In many phase II trials in solid tumours, patients are assessed using endpoints based on the Response Evaluation Criteria in Solid Tumours (RECIST) scale. Often, analyses are based on the response rate. This is the proportion of patients who have an observed tumour shrinkage above a predefined level and no new tumour lesions. The augmented binary method has been proposed to improve the precision of the estimator of the response rate. The method involves modelling the tumour shrinkage to avoid dichotomising it. However, in many trials the best observed response is used as the primary outcome. In such trials, patients are followed until progression, and their best observed RECIST outcome is used as the primary endpoint. In this paper, we propose a method that extends the augmented binary method so that it can be used when the outcome is best observed response. We show through simulated data and data from a real phase II cancer trial that this method improves power in both single‐arm and randomised trials. The average gain in power compared to the traditional analysis is equivalent to approximately a 35% increase in sample size. A modified version of the method is proposed to reduce the computational effort required. We show this modified method maintains much of the efficiency advantages. The results of the phase II trial determines whether to test the treatment in a larger, more time‐consuming, and more costly phase III trial. Because of the high cost of, and high failure rate in, phase III oncology trials,Phase II oncology trials use a variety of endpoints to evaluate the efficacy of a treatment., 3 The mCategorizing patients into responders and nonresponders is widespread and clinically appealing. However, it can have substantial statistical disadvantages. Its major limitation is that it dichotomises the continuous tumour variable, thus discarding information. This loses substantial efficiency.Focusing on response‐based endpoints, the method of Wason and SeamanThis paper is divided into 4 sections. Section 22.1fixed time and the other being BOR. Analysis at a fixed time t uses the proportion of responders at time t (those who have a tumour size shrinkage at time t above a predefined threshold and no progression up to that point). Best observed response defines patients as a responder or not according to their BOR before progression. The latest RECIST guidelinesWe use the phrase “tumour size” as shorthand for the sum of the longest diameter of target tumour lesions. We assume patients tumour sizes are recorded until progression occurs or until a preplanned number of visits. We note there are 2 ways in which a progression can occur: an increase in tumour size by more than 20% (a tumour‐growth progression) or new lesions appearing (a new‐lesion progression). Two response‐based endpoints can be used in the analysis, one being 2.2T denoting the maximum time). The tumour size at time t for patient i is denoted by zit where t=0 represents the baseline measurement. We denote G and X as the time at which a tumour‐growth progression and new‐lesion progression occurs, respectively. Once a patient progresses they are no longer followed up. The observed data is therefore  where Fi=min. We define yit as the log tumour size ratio for patient i at time t, yit=log(zit/zi0), and ct as the prespecified dichotomisation threshold for response . Further, Dit defines new‐lesion progression indicators: {Dit=1 if patient i has a progression due to new lesions occurring between time (t−1) and t, t=1,…,T }. For simplicity, we define composite indicators S for fixed time and BOR for best observed response using tumour progression relative to the baseline rather than nadir (the lowest rumour size observed so far). Note that we show how to use the true definition in the i using fixed time is defined asTumour sizes for each patient are measured at several discrete times , representing at least a 30% shrinkage in the tumour size from baseline). The mean response probability is estimated by Pr‾(S2=1|θ^)=1n∑i=1nPr(Si2=1|θ^), where θ^ is the maximum likelihood estimator of θ. A program is available in the paper which uses R2Cuba to compute the above integration. An approximately (1‐α)% confidence interval for the probability of response is constructed on the logit scale, that is, expit{l(θ^)±Φ−1(1−α2)var(l(θ^))}, where l(θ)=logPr(S2=1|θ)1−Pr(S2=1|θ) and var(l(θ^)) is obtained by using the delta method.The Augbin method makes assumptions that the log tumour size ratios follow a multivariate normal distribution, and the probability of new‐lesion progression depends only on the observed tumour size at the previous visit. The log tumour size ratios are modelled by2.4t follow‐up times. The log tumour size ratios are modelled byt is large enough). The new‐lesion progression is modelled byPr(Yi(t+1)is missing|zi0,⋯,ziT)=Pr(Yi(t+1)is missing|zi0,⋯,zit). See the “sequential missingness at random” section in i at time T can be written bycT and c are the dichotomisation cut points  and log(1.2), representing at least a 30% shrinkage in the tumour size from baseline and an increase in tumour size by more than 20%). Note that the response at a fixed time T′⩽T can be obtained by replacing T by T′ in the above formulas. The advantage is that the Equation i being a responder at time T as if they were observed until T. A potential issue of Equation θ^. An approximately (1−α)% confidence interval is constructed as described in Section We use the same assumptions and extend the Augbin to itten byPr. We note this is a strong assumption and assess the sensitivity to this assumption later on. The probability of response for patient i at a fixed time t can be written byπt be the probability of new‐lesion progression at time t,t=1,⋯,T. Note that πt is a conditional probability given no new‐lesion progression occurring at previous time points. The log tumour size ratio Yi is allowed to depend on baseline tumour size whereas new‐lesion progression depends on the previous observed tumour size at the previous visit. We can model Y byfY(·) is a joint distribution and l(·) is the logit link function. We assume that πit(z¯t−1)=πit(zt−1). The probability of response for patient i at a fixed time T can be written bycT and c are the dichotomisation threshold and θ is a vector of parameters of the models. We assume that fY(·) is the probability density function of a multivariate normal distribution. The multivariate integration can then be calculated by a highly efficient technique proposed by Genz and Bretz.i is . The l can be estimated by ωF+1+βF+1zi,F. Their probability of new‐lesion progression at time t,t⩾Fi+2 is estimated bynt is the number of patients with observed zt−1 and k is the number of patients who have log tumour size ratio yt outside of the region of integration φ of Equation k patients to avoid underestimating πit. This is similar to an idea of trimmed mean, which is used in many areas and has advantages under both normal and nonnormal distributions.The objective for this section is to efficiently estimate the mean response probability using continuous tumour‐size information in a computationally efficient way. We assume that {no new‐lesion progression occurs from time 1 to time itten byPr(SiT=1|θ consists of (T+1) parameters that make up μ, T(T+1)/2 parameters for Σ, and 2T parameters for the logistic models. The mean response probability is estimated by Pr‾(ST=1|θ^)=1n∑i=1nPr(SiT=1|θ^), where θ^ is the maximum likelihood estimator of θ. A (1−α)% confidence interval for Pr‾(ST=1|θ) can be constructed:{Pr‾(ST=1|θ^)} and transform back. Let l(θ)=logPr(ST=1|θ)1−Pr(ST=1|θ), we obtain Var(l(θ^)) by using the delta method, which is written by∇l(θ^) is the partial derivatives of l(θ). An approximately (1−α)% confidence interval for the probability of response isThe vector To summarise, the modified method uses a simplification for the relationship between new‐lesion progressions and tumour‐growth progressions to use a more efficient procedure for multivariate integration.2.6Ω1=(log(0.7),log(1.2)), Ω2=), and Ω3= as the possible regions of integration corresponding to being classified as stable disease, responder, and irrelevant variables. Let h be the time at which the patient is first classified as CR/PR. Hence, each component of Y¯T will fall into 1 of the 3 regions asi will be the sum over all possibilities of when the CR/PR is first observed. Following the concept of the eAugbin, the probability of response can be written byWe focus on the case where confirmation is not required but show briefly how the methodology can straightforwardly allow for confirmation later. By the definition of BOR, a patient is a responder if they have at least 1 log tumour size ratio smaller than log(0.7) before progression or maximum follow‐up time. We define Similarly, following the concept of the mAug, the probability of response can be written by1n∑i=1nPr(BORi=1|θ^), where θ^ is the maximum likelihood estimator of θ. As before, we work on the logit scale, use the delta method to obtain the variance and then transform back to construct the confidence interval for the mean response probability.The mean response probability is then estimated by Y1…Yh−1∈Ω1,Yh,Yh+1∈Ω2,Yh+2…YT∈Ω3) with the sum in T−1.When confirmation is required, having 2 continued responses of CR/PR before progression, one can replace 2.7The above methods can be applied to single‐arm trials. For a randomised trial where comparing the difference in response probability is of interest, a minor addition is required.i indexes the ith patient, μi1=μ1+η1R+ωzi0,μi2=μ2+η2R+ωzi0. The new‐lesion progression for T>t is modelled by using logistic models[1+exp{−(αt+γtzi(t−1))}]−1 and [1+exp{−(αt+βt+γtzi(t−1))}]−1, respectively. Let θ be the vector of parameters with length (T2+9T2+2) from the above models. The mean response probability at a fixed time is estimated byθ^ is the maximum likelihood estimator of θ. We note that patients from both arms are included in the calculation of the probability of response in an arm, as is recommended and justified in Wason and Seaman.mF(θ^) by using the delta method and use the Wald test to test whether mF(θ) is 0. Similarly, we define the mean difference in response probability for BOR asWe assume 2n patients are recruited with n patients randomised to each arm. Assumptions for log tumour size ratios and new‐lesion progression remain the same as in Section 3S=1 or BOR=1). For 2‐arm studies, the binary method is a logistic regression model that has parameters for treatment group and baseline tumour size, from which the treatment effect can be tested. The terms “Augbin,” “eAugbin,” and “mAug” refer to methods that use continuous information. They are, respectively, Wason and Seaman's methodcT and c being log(0.7) and −∞ and BOR without confirmation as the endpoints.In this section, we evaluate the performance of the proposed methods in terms of precision and power using simulations and a real data example. We use “Bin” to represent the method that just analyses the response outcomes as binary. For single‐arm trials, the binary method uses the R‐package Hmisc to construct a Wilson interval for binary success . The observed data are simulated as follows. First of all, baseline tumour size zi0 for patient i is generated from a uniform distribution and log tumour size ratios of T follow‐up time {yit:t=1,⋯,T} are generated from a multivariate normal distribution. Tumour size zit can then be calculated from zit=eyitzi0. Next, new‐lesion progression indicators are generated from logistic models with intercept α and tumour size effect γ. A nonzero γ means that probability of new‐lesion progression depends on the tumour size at the previous time point. We define time to new‐lesion progression as the first time when the new‐lesion progression occurs from the logistic models. Finally, tumour size observations of patient i after progression are replaced as missing.Following the aforementioned notation, the observed data available for each patient is (3.1.1Z0) is generated from a uniform distribution and log tumour size ratios are generated from a multivariate normal distribution for 2 to 6 follow‐up times. The  are set to  and . The value of α=−1.5 corresponds to an 18% chance of developing new lesions between each visit. The computation time for running 1 replicate using Augbin/eAugbin for 2 to 6 follow‐up times are 0.04,0.65,2.28,3.41, and 4.47 minutes; while mAug at 6 follow‐up times takes 0.09 minutes. We do not consider T>4 because of the length of time need to simulate 5000 replicates for eAugbin. The simulation settings of log tumour size ratios for 2 follow‐up times is a similar formulation to Wason and Seaman,Before generating 5000 replicates, we test the computation time for running one replicate using Augbin/eAugbin. We generated one replicate of 75 patients. Baseline tumour size ,The settings for t=2. For larger t, it appears the mAug method has a better coverage probability  than eAugbin. The reduction in confidence interval width, compared to the binary method, appears to be similar for the 2 methods. Thus for single‐arm trials it appears mAug shows a significant improvement in computational efficiency without notably poorer statistical characteristics compared to eAugbin.Table 3.1.2Z0) is generated from a U distribution. The mean log tumour size ratios between each visit are generated from a normal distribution with mean μ and variance 12. We set μ=log(0.7)+δτ+ψ, where δ=1 for control and δ=−1 for experimental arms, 2τ is the difference in the mean log tumour size ratio and ψ reflects the effectiveness of the control treatment. This is a similar formulation as.We consider a two‐arm trial with a control and experimental arm for 2 follow‐up times. Each arm has 75 patients that have been allocated at random. Baseline . For example, the case where T=4 refers to having 4 visits after baseline and μ being set to 0.25 log (0.7), 0.5 log (0.7), 0.75 log (0.7), log (0.7). For computational reasons, eAugbin was included for up to T=6. Table 2×75), to obtain a similar average width using Bin. The reduction in width is slightly higher when there is a tumour size effect on new‐lesion progression.Using the binary composite outcome, patients are classified as responders if they have a CR/PR before time F. The computation time for running 1 replicate using Augbin/eAugbin and BOR for 3 to 6 follow‐up times are 0.05,0.09,0.3, and 0.56 minutes; while mAug at 6 follow‐up times takes 0.22 minutes. Again, we use 5000 replicates of 75 patients. Baseline tumour size is generated from a uniform distribution . The log tumour size ratios are generated from multivariate normal distribution for 4,5,6, and 7 follow‐up times with 3.1.4(log(0.7)+.25δτ), (log(0.7)+.5δτ), (log(0.7)+.75δτ) and (log(0.7)+δτ), where δ=1 for control and δ=−1 for experimental arms respectively. We also consider the method of Karrison et al.To illustrate results of the mAug method for a two‐arm trial, we consider the case where each arm has 75 patients and patients are followed for 4 time points. The mean log tumour size ratios for each time point is 3.2The HORIZON II  is a 3‐arm colon cancer trial sponsored by AstraZeneca. Patients initially were randomly assigned 1:1:1 to placebo, cediranib 20 mg once daily, and cediranib 30 mg once daily. Later, subsequent patients were randomly assigned 1:2 to placebo or cediranib 20 mg.We used a permutation test to calculate the empirical type I error rate. Data from baseline, 6, 12, 18, and 24 weeks, were used. We simulated 5000 replicates, with the treatment assignment label shuffled randomly in each replicate. For each replicate, we tested the difference in probability of BOR between 2 treatment arms using mAug with 4 follow‐up times. The empirical type I error for no difference between placebo and cediranib 20 mg is 0.0558 and that between placebo and cediranib 30 mg is 0.0518. These are within Monte Carlo standard error of a true type I error of 0.05 (MC error +/− 0.006).Figure Table 4In this paper, we have considered how the augmented binary method of Wason and SeamanWe have shown that all proposed methods carry the same good properties as the augmented binary method. They provide extra precision, ie, they require a smaller sample size for the same precision  in single‐arm trials and are more powerful in comparative trials. We include Karrison's method, which directly tests the continuous tumour change outcome. As found in Wason and Seaman,The difference between the modified (mAug) and extended (eAugbin) methods is that the former uses the estimated probability of new‐lesion progression whereas the latter more correctly incorporates variation by averaging all possibilities. Estimation of probabilities using the modified method might be biased if only a few patients remain in a trial at some time point. The mAug has similar properties to eAugbin with respect to precision and power when using BOR.The extended and modified methods define progression as 20% increase from baseline, whereas RECIST defines progression as 20% increase from the minimum point observed. On the HORIZON II dataset, we examined the number of patients who had their BOR being PR or CR by both of these definitions. The number is the same for both approaches for all number of follow‐up times. This indicates that considering progression as being 20% from baseline does not substantially affect the estimation. However, we should point out that the eAugbin would be able to use the RECIST definition of progression by including a suitable indicator variable in the integrand as well as mAug by changing regions of integration of variables. Details about how to use the true definition can be found in All proposed augmented binary methods involve modelling the log tumour size ratio and new‐lesion progression indicators. The new lesion indicator can include other reasons for progression such as unequivocal progression of nontarget lesions. It may also be possible to include a second logistic regression model for the nontarget lesions separately to increase efficiency further. The log tumour size ratio has been shown to be approximately normally distributed in past data in oncology.log(0.7) to log(1.2). The joint distribution of the probability of being in each ordered category  could be estimated by suitably extending the delta‐method approach. An increasingly commonly used phase II endpoint is progression‐free survival (PFS). Response‐based endpoints focus on success, and PFS is interested in progression. Further development of the Augbin from response to progression so that it can be applied to improve analyses of PFS is an area of current work.We have only considered ORR in this work. However, the proposed method can be adjusted for using disease control rate where SD is included. The probability of patients being classified as SD can be calculated by adapting the inner integral in equations from 5https://sites.google.com/site/jmswason/supplementary-material for the methods proposed in this paper. The package includes both the eAugbin and mAug.For estimating the probability of response for fixed time and for BOR, a package mAugbin in R is available at Figure S1. Waterfall plot for individual reduction in tumour size at week 24 from the baseline in comparison to their baseline response for three arms. The colouring shows their best response over the 4 visits. Patients having new‐lesion progression before week 24 are not plotted. As seen, the proportions of responders (CR/PR) at week 24 for three arms are similar. There are cases that participants having classified as responders before progression.Figure S2. Mean estimated response probability of three methods using fixed time.The dot indicates the mean estimated response probability and the bar indicates SE.Figure S3. Residual plot of the fitted multivariate normal model for the 20 mg arm using 3‐follow up times. Residuals are obtained from a multivariate normal model with covariates (baseline and time | patients' id). Before model fitting, all negative infinity logtumour size (tumour size zero) is replaced by the minimum of the logtumour size of time 1,2,3, respectively. The colour blue indicates that one of the logtumour size of the 3‐follow up time is negative infinity. The residuals look close to normally distributed, though there is a pattern the variance of the residuals may be decreasing as the fitted values increase. In general it may be beneficial to apply a transformation such as the Box‐Cox family to ensure the normality assumption is as close to true as possible.Table S1. The width of 95% CI of the difference of mean estimated response probability between Placebo and treatments using BOR endpoints at time points 4, 5 and 6. All 95% CIs contain zero.Click here for additional data file.mAugbinClick here for additional data file.


DNA and are involved in multiple DNA replication and repair pathways. The CRFCtf18 variant complex is required for activation of the intra‐S‐phase checkpoint at stalled replication forks and aids the establishment of sister chromatid cohesion. Unlike other RFC complexes, CRFCtf18 contains two non‐Rfc subunits, Dcc1 and Ctf8. Here, we present the crystal structure of the Dcc1‐Ctf8 heterodimer bound to the C‐terminus of Ctf18. We find that the C‐terminus of Dcc1 contains three‐winged helix domains, which bind to both ssDNA and dsDNA. We further show that these domains are required for full recruitment of the complex to chromatin, and correct activation of the replication checkpoint. These findings provide the first structural data on a eukaryotic seven‐subunit clamp loader and define a new biochemical activity for Dcc1.Replication factor C complexes load and unload processivity clamps from  Accurate duplication and segregation of chromosomes is vital to maintain integrity of the genetic material. To facilitate this, cells have evolved checkpoint mechanisms to ensure that aberrant processes are detected and where possible, repaired. One such set of checkpoints occur during DNA replication. The so‐called intra‐S‐phase checkpoints are required to maintain genome integrity at stalled replication forks Ctf18 as being required for full activation of Rad53 Ctf18 is a variant replication factor C‐like complex, the basic function of which is to load or unload a DNA sliding clamp in an ATP‐dependent manner Rfc1, which loads PCNA onto DNA at replication forks and is essential for all replication and repair processes. The best understood variant is probably RFCRad24, which participates in the DNA damage checkpoint response by loading a specialised clamp formed by Rad17, Mec3 and Ddc1 Elg1 and RFCCtf18 are less clear. RFCElg1 has been implicated in PCNA unloading Ctf18 was originally identified in screens for chromosome mis‐segregation Ctf18 also contains the proteins Dcc1 and Ctf8, making it the only RFC variant formed of seven subunits. RFCCtf18 can both unload and load PCNA in vitroin vivo role is as a loader, as cells lacking Ctf18 show a reduced level of PCNA at sites of DNA replication A number of studies in yeast have shown the requirement for another protein complex, RFCCtf18 deletion and its implication in multiple cellular pathways, the biochemical and mechanistic properties of the complex are not fully understood. As the Dcc1‐Ctf8 sub‐complex is not required for the PCNA‐loading or unloading reaction C). To our surprise, we found that the C‐terminus of Dcc1 contains three‐winged helix (WH) domains, which we demonstrate can bind to both ssDNA and double‐stranded DNA (dsDNA). We further show that these domains aid recruitment of the complex to chromatin in vivo and are required for checkpoint activation.Despite the characterisation of the phenotypic effects of RFCCtf18 is formed of seven subunits: Ctf18, Rfc2, Rfc3, Rfc4, Rfc5, Ctf8 and Dcc1  with full‐length Dcc1 and Ctf8. Crystals of this complex (designated Ctf18C‐Dcc1‐Ctf8) were grown that diffracted to 2.4 Å resolution. While working on this complex, we also obtained crystals of the C‐terminal domain of Dcc1 (residues 90–380) that diffracted to 2 Å. Phase information for this crystal form was determined using multiple‐wavelength anomalous diffraction (MAD) of selenomethionine‐substituted protein. The refined structure was then used as a search model to determine the structure of the Ctf18C‐Dcc1‐Ctf8 heterotrimer by molecular replacement (MR). The final structures were of high quality with full statistics of the crystallographic analyses given in RFCC‐Dcc1‐Ctf8 is extended and looks like a “hook”, as the C‐terminus of Dcc1 folds back towards Ctf8  cannot properly activate the intra‐S‐phase checkpoint at stalled forks acts Fig A suggestCtf18 complex to stalled forks.We also find that deletion of the WH domains leads to sister chromatid cohesion defects Fig C. IntereCtf18 is a seven‐subunit complex formed of Ctf18, Rfc2‐5 and the stable Dcc1‐Ctf8 sub‐complex. The complex is required for activation of the intra‐S‐phase checkpoint and proper establishment of sister chromatid cohesion. To better understand the function of RFCCtf18, we determined the crystal structure of the heterotrimeric Ctf18C‐Dcc1‐Ctf8 complex and analysed in vitro and in vivo activities.RFCC‐Dcc1‐Ctf8 was completely unexpected and revealed that Dcc1 has three consecutive WH domains at its C‐terminus, the third of which can bind to both ssDNA and dsDNA. To our knowledge, this is a completely novel organisation of WH domains. The N‐terminus of Dcc1 and Ctf8 form a “triple” β‐barrel fold, which can then bind Ctf18. This overall architecture has previously been found in a range of transcription factors and RNase H2. Like the transcription factors, RFCCtf18 uses this domain as a bridge between a larger complex and a variable number of WH domains able to bind DNA. This suggests a common ancestor of both a subset of transcription factors and RFCCtf18. RFC complexes are able to directly recognise and bind DNA structures such as template‐primer junctions via multiple Rfc subunits Ctf18 contains additional binding sites for both dsDNA and ssDNA suggests that directed recruitment is an important part of its mechanism, as the intrinsic DNA binding activity of the core complex is sufficient for the basic loading and unloading activity The structure of Ctf18Ctf18 is able to bind to DNA polymerase epsilon (Pol ε) via the catalytic subunit Pol2 C‐Ctf8‐Dcc1 complex which we present in this study. Our structure shows that mutations in, or deletion of the Ctf18 C‐terminus would dissociate the Dcc1‐Ctf8 heterodimer from the complex, so disrupting the platform for Pol ε binding. The addition of two non‐Rfc subunits to the RFCCtf18 complex is reminiscent of the bacterial clamp loader, which employs two auxiliary subunits, χ and ѱ to link the loader to single‐stranded DNA‐binding protein (SSB) upstream of the replication fork Recent studies have also shown that RFCOur structure shows that the Ctf8‐Dcc1 interface acts as a binding site for the Ctf18 C‐terminus, which together forms a platform capable of binding Pol 2. Given the proximity of the WH domains to this binding site, it seems likely that they are also capable of interacting with the Pol ε complex, and/or DNA within the replisome. The reduced occupancy of Ctf18 on chromatin observed following WH3 deletion supports this notion. Although all the WH domains are required for full checkpoint activation, we find that only WH1 and WH2 are required for correct sister chromatid cohesion. This separation of function might be explained if the cohesion pathway is more tolerant to reduced levels of chromatin bound Ctf18 relative to the checkpoint pathway. Alternatively, the WH3 domain might form a checkpoint‐specific interaction with other proteins.Ctf18 also acts in additional pathways, for example telomere positioning and stability It has been reported that RFC6 tag and TEV protease site. These subunits were then sequenced to confirm that no mutations had been introduced. The vectors containing the subunits were transformed into BL21(DE3)‐RIL competent cells (Agilent) using standard procedures. An overnight culture of freshly transformed cells was grown at 37°C in LB, which was used to inoculate a 1 l culture. These cells were then grown at 37°C until an OD of 0.6 was reached, upon which expression of the complex was induced by the addition of 0.25 mM IPTG at 16°C for 18 h. The cells were harvested and sonicated in 50 mM Tris pH 7.5, 300 mM NaCl, 10% glycerol and 0.5 mM TCEP and then centrifuged at 35,000 × g, 4°C for 1 h. The supernatant was loaded onto a 5 ml His‐Trap HP  and eluted using imidazole. The tag was then cleaved from the Ctf18 subunit by the addition of TEV protease at 0.04 mg/ml and left overnight at 4°C. The protein was loaded onto a MonoQ 10/300 GL  column equilibrated in 20 mM Tris pH 7.5, 100 mM NaCl and 0.5 mM TCEP. The protein was eluted in a high concentration of NaCl. The complex was concentrated and loaded on an equilibrated HiLoad 16/60 Superdex 75 column  in 10 mM Tris pH 7.5, 150 mM NaCl and 0.5 mM TCEP. Eluted protein‐containing fractions were analysed for purity by SDS–PAGE. Selenomethionine incorporated protein was obtained by growing the cells in minimal media where selenomethionine was the only source of methionine and purification of the protein was identical to the native protein. The Dcc1 truncation (residues 90–380) was expressed and purified using the same protocol as for the full complex.The genes coding for the subunits Ctf18 (residues 666–741), Dcc1 (full length) and Ctf8 (full length) were amplified from genomic DNA and cloned into the vectors pET‐28a, pCDFDuet and pETDuet, respectively. The Ctf18 subunit had the addition of an N‐terminal HisC‐Dcc1‐Ctf8 was concentrated to 35 mg/ml and was crystallised in 4 μl sitting drops with equal volumes of protein to reservoir solution. The reservoir contained 0.1 M Bis–Tris propane pH 6.3, 0.2 M NaBr and 17% PEG 3350. Crystals were grown at 20°C for 10 days before harvesting in 15% ethylene glycol and stored in liquid nitrogen. Native data were collected at Diamond Light Source on beamline IO3. Molecular replacement was carried out in Phaser The selenomethionine incorporated C‐terminus of Dcc1 was concentrated to 20 mg/ml and crystallised in 2 μl sitting drops with equal volumes of protein to reservoir solution at 20°C. The reservoir contained 0.2 M potassium sodium tartrate and 18% PEG 3350. Crystals were harvested after 2 days in 20% ethylene glycol. A two wavelength MAD data set was collected at Diamond Light Source on beamline IO3. The AutoSHARP DNA binding analysis was conducted on both dsDNA and ssDNA. The dsDNA had the sequence TCTCCACAGGAAACGGAGGGGT and a fluorescent 6‐FAM tag on the 5′ sense strand. The DNA was annealed and purified using standard techniques. The ssDNA used was a 25‐mer oligo d(T), with a 5′ 6‐FAM fluorescent tag. Both oligos were synthesised and purchased from Sigma‐Aldrich. A 6% non‐denaturing DNA retardation gel was pre‐run at 4°C at 3 mA for 1 h in 0.5× TBE. DNA was at 1 μM unless otherwise stated and protein titrated at increasing concentrations. Samples were loaded and run for 75 min at 5 mA, 4°C. The gel was then analysed using a Typhoon 9400 imager excited at 488 nm, and emission was detected at 520 nm at 500 V.All strains used in this study were of W303 background and are listed in Table URA3 locus on chromosome 5, as previously described Chromatin immunoprecipitation was performed as previously described C‐Ctf8‐Dcc1 and Dcc190–380 structures have been deposited in the Protein Data Bank under accession codes 5MSM and 5MSN, respectively.Coordinates and structure factors of Ctf18in vivo experiments. BOW, FU and MRS conceived and designed the study. All authors provided input into the manuscript.BOW cloned, expressed, purified and crystallised all constructs and carried out DNA binding experiments. BOW and MRS collected and analysed X‐ray data. CPS and HWL carried out The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Review Process FileClick here for additional data file.


Early studies show that direct synthesis of H2O2 by plasma-water interactions is possible, while the factors affecting the H2O2 production in this method remain unclear. Herein, we present a study on the H2O2 synthesis by atmospheric pressure plasma-water interactions. The results indicate that the most important factors for the H2O2 production are the processes taking place at the plasma-water interface, including sputtering, electric field induced hydrated ion emission, and evaporation. The H2O2 production rate reaches ~1200 μmol/h when the liquid cathode is purified water or an aqueous solution of NaCl with an initial conductivity of 10500 μS cm−1.Hydrogen peroxide (H Obviously, H2O2 synthesis directly from its constituent elements, i.e., H2 and O2, is a simple idea452 and O2 is explosive. Some alternative direct methods do not use H2 and O2 which avoid the explosive problem and enable the in-situ continuous synthesis of H2O2, such as electrochemical synthesis from O2 and water789102 in aqueous solutions1213142 although the production rate is relatively low (0.625 μmol/h)Hydrogen peroxide HO2 has fo2O2 can be formed by the reaction of H2 + O2 → H2O2 in plasma containing H2 and O2 gases171820212223242O2 formation in plasma-water interactions. In addition, other less probable pathways such as OH + H2O* → H2O2 + H272O2 formation by discharge plasma operated over and inside aqueous solutions27282930313233342O2 production from the plasma-water interactions remain unclear. To optimize the H2O2 production from plasma-water interactions, a better understanding of the H2O2 production process is desired. Herein, we present an insight into the understanding of H2O2 production by the plasma-water interactions. The results indicate that the sputtering, the electric field induced ion emission, and the evaporation at the water surface strongly influences the H2O2 production.It has been proven that H2O2 was produced by the device schematically shown in 2O2 yield was measured at a given interval of 10 min, and the temperature, pH value, and the conductivity of the solution were also investigated.H2O2 yields at different experimental condit ions. For liquids with low initial conductivities (1.60 μS cm−1 and 1440 μS cm−1), the H2O2 production rate (the slope of the yield curve) decreases with increasing time, while they are constants for the liquids with high initial conductivities (4800 μS cm−1 and 10500 μS cm−1) [−1, the H2O2 yield for NaOH solution is almost zero after 60 min plasma treatment [2O2 production rate is enhanced [2O2 production [2O2 is formed during the plasma-liquid interactions.μS cm−1) . Even wireatment . As the enhanced . When thoduction . These rVC) will be built between the plasma and the liquid surface due to the space charge accumulation36373839VC will be located at a limited distance called cathode sheath, and for an atmospheric pressure discharge plasma, the thickness of the cathode sheath is smaller than 100 μm when the discharge current is more than 5 mAVC for liquids with different initial conductivities (see VC estimation). The following analysis will demonstrate that this cathode region near the liquid surface is very important to the water molecules transfer at the plasma-liquid interface.When the liquid acts as cathode, a cathode voltage fall (V) and the electric field (E) in the plasma-liquid system . This electric field is high enough to pull out the hydrated negative ions (carrying water molecules) from the liquid surface and transfer them to the gaseous plasma3642432O2 is formed by the combination of OH. Finally, H2O2 dissolves into the liquid to form an aqueous solution. Therefore, H2O2 production will increase with the number of water molecules entering the plasma phase which is strongly dependent on the above mentioned processes.tem ref. . Firstly00 V see  which fon) entering the plasma phase can be expressed asThe number density of water molecules , discharge current (Id), liquid surface tension etc. and it can be described as nSpu = kspuId(Ei)1/2kspu is the sputtering coeffieient, and Ei is the energy of incident ion. The positive ion in the plasma obtains its energy by passing the plasma cathode sheath, and therefore the obtained energy is proportional to the Vc. Thus, nSpu can be expressed as nSpu = kspuId(VC)1/2.nEle: the number density of water molecules entering the plasma phase due to the electric field induced hydrated ion emission, and nEle is an increasing function of the ion concentration in the liquid phase (CIon) and the electric field near the liquid surface (EC) but depends on these parameters in a complicated waynEle = f. In addition, the electric field near the liquid surface (EC) can be estimated as follows. In the cathode sheath, E = EC(1 − z/dC), and V = VC(z/dC)(2 − z/dC), where E is the electric field in the cathode sheath, z is the distance away from the liquid surface, dC is the thickness of cathode sheath, V is the voltage potential in the cathode sheath, and VC is the voltage potential near the liquid surfaceE to get V, we can find that EC = 2VC/dC. From the results and analysis in the main paper, we know that the VC in liquid cathode is in the magnitude of ~500 V, and dC is taken as 100 μm, and then EC is estimated to be in the order of 100 kV cm−1.nEva: the number density of water molecules entering the plasma phase due to evaporation. Based on the thermodynamics law, nEva can be expressed as nEva = n0exp(−W/kT), where n0 is the number density of water molecules in the liquid phase, W is the heat of evaporization, k is the Boltzmann constant and T is the liquid temperature.n can be expressed asTherefore, Id for nSpu, and on CIon for nEva, both nSpu and nEle depend on the cathode voltage fall (VC), and nEva depends on the solution temperature.Besides depending on 2O2 generation energy yields were calculated for liquids with different initial conductivities and the results are summarized in 2O2 energy yields are larger than most of the H2O2 energy yields generated from discharge plasmas which have been summarized in ref. −1, although the liquid with an initial conductivity of 1.6 μS cm−1 produces the largest generation rate. The energy yield can be expressed asBased on the discharge voltages shown in EnergyLiq and EnergyPla-Liq are energy consumed in the liquid (mostly taking a form of heating the liquid cylinder) and energy consumed in the plasma-liquid interactions, respectively, and only the latter contributes to the H2O2 production. Therefore, EnergyLiq can affect the energy yield of the H2O2 formation. The largest generation rate for the liquid with an initial conductivity of 1.6 μS cm−1 can be attributed to the high resistance of the liquid cylinder between the plasma and the graphite electrode. The Joule heating consumes a large portion of the supplied power in the liquid with the initial conductivity of 1.6 μS cm−1, but in this case, a larger cathode voltage fall is close to the liquid surface as shown in 2O2 generation rate with a lower energy yield if compared with the case using liquid with the initial conductivity of 10500 μS cm−1. In the plasma-induced H2O2 generation, to select the liquid with a high or low initial conductivity is dependent on one’s aim: to obtain high energy yield or high generation rate.where n, the water moleclues transfer from the liquid phase to the gaseous plasma is largely determined by the sputtering and the electric field induced hydrated ion emission in the case of liquid cathode since the evaporation for all cases are estimated to be similar [VC decreases with increasing time for liquids with initial conductivities of 1.60 μS cm−1 and 1440 μS cm−1, while it almost keeps constant for liquids with initial conductivities of 4800 μS cm−1 and 10500 μS cm−1. Therefore, from the expression of n and the data in n for liquids with initial conductivities of 1.60 μS cm−1 and 1440 μS cm−1 will decrease with increasing time, while it is almost constant for the liquids with initial conductivities of 4800 μS cm−1 and 10500 μS cm−1. Therefore, the H2O2 production rate decreases as time increases for liquids with low initial conductivity, while it is almost constant for liquids with high initial conductivity. Because the evaporation for all cases are estimated to be similar [see n is determined by Id, VC, and CIon. For the case with a constant Id, n is related to VCand CIon. Although CIon is low in case of the liquid with low conductivity (1.6 μS cm−1), n can be still large since the high VC (see 2O2 production rate for the liquid with a low conductivity (1.6 μS cm−1). In addition, the estimated VC are almost the same for discharge currents of 30 mA, 40 mA and 50 mA . When the discharge current increases, the ion flux (related to Id) near the liquid surface is enhanced and thus water transfer from the liquid phase to the gaseous plasma is increased. Consequently, the H2O2 yield increases with increasing discharge current as shown in VC for NaOH solution with an initial conductivity of 4800 μS cm−1 is ~30 V higher than that for the NaCl solution with the same conductivity. Based on the above anlysis, one must expect that the NaOH solution should produce more H2O2. However, the H2O2 yield for NaOH solution is almost zero as shown in 2O2 is a weak acid and it can react with OH− to form HO2− in concentrated NaOH solution (H2O2 + OH− → HO2− + H2O)45462O2 was consumed by reacting with NaOH, resulting in a very low H2O2 yield.Based on the expression of  similar . The resilar see , n is deh VC see  in this n is insignificantly small in the case of liquid anode, resulting in a low H2O2 yield as shown in When liquid acts as anode, the cathode voltage fall is formed on the tungsten steel electrode, and therefore there is no sputtering and electric field induced ion emission at the liquid surface, and evaporation is the only way to transfer water molecules from liquid phase into the gasesous plasma. Compared with the case of liquid cathode, 2O2 is directly synthesized by plasma-water interations and the H2O2 production rate can reach 1200 μmol/h when the liquid cathode is purified water or NaCl solution with an initial conductivity of 10500 μS cm−1. The H2O2 production rate strongly depends on the plasma-liquid interactions at the liquid surface: sputtering, high electric field induced hydrated ion emission, and evaporation. Liquid cathode performs much better than liquid anode in producing H2O2 by plasma-water interations. In addition, the synthesized H2O2 can be consumed if the liquid contains some constituent able to react with H2O2 such as NaOH.In summary, using water as the only consumed material, H2O2 can react with titanium sulfate. in strong acid to form H2TiO4 (Ti4+ + H2O2 + 2H2O → H2TiO4 + 4 H+) and the absorption intensity of the yellow-coloured H2TiO4 in 410 nm is proportional to the reacted H2O2 concentration1548492O2 concentration. 7.5 ml [Ti(SO4)2,120 g/l) was added to 250 ml H2SO4 (1.5 M) to obtain the test solution of titanium sulfate. We used H2O2 with standard concentrations to obtain the proportionality between the absorption intensity of H2O2 at 410 nm, and the results are presented in 2O2 yield is estimated by the following equation:Because Hk is the proportionality obtained by linearly fitting I is the absorption intensity of synthesized H2O2 at 410 nm, and V is the solution volume .where The pH value and temperature of the solution were measured by a pH detector with a temperature sensor (Yesmylab SX620), and the solution conductivity was measured by a conductivity detector (Yesmylab SX650).The voltage between the tungsten steel and the graphite electrodes was measured by a high voltage (H.V.) probe (Tektronix P6015A) and the current was achieved from dividing the voltage across a 10-Ω resistor which was in series connected with the graphite electrode.How to cite this article: Liu, J. et al. Direct synthesis of hydrogen peroxide from plasma-water interactions. Sci. Rep.6, 38454; doi: 10.1038/srep38454 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Sequential water binding enthalpies for aqueous nanodrops containing 20–500 water molecules and a 1+ to 3+ ion are reported. Hn,n–1, are important for a detailed understanding of competitive interactions between ions, water and solute molecules, and how these interactions affect physical properties of ion-containing nanodrops that are important in aerosol chemistry. Water molecule binding enthalpies have been measured for small clusters of many different ions, but these values for ion-containing nanodrops containing more than 20 water molecules are scarce. Here, ΔHn,n–1 values are deduced from high-precision ultraviolet photodissociation (UVPD) measurements as a function of ion identity, charge state and cluster size between 20–500 water molecules and for ions with +1, +2 and +3 charges. The ΔHn,n–1 values are obtained from the number of water molecules lost upon photoexcitation at a known wavelength, and modeling of the release of energy into the translational, rotational and vibrational motions of the products. The ΔHn,n–1 values range from 36.82 to 50.21 kJ mol–1. For clusters containing more than ∼250 water molecules, the binding enthalpies are between the bulk heat of vaporization (44.8 kJ mol–1) and the sublimation enthalpy of bulk ice (51.0 kJ mol–1). These values depend on ion charge state for clusters with fewer than 150 water molecules, but there is a negligible dependence at larger size. There is a minimum in the ΔHn,n–1 values that depends on the cluster size and ion charge state, which can be attributed to the competing effects of ion solvation and surface energy. The experimental ΔHn,n–1 values can be fit to the Thomson liquid drop model (TLDM) using bulk ice parameters. By optimizing the surface tension and temperature change of the logarithmic partial pressure for the TLDM, the experimental sequential water molecule binding enthalpies can be fit with an accuracy of ±3.3 kJ mol–1 over the entire range of cluster sizes.Sequential water molecule binding enthalpies, Δ Ions that are formed in the atmosphere are thought to be responsible for the fast nucleation of water molecules in the early stages of aerosol formation, where charged clusters grow faster and become thermodynamically stable at smaller cluster sizes than their neutral counterparts.7The interactions between water molecules and solute ions can significantly affect the structure, dynamics and reactivity of the solute and the dynamics and the structures of the hydrogen-bonding network of liquid water. Consequently, molecular level knowledge of these interactions is important to understanding processes in solution and in the atmosphere, such as protein folding, molecular recognition and ion-assisted aerosol formation.i.e., clusters of water molecules containing a single ion.27 In this way, any effects of impurities or counter ions are eliminated. By investigating size selected clusters, it is possible to monitor how thermodynamic quantities, such as water molecule binding enthalpies, evolve with cluster size.45 Several methods have been used to measure the sequential water molecule binding energies of hydrated ions with up to 14 water molecules attached.45 The most commonly used approaches are high-pressure ion source mass spectrometry (HPMS),34 threshold collision induced dissociation (TCID)42 and blackbody infrared radiative dissociation (BIRD).45 Singly and doubly charged hydrated ions have been studied with these methods. The results reveal that especially for the first hydration shell, the ion identity and charge state significantly influence water molecule binding enthalpies. As the result of increasing ion solvation, the influence of the sequential water binding enthalpies on the specific ion–water interactions diminishes with increasing cluster size. For example, the sequential water molecule binding enthalpies for hydrated Li+ decrease from 134 to 63 kJ mol–1 for one to six water molecules attached.37One way to obtain detailed information about ion–solvent interactions is to study hydrated ions in a well-defined environment, 47 In these experiments, the sequential water molecule binding enthalpy is deduced from the number of water molecules that are lost from the cluster upon photoexcitation with a photon of known energy. For aniline+·(H2O)n with n = 5–20 (where n is the number of water molecules) for example, the energy removed per water molecule decreases from 74.5 kJ mol–1 at n = 5 to an average value of ∼40.2 kJ mol–1 between n = 10–20.46 A very similar trend but slightly higher sequential water molecule binding enthalpies were measured for hydrated doubly charged atomic ions and paraquat.48 These UVPD studies indicate that for larger cluster sizes, the sequential water molecule binding enthalpies are close to the bulk water vaporization enthalpy  experiments have been used to deduce water molecule binding enthalpies at larger cluster sizes and these measurements can be made with high precision. The sequential water binding enthalpies for hydrated aniline, protonated proflavine, protonated tryptophan, rhodamine 590 and rhodamine 640 ions have been studied by UVPD.kJ mol–1 ) and dep7 in the atmosphere and for ion nanocalorimetry.13 In the latter method, hydrated ions are irradiated, for example, with slow electrons that can lead to a one-electron reduction of the ion.9 The recombination energy (RE), which corresponds to the energy released due to the ion-electron recombination, leads to the evaporation of water molecules from the hydrated ion. The RE can be obtained from the number of water molecules that are lost by modeling of this thermochemical process for water evaporation, which is based on the sequential water molecule binding energies as well as energy that partitions into translational, rotational and vibrational modes of the departing water molecules. By extrapolating REs measured as a function of cluster size to “infinite dilution”, absolute reduction potentials of metal ions can be measured and ultimately related to an absolute reduction potential of the standard hydrogen electrode (SHE).13 Due to the lack of thermochemical reference data for sequential water binding enthalpies of large ion-containing water clusters, the RE modeling currently uses values from the Thomson liquid drop model (TLDM).58 The TLDM combines the self-energy of solvating a charged particle in the Born solvation model with the increased energy due to the droplet surface area and the bulk vaporization energy to give sequential binding enthalpies of water molecules as a function of cluster size. Any systematic error in the TLDM will result in an error for the absolute SHE value obtained from the ion nanocalorimetry measurements, and this error increases with increasing number of water molecules that are lost as a result of ion-electron recombination.Despite the progress in obtaining thermodynamic reference data for larger hydrated ions, only limited data for sequential water binding enthalpies over a broad range of cluster sizes and for different charge states is available. These data are especially important to accurately model ion-induced water nucleationz = +1–3), ion identity, and cluster size (n = 20–500) derived from high-precision UVPD measurements. A newly developed program is used to deduce ΔHn,n–1 from experimental precursor and product cluster distributions. The various ions studied for every charge state, the different types of electronic transitions ranging from atomic transitions to electronic excitations in aromatic systems to charge transfer transitions, and the diverse chemical nature of the formed atomic, organic and inorganic ions, make the herein presented binding enthalpy trends important for many chemically relevant systems. The sequential water molecule binding enthalpies show a clear dependence on the ionic charge and cluster size and depend less on ion identity. The results are compared to binding enthalpy predictions from the TLDM. A better agreement between theory and experiment is obtained for the TLDM model employing water ice parameters at 133 K, indicating that the clusters may largely solidify under these experimental conditions.In this work, we present an extensive study of sequential water binding enthalpies as a function of charge state 48 as a result of a higher field strength magnet used in these experiments.Nanoelectrospray ionization coupled to FT-ICR mass spectrometry is used to form and subsequently trap broad distributions of hydrated ions. Two example mass spectra of (Phe + H)ged ions . For 2+ ged ions . Lower d+, Cu2+ and [Ru(NH3)6]3+ with 193.0 ± 0.5 nm laser light, 250 Hz repetition rate and 0.5 s irradiation time for isolated ensembles with n = 198–202 water molecules are shown in n = 198–202, new clusters with n = 195–197, which are formed by BIRD, appear. The mean cluster size, 〈n〉, for the precursor distributions shown in +, Cu2+ and [Ru(NH3)6]3+ ions is 198.73, 198.09 and 198.60, respectively. There are also product ion distributions at lower mass that are generated due to UVPD 6]3+. This product ion distribution has 〈x〉 = 13.50, 13.43 and 13.43 fewer water molecules than the corresponding precursor ion. Both the precursor and fragment distributions include contributions from BIRD, which should effect both populations similarly for large clusters.48 The difference in the average number of water molecules for these populations should reflect just UV absorption consistent with 〈x〉 being independent of the laser irradiation time (see ESI+·(H2O)198–202 and Cu2+·(H2O)198–202, additional product ion distributions are formed at even lower mass  and 40.77 (40.31) water molecules are lost from the precursor ion distribution. For the UVPD mass spectra shown in i.e., one, two and three 193 nm UV photons are absorbed sequentially to form the first, second and third product ion distribution, respectively, and not to multiphoton processes. A detailed discussion of sequential UV photon absorption on experimental UVPD results is provided in Results of UVPD of (Phe + H) to UVPD . The avee see ESI. For (Phwer mass . For thex〉 for (Phe + H)+·(H2O)198–202, Cu2+·(H2O)198–202 and [Ru(NH3)6]3+·(H2O)198–202 do not significantly differ (+·(H2O)198–202 to 31% and 12% for Cu2+·(H2O)198–202 and [Ru(NH3)6]3+·(H2O)198–202, respectively. (Phe + H)+ in aqueous solution has a strong absorption band between 190–210 nm as a result of the phenyl group.59 The UV absorption at 193 nm for [Ru(NH3)6]3+ is due to a ligand-to-metal charge transfer transition.60 The molar extinction coefficient for (Phe + H)+ in the water at 193 nm is about six times larger than that for [Ru(NH3)6]3+,60 consistent with the higher photoproduct yield for (Phe + H)+·(H2O)198–202 compared to [Ru(NH3)6]3+·(H2O)198–202. No solution phase reference data is available for Cu2+ ions  at 193 nm precluding a comparison to this ion. However, the photoproduct yield for Cu2+·(H2O)198–202 and other divalent transition metal ions used in this study 198–202, Cu2+·(H2O)198–202 and [Ru(NH3)6]3+·(H2O)198–202 lose ∼13.5 water molecules upon UVPD at 193 nm. Previously reported sequential water molecule binding energies, En,n–1, are ∼0.45 eV per water molecule for clusters with n = 10–124.48 Assuming that water molecules are lost sequentially as a result of full internal conversion from an excited electronic state to the ground electronic state, the number of water molecules that are lost is estimated to be 14.2 for a 6.41 eV photon. This is ∼0.7 water molecules higher than the experimental value of ∼13.5. Because this simple calculation does not take into account energy partitioned into the degrees of freedom of the water molecules that evaporated, this calculation overestimates the number of water molecules that are lost. Hence, our experimental results are consistent with water loss due to full internal conversion of the absorbed photon. Further evidence for sequential water molecule loss and full internal conversion producing the product distributions at about 〈x〉 = hν/En,n–1 below the precursor distribution in UVPD experiments of hydrated ions comes from the wavelength-dependent UVPD measurements of anilinium·(H2O)69–71 shown in x〉 = 13.47 water molecules compared to the precursor ion distribution whereas the other distribution is shifted by 〈xf〉 = 7.22. The product distribution with the highest water loss is consistent with the expected loss of ∼13.5 water molecules due to full internal conversion of a 193 nm photon, and the additional product ion distribution is due to partial internal conversion followed by fluorescence with an emitted photon wavelength of ∼2.80 eV.47 The fluorescence quantum yield, obtained from the relative ion abundances of the two product ion distributions, is 0.11. UVPD of anilinium·(H2O)69–71 at 248 nm results in only a single product ion distribution corresponding to 〈x〉 = 10.68  = 10.68 . For thehν/En,n–1 are consistent with a full internal conversion process and additional ion product distributions are identified for partial internal conversion/fluorescence processes, we conclude that full internal conversion and sequential water loss are the major processes leading to product ion distributions at 〈x〉 ≈ hν/En,n–1 in UVPD.Because the number of water molecules lost from precursor ion distributions to product ion distributions at ∼61 The potential influence of measurement time on the product ion cluster size is a kinetic shift effect. The extent to which a kinetic shift affects these measurements is measured by varying the delay time between laser irradiation and ion detection until the maximum number of water molecules, 〈xmax〉, that are lost, is observed. Results of these kinetic shift measurements as a function of cluster size, charge state and laser wavelength are shown in n ≥ 200 (x〉 for Cu2+·(H2O)198–202, Cu2+·(H2O)298–302 and Co2+·(H2O)398–402 198–202, Cu2+·(H2O)298–302 and Co2+·(H2O)398–402, a delay time between photoexcitation and ion detection of 250 ms, 500 ms and 1000 ms are required so that 〈x〉 approaches 〈xmax〉 to within 0.05 water molecules. The kinetic shift does not depend significantly on laser wavelength within this range of photon energies (2+·(H2O)198–202 at 193 nm and Fe2+·(H2O)218–222 at 248 nm lose 0.23 and 0.39 water molecules less than 〈xmax〉 and both approach 〈xmax〉 at about 250 ms. The independence of the kinetic shift on laser wavelength is consistent with the time necessary for the water evaporation process to occur being mainly limited by the last water molecule that is lost from the cluster, which depends primarily on cluster size (degrees of freedom) and not on the initial energy deposited. The bigger difference at zero detection delay for the iron compared to the copper containing water cluster is consistent with the slightly larger cluster size of the former. The charge state dependence of the kinetic shift is investigated for Cu2+·(H2O)298–302 and [Ru(NH3)6]3+·(H2O)298–302 at 193 nm 6]3+ with a median of 30 water molecules attached to a maximum of 14.01, 13.67 and 13.44 for mono-, di- and trivalent ions, which occurs at median cluster sizes of 70, 110 and 210, respectively. Beyond the maxima, the number of water molecules that are lost decreases monotonically with cluster size. Similar changes in 〈x〉 with cluster size also occur for 248 nm UVPD. With 248 nm photons, 〈x〉 is about three water molecules less than that with 193 nm photons, but the qualitative dependence of 〈x〉 with median cluster size follows the same trends as for the 193 nm data 6]3+ and [Co(NH3)6]3+ with 100 water molecules or [Ru(NH3)6]3+ and [Cr(NH3)6]3+ with 350 water molecules lose 13.21 ± 0.03 and 13.16 ± 0.03 or 13.21 and 13.26 water molecules at 193 nm excitation wavelength. Similarly, 〈x〉 differs only by 0.02 for 248 nm UVPD of (Phe + H)+ and anilinium with 120 water molecules attached. Although the number of water molecules lost following photoabsorption is minimally affected by ion identity for the majority of the ions investigated, 〈x〉 at 193 nm for hydrated Mn2+ and Fe2+ ions differs significantly from that for Cu2+ and Co2+ at precursor sizes with n > 130. At a precursor ensemble size of 220, 〈x〉 is 13.38 ± 0.04 and 12.94 ± 0.19 for Cu2+ and Fe2+, respectively. More water molecules can be lost from a cluster either as a result of lower water molecule binding energies or competition between water molecule loss and internal energy conversion. If internal conversion is not instantaneous, ions are not heated to as high of an effective temperature compared to when internal conversion is instantaneous because water molecules that evaporate during internal conversion take away energy. Thus, less energy partitions into translational, rotational and vibration energy of evaporated water molecules. There is evidence for a long-lived excited state for Cu2+ in smaller nanodrops for which competition between water loss and internal conversion could be relevant, but this effect should be negligible for the larger clusters investigated here owing to a slower water molecule evaporation rate.62 Thus, the different number of water molecules lost for Cu2+/Co2+ compared to Fe2+/Mn2+ likely reflect a difference in water molecule binding enthalpy. A comparison between the divalent ions at 248 nm is not possible because UVPD products are only observed for Fe2+. However, the decrease of 〈x〉 for hydrated Fe2+ at 248 nm with increasing cluster size, even below 〈x〉 for trivalent ions, is also consistent with an increasing binding enthalpy with increasing cluster size n. Evidence for the influence of ions on the water phase in large water clusters comes from a recent study that showed that La3+ ions can affect the onset of crystallinity in clusters as large as 375 water molecules.63The identity of the ion of a given charge state and at the same cluster size has only a small effect on the number of water molecules lost . For exater size . This is2+ cluster sizes (2+·(H2O)40 and Cu2+·(H2O)400 prior to any water molecule loss increases to ∼550 K and ∼190 K, respectively. Both clusters cool back down to ∼130 K after 13 water molecules are lost. The higher initial effective temperature of Cu2+·(H2O)40 compared to Cu2+·(H2O)400 is due to the lower number of degrees of freedom of the smaller cluster. The energy partitioned into the degrees of freedom for the sequentially evaporated water molecules depends on the effective cluster temperature at which each water molecule is lost (ESI eqn (3)2+·(H2O)400 than that for Cu2+·(H2O)40 results in less 〈EVRT〉 (see EVRT〉 = 8.16 and 3.68 kJ mol–1 for Cu2+·(H2O)40 and Cu2+·(H2O)400, respectively). The sequential water molecule binding energies for Cu2+·(H2O)40 and Cu2+·(H2O)400 obtained by modeling the water molecule loss are 39.37 kJ mol–1 and 43.85 kJ mol–1, respectively. The relative energy contribution from the total energy release of 8.16 and 3.68 kJ mol–1 for Cu2+·(H2O)40 and Cu2+·(H2O)400 to the deposited energy of 6.41 eV is 17% and 8%, respectively, and decreases with increasing cluster size. Thus, any systematic error in the computed binding energies introduced by using this energy release model decreases with cluster size. For mono-, di- and trivalent clusters with up to 350 water molecules, the difference between calculated binding energies obtained from 248 nm and 193 nm UVPD experiments are shown in Fig. S6.–1. The average difference of 0.4 kJ mol–1 for 248 nm compared to 193 nm binding energies indicates that the model may slightly overestimate the kinetic energy release, but this difference is well within the uncertainty of our measurements. This indicates that the kinetic energy release model introduces no significant systematic error for the calculation of average sequential binding enthalpies from UVPD experiments.After absorption of one UV photon and full internal conversion back to the electronic ground state of an ion, the effective temperature of the cluster increases from its initial effective temperature of ∼133 K established by the interaction of the ions with the blackbody radiation field inside the ion cell prior to photoexcitation. Absorption of a photon shifts the initial internal energy distribution of the cluster by a value corresponding to the energy of the photon. The extent of this effective temperature increase depends on the cluster size and the energy of the absorbed photon and is modeled as described in the ESI.er sizes . Upon ab45 as a function of 〈n〉 – 〈x〉/2 are shown in Hn,n–1 values are ±〈x〉/2 = ±5.5–7.5 water molecules and ±0.4–1.2 kJ mol–1, respectively. The average binding enthalpies for clusters with 20 to 500 attached water molecules decrease for the smallest investigated clusters with increasing cluster size until a minimum in ΔHn,n–1 is reached +·(H2O)198–202, Cu2+·(H2O)358–362 and [Co(NH3)6]3+·(H2O)498–502, respectively. The average water molecule binding enthalpies of hydrated Cu2+/Co2+ ions approach a value of ∼46.0 kJ mol–1 at large cluster size, whereas that for hydrated Fe2+/Mn2+ ions is 48.53 kJ mol–1  but exceed the enthalpy of vaporization at 273 K (ΔHvap = 44.8 kJ mol–1) for large cluster size.49The average sequential binding enthalpies for mono-, di- and trivalent ions obtained from these UVPD experiments, along with previously published data for clusters with fewer than 15 water molecules reached . Namely,kJ mol–1 . For 〈n〉–1, 2.47 kJ mol–1, 2.01 kJ mol–1 and 1.38 kJ mol–1 at 313 K, 298 K, 273 K and 133 K, respectively. The use of ice parameters for the TLDM calculations is consistent with recent results, which indicate that large clusters are “ice-like” at low temperature.66 It is still debated at what cluster size the thermodynamic concept of phase is applicable to nanodrops and it has been shown that spectroscopic features for amorphous and crystalline ice can coexist for clusters with up to 550 water molecules,63 hence, we use the phrase “ice-like” in what follows as a synonym for the presence of amorphous or crystalline ice phases.Also shown in Hn,n–1 with cluster size above 〈n〉 – 〈x〉/2 ≈ 175. In order to improve the agreement between values from the TLDM and the experiment at large cluster size changing the least number of parameters in the model, a sensitivity analysis was performed. The surface energy, γ, and the change of the logarithmic partial pressure with temperature, ∂ln(p/p0)/∂T (p0 is the standard pressure), have the biggest effect on the TLDM at large cluster size. For mono-, di- and trivalent ions, these two parameters were optimized and the resulting fits are shown in –1, 1.92 kJ mol–1 and 2.05 kJ mol–1 for the 133 K TLDM to 0.92 kJ mol–1, 1.17 kJ mol–1 and 0.67 kJ mol–1 for the corresponding fits for mono-, di- and trivalent ions, respectively. The surface energy and ∂ln(p/p0)/∂T increase for all charge states compared to bulk 133 K parameters and the extent of the increase depends on charge. For example, the surface tension for divalent ions is 215 mN m–1, which is nearly double the corresponding bulk value. Excluding the Fe2+/Mn2+ data for the fit of the TLDM for divalent ions, i.e., only using ΔHn,n–1 values for Cu2+ and Co2+ 6].67 Even though these clusters contain only one isolated cation and up to 500 water molecules, solution data provide support for the use of a higher surface energy in the TLDM that should be charge dependent. This indicates that hydrated ions can influence the properties of water, such as surface energy, in clusters containing up to 500 water molecules. The higher ∂ln(p/p0)/∂T for clusters compared to bulk water is in qualitative agreement with the Kelvin equation, which predicts an increase of droplet partial pressure compared to bulk water.68Although the TLDM using 133 K bulk ice parameters provides binding enthalpies that are more similar to the experimental values for small clusters compared to those using liquid water parameters, the model does not accurately account for the increase of Δ Fig. S10. The higγ and ∂ln(p/p0)/∂T are averaged over all cluster sizes between n = 20–500 so there is no explicit size dependence of these parameters. The TLDM also has difficulties in accurately reproducing ΔHn,n–1 for small hydrated ions because specific bonding in the first and second solvation shells is not taken into account by the TLDM.52 Finally, large and hydrophobic ions like (Phe + H)+ or PTMA may not be fully solvated or the nanodrop may not be spherical at small cluster sizes. Despite these factors, the close agreement with experimental data justifies the use of the modified TLDM. Additionally, the relatively close correspondence of the optimized parameters to the bulk ice parameters at 133 K, considering the discussed uncertainties, is consistent with isolated ions in larger nanodrops as “ice-like” particles in the gas phase.Although optimization of the 133 K TLDM results in an improved fit to the experimental data, it is difficult to interpret the physical relevance of the extracted parameters. The values of –1 precision. These data have a minimum in ΔHn,n–1 at around 75, 45 and ∼15 water molecules for 3+, 2+ and 1+, respectively. This minimum in the binding enthalpies is predicted by the TLDM and stems from the counteracting energetic contributions of the surface and solvation energies. Therefore, these results are consistent with all qualitative predictions of the TLDM. However, the precision of the UVPD results reveal that the unmodified TLDM does not adequately fit the experimental data and the closest agreement between experimental and TLDM values for ΔHn,n–1 is achieved using 133 K parameters. Indications for a water–ice phase transition for water clusters with 60–79 water molecules at 133 ± 6 K and for a phase transition to crystalline ice for La3+·(H2O)375 at 133 K have been reported previously.66 Therefore, our interpretation of the increased average water molecule binding enthalpies within a modified 133 K TLDM is consistent with these findings. With bulk ice parameters and optimizing γ and ∂ln(p/p0)/∂T, the experimental values can be reproduced with accuracies of ±0.8 kJ mol–1 between n = 20–500 water molecules. Even though the TLDM is a very simplified model of these complex systems, it does a remarkable job in qualitatively explaining the experimental results. The physical significance of the optimized γ and ∂ln(p/p0)/∂T values is difficult to evaluate, but the agreement between experimental ΔHn,n–1 values and the TLDM with modified ice parameters indicates that the larger clusters are “ice-like” under our experimental conditions. These binding enthalpies can serve as valuable reference values for simulations of ion-induced nucleation. Additionally, values will further improve the accuracy and precision of absolute reduction potential values deduced from ion nanocalorimetry measurements of ions in nanodrops.High-resolution UVPD results as a function of charge state and cluster size for a diverse set of ion-containing nanodrops containing up to 500 water molecules, which include data for trivalent ions for the first time, are presented. Average sequential water molecule binding energies are obtained with ±0.8 kJ mol


Electron attachment to mixed HNO3/H2O clusters yields several atmospherically relevant species such as NO3–, HONO and OH radical. 3)m(H2O)n, m ≈ 1–6, n ≈ 1–15, in a laboratory molecular beam experiment using electron attachment and mass spectrometry and interpret our experiments using DFT calculations. The reactions are triggered by the attachment of free electrons (0–14 eV) which leads to subsequent intracluster ion–molecule reactions. In these reactions, the nitrate anion NO3– turns out to play the central role. This contradicts the electron attachment to the gas-phase HNO3 molecule, which leads almost exclusively to NO2–. The nitrate containing clusters are formed through at least three different reaction pathways and represent terminal product ions in the reaction cascade initiated by the electron attachment. Besides, the complex reaction pathways represent a new hitherto unrecognized source of atmospherically important OH and HONO molecules.Polar stratospheric clouds, which consist mainly of nitric acid containing ice particles, play a pivotal role in stratospheric chemistry. We investigate mixed nitric acid–water clusters (HNO Free electrons originate from, e.g., photoionization, solar and magnetospheric particles or cosmic rays and the corresponding secondary ionization processes. Typically, the electrons are effectively thermalized to low energies (<1 eV) via multiple inelastic collisions.5 At these low energies, they can attach to molecules and clusters in the atmosphere.Heterogeneous chemistry involving ice particles, cloud droplets and aerosols plays an important, albeit not yet fully understood, role in the atmospheric chemistry.3 molecules have been studied only using flowing afterglow techniques,8 and we are not aware of any crossed-beam electron attachment data. It has been initially concluded6 that the dissociative electron attachment (DEA) in HNO3 leads only to the NO2– product ion which is the most exothermic channel with a reaction enthalpy of ΔrH298K = –0.13 eV. Recently, Shuman et al.8 reported a weak endothermic channel producing OH– (ΔrH298K = 0.31 eV). The formation of NO3– is rather endothermic (ΔrH298K = 0.45 eV) and has not been observed experimentally so far. In complexes with ice nanoparticles, the outcome of the electron-induced reactions can be significantly affected by the environment and by acidic dissociation of HNO3.The gas-phase reactions between free electrons and HNO3)m(H2O)n clusters by supersonic expansions of HNO3/H2O vapor mixtures at different temperatures and buffer gas pressures. We analyzed the composition of the clusters generated under these conditions in our previous investigations with electron-impact ionization and sodium doping.10 These clusters then interact with the electron beam and the negative ion production is monitored by a mass spectrometer. In order to provide a reference gas-phase spectrum, we have also performed electron beam attachment experiments to gas-phase HNO3. Employing quantum chemical methods, we characterize the small neutral complexes of (HNO3)m(H2O)n, calculate the energetics of the initial dissociation step, i.e. the generation of NO2–, NO3– or OH– from HNO3, and sample the potential energy surface to locate the energetically most stable isomers and intracluster rearrangements.In the present experiment, we produce mixed  and helium  as a buffer gas through a divergent conical nozzle of 100 μm diameter, 2 mm long, and approximately 30° full opening angle into the vacuum. The clustering conditions were controlled by heating the HNO3 solution in the reservoir and by the stagnation pressure of the buffer gas. Reservoir temperatures between 60 and 80 °C and buffer gas pressures between 1 and 2 bar were employed. The buffer gas carried the HNO3/H2O vapor to the nozzle heated independently to a higher temperature  to avoid any condensation. However, changing the source conditions within the present range had little effect on the mass spectra. We have analyzed the composition of the neutral clusters previously from the positive mass spectra.10 From this analysis, the present neutral clusters have a mixed composition of (HNO3)m(H2O)n, with m ≈ 1–6 and n ≈ 1–15.The experiments were carried out on the CLUster Beam (CLUB) apparatus in Prague.3)m(H2O)n clusters were ionized using free electrons (0–14 eV) from a pulsed electron gun at 10 kHz frequency during 2 μs in the extraction region of the spectrometer. After 0.5 μs delay to exclude the effects of any free electrons, a 2 μs extraction pulse was applied to extract the negative ions, which were detected on the Photonics MCP detector in the Chevron configuration. The electron attachment experiment was described in our recent paper,12 typical electron current at the present experiment was 0.2 μA at 5 eV. The electron-energy scale was calibrated using the 2.2 eV resonance in the O– production from N2O. The individual mass spectra recorded with an electron energy step of 0.25 eV in the range 0–14 eV allow for the evaluation of the electron energy dependence for each individual peak in the mass spectra, however, a detailed analysis of the electron attachment efficiency dependence is beyond the scope of the present study and will be the subject of future work.The cluster beam was skimmed and passed through three differentially pumped chambers before entering the ion source of the perpendicularly mounted reflectron time-of-flight mass spectrometer. The voltages on the extraction plates were set to detect negative ions. The  a negative voltage of –300 V is pulsed across the chamber which pushes the anions formed in the cell towards the ion time-of-flight mass analyzer in the direction perpendicular to the electron beam. The anions are detected by a microchannel plate, counted, and their arrival times are analyzed.The HNO3 and H2O molecules. The presence of water vapor in the sample does not interfere with the results shown in 2O leads to H– and O– production,15 none of the fragments reported form HNO3  originate from water. The spectra recorded after a longer time (tens of minutes) after the pump–freeze–thaw purification of the sample contained a weak O– signal. The electron-energy dependence of this signal was identical to that of O– originating from DEA to NO2.16 Since HNO3 is known to release NO2, we conclude that the detected O– does not originate from DEA to HNO3.The target chamber is filled with the vapor of concentrated nitric acid solution in water (70%). The vapor thus consists of a mixture of gas-phase HNO3)m(H2O)n, m = 1–3, n = 0–5, (HNO3)m(H2O)nNO2–, m = 0–2, n = 0–5, (HNO3)m(H2O)nNO3–, m = 0–2, n = 0–5 , (H2O)nOH–, n = 0–5. For all clusters with more than two composing molecules, molecular dynamics was run to find the most stable isomers, using the BLYP/6-31+g* method, with a time step of 40 a.u. (∼1 fs). First, 10 000 steps were performed at 400 K, then another 10 000 steps at 200 K starting from the last geometry of the first molecular dynamics run. The temperature was maintained by a Nosé–Hoover chain of 4 thermostats with a relaxation time of 0.0015 a.u. The structures were re-optimized at the M06-2X/aug-cc-pVDZ level after every 1000 steps; the M06-2X functional was chosen due to its performance for flexible anionic clusters.17 Only the most stable isomers were considered, the respective structures are shown in Fig. S2 (ESI3(H2O)n, n = 4, 5, ion pair structures were also included, based on the structures presented elsewhere.18 Localized structures are in good agreement with those found by other studies.21 All reported energies were calculated at the M06-2X/aug-cc-pVDZ level and include zero-point-energy corrections . Electronic structure calculations were performed using the Gaussian09 program,22 the ABIN code was used for molecular dynamics.23 Further details and benchmark calculations can be found in the ESI.Interpretation of the experimental results was supported by DFT calculations. We included the following clusters: , a weak OH– fragment and, for the first time detected, a NO3– fragment. The NO2– : OH– : NO3– intensity ratio is 96.5 : 3.4 : 0.03. The ratio of the first two fragments is in very good agreement with that observed in mass spectrometry of flowing afterglow plasma,8 the electron energies in that type of experiment, however, were probably too low to reach the threshold for NO3– production. of HNO3  is domin2–. This is not surprising as a part of the present signal originates from HNO3 monomers in the beam. However, among the complex anions (with the mass higher than the HNO3 monomer), the dominant species are ions containing NO3–. The total intensity ratio of peaks from complexes containing NO3– : NO2– : HNO3– : OH– is 57 : 32 : 8 : 2. The dramatic increase in the NO3– anion signal with respect to the spectrum of gas-phase HNO3 indicates rich intracluster chemistry. In the gas-phase reactions, the NO3– anion is often seen as the terminal product of ion–molecule reactions involving HNO3.28 In order to elucidate the pathways leading to its production in the present species, The cluster beam spectrum  is also 30 Electron attachment is a vertical process and the decisive factor for breaking specific bonds, in case of a prompt dissociation, is often the direction of the gradient of the anionic potential energy surface at the initial structure. However, in systems with sufficient degrees of freedom such as clusters, the excitation energy may be redistributed over the vibrational degrees of freedom, with the decay proceeding statistically. In this case, the product distribution is dominated by the energetics of the dissociation channels. We identify three mechanisms leading to NO3– production, one proceeding in the first described way, i.e. kinematically driven, the other two driven by the asymptotic energetics.It should be noted that the efficiency of DEA channels does not primarily depend on asymptotic energetics.3 can acidically dissociate in sufficiently large water clusters to form a nitrate anion NO3– and a hydronium cation H3O+:33 showed that the acidic dissociation (10a) might occur in (H2O)nHNO3 clusters with n ≥ 4–5. At the M062X/aug-cc-pVDZ level, the acidically dissociated clusters are less stable by 0.01 eV for n = 4 and more stable by 0.11 eV for n = 5 with respect to the non-dissociated ones. For (H2O)n(HNO3)m species, the formation of the NO3–···H3O+ ion pair is predicted to be energetically preferred for 3 and 4 water molecules for m = 2 and 3, respectively, see Fig. S2 in the ESI.31 Previous ab initio molecular dynamics showed that acidic dissociation immediately leads to the formation of a “solvent-separated” ion pair and the proton migrates across the cluster even on cold ice particles.34In the neutral state, before any interaction with free electrons, HNO3O+/NO3– ion pair with a free electron will most probably lead to recombination, forming a metastable H3O radical that subsequently dissociates, yielding a hydrogen atom and water.35 The energy released upon recombination will lead to the evaporation of the hydrogen atom, which is only weakly bound:36 The cross section for electron capture rapidly increases with the strength of the long-range electron–target interaction,37 one can thus assume a high probability of reaction (10b).Collision of the neutral cluster containing an H2– production from gas-phase HNO3via DEA is the most prominent channel due to its exothermicity. Upon hydration of HNO3, the exothermicity of this channel  and (8).The NO3)m(H2O)nNO2– is equal to that of HONO(HNO3)m–1(H2O)nNO3–, these ions cannot be distinguished in the mass spectrometry experiment. However, our molecular dynamics calculations indicate that the HONO formation proceeds irreversibly. The gas-phase reaction HNO3 + NO2– → NO3– + HONO has an energy of –0.74 eV . The considerably larger acid strength of HNO3 compared to that of HONO suggests that the proton transfer reaction from HNO3 to NO2– can be expected in small mixed clusters nOH– becomes the most exothermic channel for n > 1. As shown previously, it is more exothermic than the (H2O)nNO2– channel due to the stronger interaction of water with OH– than with the NO2– ion.38 If more than one HNO3 molecule is present in the neutral complex, the OH– anion reacts rapidly with HNO3 and forms NO3–, reactions (6) and (9).Upon hydration of one HNO3 + OH– → NO3– + H2O reaction is –3.05 eV. With an increasing degree of hydration, the reaction retains its exothermic character, see Again, such intracluster rearrangement cannot be followed by the mass spectrometry experiment. In our MD simulations, this reaction is seen to proceed spontaneously within 1 ps. It is strongly preferred already in the gas phase, the calculated energy of the corresponding HNO3– containing complexes as there are factors favoring each of them. As described above, a significant portion of neutral complexes will be acidically dissociated, leading to the first proposed channel. However, it is reasonable to assume that these will yield exclusively NO3–-containing anions, since NO3– is already present in the cluster before the interaction with the free electron takes place, and the electron is attracted by the positive charge center H3O+. The significant abundance of complexes containing NO2– and HNO3– in via the first channel is not the only process occurring. The fact favoring the second channel is that its first step, DEA to HNO3 yielding NO2–, has a high cross section. The third channel is the most exothermic one, it however requires OH– production as its first step, which has a low probability for the gas-phase HNO3 (We cannot determine the relative importance of these three channels leading to NOase HNO3 .2–. The formation of (H2O)nNO2– clusters is predicted to be the most exothermic channel for n < 2. With increasing degree of hydration, the formation of (H2O)nOH– prevails energetically as discussed above. For complexes with more than one HNO3 molecule, reactions (5) and (8) have an energy of about –1.5 eV and –2.0 eV for 2 and 3 HNO3 molecules, respectively. The formation of NO2– is then followed by an intracluster rearrangement to form HONO and NO3–.The second strongest progression in 2O)n(HNO3)m– represent 8% of the total signal. For small cluster sizes (n ≤ 4), HNO3 is not ionically dissociated in the neutral cluster. Moreover, HNO3 has an appreciable electron affinity of 0.56 ± 0.17 eV.39 HNO3– valence anions are stable, decomposition into NO2– + OH is mildly exothermic. It is therefore entirely plausible that (H2O)n(HNO3)m– clusters contain a valence-bound HNO3– anion, which may decompose upon hydration with a small number of water molecules. In the hydrogen bonded network, it is also possible that NO2– + OH dissociation products remain in the cluster if the dissociation is not exothermic enough to cause product evaporation.Anions of the nominal composition (H2O)nOH– cluster series represents about 2% of the total signal. Its presence can be explained by (H2O)n(HNO3)m– dissociation into OH– + NO2, with NO2 not forming strong hydrogen bonds. This would be in line with our recent work,38 where OH– formation is observed in binary collisions of hydrated electrons with HNO3 in the gas phase. Electron attachment to a neutral (H2O)nHNO3 cluster would form the same initial state, a hydrated valence-bound HNO3, which quickly undergoes O–N bond cleavage to form (H2O)nOH–.The  can be produced. For clusters with more than one HNO3 molecule, clusters containing a NO3– core anion play the central role and represent the terminal product for all considered dissociation channels.The present experiment shows that low energy free electrons are efficiently trapped in the mixed HNO3 on heterogeneous chemistry. In particular, we suggest that DEA to molecules in PSC particles results in the formation of NO3– ions, which are among the most abundant anions in the stratosphere. As an example, (HNO3)2NO3– dominates among all negative stratospheric ions in the PSC region at heights below 30 km.42 We show that although DEA to HNO3/H2O particles leads to a cascade of reactions yielding reactive OH and HONO molecules, it is terminated by relatively stable NO3–. It is unlikely that this ion takes part in further reactions, and it rather promotes nucleation of new particles.43 However, the OH and HONO molecules released from the mixed nitric acid–water clusters upon the electron attachment play a pivotal role in the atmospheric chemistry. The atmospheric models do not account for the total yield of HONO in the atmosphere.44 It was indicated that a further, hitherto unrecognized, source of HONO must exist in the atmosphere and heterogeneous chemistry involving atmospheric aerosol particles was suspected to represent such a source. The electron attachment to the nitric acid–water clusters and our suggested reaction pathways represent one of the possible HONO sources.There is a close analogy between the composition of the studied clusters and PSCs. Therefore, our results can have important implications for the impact of HNO3)m(H2O)n, m = 1–6, n = 1–15, in a crossed beam experiment. The products were analyzed using negative ion mass spectrometry and the results were interpreted by DFT calculations. The reactions induced by the electron attachment in the range of 0–14 eV yielded predominantly NO3– ion containing species through different reaction pathways. This is in contrast to both the electron attachment to gas-phase HNO3, yielding primarily NO2–, and the reaction of gas-phase HNO3 with a hydrated electron, which leads to the formation of hydrated OH– clusters. The initial driving force in the present DEA to HNO3/H2O complexes is the proton transfer to form the H3O+/NO3– ion pair where the electron recombines with the proton, yielding the hydrogen atom and NO3–. The NO3– anion represents the terminal product ion also in the intracluster reaction pathways driven by the energetics, which yield also OH and HONO molecules.In order to mimic electron-triggered reactions in PSC particles we investigated electron attachment to mixed nitric acid–water clusters (HNO


SARI) to identify the attribution of influenza to reported cases. We aim to highlight the proportion of influenza‐associated SARI from September 1st, 2015 to August 31st, 2016 in 2 Lebanese hospitals.Given the sparse information on the burden of influenza in Lebanon, the Ministry of Public Health established a sentinel surveillance for severe acute respiratory infections  and influenza A (H1N1) (41%) was evident. A high proportion of cases was reported in children <2 years 274 (37%). The proportional contribution of influenza‐associated SARI to all‐cause hospital admissions was high in children <2 years in the south (4.5% [95% CI: 3.1‐6.5]) and in children <5 years in Beirut (0.7% [95% CI: 0.6‐0.8]).The 2 sentinel sites reported 746 SARI in 2 hospitals in Lebanon. The findings will be beneficial for supporting respiratory prevention and immunization program policies.This is the first study to highlight the proportion of influenza‐associated  At the start of this pilot, a total of 11 general hospitals were selected for the SARI surveillance. However, this study will present data for the 2 pilot sites as they had complete seasonal data.A focal point appointed by the sentinel site was responsible for case finding and reporting since February 2015. The SARI case definition was as per WHO guidelinesThe focal point collected data through passive and active surveillance: passively by receiving notification of cases from medical teams within the hospital and actively by visiting all hospital wards searching for cases. The same method of case finding was applied at both sites.Specimens were collected using nasopharyngeal or oropharyngeal swabs and stored in viral transport media (VTM) at the sentinel site laboratory for 48‐72 hours at 2‐4°C. If the storage time exceeded 72 hours, the specimens were stored at −80°C. The specimens were transferred to the National Influenza Center (NIC) for testing in a cold box with frozen ice packs. At the NIC, specimens were tested by RT‐PCR for influenza types A and B, and type A was further subtyped. Influenza virus RNA was extracted from 200 mL of VTM using High Pure Viral Nucleic Acid Kit . RT‐PCR was conducted on 5 mL of extracted RNA using the Superscript III Platinum One‐step qRT‐PCR system kit, where the sample was mixed with influenza A primers , influenza B primers , and RP primers. The specimen is run on Bio‐Rad IQ5 thermal cycler  with the following conditions: 50°C for 30 minutes, 95°C for 2 minutes, 95°C for 15 seconds, run for 45 cycles, and 55°C for 30 seconds.Data on demographic, clinical, and laboratory findings were entered into a SARI database  and sent via email to the Epidemiologic Surveillance Unit (ESU) at the MOPH on a weekly basis.2.2To ensure that a complete influenza season is demonstrated, SARI data from September 1st, 2015 to August 31st, 2016 were used for the analysis.Data reported from September 1st, 2015 to August 31st, 2016 were reviewed at the sentinel site, and total numbers of all‐cause hospital admissions were obtained. An analysis plan was developed based on WHO's guideline “A Manual for Estimating Disease Burden Associate with Seasonal Influenza,” and analysis was conducted accordingly.3The total number of reported cases from the 2 sentinel sites was 746, of which 467 (63%) were from the southern sites and 279 (37%) from the Beirut suburbs site. At the southern site, children <2 years of age constituted 208 (45%) of all reported cases and 275 (59%) of all cases were males. Only 4 (1%) cases were admitted to the intensive care unit and required ventilation. Majority of cases resided in the southern governorate of Lebanon, with a total of 437 (94%) cases. Similarly, data from the Beirut suburb site revealed 66 (23%) of cases were children <2 years of age and 143 (51%) were males. The reported cases requiring intensive care admissions were 77 (28%) and 28 (10%) required ventilation. Cases admitted to the Beirut suburbs hospital were predominantly residents of the capital Beirut and the district of Baabda, with a total of 85 (30%) cases each.The highest wave of SARI reports for both the southern and Beirut suburbs sentinel sites was evident between weeks 1 and 10 of the year 2016 (January 4 to March 13).Samples for influenza testing were collected from all SARI cases 279 100%) at the Beirut suburb site and from 463 (99%) at the southern site, of which 30 (11%) and 70 (15%) were positive, respectively. At the southern site, influenza B accounted for 29 (41%) of positive cases followed by 27 (39%) influenza A (H1N1)pdm09 and 14 (20%) influenza A (H3N2). Of the 30 cases positive for influenza B, children <2 years of age accounted for 67%. On the other hand, at the Beirut suburbs sentinel site, influenza B and influenza A (H1N1)pdm09 had the same numbers of 14 (47%), while influenza A (H3N2) constituted 2 (6%) of positives at the site . At the southern sentinel site, ages 15‐49 years sampled for influenza testing had the highest positive results with a proportion of influenza of positive 26%, followed by ages 65 years and older with a proportion of influenza positive of 22%. At the Beirut suburb sites, ages 5‐15 years had the highest proportion of influenza positive (21%) followed by ages 50‐64 years , yet with further age breakdown it was higher in children <2 years of age in particular  and influenza A (H1N1) (41%) was evident. A high proportion of the SARI cases were reported in children <2 years of age (37%). The proportional contribution of influenza‐associated SARI to all‐cause hospital admissions was high in children <2 years of age in the south (4.5% [95% CI: 3.1‐6.5]) and in children <5 years in Beirut (0.7% [95% CI: 0.6‐0.8]).Severe acute respiratory infections surveillance systems have proved to be a good source for obtaining estimates of influenza trends as well as estimating the proportion of influenza‐related SARI.While reviewing the data at the sentinel sites, the possibility of using ICD‐10 codes in addition to reported SARI cases was explored; however, this was finally disregarded as codes used in WHO's manual did not correspond to what codes were used at the hospital admission logs. ICD‐10 for influenza (J09‐J11) are scarcely used given the late or lack of influenza results by the time of patient discharge. Most codes used are based on upper respiratory tract infections  or specific symptoms such as fever (R50) and cough (R05). This made it difficult for estimating the total number of respiratory‐related admissions at the sites.The findings of the study revealed that the proportion of influenza‐associated SARI to all hospital admissions at the southern sentinel sites was 0.9% [95% CI: 0.7‐1.1], while in Beirut, it was 0.2% [95% CI: 0.1‐0.3]. There are several explanations for the small proportion shown at the 2 sentinel sites. One explanation is that the denominator used was all‐cause admissions and was not narrowed down to respiratory illness due to the aforementioned difficulty of having total numbers of respiratory illness at the sites. Additional studies are suggested to study the hospital profiles and the overall respiratory illness‐related admissions at the sentinel sites.Taking the southern sentinel site alone, and dividing into age groups, children less than the age of 5 years have a high influenza‐associated SARI to all‐cause hospital admissions (2.9% [95% CI: 2.1‐3.9]). Children <2 years of age have a higher proportion of 4.5% [95% CI: 3.1‐6.5] when compared to the Beirut suburb site (0.5% [95% CI: .1‐2]). An explanation for these slight differences might be the hospital profile and differences in admission patterns in particular. The southern district had higher admissions in pediatric wards 80%) compared to the Beirut suburb site (30%). These proportions among children, however, might still be considered small given other pathogens might be causing increased admissions of children <5 years such as respiratory syncytial virus (RSV) and others 0% comparComparing our data with the literature, the influenza‐associated SARI seemed to be low, yet the fact that children less than 5 years of age have the highest risk is commonly reported. Data from 2008 to 2014 in Jordan revealed that 57% of influenza positive SARI cases are children aged less than 5 years old.The limitations of the study are many, and 1 major limitation is that only 2 sentinel sites were selected for estimating influenza‐associated SARI. The sentinel sites were selected given their complete seasonal data, yet this can lead to difficulty generalizing the findings. In addition, a relatively low number of influenza positive cases was observed. Therefore, the data should be interpreted carefully and may not be directly compared to data from the southern province. The other aspect is that SARI surveillance only covers severe cases admitted to the hospital, and hence, the estimates here include only severe influenza cases. A general understanding of influenza burden in Lebanon can be developed once the results in SARI surveillance system are combined with an influenza‐like illnesses (ILI) sentinel surveillance system once initiated.This is the first study to highlight the proportion of influenza‐associated SARI cases using data from 2 governmental hospitals in Beirut suburbs and the southern governorate in Lebanon and using a national surveillance system. The study is a basis for replication in the other SARI sentinel sites to be able to have national findings on influenza‐associated SARI. This study is the first step in better understanding severe influenza in Lebanon, and this information can be used for better control programs such as immunization and awareness campaigns.