Herein, we study activation of parahydrogen (nuclear spin‐0 isomer of H2) by a number of 4‐ and 5‐membered pnictogen biradicaloids based on hetero‐cyclobutanediyl [X(μ‐NTer)2Z] and hetero‐cyclopentanediyl [X(μ‐NTer)2ZC(NDmp)] moieties . The concerted mechanism of this reaction allowed observing strong nuclear spin hyperpolarization effects in 1H and 31P NMR experiments. Signal enhancements from two to four orders of magnitude were detected at 9.4 T depending on the structure. It is demonstrated that 4‐membered biradicaloids activate H2 reversibly, leading to SABRE  hyperpolarization of biradicaloids themselves and their H2 adducts. In contrast, the 5‐membered counterparts demonstrate rather irreversible parahydrogen activation resulting in hyperpolarized H2 adducts only. Kinetic measurements provided parameters to support experimental observations.Biradicaloids attract attention as a novel class of reagents that can activate small molecules such as H Metal‐free parahydrogen activation by a series of four‐ and five‐ membered pnictogen biradicaloids is reported. Strong 1H and 31P hyperpolarization effects induced by parahydrogen were observed and discussed. The symmetry of the activator biradicaloids strongly affected the shape and amplitude of the hyperpolarization signals. Signal enhancements of more than three orders of magnitude were detected at 9.4 T. For instance, recent applications include photoswitching,1–8). These are mainly N−B intramolecular frustrated Lewis pairs (FLP) (1–7) such as ansa‐amino boranes,7.8 was demonstrated to show hyperpolarization effects both in 1H and 31P NMR spectra while activating parahydrogen molecules.8‐H2 adduct led to observation of unusual 1H and 31P signal multiplets with enhancements exceeding at least one order of magnitude at 7 Tesla.So far, a few metal‐free systems were demonstrated to produce nuclear spin hyperpolarization effects with parahydrogen Scheme , 1–8. Th9–12, addressing the nuclear spin hyperpolarization effects observed upon activation of parahydrogen molecules. We show that all these biradicaloids can activate H2, which leads to strong hyperpolarization of biradicaloid‐parahydrogen adduct molecules. We demonstrate that H2 activation properties of four‐ and five‐membered biradicaloids are different since the former ones show facile reversible activation that leads to metal‐free SABRE of the biradicaloids themselves and incorporation of As atoms promotes the reversibility. Unusual hyperpolarization effects related to the biradicaloid structures were detected.Herein, we study promising novel pnictogen  biradicaloids 8–10 at room temperature. The experiments were performed by bubbling parahydrogen gas through biradicaloid solutions in a 9.4 T magnet, which was followed by a π/4‐pulse and NMR signal detection (see Supporting Information). The corresponding 1H NMR spectra are shown in Figure First, we studied nuclear spin hyperpolarization effects for the four‐membered biradicaloids 9 demonstrated the most prominent hyperpolarization effect, leading to a substantial enhancement of P−Ha and As−Hb 1H NMR signals of 9‐H2 adduct. As the parahydrogen activation takes place at the high magnetic field, the antiphase signals are observed in this case, as it should be for a weakly coupled spin system according to PASADENA experiment in theory.[J‐coupled 31P, the enhanced antiphase resonances are split for the both Ha and Hb protons . The measured enhancement factor ϵ is ca. 250 at 9.4 T and 298 K.The asymmetrical As−P biradicaloid n theory.8a, 9 10) and P−P (8) counterparts cannot be interpreted in a straightforward way as compared to 9 , and this feature complicates corresponding NMR signal shapes significantly. Since the spin pairs of the same kind are strongly coupled, the resulting NMR spectra are expected to have complex signal structures due to the second‐order effects.Hyperpolarization effects for the symmetrical As−As 0 and P−P10 has a clear central transition in the thermal 1H NMR spectrum, while the strongest hyperpolarization effects are observed at the side bands . Our attempts to simulate thermal and hyperpolarized spectra based on computed J‐couplings  didn't provide good matching of the experiment and theory, most likely due to the quadrupolar nature of 75As. Indeed, among possible reasons for the discrepancy are errors in the computed parameters, presence of relatively fast chemical exchange that modulates J‐coupling constants and strong quadrupolar relaxation of 75As (spin‐3/2). The latter two sources should lead to shifts of spectral lines and to the scalar relaxation of the first and the second kinds, respectively.Our measurements show that As−As derivative 1H signal enhancements for 8 are more than one order of magnitude, as was also shown elsewhere.8 earlier.9, the substantial 1H polarization can be efficiently transferred to 31P nuclei. For instance, we utilized recently introduced ESOTHERIC (Efficient Spin Order Transfer via Relayed Inept Chains)31P NMR spectra at 308 K after the polarization transfer and after the relaxation to thermal equilibrium. The comparison reveals a 31P signal enhancement factor (ϵ) of 2000 at 9.4 T for 9‐H2 adduct, demonstrating the high efficiency of this process. We should note that this is an unusually high signal enhancement for metal‐free parahydrogen activations, highlighting the high efficiency of this biradicaloid system. Moreover, the reversibility of H2 activation/addition by 9 opens a way for hyperpolarizing this molecule simply by waiting for 9‐H2 to dissociate. Indeed, setting a 300 ms delay between polarization transfer pulse‐sequence and acquisition leads to the 31P NMR spectrum depicted in Figure 9‐H2 the initial resonance of 9 becomes strongly enhanced with the factor of ca. 1000‐fold. The hyperpolarization of molecules without the actual chemical modification is the feature of SABRE technique. There is a natural difference between our observations and the original SABRE experiment,15N hyperpolarization with FLPs based on ansa‐aminoboranes.In the case of mixed As−P biradicaloid 9, heteronuclear hyperpolarization with the symmetrical four membered bicadiacaloids 8 (31P) and 10 (75As) may not require the use of any polarization transfer pulse sequences. In both cases, the symmetry properties of the resulting AA'XX’, wherein A and A’ nuclei come from parahydrogen, allows inherent hyperpolarization of X nuclei. This feature was demonstrated for 31P in 8‐H2 adduct,75As in 10‐H2, but no visible enhancement was detected. Most likely, this is due to the destructive effect of the fast 75As relaxation leading to extremely broad 75As NMR signals and collapsing partially the multiplet structure.In contrast to 8–10 is the reversibility of H2 addition under ambient temperatures, which leads to the dynamic exchange between bound and free H2 pools. This feature allows one to hyperpolarize the corresponding adducts constantly, provided that the continuous supply of parahydrogen is present. It should be noted that the reverse reaction rate increases with replacement of P atoms with As. For instance, at room temperature the dissociation of baradicaloid‐H2 adducts is characterized by the experimentally measured rate constants (kdis) 1×10−4, 0.15, 24 s−1 for 8, 9 and 10, correspondingly. The replacement of P with As leads to a clear gradual decrease of the activation energy .In contrast to the four‐membered biradicaloids, the five‐membered ones, 11‐H2. The direct comparison of the resulting signal amplitudes with those in thermal spectra shows ca. 120‐fold signal enhancement. We should note, however, that the H−H spin coupling in the adduct is only about 2.5 Hz whereas the linewidth is ca. 2 Hz, which led to a significant mutual cancellation of the signal components of the antiphase doublets. The lineshape fitting predicted at least 250‐fold enhancement. Moreover, the determination of the exact enhancement factor through comparison to the thermal spectra is not a straightforward procedure under such experimental conditions because of the continuous accumulation of the adduct 11‐H2. Therefore, 250 is the lowest possible estimate. The 1H hyperpolarization was easy to transfer to 31P nuclei via coherent polarization transfer with ESOTHERIC, providing 130‐fold signal enhancements for both phosphorus centers in 11‐H2 .Due to the reaction irreversibility, the hyperpolarization effects were observed only at the first moments after parahydrogen bubbling for adduct 12 with parahydrogen at room temperature resulted in observation of thermally polarized signals of 12‐H2 in 1H NMR spectra , meaning that this process does not lead to the observable polarization effects. The mechanism of parahydrogen activation with this biradicaloid most likely is still pairwise, but H atoms originating from parahydrogen are not coupled strongly enough by J‐coupling. In this circumstance, the parahydrogen nascent nuclear spin order cannot be efficiently converted into the observable magnetization, thus effectively producing no hyperpolarization effects. The corresponding J‐coupling constant of ≤1 Hz estimated by analysis of experimental thermal spectra supports this conclusion.The reaction of 1H and 31P NMR spectra of 12‐H2 were nevertheless observed by heating its equilibrium solution in the presence of parahydrogen. For instance, in‐phase doublet of 12‐H2 in 31P NMR spectrum was converted into antiphase doublet at 334 K, indicating the weak but still detectable reversibility of the H2 activation by 12 . Similar effect was observed also by heating 11‐H2 but at a higher temperature (373 K) , implying that activation H2 by 11 also shows signs of reversibility. Attempts to synthesize the five‐membered As−As analogue of 11 and 12 were not successful, since the initial four membered biradicaloid did not react with 2,6‐dimethylphenyl‐isonitrile to give the desired product.Interestingly, weak hyperpolarization effects in 9 in both 1H and 31P NMR spectra. The experimentally measured signal enhancements exceed three orders of magnitude at 9.4 T, i. e., correspond to at least 3 % spin polarization for both nuclei. To date, this is a record high signal enhancement that was observed using metal‐free parahydrogen activation. Comparing to much more widely explored metal‐free activation with ansa‐aminoboranes,8‐H2 and 10‐H2) form symmetrical AA'XX’ spin systems  resulting in complex NMR multiplets having features of second‐order spectra.1H and pnictogen spin pairs at the simultaneous magnetic inequivalence of spins in these pairs. This allows one to observe unusual effects such as hyperpolarization of 31P without a need for polarization transfer radio‐frequency pulses for 8‐H2 or strongly enhanced 1H side band resonances in the case of 10‐H2. Among five‐membered biradicaloids, P−P based 11 provided strong signal enhancements of at least 250‐fold, and all studied members of this class are shown to activate H2 rather irreversibly, as compared to four‐membered biradicaloids. However, the experiments with 11‐H2 and 12‐H2 performed at high temperatures (334 and 373 K) under parahydrogen pressure showed slight hyperpolarization of these adducts, indicating a weak reversibility of H2 activation that was practically impossible to detect without the hyperpolarization. Altogether, we demonstrated versatile possibilities of hyperpolarization using metal‐free pnictogen biradicaloids. Immediately, these biradicaloids cannot be used for NMR sensitivity enhancement in vivo because of air‐sensitivity and toxicity of As. So far, they were not shown to mediate hydrogenation reactions, either. However, we would expect that a rational design of biradicaloids for parahydrogen activation may lead to applications in these areas in the future.Herein, we considerably expanded the range of metal‐free biradicaloids that demonstrate hyperpolarization effects upon interaction with parahydrogen. The strongest hyperpolarization effects are observed for As−P four‐membered compound 8–12 were synthesized using modifications of synthetic approaches described elsewhere,[Biradicaloids lsewhere,1, 19 The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


The knowledge on the in vitro Y122. structure and its radical distribution within the β2 subunit has accumulated over the years; yet little information exists on the in vivo Y122.. Here, we characterize this essential radical in whole cells. Multi‐frequency EPR and electron‐nuclear double resonance (ENDOR) demonstrate that the structure and electrostatic environment of Y122. are identical under in vivo and in vitro conditions. Pulsed dipolar EPR experiments shed light on a distinct in vivo Y122. per β2 distribution, supporting the key role of Y. concentrations in regulating RNR activity. Additionally, we spectroscopically verify the generation of an unnatural amino acid radical, F3Y122., in whole cells, providing a crucial step towards unique insights into the RNR catalysis under physiological conditions.The E. coli ribonucleotide reductase (RNR), a paradigm for class Ia enzymes including human RNR, catalyzes the biosynthesis of DNA building blocks and requires a di‐iron tyrosyl radical (Y The in vivo structure of the redox‐active tyrosyl radical in ribonucleotide reductase that catalyses the biosynthesis of DNA building blocks was explored using advanced EPR spectroscopic techniques. Furthermore, the radical form of a tyrosine derivative within the cells was generated. Our results mark the initial steps towards gaining unique insights into the redox reactions involving tyrosyl radicals under physiological conditions. E. coli class Ia RNR, a paradigm for class Ia enzymes including human RNR, consists of two homodimeric subunits, α2 and β2. The active α2β2 complex is formed transiently upon substrate and effector binding.E. coli class Ia RNR relies on at least five redox‐active amino acids, which are involved in an unprecedented reversible radical transfer that occurs over ≈32 Å.122. and the redox‐active tyrosine residue Y356. α contains the active site for nucleotide reduction, two allosteric effector binding sites and redox active residues Y731, Y730, and C439 that are a part of the radical transfer pathway. Y122. reversibly oxidizes C439 to a thiyl radical, which in turn initiates the irreversible catalytic reaction.Ribonucleotide reductases (RNRs) catalyse the conversion of nucleotides to their corresponding deoxynucleotides in every living cell.. is generated by self‐assembly requiring Fe2+ and O2, both in structurally homogenous E. coli and Saccharomyces cerevisiae (S. cerevisiae) class Ia RNRs.E. coli RNR showed that usually 1.2 Y./wt‐β2 were generated, suggesting ≈60 % of the β2 population is active and ≈40 % possesses inactive diferric clusters without any Y.s .. levels, however, were substoichiometric compared to that of iron loading.E. coli  remains unknown. In contrast, studies with S. cerevisiae revealed stoichiometric amounts of Y. with 1 Y. per ββ′ heterodimer both in vivo and in vitro.[S. cerevisiae RNR activity is not regulated by the modulation of Y. concentrations, but Y. concentrations play a key role in regulation of E. coli RNR activity in cells.As di‐iron tyrosyl radical cofactor initiates the catalytic reaction, radical generation at this tyrosine residue is essential for RNR activity. YE. coli RNR catalytic reaction, which is the conformational change(s) upon substrate and allosteric effector binding, has prevented spectroscopic detection of other redox‐active amino acids in wild‐type enzyme.. intermediates in RNR catalytic reaction was evidenced by site‐specific incorporation of unnatural amino acids (UAAs) with altered pKas and reduction potentials such as DOPA, NH2Y, NO2Y, and FnYs.731 in the α2β2 complex for the first time.3Y122..The rate‐limiting step in the 122., the stable di‐iron tyrosyl radical cofactor of E. coli class Ia RNR in whole cells. We characterized its in‐cell conformation and H‐bonding environment at nanometre‐scale resolution using multi‐frequency EPR and electron‐nuclear double resonance (ENDOR) spectroscopies. Subsequently, we detected in vivo distances between two Y122.s by DEER  at residue 122, generated and identified its radical form F3Y122. in the cells, and obtained in vivo distances between F3Y122.s. These results verify the generation of an unnatural amino acid radical in whole cells and show that F3Y incorporation does not alter protein structure within cells.One of the most challenging tasks for techniques used in structural biology is to achieve high resolution within the native environment of biomolecules, that is, the cells. Among the methods employed, EPR spectroscopy is an emerging tool for in vivo studies.122 and for the catalytic activity of E. coli RNR. Previously, it was shown that in vitro incubation of iron‐free, apo‐β2 with FeII and O2 resulted in the reconstitution of the radical state of Y122, and thus led to recovery of the RNR activity.E. coli was over‐expressed because Y122 concentrations are below detection limit under normal growth conditions.II for 10 min. Next, the cell suspension was saturated with O2 and transferred into EPR tubes (Supporting Information (SI) 1.1). Prior to EPR experiments, the viability of the cells was checked using cell counting experiments (SI 1.3). These proved that the cells used for EPR measurements were intact and alive. In addition, the possibility of cell damage, and thus high amounts of protein leakage from the cells into the suspension buffer was ruled out using polyacrylamide gel electrophoresis and EPR experiments (SI 2.1).Presence of the di‐iron centre is prerequisite for the radical state of YE. coli cells showed that the FeII and O2 treatment led to the generation of a radical species whose EPR signature is similar to that of Y122. (SI 2.2). To examine the generated radical, we recorded multi‐frequency  EPR spectra of treated whole E. coli cells (termed in‐cell) and analysed the data using spectral simulations  were in excellent agreement with the multi‐frequency EPR/ENDOR dataset.122. having an average bulk concentration of 18±5 μm (see SI 2.3 for concentration determination).Comparison of 9.6 GHz EPR spectra of treated and untreated whole 122. is also shown in Figure g‐ and hyperfine anisotropy and is highly sensitive to molecular environment of tyrosyl radicals.[gx value reports on the electrostatic environment of a tyrosyl radical and is affected by the changes in the radical's local environment, such as addition or loss of an H‐bond.[gx value indicate multiple molecular orientations and/or radical environments. Another important structural parameter is the strength of the hyperfine couplings of Cβ‐methylene protons. They are related to the dihedral angle between Cβ−H bonds and the ring plane, and therefore provide information on the structure of tyrosyl radicals.94 GHz EPR spectrum of in vitro Y122. would result in a different β‐methylene proton hyperfine coupling, and thus distinct EPR line shape (SI 2.4), as it has been observed previously for residue Y731.gx value for in‐cell Y122. that is identical to that of in vitro one, resulting in nearly identical EPR line shapes for in‐cell and in vitro Y122.. The only marginal difference observed in the line shapes is due to the Mn2+ species that is inherent to the cells . Overall, all of the EPR data and simulations clearly show that Y122. generated within the cells is a single radical species with one set of magnetic parameters and a well‐defined orientation. Additionally, our results demonstrated that structure and environment of Y122. in the cells are highly similar to those of Y122. in vitro. These results were not unexpected considering the isolated nature of Y122. inside the protein, around 10 Å away from its surface.Therefore, a substantial change in the structure of Y122. in the cells. Orientation‐selective ENDOR reports on the number, separation distance  and orientation of the magnetic nuclei that are coupled to the observed radical, and thus provides structural information.122. EPR line corresponding to gxy, gy and gyz molecular orientations shown in SI 2.6. Figure 1H Davies ENDOR spectra of Y122. in the cells (black lines) and in vitro (blue lines) with the corresponding simulations (red dotted lines). Based on the previous 1H ENDOR studies on Y122. conducted at 9 up to 263 GHz,[1H hyperfine couplings were included in the simulations of the in vitro and in‐cell spectra (see Table S3 and Scheme S1). As expected, the simulations were in excellent agreement with in vitro ENDOR and multi‐frequency EPR data.Next, we employed ENDOR spectroscopy at 34 GHz to probe the H‐bonding environment of Y1H Davies ENDOR line shape of the in‐cell Y122. contained additional spectral features compared to the in vitro Y122.. Therefore, the in vitro spectrum was subtracted from the in‐cell one after normalizing the spectra to the C1β‐methylene proton ENDOR intensities . This comparison clearly demonstrated that the additional proton hyperfine couplings observed for the in‐cell sample are not related to Y122., but rather to Mn2+ in E. coli cells as the only clearly detectable paramagnetic species in these cells was Mn2+ (SI 2.6). Indeed, the additional 1H ENDOR features detected in E. coli cells closely resemble the in‐cell Mn2+ ENDOR line shape found in literature,1H Larmor frequency  indicative of the water‐ligated Mn2+ species.2+ species is in line with identical gx values detected for in‐cell and in vitro Y122.. An extra H‐bond to Y122. would make its local environment more polar and shift its gx to lower values, as reported previously for tyrosyl radicals of distinct proteins coupled to hydrogens of similar strengths.[1H hyperfine coupling observed in the difference spectrum, which is similar to the Ay component of C3/C5 protons, would result in additional splittings in the 94 GHz EPR spectrum as shown in Figure S4, which were not observed. Overall, the analysis of our orientation‐selective 1H Davies ENDOR data combined with the gx value and EPR line shape information demonstrated that the number of hydrogens coupled to Y122. as well as the coupling strengths are the same in the cells and in vitro.The 122.′s, each residing in a β monomer of E. coli RNR in whole cells . Fourier‐transform of in‐cell and in vitro summed traces led to almost ideal Pake patterns (SI 2.7). The resulting orientation‐averaged form factors and the extracted distance distributions are displayed in Figure σ=0.09 nm . This distance belongs to the in‐cell Y122.‐Y122. pairs, as it is identical to in vitro Y122.‐Y122. distances detected in this work (3.32 nm with σ=0.07 nm) and previously (3.31 nm122. and Mn2+ EPR features (see SI 2.8 for details). The detected narrow in‐cell distance distribution (σ<0.1 nm) and the presence of orientation selectivity demonstrated the rigid nature of Y122.s, and more importantly, of the β2 subunit within the cells. This conclusion is also supported by our EPR data that established Y122. generated within the cells as a single and structurally defined radical species.In order to get insights into the wt‐β2 structure and in‐cell radical distribution, we measured the distance between two Ys Figure . As orie122.‐Y122. distance in the cells is not self‐evident because in vivo radical distribution of β2 is not known. In order to investigate the radical distribution within the cells, which was suggested to play a key role in RNR regulation and activity,[2+ to the EPR signal at the observe position . Δ of the in vitro wt‐β2 samples without Mn2+ depends neither on the protein nor on the spin concentration of the sample,122.‐Y122. pair or no radical at all .To decipher whether Mn2+ alone. As Y122. and Mn2+ were the only paramagnetic centres detected within the cells  showed significantly steeper backgrounds . The detection of low intracellular Y122. concentration is further supported by echo decay measurements. Tm of Y122. within cells is either longer or the same as that of in vitro samples .Additionally, we compared background slopes of the DEER dipolar traces, as the slope reports on the local spin concentrations in a sample.3Y122. was fundamental to being able to investigate in vitro radical transfer mechanism in RNR and to obtain the cryo‐EM structure of the α2β2 complex. Therefore, we explored the possibility of observing F3Y. in whole E. coli cells. Successful generation of F3Y122. within the cells would be the first step towards gaining unique insights into the RNR catalysis under physiological conditions. In addition, it may serve as a new exciting probe to study in vivo radical transfer steps in other fundamental proteins containing amino acid radicals.Incorporation of F3Y122., we followed the same procedure as that described for the in‐cell generation of Y122.. After over‐expressing apo‐F3Y122‐β2 in the cells, we treated the harvested and washed cell cultures with FeII and O2 to generate F3Y122. (SI 1.2). Prior to EPR experiments, the viability of the cells and protein leakage from the cells were checked using cell counting and EPR experiments, respectively (SI 1.3 and 2.13). The resulting data showed that the cells used for EPR measurements were intact and no detectable amounts of F3Y122.‐β2 protein pass through the E. coli cell wall. 9.6 and 34 GHz EPR measurements along with spectral simulations revealed the presence of the F3Y122. species in the cells (SI 2.13). Spin quantification experiments yielded a 17±5 μm in‐cell sample concentration of F3Y122. (SI 2.13), in good agreement with that of the wt‐β2 in‐cell samples.In order to generate F3Y122. were performed . Such orientation‐selective DEER measurements were not feasible with the in‐cell sample because of poor SNR. We recorded an in‐cell DEER trace at a molecular orientation at which the distance vector rF3Y122‐F3Y122 is perpendicular to the magnetic field because the correct mean distances can be directly obtained from dipolar frequencies at this orientation.3Y122.‐F3Y122. pair as it agrees extremely well with the in vitro distance detected here (3.36 nm with σ=0.11 nm) and with the distance between oxygen atoms of F3Y122′s in the recent crystal structure of F3Y122‐β2 (SI 2.15).Subsequently, in‐cell and in vitro DEER measurements with samples containing Fd Figure . Four in3Y122. DEER traces. Similar to the results obtained with Y122., this analysis showed that local F3Y122. concentration within the cells is substantially lower than the detected in vitro concentration of 130±30 μm .Furthermore, we compared background steepness of in‐cell and in vitro F122. in E. coli RNR are identical under in vivo and in vitro conditions. Here, we presented distance measurements between two native paramagnetic centres residing in an enzyme in intact and living cells where the protein under investigation is expressed. Distance measurements within the Y122.‐Y122. pair provided insights into the in‐cell structure and conformational rigidity of the β2 subunit, and suggested a distinct in‐cell radical distribution within this subunit. Detection of β2 subunits having only one Y122. strongly supports a model in which E. coli RNR activity is regulated by modulation of Y. concentrations in the cells.3Y incorporation does not affect the in vivo protein structure. Successful generation of F3Y122. within the cells is the first step towards gaining unique insights into the RNR catalysis under physiological conditions. Furthermore, it showcases the possibility of unravelling the in vivo structure and role of tyrosyl radicals involved in other fundamental processes, such as photosynthesis, reduction of O2 to water, and DNA repair by unnatural amino acid incorporation.This study has revealed that the structure and electrostatic environment of the essential di‐iron tyrosyl radical cofactor YThe authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.


The introduction of a second light‐harvesting dye in the Ru‐based dye‐WOC‐dye supramolecular complex enables two separate parallel electron‐transfer channels, leading to a five‐step catalytic cycle with three intermediates and two doubly oxidized states. The total spin S=1 is conserved during the catalytic process and the system with opposite spin on the oxidized NDI proceeds from the Ru=O intermediate to the final Ru‐O2 intermediate with a triplet molecular 3O2 ligand that is eventually released into the environment. The in‐depth insight into the proposed photocatalytic cycle of the two‐channel model provides a strategy for the development of novel high‐efficiency supramolecular complexes for DS‐PEC devices with buildup and conservation of spin multiplicity along the reaction coordinate as a design principle.To improve the performance of dye‐sensitized photoelectrochemical cell (DS‐PEC) devices for splitting water, the tailoring of the photocatalytic four‐photon water oxidation half‐reaction represents a principle challenge of fundamental significance. In this study, a Ru‐based water oxidation catalyst (WOC) covalently bound to two 2,6‐diethoxy‐1,4,5,8‐diimide‐naphthalene (NDI) dye functionalities provides comparable driving forces and channels for electron transfer. Constrained  Change the channels: The inclusion of two channels for the photoinduced electron injection in the photoanode is proposed as a strategy for the development of novel high‐efficiency supramolecular complexes for dye‐sensitized photoelectrochemical cell devices with buildup and conservation of spin multiplicity along the reaction coordinate as a design principle. Artificial photosynthesis, inspired by nature, with the goal of conversion of solar energy into chemical energy, has attracted growing interest in the past decades.The solar‐driven four‐photon water oxidation half‐reaction occurs at the photoanode, and is always initiated by light absorption at the molecular sensitizers and subsequent electron injection from the dye in the excited state into the electrode.S‐state cycle model of photosynthetic water oxidation involving five oxidation states (S0→4) has been the paradigm for the understanding of oxygen evolution.S‐state cycle has been introduced by Dau et al., in which eight successive steps starting from I0 lead to I8 and only then the O2 is formed and released.I‐cycle model involves not only four oxidizing equivalents but also four bases prior to the dioxygen formation. For sequential alternating proton and electron transferSince 1970, Kok's classical 1([(cy)RuIIbpy(H2O)]2+‐NDI)  for photocatalytic water splitting, which has been systematically investigated in silico recently,[1(NDI1‐[(cy)RuIIbpy(H2O)]2+‐NDI2) with the total spin S=0 (denoted as 1(NDI1‐[RuII‐H2O]2+‐NDI2) in Scheme In this work we explore how the catalytic cycle could be affected by introducing two excited dye motifs in parallel, and thereby combining two PCET steps without stable intermediates in between. For this aim, an additional dye molecule is introduced in the catalyst‐dye supramolecular complex 2 semiconductor conduction band on a time scale of picoseconds upon photooxidation.[IIbpy(H2O)]2+ has been investigated using online electrochemical mass spectrometry (OLEMS) and in situ surface enhanced Raman spectroscopy (SERS) in our previous work.+. and NDI2+.. Scheme α unpaired electron (↑) on each NDI dye with total spin S=1. This choice is based on the previous finding for the one‐channel model where the triplet spin configuration was found to be more favorable for the second PCET step. Instead, for the third step involving the O−O bond formation process in the one‐channel model, it is found that the antiparallel spin alignment of the unpaired electrons on the WOC (↑ ↑) and dye (↓) is essential for this reaction. Thus, for the second half of the cycle, the antiparallel spin alignment of unpaired electrons on the two NDI dyes is considered: in this way the total spin S=1 is preserved along the whole cycle until the formation of the triplet oxygen (3O2), which eventually leaves the complex being exchanged by a water molecule and brings the spin multiplicity back to the singlet state   simulations, which can provide accurate predictions of the reaction mechanism and activation energy barrier.1(NDI1‐[RuII‐OH2]2+‐NDI2) (S=0) and ending with the intermediate 3(NDI1‐[RuIV=O]2+‐NDI2) . In this process, we assume that one attacking water molecule is approaching Hiii while at the same time another attacking water molecule approaches Hiv . The spins are parallel on the NDI in the initial photooxidized state  and the spin on the WOC is built up from S=0 to S=1 during the two PCET steps.To verify the feasibility of the entire proposed photocatalytic cycle for the two‐channel model, the first half of the photocatalytic cycle is first investigated. Here we only give the main results . According to our simulations, the first and second catalytic steps, starting from the initial intermediate 3(NDI1‐[RuIV=O]2+‐NDI2) was optimized at the DFT level employing the OPBE exchange‐correlation functional3(NDI1+.‐[RuIV=O]2+‐NDI2+.) with total spin S=1  and for the final (3[RuII‐O2]2+) intermediates, thus avoiding the need for intersystem crossing during the reaction: The electronic ground state of the 3[RuIV=O]2+ WOC is a triplet configuration, whereas the two unpaired electrons on the photooxidized dyes are in an antiparallel arrangement. We also checked the case of the parallel spin configuration for the two unpaired electrons on the dyes, leading to a quintet state 5(NDI1+.‐[RuIV=O]2+‐NDI2+.). The result shows that the bond energy difference between the triplet 3(NDI1+.‐[RuIV=O]2+‐NDI2+.) and the quintet state 5(NDI1+.‐[RuIV=O]2+‐NDI2+.) is negligible (see the Supporting Information S5). This is not surprising considering the relatively large distance between the two dyes, thus an intersystem crossing could easily take place between these two energetically degenerate states.The initial geometry of the dye‐WOC‐dye complex ESOMO‐1≈0.18 and ΔESOMO‐2≈0.21 eV, respectively (see Table S2).It is found that the alignment of the energy levels is favorable for the subsequent ET steps involving the O−O bond formation since the SOMOs localized on the NDI dyes (SOMO dye1 and SOMO dye2) with antiparallel spins are lower in energy than the HOMO of the dye‐WOC‐dye complex localized on the WOC . The orbital energy difference between the SOMO WOC and the SOMO dyes is Δ3 with periodic boundary conditions containing the dye‐WOC‐dye solute 3(NDI1‐[RuIV=O]2+‐NDI2) (S=1) together with 212 explicit water molecules was used in the AIMD simulations to get accurate predictions of the catalytic reaction and free energy profile. AIMD simulations were carried out with the Car‐Parrinello molecular dynamics (CPMD) program.An orthorhombic box of dimensions 25.5×22.4×15.4 Å+.‐[WOC]2+‐dye+. at the beginning of the constrained AIMD simulation for the second half of the cycle, since the photoinduced electron injection from the selected NDI to a TiO2 semiconductor surface can be achieved on a time scale of approximately 1 ps, as has been demonstrated in previous work.2+‐NDI2+.) (S=1).The system is assumed to be already in its doubly‐oxidized form of dyei on the Ru complex and the Oii oxygen of the attacking water indicated by the red double‐sided arrow in Scheme To explore the catalytic water oxidation steps involving bond‐forming and bond‐breaking processes, which are normally considered as rare events on the characteristic AIMD simulation time scale, the constrained MD approach was employed in the simulations to control the reaction coordinate after the re‐equilibration of the photooxidized system.3(NDI1+.‐[RuIV=O]2+‐NDI2+.) (S=1) complex =2.5 Å =2.1 Å.d(Oi←Oii) from 2.5 Å to 2.0 Å induces the complete ET from the WOC to the oxidized NDI1 with spin density localized on the WOC fluctuating around an average value of −1. After short‐term fluctuations of spin density localized on the WOC, the dye system that is initially in the dye+.‐[WOC]2+‐dye+. state ends up with one unpaired α electron (↑) localized on the WOC and one unpaired α electron (↑) on the NDI2 at the end of the constrained 1.8 Å MD simulation. Moreover, the proton transfer (PT) from the attacking water molecule to the solvent is first observed during the constrained 1.8 Å MD simulation when tracking the distance between Ru and the OH d(Ru‐OH) along the constrained MD trajectories , the variation of the total spin density localized on the WOC, and the distance between Ru and H3O+ (defined as an O atom with three H within a radius of 1.2 Å) along the free MD trajectory after releasing the constraint are collected in Figure At the end of the constrained 1.6 Å MD simulation, the constraint on the reaction coordinate was released and the system is allowed to evolve freely. The time evolution of the distance between the oxygen atoms Od(Oi‐Oii)≈1.35 Å  diffuse independently from each other into the solvent at this stage and we only focus on the second released proton Hii in Figure i and Oii equilibrates quickly to an average value d(Oi‐Oii) of about 1.25 Å. Although we have a higher proton density compared to the one‐channel model, we observe that the fourth PCET catalytic water oxidation step proceeds spontaneously following the formation of the O−O bond and the system reaches the final intermediate 3(NDI1‐[RuII‐OO]2+‐NDI2) .Based on these data, the O−O bond distance relaxes within a very short time of about 0.2 ps to an average value of d(Oi‐Oii)≈1.25 Å two unpaired α electrons (↑) are localized on the dioxygen ligand  (S=1) proceeds combining two sequential steps without stable intermediates in between, it is relevant to evaluate how difficult it is to activate this reaction in such a two‐channel model. In recent years, metadynamics simulations have been increasingly used as an alternative enhanced sampling method in similar computational works on O−O bond formation, which allows to sample the entire free energy landscape.d(Oi←Oii) is constrained to a series of fixed values to estimate the free energy profile along this reaction pathway using the Blue Moon ensemble approach and thermodynamic integration.2+‐NDI2)  S=1/2)The calculated activation free energy barrier  obtained for the two‐channel model can be reasonably attributed to the accomplishment of the barrier‐less fourth catalytic water splitting PCET step under the condition that the second dye NDI2 is photooxidized. This result suggests a relatively stable final intermediate 3(NDI1‐[RuII‐OO]2+‐NDI2)  . Finally, the cycle is completed by replacing the dissociating triplet molecular oxygen with a water molecule, leading to the initial singlet 1(NDI1‐[RuII‐OH2]2+‐NDI2) state. Hence, the Ru metal ion operates as a spin shuttle during catalysis. In the first part of the cycle, it selects the matching spin from the NDI to build up spin multiplicity, and in the second part it preserves the spin multiplicity and passes on a triplet to the oxygen.In addition, the larger thermodynamic driving force Δ3O2 product. Overall, this study suggests that having the WOC coordinated to more than one dye at the photoanode of a DS‐PEC device can have beneficial effects in the rate and efficiency of the photocatalytic cycle: this is achieved by having the co‐photooxidation of the two dyes and an antiparallel spin alignment of the unpaired electrons on the dyes.In conclusion, the introduction of the second NDI dye in the dye‐WOC‐dye complex for photocatalytic water splitting provides an extra channel for ET, which enables the sequential event of ET from the WOC to the two separate NDI dyes. The dynamical description of the proposed photocatalytic cycle of the two‐channel model obtained with adiabatic AIMD simulations and explicit solvation demonstrates that the third and fourth catalytic steps can proceed one after the other without stable intermediates in between. Although the estimated activation free energy barrier of the combined third and fourth catalytic steps for the two‐channel model is similar to that of the one‐channel model, the introduction of the second ET channel removes one intermediate in the cycle: the system can now proceed without changing the total spin of the supramolecular complex, from the Ru=O intermediate to the final intermediate with a triplet molecular The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.


Graph theory is applied in order to visualize underlying trends in the transformation of high-throughput data into networks, which are then used to design new catalysts that potentially result in high C2 yields during the OCM reaction. Transforming high-throughput data in this manner has resulted in a representation of catalyst data that is more intuitive to use and also has resulted in the successful design of a myriad of catalysts that elicit high C2 yields, several of which resulted in yields greater than those originally reported in the high-throughput data. Thus, transforming high-throughput catalytic data into catalyst design-friendly maps provides a new method of catalyst design that is more efficient and has a higher likelihood of resulting in high performance catalysts.Designing high performance catalysts for the oxidative coupling of methane (OCM) reaction is often hindered by inconsistent catalyst data, which often leads to difficulties in extracting information such as combinatorial effects of elements upon catalyst performance as well as difficulties in reaching yields beyond a particular threshold. In order to investigate C 2 yields during the OCM reaction.Catalyst data created through high-throughput experimentation is transformed into catalyst knowledge networks, leading to a new method of catalyst design where successfully designed catalysts result in high C Here, graph theory is proposed as a means to represent the information and knowledge found within catalyst big data where the relationships within catalyst data are represented as complex networks.18 Doing so would thus assist in revealing the underlying knowledge in catalyst big data in a comprehensive manner, leading towards a more informed way of designing catalysts.The introduction of catalyst informatics has innovated how catalysts are designed and understood based on the trends and patterns that lie within catalyst data.4 to C2H4 and C2H6.19,20,23 Big data focused on OCM catalysts are previously collected using high throughput experiments where the dataset consists of 291 catalysts with experimental conditions that result in maximum catalytic performance.4,5 If the relationships between chemical element combinations in catalysts and experimental conditions as well as catalytic performance are uncovered, it becomes possible to find key combinations for chemical elements and corresponding experimental conditions that result in high C2 yields. Here, the relationships within the OCM catalyst big data are expanded into networks that provide a basis for designing and understanding the OCM reaction from complex networks.Catalyst big data for oxidative coupling of methane (OCM) is investigated where OCM aims to directly convert CH5 It has two important features that few other catalyst datasets possess. The first feature is the consistency, which arises from the fact that the catalysts were prepared and evaluated by the exactly same methods. When datasets consist of catalyst data collected from multiple references, data inconsistency due to discrepancies in catalyst preparation and evaluation methods is a major obstacle. There are few datasets of this scale that are collected in a consistent manner, and this was achieved through high-throughput experimentation.4,5 The second feature is that these 291 catalysts are randomly selected from 36 540 compositions that can be created by combining 28 elements and 9 oxides. The frequency of appearance of individual elements and supports is uniform, without any biases toward known effective compositions, i.e. free of sampling bias. The performance of a catalyst as part of a chemical process is sensitive to reaction conditions. Evaluation under specific conditions tends to favor the catalyst that is best suited to those conditions rather than the catalyst that is truly superior. In this dataset, each catalyst is evaluated under 135 reaction conditions with different temperatures and gas compositions, and the data points with the best C2 yield are extracted and collected.The dataset used in this study is a collection of the OCM data for 291 quaternary catalysts represented by M1–M2–M3/support.5 Briefly, catalysts are prepared based on a wet impregnation method: a specified support (1.0 g) is loaded with precursors of the elements specified as M1–M3 (0.37 mmol for each), followed by drying and calcination at 1000 °C to obtain a catalyst. Support materials and precursors used are the same as those described in the literature.5 The OCM performance of the catalysts is acquired using a high-throughput screening instrument developed by some of us.4,5 The instrument automatically acquires the performance of 20 catalysts under a pre-programmed set of reaction conditions in a fixed-bed flow reactor configuration. Catalyst beds consist of quartz reaction tubes with an inner diameter of 4.0 mm filled with catalyst powder at a bed height of 10 mm. A gas mixture of a specified composition is simultaneously flowed through 20 catalyst beds heated at a specified temperature, and the composition of the effluent gas is measured using a quadrupole mass spectrometer (QMS) equipped with an auto-sampling system. The catalyst performance is obtained for 135 reaction conditions differing in the temperature and feed volume of CH4, O2, and Ar, where Ar serves as a carrier gas as well as an internal standard in QMS. As in the original dataset, the data point corresponding to the best C2 yield out of the 135 conditions is extracted, which represents the performance of a catalyst.Validation experiments are performed on the catalysts proposed from the analysis of network information. The methods of catalyst preparation and evaluation are exactly the same as those used to create the original dataset.via Gephi.21 Data from the dataset are extracted and preprocessed to account for graph nodes, edges, and edge weights. Here, graph nodes are objects that represent the catalysts, catalyst supports, corresponding experimental conditions, and the resulting C2 yields when tested via high throughput experiments. Edges represent the connections shared between two nodes while the edge weight is set to 1. In particular, the following data are extracted for network analysis: atomic elements, catalyst supports, C2 yields of the individual catalysts, C2 yield groups , CH4/O2 ratio , CH4 flow, O2 flow, Ar flow, and temperature . Note that in the case of C2 yield groups, each catalyst is assigned to a C2 yield group according to the individual C2 yield produced during the high throughput experiments . Based on previous reports, cut-off points are based on catalyst-free OCM which produces a C2 yield of 10% with a ±2% range for the “neutral” group (“C2 yield 8–12%”).5 C2 yields that are less than 8% can be seen as yields that are negatively affected by catalytic activity while catalysts with C2 yields greater than 12% can be seen as exhibiting higher degrees of catalytic activity. The preprocessed data are then transformed into an undirected graph through the Force Atlas 2 algorithm where node placement is influenced by how often nodes access other nodes (e.g. nodes that share many connections are closer to each other within the network).22 Note that node sizes and colors are adjusted for visualization purposes.Networks of the created datasets are constructed 2 yield group “C2 yield 12+%” or are found in grey areas between C2 yield groups but are found to be closer to the C2 yield group “C2 yield 12+%”. Additionally, element combinations are chosen based on how often certain element pairs appear near the C2 yield group “C2 yield 12+%” and how likely they are to pair with particular supports.Proposed catalysts are designed based on observations and information gathered from the catalyst networks illustrated in 2 yields and can be found in 4 flow (blue), O2 flow (red), Ar flow (brown), temperature (pink), CH4/O2 ratio (purple), and C2 yield group (yellow). Individual C2 yields are listed by their value.High-throughput experimental data of catalysts used towards the OCM reaction are preprocessed and transformed into a network in order to analyze how various components of data relate to others. By visualizing the data as a network, it becomes possible to make several valuable observations about the catalyst that would otherwise be difficult to obtain when analyzing it in textual format. A network is generated from the collected and preprocessed data using Gephi where individual atomic elements of each catalyst are plotted with their corresponding supports, experimental conditions, and C2 yield groups rather than others. For example, atomic element nodes such as Pd and Cu are close to the C2 yield group “C2 yield 0–8%” while atomic element nodes such as Ti and Nd are close to the C2 yield group “C2 yield 8–12%”. This suggests that these elements have a clearer tendency to result in a particular range of catalytic activity, e.g. Pd and Cu tend to result in lower degrees of catalytic activity while Ti and Nd tend to result in a neutral level of catalyst activity when compared to the catalytic activity of other catalysts in this study. In the case of the C2 yield group “C2 yield 12+%”, it becomes less obvious where the boundaries between the C2 yield groups lie. Their location between C2 yield groups “C2 yield 8–12%” and “C2 yield 0–8%” results in many elements being placed in the shared spaces between “C2 yield 0–8%” and “C2 yield 12+%” and between C2 yield groups “C2 yield 0–8%” and “C2 yield 12+%”. Further analysis of the data reveals that atomic elements that fall within these grey areas between C2 yield groups “C2 yield 0–8%” and “C2 yield 12+%” and between C2 yield groups “C2 yield 0–8%” and “C2 yield 12+%” will result in varying levels of C2 yields depending on their companion elements, supports, and experimental conditions. From this, one can understand that elements that fall within these so-called grey areas can be treated as elements whose catalytic performance is influenced by other elements or experimental conditions. Thus, the figure successfully illustrates the importance of combinatorial effects in the design of high-performance catalysts.From 4 flow, O2 flow, and Ar flow conditions are found to closely associate with particular conditions. For instance, nodes representing the CH4 flow, O2 flow, and Ar flow tend to congregate around the nodes representing temperature. For example, CH4 flows 6.0 and 11.33, O2 flows 2.83 and 3.0, and Ar flow 6.0 are found in close proximity to the node representing“700 °C” and, as a set of conditions, are close to node “C2 yield 8–12%”, this suggests that these particular experimental conditions are likely to be the conditions that elicit the best catalytic performance of the catalysts that fall within this range. Similarly, the network illustrates that the nodes representing gas flows tend to congregate around temperature nodes where particular temperatures will show closer proximity to certain C2 yield groups. Given these observations, one can understand two points: (1) gas flows tend to have share more connections with particular temperatures as seen by their congregation patterns, and (2) temperatures show more connections to some C2 yield groups over others. One can therefore treat these gas flow/temperature combinations as sets of conditions that have a stronger correlation with particular C2 yields.2 yields that fall under 8% become strikingly clear. Immediately, one can see that a temperature of 900 °C is strongly related to the C2 yield group “C2 yield 0–8%” along with CH4/O2 ratios of 4 and 6. One can also see that a large array of CH4 flow and O2 flow nodes also exhibit a strong correlation with the C2 yield group “C2 yield 0–8%” along with atomic elements Cu, Pd, Zn, and Ni. Thus, the network better illustrates elements and supports that associate with conditions that correlate with low C2 yields and therefore it may be better to avoid them when designing high-performance catalysts.While the development of the network illustrated in 2/O2 ratios. The location of the node representing the CH4/O2 ratio of 2 within the network reflects how commonly this ratio is involved with the various types of catalysts, supports, and experimental conditions that were tested through high-throughput experimentation. Given its location at the center of the network, one can assume that this particular ratio does not show preference to any particular C2 yield outcome, thereby suggesting that other factors may be at play when determining C2 yields for the cases where the CH4/O2 ratio of 2 is involved. Meanwhile, CH4/O2 ratios of 6 and 4 are clearly close to the C2 yield group “C2 yield 0–8%”, suggesting that using these particular ratios when designing experiments to test catalysts will likely hinder catalytic performance.Interestingly, transforming catalytic data into a network clarifies the outcomes of choosing different CH2 yield groups. Various elements and experimental conditions are found in areas where they share equal or similar distances between more than one C2 yield group, suggesting that particular elements or experimental conditions may associate with a particular C2 yield group depending on the other elements, supports, and experimental conditions that they may be paired with. For instance, elements such as Sr or Cs can lead to C2 yields that fall within the C2 yield range of 8–12% or lead to a yield greater than 12% depending on what they are coupled with. Similarly, elements such as Zr, Mg, and Ba fall within a grey zone between C2 yield ranges of less than 8% and greater than 12%, suggesting that the elements' ability to invoke a higher C2 yield may depend on the elements or experimental conditions that they are partnered with. While these grey zones provide insights towards designing catalysts that result in higher C2 yields, the pairing effect that occurs between elements is still largely unknown.Finally, by analyzing 2 yield of a given catalyst. Not only can one understand the likely C2 yield outcome of using different elements when designing a catalyst, but can also understand which experimental conditions can enhance the catalytic activity of the catalyst in question. Visualizing the data in this manner can therefore improve the efficiency of the catalyst design process and allow researchers to extract knowledge and apply it towards new catalysts and experimental designs.From these results, it becomes clear that transforming catalytic data into a network provides a wealth of information regarding how various components affect the C2 yields, the network is redesigned by representing element combinations as element pairs. The catalytic data are preprocessed in the same manner as previously discussed; however, catalysts are represented by the possible element pairs that can be made with the individual elements of the catalyst instead of individual atomic elements. For instance, catalyst LiKMn–MgO, which was previously represented in the network as nodes “Li”, “K”, “Mn”, and “MgO”, is now represented as the following: “LiK”, “LiMn”, “KMn”, and “MgO”. By representing catalysts by their element pairs, the ability to design new catalysts that elicit a high C2 yield based on the network visualization becomes possible as it can potentially help clarify positive combinations of elements that may have otherwise fell within the “grey areas” between C2 groups as found in 2 yields. Supports, CH4 flow, O2 flow, Ar flow, CH4/O2 ratios , temperatures , and C2 yield groups “C2 yield 0–8%”, “C2 yield 8–12%”, and “C2 yield 12+%” are also defined as nodes. Nodes are also colored according to the type of information they represent and are colored as the following: atomic element pair (light green), support (dark green), CH4 flow (blue), O2 flow (red), Ar flow (brown), temperature (pink), CH4/O2 ratio (purple), and C2 yield group (yellow). Edges represent the connections shared between two nodes while the edge weight is set to 1. For the new network, individual C2 yield values are excluded in order to focus on the element pair nodes.In order to better understand the effects of element pairing in relation to various experimental conditions and resulting C2 would be represented as LiEu, LiW, and EuW, respectively, while its support ZrO2 is represented separately. By representing the elements in this manner, the pairing effect becomes clearer. For instance, in 2 yield 12+%” and “C2 yield 0–8%”. However, when represented as pairs, one can see that element pair BaEu correlates more with the yield group “C2 yield 12+%” than with the C2 yield group “C2 yield 0–8%”. Cases like W also prove to be interesting when comparing the location of nodes between networks. In 2 yield 12+%”. In 2 yield 12+%” when paired with elements such as Cs, Mo, Hf, and Li. Meanwhile, W more closely relates to the yield group “C2 yield 0–8%” when paired with elements Pd and Sr. This therefore illustrates that the catalytic performance of elements is affected by the elements they are paired with, which can improve or worsen the catalytic activity of the catalyst.2 yield group “C2 yield 0–8%”; however, 2 yield group “C2 yield 8–12%”. The elements Ti, Ba, and Co, in the meantime, are positioned near the C2 yield group “C2 yield 8–12%” or within the grey zone between C2 yield groups “C2 yield 12+%” and “C2 yield 0–8%”. This suggests that elements that may be considered to traditionally have poor catalytic performance could potentially be improved by pairing with elements that are typically viewed as having good catalytic performance. Furthermore, the network in 2 yield groups in 2 yields higher than 12% by combining elements and experimental conditions that fall within the vicinity of the C2 yield group “C2 yield 12+%”.Representing elements in this manner also helps dispel preheld ideas that particular elements are considered to be poor. As seen in via high-throughput experiments. Atomic element combinations and potential experimental conditions are proposed using the networks illustrated in 2 yield group “C2 yield 12+%” while atomic elements such as Ca, V, Mn, and Tb are found in a grey area between C2 yield groups “C2 yield 8–12%” and “C2 yield 12+%”. Given that these elements are involved in designing catalysts that result in various C2 yields, a more detailed network like the one shown in 2 yield.In order to test the efficiency of designing catalysts based on network visualization, 32 catalyst combinations are designed and then tested 2O3 are strongly associated with the C2 yield group “C2 yield 12+%”, suggesting that these supports have a higher likelihood of resulting in C2 yields when used experimentally. From there, element combinations that are found close to these supports are analyzed. Closer analysis of 2 yield group “C2 yield 12+%” in 2 yield group “C2 yield 12+%”. Similar observations are made for elements such as Ca and Tb with pairs such as CaK, CaTi, CaNd, FeTb, MoTb, and TbTi. By listing the atomic elements according to the additional atomic elements they are paired with, it becomes easier to understand which particular combinations of elements may result in a higher C2 yield. This can help clarify cases where atomic elements fall within grey zones as the element pairs can clarify which particular combinations of elements will fall under different C2 yield groups.An initial glance at 2 yield group “C2 yield 12+%” or are found in grey areas between C2 yield groups but also show affinity for “C2 yield 12+%”. 2 yield group “C2 yield 12+%” as seen in 2 yield 12+%” range. Also, element combinations are chosen based on elements that are found to be common in element pairs near a particular support.Designing catalysts according to node placements within the networks is further investigated in order to determine the accuracy and efficiency of designing catalysts in this manner. 2 yield that can be categorized as “C2 yield 12+%”, 8 cases result in a C2 yield that can be categorized as “C2 yield 8–12%”, and 1 case results in a C2 yield that can be categorized as “C2 yield 0–8%”. From this, one can see that over half of the suggested elemental combinations result in high C2 yields; more specifically, 70% of the catalysts produced a C2 yield of 12% or greater when tested via high throughput experiments. In particular, catalysts EuMgZr–BaO, MoKW–BaO, and KYV–BaO result in C2 yields (%) of 18.82, 18.36, and 18.21, respectively, while catalysts MoCsZr–BaO, KYMO–BaO, TiCsW–BaO, MoCsW–BaO, CsZrW–BaO, and TiTbW–BaO resulted in C2 yields (%) of 17.96, 17.60, 17.45, 17.39, 17.32, and 17.14, respectively. One can therefore understand that using the constructed network to represent catalysts and experimental conditions with their respective yields can help increase the likelihood of designing a catalyst with higher C2 yields.The catalysts suggested in 2 yields of 18% – Eu, Mg, Zr, Mo, K, W, Y, and V – are highlighted in 2 yield groups “C2 yield 12+%” and “C2 yield 0–8%”. Elements that make up the catalysts that result in C2 yields of 17% – Mo, Cs, Zr, K, Y, Ti, W, Li, and Tb – are also not only found within the grey areas between C2 yield groups “C2 yield 12+%” and “C2 yield 0–8%”, but in some cases are also between C2 yield groups “C2 yield 12+%” and “C2 yield 8–12%”. From this, one can come to the understanding that the efficiency of these elements is affected by the elements that they are paired with.The elements of these catalysts are compared against their locations within the created networks in order to better understand the reliability of network-based catalyst design. To start with, the elements that make up the catalysts that result in C2 yield groups when represented by their element pairs as listed in 2 yields become clearer. For instance, in the case of proposed catalyst “EuMgZr–BaO”, the element pair “EuMg” is found closer to the C2 yield group “C2 yield 0–8%” while element pairs “MgZr” and “EuZr” are found closer to the C2 yield group “C2 yield 12+%” and in the grey area between groups “C2 yield 12+%” and “C2 yield 0–8%”, respectively. Here, one can see that while “EuMg” may be more associated with catalysts that result in C2 yields that are low, their combination with element Zr improves the C2 yield (as seen by the placements of “MgZr” and “EuZr”). This effect is also seen with proposed catalysts MoKW–BaO and KYV–BaO, where element pairs “MoK” and “VY” share association with the C2 yield group “C2 yield 8–12%” and the remaining element pairs are found near the C2 yield group “C2 yield 12+%”. By studying the locations of these element pairs, it becomes possible to not only improve the efficiency of a designed catalyst by choosing element combinations that strongly associate with high C2 yields but also can potentially improve the efficiency of catalysts with poor performance by selectively replacing elements with other elements that result in higher catalytic performance.2 yields that fall within the category of “C2 yield 12+%” while the remaining two produce C2 yields that fall within the category “C2 yield 8–12%”. No catalysts produce yields that would fall within the C2 yield category “C2 yield 0–8%”. Thus, one can see that using the created networks to design catalysts in an informed manner can help decrease time and resources spent on catalyst development and testing while also have a higher chance of successfully returning a C2 yield that is considered to be high.A second batch of catalysts are then proposed and are presented in 2 yields of 20.38% and 19.03%, respectively, which outperform those of the remaining proposed catalysts and have also not been previously reported. Further analysis is conducted in order to better understand why these combinations may have resulted in such high yields. 2 yield groups “C2 yield 12+%” and “C2 yield 0–8%”. Given that the element pair nodes are located in the grey area between the two C2 yield groups, it is likely that the success of these elements is in someway dependent on the supports and gas flows that accompany them. For instance, supports BaO and CaO are seen to have a strong correlation with the C2 yield group “C2 yield 12+%” while support CeO2 strongly correlates with “C2 yield 0–8%”. A similar effect is also seen with LiTiW–BaO, where element pairs LiTi and TiW are seen near the C2 yield group “C2 yield 8–12%” and LiW is found within the grey area between C2 yield groups “C2 yield 12+%” and “C2 yield 0–8%”. Interestingly, the network did not include a case where any of these element pairs are connected with the support BaO. Given that the node for support BaO correlates strongly with the C2 yield group “C2 yield 12+%”, it is reasonable to believe that pairing the mid-level performing elements with a potentially high-level performing element with a support like BaO can improve the catalytic performance of the proposed catalyst. Further studies, however, are required in order to determine the long-term stability of these catalysts. These results thereby show that targeted design of new catalysts can be carried out more efficiently with the relational information that can be extracted through studying a network representation of catalytic data.Catalysts KVEu–BaO and LiTiW–BaO are also found to elicit C2 yields produced during the OCM reaction. One can see that elements either have clear associations with a particular C2 yield group or are found in areas between groups, which suggests that the performance of these so-called “grey-area” elements is influenced by other factors such as the elements they are paired with or other experimental factors such as temperature. The pairing effect of elements on the performance of catalysts is easier to understand when the data are retransformed into a network where catalysts are represented by their possible element pairs. Thirty-two catalysts are then designed using the constructed networks and then tested via high-throughput experiments with the aim of producing catalysts that result in high C2 yields during the OCM process. Out of the 32 catalysts, 23 are found to result in C2 yields greater than 12%, with 9 catalysts resulting in C2 yields of 17% or greater. Further analysis of these catalysts shows that elements that are found in grey areas are improved by elements that had correlations with high yield-producing catalysts, thereby demonstrating that catalyst performance can be enhanced through deliberate elemental pairings. Additional catalysts are designed and tested in order to confirm the efficiency of catalyst design via a network, where 7 out of the 9 catalysts are found to have C2 yields greater than 12%. Two catalysts in particular – KVEU–BaO and LiTIW–BaO – are found to elicit C2 yields of 20.38% and 19.03% and have not been previously reported, though long-term stability requires further investigation. Catalyst knowledge networks provide a way to design catalysts based on the relationships provided by catalyst data. In particular, this enables the ability to design highly active OCM catalysts. One can consider that the networks can assist further developments of catalysts, e.g. through doping or optimization of composition ratios, by providing information that could potentially lead to the enhancement of catalytic activity. If catalyst big data contains doping and ratio of composition information of catalysts, a further detailed catalyst knowledge network can, in principle, be developed. Thus, by creating networks of catalysts and experimental conditions of data produced via high-throughput experiments, catalysts with high performance can be designed in a much more efficient manner with a higher likelihood of success than traditional methods used during the catalyst design process.Transforming catalyst data generated from high-throughput experiments into networks has proven to be beneficial in several ways. To start with, by visualizing the transformation of catalyst data into networks, it becomes easier to understand correlations between atomic elements, their supports, and corresponding experimental conditions in relation to CData used to construct the networks presented in Fig. 1 and 2 have been uploaded as part of the ESI.†LT and KT conceived the idea for this analysis, determined methodologies, and wrote and reviewed the published work. LT curated catalyst data and applied network-related methods for formal analysis and visualization. TNN, SN, and AF tested designed catalysts in experiment. TT reviewed the published work and provided resources for experimental investigations. KT acquired funding for this published work.There are no conflicts of interest to declare.SC-012-D1SC04390K-s001SC-012-D1SC04390K-s002SC-012-D1SC04390K-s003SC-012-D1SC04390K-s004SC-012-D1SC04390K-s005SC-012-D1SC04390K-s006


Electrochemical impedance spectroscopy (EIS) was applied to study the electrical properties of this polymer electrolyte, such as ionic conductivity at room and different temperatures, dielectric and modulus properties. The highest conducting film has been obtained at 1.48 × 10−5 S·cm−1 by doping 30 wt% of Mg(NO3)2 into the polymer matrix at room temperature. This high ionic conductivity value is achieved due to the increase in the amorphous nature of the polymer electrolyte, as proven by X-ray diffractometry (XRD), where broadening of the amorphous peak can be observed. The intermolecular interactions between agarose and Mg(NO3)2 are studied by Fourier transform infrared (FTIR) spectroscopy by observing the presence of –OH, –CH, N–H, CH3, C–O–C, C–OH, C–C and 3,6-anhydrogalactose bridges in the FTIR spectra. The electrochemical properties for the highest conducting agarose–Mg(NO3)2 polymer electrolyte are stable up to 3.57 V, which is determined by using linear sweep voltammetry (LSV) and supported by cyclic voltammetry (CV) that proves the presence of Mg2+ conduction.Polymer electrolytes based on agarose dissolved in DMSO solvent complexed with different weight percentages of Mg(NO Among these promising multivalent ion batteries, Mg-ion batteries represent a significant advance in battery technology due to their highest theoretical volumetric energy density (3866 mAh cm−3), which is higher than that achievable by a Li anode (2066 mAh cm−3) [2+) [A significant increase in interest can be seen in the research activities for formulating suitable materials for next-generation advanced rechargeable batteries by virtue of high energy density in theory . MultivaAh cm−3) . HoweverAh cm−3) ,5,6, incAh cm−3) , the sol−3) [2+) ,9.Research on amorphous polymer-salt electrolytes is widely reported due to their wide variety of applications in devices such as fuel cells, supercapacitors and electrochromic windows ,10. This−5–10−4 S·cm−1 [2+ conduction. The Mg2+ conduction is supported by the hydroxyl groups in the repeating units of agarose, which contain an electronegative atom (oxygen). The presence of oxygen atoms also results in the formation of a cross-linking network to other constituents that can promote the transportation of ions in the polymer matrices [3SO3) liquid electrolyte that reached 2.80 × 10−3 S·cm−1 that has been recorded by Perera and Dissanayake [−3 S·cm−1 achieved when 40 wt% potassium iodide (KI) was infused in agarose matrices [−3 S·cm−1 when doped with 20 wt% LiI [Earlier studies on biopolymers as amorphous polymer salt complexes have shown that these naturally abundant polymers exhibited good film producibility . Collect4 S·cm−1 ,14,18. Amatrices ,21. The sanayake ,22. Sing wt% LiI . A parti wt% LiI ,25. Thes2+ on the conductivity, dielectric and complexation nature in agarose–Mg(NO3)2 as a freestanding film prepared by means of a solution casting method. Mg(NO3)2, as dopant salt with a high lattice energy of 2429.51 kJ·mol−1 [The present work was carried out to investigate the effect of MgkJ·mol−1 , was chow: 630.5 g·mol−1) obtained from Next Gene  was used as host polymer, and magnesium nitrate salt (Mg(NO3)2; Mw: 256.41 g·mol−1, ACS reagent 99%) purchased from Sigma-Aldrich  was used as a dopant. Dimethyl sulfoxide (DMSO) with ≥99.7% purity and 78.129 g·mol−1 molecular weight was purchased from Fisher Scientific  to function as a solvent in the preparation of polymer electrolyte films.Agarose 2 polymer electrolyte films. Thus agarose powder (0.5 g) was dissolved in dimethyl sulfoxide  before different amounts of Mg(NO3)2 was added to the solution. The Mg(NO3)2 ranged from 0 to 35 weight percent (wt%) with 5 wt% intervals. The solution of agarose–DMSO–Mg(NO3)2 was stirred magnetically until the solution became clear and homogeneous. Then, the samples were cast into a clean Petri dish and left to dry until a colourless thin film of polymer electrolyte was formed. The electrolyte films were tagged as AMg0, AMg5, AMg10, AMg15, AMg20, AMg25, AMg30 and AMg35 for 0, 5, 10, 15, 20, 25, 30 and 35 wt% Mg(NO3)2 infused into the agarose matrices, respectively.A solution casting method was used to prepare agarose–Mg is the ionic conductivity of polymer electrolyte, A (cm2) is the electrode–electrolyte contact area, t (cm) is the thickness of the thin film, and Rb is the bulk impedance. Other parameters related to the ion conduction mechanism, i.e., dielectric and modulus, were also plotted and discussed in this work.Electrochemical impedance spectroscopy (EIS) was performed using a HIOKI 3532-50 LCR Hi-Tester at various temperatures ranging from 300 to 373 K. The samples were sandwiched between two symmetrical stainless-steel electrodes with a contact area of 0.7854 cm3)2 in agarose–based polymer electrolytes were analysed by FTIR spectroscopy using Perkin Elmer  model spectrum 400 instrument. The spectra were recorded in the absorbance mode in the wave region ranging from 650 cm−1 to 4000 cm−1 with a spectral resolution of 2 cm−1. This measurement was conducted at room temperature by means of an attenuated total reflection (ATR) device.The molecular interactions in different compositions of Mg(NOα radiation (λ = 1.5418 Å) to determine the nature of polymer electrolyte formed either crystalline or amorphous. In XRD measurement, the Bragg angle, 2θ was varied from 5° to 90° at room temperature (27 °C).X-ray diffraction (XRD) patterns were measured using an PANalytical X’pert PRO diffractometer with CuK3)2 polymer electrolyte that would act as a separator was placed in between stainless steel (SS) and magnesium metal (Mg). The stainless steel served as a working electrode and the magnesium metal as a reference/counter electrode. LSV was performed at room temperature at a fixed potential range from 0 to 4 V at a scan rate of 5 mV·s−1. The cyclic voltammetry (CV) was performed in two-electrode configurations at a scan rate of 5 mV·s−1 in the voltage range of −1.5 V to 3.5 V. By carrying out this characterisation, the presence of the redox reactions in agarose-based polymer electrolyte can be discussed.The electrochemical stability of polymer electrolytes was studied by linear sweep voltammetry (LSV) using an Automatic Battery Cycler . The highest conducting agarose–Mg(NO3)2 electrolyte films presented in DC value. Commonly, a semicircle at the higher frequency in the impedance spectra is recorded, which refers to the ion transport through the bulk electrolyte with a particular ionic conductivity [b value is successfully determined using a manual graphical approach for pure agarose films to calculate the conductivity. The conductivity of pure agarose film is 1.75 × 10−8 S·cm−1. However, as seen clearly from x-axis with the end-tail of the semicircle is used to determine the Rb [3)2 polymer electrolyte at various weight percentages (wt%) of Mg(NO3)2 as displayed in −6 S·cm−1 upon addition of 25 wt% Mg(NO3)2 and further increased to 1.48 × 10−5 S·cm−1 when 30 wt% Mg(NO3)2 is dissolved into the agarose matrix. The enhancement of ionic conductivity is attributed to the higher number of ion dissociation events obtained as a consequence of the addition of salt Mg(NO3)2 into the agarose-based polymer electrolyte system that results in the production of free ions (Mg2+ cation and NO3− anion) that can be incorporated in the polymer host matrix [3)2 polymer electrolyte system. On the other hand, the increase in amorphous nature of the system also affects the enhancement of ionic conductivity as it leads to flexibility of the agarose polymer chain and promotes the segmental motion in this system as represented in 3)2 with the value of 2.47 × 10−6 S·cm−1 due to the aggregation of ions, which leads to the formation of ion cluster carriers or overcrowded ions that cause a decline in both the number of ions and their mobility [i-carrageenan-Mg(NO3)2 [3)2 [−4 S·cm−1 and 1.34 × 10−5 S·cm−1, respectively.The impedance spectra of selected agarose–Mg2  and poly3)2 [3)2  polymer k) and ions mobility (µk) which can be mathematically described from the equation:Generally, the conductivity is related to the ion charge carriers concentration (nσ) with charge carrier concentration and/or ions mobility is directly explained by the influence of salt concentration on the conductivity. According to the past reported conduction mechanism of polymer-salt systems, the independent charge transport species from the disjointed cation-anion move under an applied electric field. The cation leaps from one electronegative atom to another, creating a vacancy that another readily available cation present can fill at the neighbouring atom. This repetitive leaping of charge transport species is known as the Grotthus mechanism and is related to cation-anions disjointed via a dissociation process [3)2 is 2429.51 kJ·mol−1 [σ vs. 1000/T for agarose–Mg(NO3)2 polymer electrolytes with selected salt concentrations. Here, the conductivity of polymer electrolyte films is linearly dependent of the temperature, and this relation can be related to the Arrhenius rule as given in the following equation:σ0 is the pre-exponential factor, aE is the activation energy, and Bk is the Boltzmann constant. The supplied thermal energy weakens the recombination of ions during the solvent desiccation step in the film formation. Therefore, more independent ions are available for conduction as the temperature increases, contributing to higher conductivity values. The activation energy of the selected AMg25 and AMg30 samples are determined as 0.142 eV and 0.044 eV, respectively. The highest conducting of agarose–Mg(NO3)2 polymer electrolyte, AMg30, the activation energy has recorded a low value implying that low energy is required to free the ions hopping to another coordinating site [The proportionality of conductivity  consist of the real and imaginary part of dielectric, and their relationship is as in the equation below:Dielectric studies were performed to get further information on the conductivity behaviour of the studied divalent ion-based electrolyte. The complex permittivity . The angular frequency, 3)2 at various temperatures. It indicates that δ) and the relaxation time (The imaginary component (dielectric loss), reversed  as shownle layer . In addile layer . Figure i values . The furδ as a function of the logarithm of frequency (f) of agarose–30 wt% Mg(NO3)2 polymer electrolyte at different temperatures. The temperatures stimulate the frequencies position and the intensities of the peak in the plots. Such stimulation authenticated the dielectric relaxation process that is thermally activated. The relaxation time 2 electrolyte system are displayed in −1, 2900 cm−1, and 1641 cm−1 can be ascribed to the O–H stretching vibration [Referring to the molecular structure of agarose, multiple oxygen atoms bear lone pair electrons for the conduction of cations . Therefoibration  and N–H ibration , respect3 bond bending of an alkene group, C–O–C, C–OH and 3,6–anhydrogalactose bridge vibration modes that are registered at 1368 cm−1 [−1 [−1 [−1 [3)2. From the figure, the addition of salt shifts the OH stretching vibration peak of agarose to the 3397–3387 cm−1, region (i). The peak observed at 2930 cm−1 attributable to –CH stretching vibrations has shifted to 2922 cm−1 due to the increase of Mg(NO3)2 salt in the polymer matrix, resulting in higher energy vibration. At the same time, the N–H and CH3 bending in this work are detected at 1646–1652 cm−1 and 1364–1332 cm−1, respectively. The wavenumbers of C–O–C stretching vibration modes have been relocated from 1152 cm−1 to 1154 cm−1, proving the presence of carbohydrates units in the agarose-based polymer electrolyte [3)2 polymer electrolyte peak is recorded at 1070 cm−1 to 1072 cm−1 as observed in the region (vii). The vibration peak of the 3,6–anhydrogalactose bridge from the agarose polymer, has remained at a wavenumber of 930 cm−1 [3)2, as shown in the figure, we can verify the presence of the plane deformation modes of NO3− at the peak of 819 cm−1, as mentioned by a work of Manjuladevi and his team for a peak between 818–820 cm−1 [3)2 polymer electrolyte spectra.Other peaks belonging to agarose are the CH368 cm−1 , 1152 cmcm−1 [−1 , 1064 cm [−1 [−1  and 929  [−1 [−1 , respectctrolyte ,44,45. A930 cm−1 ,43. For 820 cm−1 ,48,49. H3)2 polymer electrolyte samples  in the 1320 cm−1 to 1450 cm−1 spectroscopic region. A band at a wavenumber of 1435–1438 cm−1 represents the isolation of C-C stretching vibrations for every composition of the agarose-Mg(NO3)2 polymer electrolyte [−1 which is important to clarify the conductivity trend in the sample [2+------NO3−) is effected in the polymer electrolyte due to the electrostatic forces that form between oppositely charged ions in the electrolyte. Another type of coexistent species is the ion aggregates (Mg2+------NO3−------Mg2+) that originate from the interactions among the Mg(NO3)2 units in the agarose matrix and are assumed to be larger than ion pairs. The vibrational frequencies encountered at 1419–1423 cm−1 and 1340–1366 cm−1 are assignable to ion pairs and ion aggregates, respectively [3)2 to the agarose based electrolyte, the area of the free ions peak starts to increase and this causes a decrease of the area under the peak of the contact ions region as the salt begins to dissociate in this composition. The samples of polymer electrolyte labelled as AMg10, AMg15, AMg20, and AMg25 record an area under the peak of free ions that is almost similar to that of the the sample of AMg5 as these concentrations exhibit a comparable ionic conductivity value, which is about 10−7 to 10−6 S·cm−1. However, the band attributed to free ions has shifted from 1408 cm−1 in AMg0 to 1404 cm−1 in AMg25, while for AMg30, the area under the free ions peak is larger than the peak area of the contact ions, which reveals that the lowest percentage of contact ions is achieved in the highest conducting agarose–30 wt% Mg(NO3)2 polymer while the percentage of free ions is the highest, reflecting the occurrence of a high degree of ion dissociation in the sample as illustrated in 3)2, induced by ion recombination to form ion pairs. A plausible mechanism for the complexation between agarose molecules and Mg(NO3)2 is illustrated in 2+) and nitrate (NO3−) ions are coupled to the ether oxygens of agarose. The Mg2+ are more tightly bound within the matrix, while the NO3− are loosely attached. In the presence of a small DC electric field, the weakly bound NO3− can rapidly dissociate and are able to hop from one coordinating site to another.Further, the free ions and contact ions percentages were calculated from the following formulas:e sample . The con3)2 in the agarose molecules has been highlighted in the FTIR as evidenced by the shifting of OH, –CH, N–H, –CH3, C–O–C, C–OH, C–C vibrational modes. XRD diffraction peaks of pure salt, Mg(NO3)2 are observed at 2θ = 20.12°, 26.99°, 29.56°, 38° and 43.53° as illustrated in The semicrystalline properties nature of the polymer electrolyte has been studied by using X-ray diffractometry (XRD). A substantial defect formation effect in the semicrystalline matrix increases the amorphous domain that generally improve the ion conduction for conductivity enhancement. The role of the defect agent Mg(NO3)2 polymer electrolytes shown in 3)2 in a polymer matrix (AMg0) [3)2 to the polymer matrix results in the peak becomes broader as can be observed from the XRD patterns of AMg5 to AMg30, thus, proving the increased amorphous nature of the polymer electrolyte system, that causes the high dissociation of the salt into free ions and increases the migration of ions through the polymer chains. In AMg35, the peak becomes narrow, indicating a decrease in the amorphous nature of the electrolyte due to the reassociation of ions in the polymer matrix.However, these peaks are absent in the XRD pattern of agarose–Mg(NOx (AMg0) . The add3)2 polymer electrolyte system and represent the trend of amorphousness nature as illustrated in L is the crystallite size, FWHM is the full width at half maximum (a measure of the peak broadness) and θ is Bragg’s diffraction angle.These observations are confirmed through the XRD deconvolution technique, which is analysed using a Gaussian function to identify the exact position of peaks that appear in selected samples of agarose–Mg(NOectively :(13)Xc=AFWHM value leads to the reduction of crystallite size from 6.62 nm to 3.17 nm, as presented in From the XRD deconvolution pattern for electrolyte shown in 3)2 in the SS//AMg30//Mg configuration. The current is swept from 0 V towards the positive range to determine the stability of the polymer electrolyte film that is important for further practical application. The voltage stability ceases when the cell does not withstand the potential. At this point, a rapid increase in current occurs due to the decomposition of the electrolyte at the inert electrode interface, known as the onset current. The onset current is marked at 3.57 V, inferring the stability of agarose–30 wt% of Mg(NO3)2, which is a good range for electrochemical applications in magnesium batteries [3)2 exhibits voltage stability of 3.4 V with the conductivity of 1.7 × 10−3 S·cm−1 [atteries ,52. Manj3 S·cm−1 . In anot3 S·cm−1 .2+ conduction in the polymer electrolyte system [The cyclic voltammetry (CV) pattern of the highest conducting sample of agarose-based polymer electrolyte, AMg30, that had been sandwiched between a stainless steel and a magnesium electrode in the range of 0 to 3.5 V is illustrated in e system .3)2 and exhibit the highest conductivity at a value of 1.48 × 10−5 S·cm−1 and a lowest activation energy value of about 0.044 eV due to the higher degree of ion dissociation. Beyond 30 wt% of salt in the polymer matrix, the ionic conductivity becomes reduced as the aggregation of ions starts to occur. The correlation of ionic conductivity with temperature can be explained by an enhancement of the mobility of ions at high temperatures, thus promoting the ionic conductivity of agarose–Mg(NO3)2 polymer electrolyte. FTIR studies show the presence of 3), 3)2 polymer electrolyte is in accordance with its intermolecular and amorphous behaviour. Furthermore, the agarose–Mg(NO3)2 electrolyte system is proven to exhibit excellent electrochemical properties as it can attain a sufficient range of electrochemical stability up to 3.57 V, which makes it applicable in magnesium batteries.Agarose–based polymer electrolytes shows their amorphous nature behaviour with the addition of 30 wt% of Mg(NO


Here, we demonstrate by mass spectrometric experiments that RhTiO2− promotes the co‐conversion of CH4 and CO2 to free 2 H2+CO and an adsorbed CO (COads) at room temperature; the only elementary step that requires the input of external energy is desorption of COads from the RhTiO2CO− to reform RhTiO2−. This study not only identifies a promising active species for dry (CO2) reforming of methane to syngas, but also emphasizes the importance of temperature control over elementary steps in practical catalysis, which may significantly alleviate the carbon deposition originating from the pyrolysis of methane.Catalytic co‐conversion of methane with carbon dioxide to produce syngas (2 H 4 and CO2 to syngas mediated by single Rh atom‐doped titanium oxide anions (RhTiO2−) has been experimentally identified. The production of free 2 H2+CO and an adsorbed CO (COads) could be achieved at room temperature while the only elementary step that requires the input of external energy is desorption of the COads from RhTiO2CO−.The catalytic co‐conversion of CH The CID experiments of mass‐selected intermediate complexes RhTiO213CH4CO2−  and (3c), respectively. The desorption of 13CH4 molecule from 3III supports reaction (4).In order to confirm the proposed sequential desorption mechanism, we spatially separated the addition of the reactants using two ion trap reactors (instead one). Product ions RhTiOr Figure . These e4 and CO2 are co‐converted to 2 H2+CO at room temperature in the presence of RhTiO2− and form RhTiO2CO− as the product ion. The CID experiments of mass‐selected RhTiO2CO− with Xe were carried out. When the center‐of‐mass collisional energy was increased to above 3 eV, the loss of the second CO and regeneration of RhTiO2− were observed  and [CCO2−OCO2], bound to the Rh atom. Either CCH4Ocluster or (CO)CO2can be desorbed to produce the ionic product RhTiO2CH2O− (P2), consistent with the observed reaction channels in the isotopic labeling experiment  was also considered. Although it is thermodynamically more favorable than P1→RhTiO2CH2O−+CO, a high energy barrier  is encountered before H2 desorption. The rate (4.3×108 s−1) of traversing TS49 from P1 was estimated to be two orders of magnitude lower than that for CO evaporation  so that CO is preferentially desorbed from P1. The RhTiO2C2O2−+H2 channel was not observed. After dehydrogenation of RhTiO2CH2O− (P2→P3), the RhTiO2CO− ion with CO bound to Rh is formed. Desorption of the second CO to regenerate RhTiO2− and close the catalytic cycle of DRM to syngas is overall endothermic by 2.74 eV . This requires supplying additional external energy as shown by the CID experiments. The direct elimination of HCHO and CH3OH from the reaction of RhTiO2CH4− with CO2 was also considered. These reaction pathways  are kinetically less favorable than the H2 loss channel [reaction (3a)] and ultimately also entropically unfavorable. Therefore, we conclude that the probability for formation of HCHO or CH3OH is negligible.P1 still possesses sufficient internal energy to enable desorption of the first CO molecule , the single Rh atom is sufficiently active to enable the co‐conversion of methane with carbon dioxide to syngas, in sharp contrast to the VOx cluster support, for which the Rh2 dimer is indispensible for syngas production.Methane conversion has been extensively explored in gas phase studies, which revealed that only a few transition metal‐containing oxide ions react with a single CH4→C+2 H2), leading to carbon deposition and catalyst deactivation.2− provides first experimental evidence that both the conversion of CH4 to 2 H2,gas+COgas/COads and reduction of CO2 to COads/COgas can be achievable at room temperature, the only elementary step that requires high temperatures is COads desorption (rate‐determining step). The insights into the elementary steps of this study should motivate the employment of temperature‐programmed methodsIn condensed phase thermocatalysis of DRM to syngas, methane activation is generally viewed as the rate‐determining step and almost all the elementary reactions are performed at the same high temperatures that easily caused pyrolysis of methane  to complete the full catalytic cycle. The working conditions for each elementary step of syngas production identified herein underline that the temperature control is very important for optimization of the reaction process and reduction of carbon deposition and energy consumption in practical catalysis.In conclusion, the co‐conversion of CHThe authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.