 redox reaction derived from the LiNO3 salt is not very effective in solvents other than TMS. Compared with other common Li‐O2 solvents, TMS seems optimum solvent for the efficient use of LiNO3 salt. Good compatibility with lithium metal, high dielectric constant, and low donicity of TMS are considered to be highly favorable to an efficient NO2−/NO2 redox reaction, which results in a high‐performance Li‐O2 battery.To fabricate a sustainable lithium‐oxygen (Li‐O The carbon cathode is prone to an oxidative attack by active species at high overpotential to produce parasitic gas CO2, resulting in poor rechargeability in the cell.2 cell components. The cell electrolyte is exposed to nucleophilic attack by the reactive reduced products  present on the cathode during the discharge process, and is vulnerable to reactive lithium metal on the anode during cycling. The by‐products from the electrolyte decomposition produce a significant increase in the cell voltage during the charging process, which promotes oxidative decomposition of the electrolyte. Repeated cycling of the cell produces side reactions, and the resulting parasitic products accumulate on the cathode, which eventually results in cell failure after a brief cycle life.2 battery rests on identifying and developing a highly stable and optimized electrolyte.The lithium‐oxygen Li‐O battery 2 battery chemistry. In this work, tetramethylene sulfone (TMS) was found to afford much higher stability than other typical Li‐O2 solvents such as dimethylacetamide (DMA), dimethyl sulfoxide (DMSO), and tetraethylene glycol dimethylether (TEGDME). In spite of its stability, however, the cycle performance of the Li‐O2 cell employing a TMS‐based electrolyte was far from satisfactory. Other studies have reported that TMS electrolytes exhibited a high charge potential above 4 V, low capacity retention on cycling, and rapid capacity fading over a short cycle life.2 battery.Lithium battery electrolytes generally comprise aprotic, organic solvents, and lithium salts. The organic solvents must have several desirable properties including oxidative stability, compatibility with lithium metal, low volatility, and high solubility for lithium salt. Of these properties, stability to oxidative decomposition and compatibility with lithium metal are of the utmost importance for stable operation in the Li‐O3) salt was included in the electrolyte in a Li‐O2 cell, the cell exhibited better cycle performance than when other lithium salts were used, including lithium bis(trifluoromethane) sulfonimide (LiTFSI).3 in place of LiTFSI resulted in the production of a stable solid‐electrolyte interphase (SEI) on the negative electrode that protected the labile DMA solvent from the lithium metal anode and the stable Li‐O2 cell successfully executed more than 80 cycles. Sharon et al.3 in a diglyme solvent to form an electrolyte that exhibited lower charging overpotential than a similar electrolyte containing LiTFSI. In our recent work,In several recent literature reports, when the lithium nitrate  analysis, which showed that a Li‐O2 cell charged with this electrolyte exhibited far superior performance than similar TMS‐based electrolytes containing other lithium salts. In an extended study, other typical solvents such as DMA, DMSO, and TEGDME were also combined with LiNO3 in a comparative study of their performance. Interestingly, the experimental results indicated that the performance of the salt is strongly affected by the solvent used to prepare the electrolyte.Encouraged by these results, in this current work TMS was combined with LiNO3 salt promoted improved performance in Li‐O2 and to identify the reasons why the improved effect of the salt is strongly dependent on the solvent. Employing linear sweep voltammetry (LSV) combined with DEMS analysis, the formation of NO2− was quantified and the oxidation potentials of the redox mediators, NO2− to NO2, were determined. The formation of NO2− and the subsequent NO2−/NO2 redox shuttle reactions were found to be greatly dependent on the solvent properties including stability, dielectric constant, and donicity. By comparing all of the quantitative DEMS results of the various electrolytes, the solvent that exhibited better performance with LiNO3 was identified. Consequently, these results may provide a suitable guide for the identification of an optimum electrolyte for the high‐performance Li‐O2 battery.The main objectives of this study are to determine why the LiNO22.1Figure2 cell used for this work. In order to compare the oxidative stability of the solvents, the Li‐O2 cells were prepared by assembling all the cell components after they had been individually saturated with each solvent containing 1 m LiTFSI. The LSV‐DEMS analysis was conducted on the pristine Li‐O2 cells under an argon atmosphere subjected to a voltage sweep from open‐circuit voltage (OCV) to 5.0 V to measure the change in the anodic (oxidative) current as well as the evolution of CO2 and H2 gases due to any solvent decomposition reactions.Figure2 electrolyte solvents such as DMA, DMSO, and TEGDME. Oxidative decomposition of the solvents occurs primarily at high potentials during the charging process in the Li‐O2 cells.2 cells should have a wide electrochemical voltage window to ensure good oxidative stability at high potentials. Figure 2 cells containing TMS, DMA, DMSO, and TEGDME electrolytes. As can be seen, the Li‐O2 cell with the DMA generated a significant amount of anodic current even at potentials of 3.35 V, followed by a substantial increase in the current from 4.5 V. The DMSO‐LiTFSI Li‐O2 cell exhibited a gradual increase in anodic current from 4.0 V and then exhibited an abrupt increase at 4.4 V, indicating that a considerable amount of oxidative decomposition occurred in the electrolyte at this potential. Although the Li‐O2 cell with the TEGDME electrolyte displayed better stability as evidenced by the small amount of anodic current generated to 4.5 V, after reaching 4.7 V the cell exhibited a significant increase in the anodic current, indicating that some electrolyte decomposition had occurred in the cell at that potential. Contrary to the other solvents, no detectable current was observed in the cell containing the TMS electrolyte until the cell reached 4.7 V and even after this potential was attained, only a small amount of current was detected, which was indicative of the better oxidative stability of the TMS electrolyte. This result agreed well with the experimental results presented in a recent report,2 solvents such as DMSO (4.8 V), TEGDME (5.3 V), and dimethyl formamide . Bardé et al.2 gas, which is a typical parasitic gas that results from the oxidative decomposition of the solvents, was assessed for the various test cells during the LSV scan, and the results are presented in Figure 2 gas beginning at a cell voltage of 4.4 V, which was the lowest onset potential of all the solvents. At 4.7 V, DMA and TEGDME exhibited a higher onset potential than DMSO. In the Li‐O2 cells containing TMS, a negligible amount of CO2 gas was detected even up to a cell voltage of 5.0 V. Based on the results of the highest onset potential for the generation of anodic current and CO2 evolution, together with the results for the amount of gas generated during the LSV scan to 5.0 V, it was concluded that TMS possessed the highest oxidative stability of the solvents tested.Evolution of CO2, another parasitic reaction gas, during the LSV testing. In the case of DMA, H2 gas began being evolved at 3.5 V, which is the lowest onset potential of all the solvents, and after this potential was reached, a significant increase in the rate of H2 gas evolution was observed at 4.4 V. A possible source for the evolution of H2 gas in the cell may be found in the reductive decomposition of the solvents as a result of their reaction with the lithium metal anode.m LiNO3 was used as a supporting salt in place of 1 m LiTFSI, the DMA‐LiNO3 electrolyte exhibited a much higher onset potential for H2 gas evolution than the DMA‐LiTFSI electrolyte as shown in Figure S1b (Supporting Information). This was interpreted to mean that a surface layer had been formed on the lithium metal by its reaction with the LiNO3 salt, which passivated the lithium prohibiting the DMA solvent from contact with the native lithium metal as suggested in the previous publications.2 gas with the addition of LiNO3 suggested that the reactive lithium metal was a possible cause for the parasitic H2 gas evolution. It is noteworthy that the evolution profile of the H2 gas closely resembled the profile of the anodic current during the LSV potential scan. When the lithium salt in the electrolyte was changed from LiTFSI to LiNO3 in DMA, the evolution of H2 gas was suppressed and the onset potential for gas evolution shifted from 3.5 to 4.0 V . A similar result was observed in the profile of the anodic current with the onset potential shifting from 3.3 to 4.0 V . Replacement of the lithium metal with partially charged lithium iron phosphate (LFP) as an anode in the Li‐O2 cells containing the TEGDME‐LiTFSI electrolyte confirmed that the pure lithium metal may react with the electrolyte solvents resulting in the evolution of H2 gas . The H2 gas evolution was substantially subdued up to 4.8 V with the LFP as the anode in the cell, whereas the Li‐O2 cell with pure lithium metal anode generated a considerable amount of H2 gas beginning at 4.4 V. Therefore, it appeared that the stability of the solvents to lithium metal could be estimated by observing the onset potential and amount of H2 gas evolved during LSV‐DEMS analysis. From this perspective, TMS was considered to be far more compatible with lithium metal than the other solvents, because this solvent showed the highest onset potential and the lowest amount of H2 evolved as depicted in Figure 2 evolution. In addition, H2 gas began to evolve at 4.2 V for DMSO and 4.4 V for TEGDME, implying that DMSO was less stable to lithium metal than TEGDME. Overall, the LSV‐DEMS results in Figure Figure 2.22 cells was investigated using in situ DEMS as shown in Figure2 gas evolution during charging of the cell, which was indicative of oxidative parasitic reactions. The CO2 gas ratio (rCO2) in the first cycle was not very high (1.2%), but the value greatly increased from the second to the fifth cycles, eventually reaching more than 11%. This implied that additional oxidative decomposition reactions were occurring as the cell was cycled  was greatly decreased from 76% in the first cycle to a mere 44% in the fifth cycle .The TMS electrolyte was prepared by dissolving a typical lithium salt, LiTFSI, in TMS and the performance of the resulting Li‐Od Figure b,h. The e Figure c,i. CyclFigure2 cell loaded with Li2O2 that was produced by performing two discharge–charge cycles and a third discharge process to a cell discharge capacity of 1000 mA h gc−1. The LSV‐DEMS analysis monitored the evolution of O2 gas, which resulted from the oxidation of Li2O2, and the generation of CO2 gas, which was due to the oxidative decomposition of the parasitic reaction products during the oxidative scan from OCV to 4.7 V. This LSV‐DEMS study of the cell containing deposited Li2O2 provided information of the onset potential and peak potential, where the evolution of O2 and CO2 gases began and reached their maximum, as reported in our previous publications.2 gas evolved at 3.2 V as shown in Figure 2 evolution occurred from 3.8 to 4.5 V with a maximum value at 4.35 V. The CO2 gas was detected from 4.3 to 4.6 V with a peak value at 4.45 V, which resulted in a large overlap in the gas evolution potential regions of these two gases (O2 and CO2). In other words, the oxidation of Li2O2  was inevitably accompanied by a parasitic reaction and evolution of CO2 gas, which occurred from 4.3 to 4.6 V. When the CO2 gas evolution profile in the Li2O2‐loaded Li‐O2 cell . Actually, there was a kind of similarity in the cycle performance of the TMS electrolytes containing LiTFSI, LiClO4, and LiTf. As the various Li‐O2 cells with these three lithium salts proceeded from the first to the fifth cycle, the charge potential increased (and hence the energy efficiency decreased by approximately 3%), the oxygen efficiency during charge deteriorated by ≈30% for LiTFSI, and ≈20% for LiClO4 and LiTf, and the CO2 gas evolution increased by ≈10%. Similar amounts and trends were observed in the evolution of CO2 gas during five‐cell cycles . This low overpotential during cell charging in TMS‐LiNO3 electrolyte enhanced the cell's energy efficiency during cycling to 80%, which was about 10% higher than that of the TMS electrolytes with other lithium salts . It is noteworthy that the TMS‐LiNO3 electrolyte exhibited a stable evolution of oxygen during the whole charge process and during the five cycles. Oxygen gas was evolved in nearly similar quantities from the start to the end in the charge process in the TMS‐LiNO3 electrolyte as shown in Figure 2 fluctuated during the charge process in the TMS electrolytes prepared with lithium salts other than LiNO3 . In other words, the lower charge overpotential in the TMS‐LiNO3 electrolyte was the key factor in preventing a parasitic reaction and suppression of CO2 gas evolution in that Li‐O2 cell.When the evolution patterns and potentials of the O2.33 electrolyte exhibit such a low potential for oxidation of Li2O2 will be addressed next. A LSV‐DEMS investigation of the pristine Li‐O2 cell (without loaded Li2O2) with the TMS‐LiNO3 electrolyte confirmed that NO2− existed in the electrolyte and it was oxidized to NO2 during the oxidative scan. Three kinds of Li‐O2 cells each with a different TMS electrolyte were tested. These electrolytes separately contained (a) 1 m LiTFSI and 0.02 m NaNO2, (b) 1 m LiTFSI, and (c) 1 m LiNO3 . The three individual cells were scanned from OCV to 4.5 V while monitoring the anodic current and NO2 gas evolution using DEMS. In the TMS‐LiTFSI electrolyte with 0.02 m NaNO2, a noticeable amount of oxidative current and NO2 gas was observed in the potential range between 3.5 and 3.7 V as shown in Figure S5a (Supporting Information). However, there was no detectable current or NO2 gas in the TMS‐LiTFSI electrolyte in the same potential region . It was believed that NO2− in the TMS‐LiTFSI electrolyte with NaNO2 was oxidized to NO2 during the LSV scan and then it was evolved as a gas. NO2 is capable of oxidizing Li2O2 to produce Li+ and O2 when it is reduced to NO2− as verified in a recent report by Uddin et al.2 + Li2O2 → 2NO2− + 2Li+ + O2. It would appear then that NO2 played the role of redox mediator for the oxidation of Li2O2, which resulted in the evolution of O2 gas in the potential of the NO2−/NO2 redox reaction. To confirm this speculation, a Li‐O2 cell that contained the TMS‐LiTFSI electrolyte with NaNO2 was prepared and then Li2O2 was deposited on the cathode using the previously described discharge process to a cell capacity of 1000 mA h gc−1. As shown in Figure S6a (Supporting Information), the LSV‐DEMS analysis of this cell revealed that O2 gas was indeed generated in a similar potential region to that of NO2 gas evolution as shown in Figure S5a (Supporting Information). When this result was compared with the O2 evolution profile of the TMS‐LiTFSI electrolyte in Figure 2 evolution  was significantly lowered by the addition of 0.02 m NaNO2 as shown in Figure S6a (Supporting Information). The peak potential, at which a maximum amount of O2 is evolved, was shifted from 4.45 V . As a result, the charge overpotential was also considerably reduced by the addition of 0.02 m NaNO2 as shown in Figure S6b (Supporting Information). There was an evident flat plateau at 3.5 V in the charging of Li‐O2 cell containing the TMS‐LiTFSI electrolyte with NaNO2, whereas the TMS‐LiTFSI electrolyte alone showed a much higher overpotential during charge as shown in Figure The question of what makes the TMS‐LiNO3 electrolyte exhibited an anodic current and NO2 gas evolution in the same potential region of 3.5–3.7 V as that of TMS‐LiTFSI with 0.02 m NaNO2. These LSV‐DEMS results appeared to indicate that NO2− was present in the TMS‐LiNO3 Li‐O2 cell and it could act as a redox mediator as was the case in the TMS‐LiTFSI with NaNO2. The NO2−/NO2 redox shuttle reaction was believed to be the reason why the TMS‐LiNO3 electrolyte exhibited a low potential of 3.55 V for the oxidation of Li2O2 , the concentration of NO2− contained in the TMS‐LiNO3 Li‐O2 cell was estimated to be 0.011 m, assuming that the amount of evolved NO2 gas was proportional to the concentration of NO2− ((0.70/1.23) × 0.02 m = 0.011 m).In Figure S5c (Supporting Information), it can be seen that TMS‐LiNO2 Figure d, and he2.43 salt is also as effective in other solvents such as DMA, DMSO, and TEGDME. The LSV‐DEMS data shown in Figure2 gas and NO2−/NO2 redox reaction were not as effective in the other solvents containing LiNO3. In the DMA and DMSO electrolytes containing LiNO3, no NO2 gas or anodic current was detected in the potential range of 3.5–3.8 V in the LSV‐DEMS scans . This result indicated that the quantity of NO2 gas evolved was dependent on the concentration of NO2− in the electrolyte. Therefore, no detection of NO2 gas in DMA‐LiNO3 and DMSO‐LiNO3 electrolytes meant that NO2− was not present in the electrolytes and the NO2−/NO2 redox shuttle reaction would not work during the charging process.The next question to be answered is whether the LiNOm LiTFSI and 0.02 m NaNO2 (except for DMA with 1 m LiNO3 and 0.02 m NaNO2) and (b) 1 m LiNO3 (except for TEGDME with 0.5 m LiNO3) are compared in Figure2 exhibited a flat plateau in the potential of around 3.5 V during the charging process, which seemed to indicate that oxidation of Li2O2 via NO2−/NO2 redox shuttle reaction occurred. A similar profile was also observed in TMS‐LiNO3 electrolyte, possibly due to the same redox reaction. However, the DMA and DMSO electrolytes containing NaNO2 displayed a charge plateau at 3.55 V, whereas the electrolytes with LiNO3 exhibited a charge profile at much higher potential of about 3.7 V. It is believed that this difference occurred, because the NO2−/NO2 redox shuttle reaction was not working during the charging process in DMA or DMSO electrolytes containing LiNO3.The voltage profiles of the solvents with (a) 1 3 electrolyte, NO2 gas was detected in the potential region of 3.5–3.7 V . It seemed that such a low quantity of NO2 gas was not adequate for an efficient NO2−/NO2 redox shuttle reaction. The charge profile of TEGDME‐LiNO3 electrolyte in Figure 2−/NO2 redox shuttle reaction may have occurred, but after this region another mechanism for Li2O2 oxidation was activated and it became dominant. The concentration of NO2− in the TEGDME‐LiNO3 electrolyte might not be adequate for the redox reaction to be maintained during the entire charge process. Hence, the redox reaction occurred in the early stage of the charge process, which was followed by another oxidation process for Li2O2 in the remaining charge period. For comparison, TEGDME‐LiTFSI with the NaNO2 electrolyte contained sufficient NO2−, and, as a result, it maintained a flat voltage plateau from the beginning to the end of the oxidation of Li2O2 as shown in Figure In the case of the TEGDME‐LiNO2.52− in the solvents containing LiNO3 was greatly affected by the solvent's stability with the lithium metal anode. As previously mentioned and illustrated in Figure 2− can be produced by the reaction of LiNO3 with lithium metal and it acts as a redox mediator in the oxidation of Li2O2 following the sequential reactions of; (1) generation of NO2− on the lithium metal anode: 2Li + LiNO3 → Li2O + LiNO2; (2) oxidation of NO2− on the cathode: NO2− → NO2 + e−; and (3) reduction of NO2 with the reaction of Li2O2: 2NO2 + Li2O2 → 2NO2− + 2Li+ + O2. To generate NO2−, lithium metal must react with LiNO3 via the above reaction pathway (1). When the solvent has insufficient stability and the reaction of lithium metal with the solvent is dominant, the formation of NO2− via pathway (1) might be greatly impeded. As discussed in Figure 2 gas. It appeared that the high reactivity of these solvents severely interfered with the formation of NO2−. It is also possible that after NO2− was formed by the reaction of lithium metal and LiNO3, NO2− was attacked or damaged by the reaction between the lithium metal and the solvent, and was transformed into other chemical species. However, the LSV‐DEMS results for the four types of solvents with 0.02 m NaNO2 , which compares the DMA‐LiTFSI and DMA‐LiNO3 electrolytes. This higher stability may result from the SEI layer formed by the reaction of LiNO3 with the lithium metal. Aurbach et al. confirmed that insoluble LixNOy species are formed on the surface of lithium metal upon reaction with LiNO3 in their detailed investigation performed using Fourier transform‐infrared (FT‐IR) and X‐ray photoelectron spectroscopy (XPS) with a specialized apparatus.In a recent study by Walker et al.3 produces NO2 gas due to its higher stability, but the quantity of the gas is much smaller than in TMS. It should be noted that TEGDME has much lower dielectric constant than the other solvents ,3 salt. Dielectric constant is known a rough measure of polarity of solvents,3 to produce a 0.5 m solution, in our investigation. It can be possible that such low polarity is not favored for the effective formation of NO2− via the above reaction pathway (1) or limits solubility of NO2− in TEGDME solvent.Compared to DMA and DMSO, TEGDME with LiNO2 gas evolution during the LSV scans was dependent on the solvents as shown in Figure 2 evolved NO2 gas in the potential region of 3.5–3.7 V with a maximum at 3.55 V, which was similar to TEGDME. In the case of DMA and DMSO, the NO2 gas evolution occurred at a higher potential; the maximum potential was 3.7 V for DMA and 3.75 V for DMSO. The different potentials for the gas evolution was believed to be closely related to the solvating power of the solvents, which can be expressed by Gutmann donor number.−1, respectively).2− could interact with a cation  and the resulting agglomerates of Li+ and NO2− would be solvated by the solvent molecules, which is strongly affected by the donicity of the solvent.+ and NO2− would be strongly solvated by the solvent molecules, which would impede the oxidation reaction of NO2− on the cathode surface and result in a high polarization. Therefore, the oxidation of NO2− would occur at higher potentials during the LSV scan than in the case of TMS or TEGDME with a lower donicity. This would result in the evolution of NO2 gas at a higher oxidation potential in DMA and DMSO. This shift in the NO2 evolution potential would also influence the charge potential as shown in Figure 2 showed a higher charge potential than the TMS and TEGDME electrolytes. This indicated that the lower donicity of TMS was favorable to a lower overpotential in the charge process as a result of the NO2−/NO2 redox reaction. Overall, with high stability, high dielectric constant, and low donicity, TMS appeared to be the optimum solvent for an efficient NO2−/NO2 redox reaction derived from LiNO3.In another interesting point, it should be noted that the potential for NO2.63 electrolyte exhibited superior cycle performance in comparison to other solvents containing LiNO3. In Figure Figure2 and CO2 gases in the Li‐O2 cells with TMS, DMA, DMSO, and TEGDME electrolytes containing LiNO3 are shown during a five‐cycle test, which was performed using DEMS. Figure 2 ratio, and charge oxygen efficiency of the four electrolytes, and it can be seen that the performance of the TMS electrolyte was excellent. The first attribute of the TMS‐LiNO3 can be seen in the energy efficiency  increased from 2.4% in the first cycle to 4.6% in the fifth cycle. In DMSO, the value increased from 3.1% in the first cycle to 5.5% in the fifth cycle. In TEGDME electrolyte, rCO2 increased from 0.6% in the first cycle to 2.6% in the fifth cycle. In the area of oxygen efficiency, the TMS‐LiNO3 electrolyte was far better than other electrolytes  of as much as 81% in the first cycle, which was 10–20% higher than the other electrolytes . In addition, the gap in the oxygen efficiency became wider as the cycling proceeded. The TMS‐LiNO3 electrolyte achieved an 83% oxygen efficiency at the fifth cycle, while the DMA, DMSO and TEGDME electrolytes exhibited only 62%, 53%, and 64%, respectively.As a result, the TMS‐LiNOy Figure j. TMS eln Figure k. All ofs Figure l. The TM3 electrolyte, its oxygen efficiency and CO2 gas evolution were measured over 20 cycles using DEMS. Although there was some fluctuation, the oxygen efficiency at charge maintained a relatively stable value at around 80% during the 20 cycles . The Li‐O2 cell with the DMA electrolyte showed an abrupt capacity fade after just 20 cycles, which may have been due to continuous parasitic reactions and the accumulation of byproducts. The DEMS results in Figure S10b (Supporting Information) clearly show that the Li‐O2 cell maintained a stable oxidation of Li2O2 at charge achieving a charge oxygen efficiency of 80% and, moreover, the cell evolved almost no CO2 gas even at the 102nd cycle. All these results demonstrated that the TMS‐LiNO3 electrolyte sustained superior cycle performance for more than 100 cycles.In order to investigate the long‐term cycle performance of the TMS‐LiNO2 cell with the TMS‐LiNO3 electrolyte achieved a full capacity of about 6000 mA h gc−1 at a current density of 300 mA gc−1, which required more than 40 h for discharge and charge cycling as shown in Figure S12a (Supporting Information). There was a noticeable plateau at 3.5 V during the charge profile at full depth of discharge, which was indicative of good operation of the NO2−/NO2 redox shuttle, similar to that at the controlled capacity of 1000 mA h gc−1 as shown in Figure c−1, together with a very low amount of CO2 gas (rCO2 < 0.3%). The scanning electron microscopy (SEM) image and X‐ray diffraction (XRD) data in Figure S12c,d (Supporting Information) revealed that the discharge product was a crystalline Li2O2 with a toroidal shape with a diameter of about 0.5 µm. The toroidal Li2O2 particles deposited on the carbon cathode during the discharge were completely removed from the cathode surface following the charge process, as presented in SEM images in Figure S13 (Supporting Information) and XRD peaks in Figure S12d (Supporting Information). It is noteworthy that the large particles of Li2O2 are indicative of dominant occurrence of solution mechanism during the discharge process.3 electrolyte is favorable for enhancing discharge capacity.The Li‐O33 is very effective in reducing the charge overpotential of a Li‐O2 battery through a NO2−/NO2 redox couple that suppresses parasitic side reactions, producing long‐term reversible cycling. In addition, TMS was found to be the optimum solvent making effective use of the LiNO3 salt in an electrolyte compared to other typical solvents such as DMA, DMSO, and TEGDME. A detailed investigation using LSV‐DEMS analysis revealed that the TMS‐LiNO3 electrolyte effectively produces NO2−, which initiates the NO2−/NO2 redox reaction. The high compatibility of TMS with lithium metal was assumed to play a key role in the effective generation of NO2−, compared to DMA and DMSO. The high dielectric constant of TMS is yet another advantage of this solvent over TEGDME, which helped to facilitate the generation of NO2− in the electrolyte. The low donicity of TMS allowed a lower redox potential of NO2−/NO2 couples and a reduced potential at the charging process. Overall, TMS appeared to be highly favorable for fostering an efficient NO2−/NO2 redox reaction, which produced a very low charge overpotential in the Li‐O2 cell, suppressed CO2 evolution, improved the oxygen efficiency and energy efficiency of the cell, and promoted a stable, long‐term cycle behavior for more than 100 cycles.In this work, as a result of a thorough evaluation and careful analysis, it is concluded that LiNO42 CellsPreparation of Li‐O: Coin‐type Li‐O2 cells was composed of a Li metal anode, a glass microfiber membrane separator, and a Ketjen black  cathode. In the cycling test system, high‐purity oxygen gas (>99.999%) was fed through the inlet capillary attached to the upper side of the cell holder into the KB cathode. In a typical Li‐O2 cell cycle, a current of 200 mA gc−1 (gc: weight of KB in the cathode) was applied for 5 h for both the discharging and charging of the cell with a cutoff potential of 2.0 V for the discharge and 5.0 V for the charge. All the potentials reported in this paper were displayed relative to the voltage of the Li/Li+ couple, unless otherwise stated.In Situ DEMS Analysis: The consumption of O2 during the cell discharge and evolution of O2 and other gaseous products during the cell charge were quantitatively measured using in situ DEMS analysis. The details of the experimental apparatus and the operation of the in situ DEMS for the Li‐O2 cells have been reported in our previous publications.LSV‐DEMS Analysis: The Li‐O2 cells containing a Li metal anode, an electrolyte‐soaked separator, and the KB cathode were assembled in an Ar‐filled glove box and then used for LSV‐DEMS analysis according to the methods reported in our previous publications.−1.The authors declare no conflict of interest.SupplementaryClick here for additional data file.