{"title":"Flexible model of water based on the dielectric and electromagnetic spectrum properties : TIP4P/$epsilon$ Flex.","authors":"Ra'ul Fuentes-Azcatl","doi":"10.1016/J.MOLLIQ.2021.116770","DOIUrl":"https://doi.org/10.1016/J.MOLLIQ.2021.116770","url":null,"abstract":"","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83644685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Stender, Keisuke Obata, Max Baumung, F. Abdi, M. Risch
{"title":"Characterization of a Modular Flow Cell System for Electrocatalytic Experiments and Comparison to a Commercial RRDE System","authors":"F. Stender, Keisuke Obata, Max Baumung, F. Abdi, M. Risch","doi":"10.26434/chemrxiv.13308458","DOIUrl":"https://doi.org/10.26434/chemrxiv.13308458","url":null,"abstract":"Generator-collector\u0000experiments offer insights into the mechanisms of electrochemical reactions by\u0000correlating the product and generator currents. Most commonly, these\u0000experiments are performed using a rotating ring-disk electrode (RRDE). We\u0000developed a double electrode flow cell (DEFC) with exchangeable generator and\u0000detector electrodes where the electrode width equals the channel width.\u0000Commonalities and differences between the RRDE and DEFC are discussed based on\u0000analytical solutions, numerical simulations and measurements of the\u0000ferri-/ferrocyanide redox couple on Pt electrodes in a potassium chloride\u0000electrolyte. The analytical solutions agree with the measurements using\u0000electrode widths of 5 and 2 mm. Yet, we find an unexpected dependence on the\u0000exponent of the width so that wider electrodes cannot be analysed using the\u0000conventional analytical solution. In contrast, all the investigated electrodes\u0000show a collection efficiency of close to 35.4% above a minimum rotation speed\u0000or flow rate, where the narrowest electrode is most accurate at the cost of\u0000precision and the widest electrode the least accurate but most precise. Our\u0000DEFC with exchangeable electrodes is an attractive alternative to commercial RRDEs\u0000due to the flexibility to optimize the electrode materials and geometry for the\u0000desired reaction.","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80945722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emma Lumiaro, M. Todorovi'c, T. Kurtén, H. Vehkamäki, P. Rinke
{"title":"Predicting Gas-Particle Partitioning Coefficients of Atmospheric\u0000Molecules with Machine Learning","authors":"Emma Lumiaro, M. Todorovi'c, T. Kurtén, H. Vehkamäki, P. Rinke","doi":"10.5194/ACP-2020-1258","DOIUrl":"https://doi.org/10.5194/ACP-2020-1258","url":null,"abstract":"Abstract. The formation, properties and lifetime of secondary organic aerosols in the atmosphere are largely determined by gas-particle partitioning coefficients of the participating organic vapours. Since these coefficients are often difficult to measure and to compute, we developed a machine learning model to predict them given molecular structure as input. Our data-driven approach is based on the dataset by Wang et al. (Atmos. Chem. Phys., 17, 7529 (2017)), who computed the partitioning coefficients and saturation vapour pressures of 3414 atmospheric oxidation products from the master chemical mechanism using the COSMOtherm program. We trained a kernel ridge regression (KRR) machine learning model on the saturation vapour pressure (Psat), and on two equilibrium partitioning coefficients: between a water-insoluble organic matter phase and the gas phase (KWIOM/G), and between an infinitely dilute solution with pure water and the gas phase (KW/G). For the input representation of the atomic structure of each organic molecule to the machine, we tested different descriptors. We find that the many-body tensor representation (MBTR) works best for our application, but the topological fingerprint (TopFP) approach is almost as good, and is significantly more cost effective. Our best machine learning model (KRR with a Gaussian kernel + MBTR) predicts Psat and KWIOM/G to within 0.3 logarithmic units and KW/G to within 0.4 logarithmic units of the original COSMOtherm calculations. This is equal or better than the typical accuracy of COSMOtherm predictions compared to experimental data (where available). We then applied our machine learning model to a dataset of 35,383 molecules that we generated based on a carbon 10 backbone functionalized with 0 to 6 carboxyl, carbonyl or hydroxyl groups to evaluate its performance for polyfunctional compounds with potentially low Psat. The resulting saturation vapor pressure and partitioning coefficient distributions were physico-chemically reasonable, and the volatility predictions for the most highly oxidized compounds were in qualitative agreement with experimentally inferred volatilities of atmospheric oxidation products with similar elemental composition.\u0000","PeriodicalId":8439,"journal":{"name":"arXiv: Chemical Physics","volume":"221 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79877273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}