Faraday Discussions最新文献_第5页

Electrochemical Nucleation and Growth Kinetics: Insights from Single Particle Scanning Electrochemical Cell Microscopy Studies 电化学成核和生长动力学：单颗粒扫描电化学电池显微镜研究的启示

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-06 DOI: 10.1039/d4fd00131a

Kenneth Osoro, Caleb Hill

{"title":"Electrochemical Nucleation and Growth Kinetics: Insights from Single Particle Scanning Electrochemical Cell Microscopy Studies","authors":"Kenneth Osoro, Caleb Hill","doi":"10.1039/d4fd00131a","DOIUrl":"https://doi.org/10.1039/d4fd00131a","url":null,"abstract":"The kinetics of particle nucleation and growth are critical to a wide variety of electrochemical systems. While studies carried out at the single particle level are promising for improving our understanding of nucleation and growth processes, conventional analytical frameworks commonly employed in bulk studies may not be appropriate for single particle experiments. Here, we present scanning electrochemical cell microsocpy (SECCM) studies of Ag nucleation and growth on carbon and indium tin oxide (ITO) electrodes. Statistical analyses of the data from these experiments reveal significant discrepancies with traditional, quasi-equilibrium kinetic models commonly employed in the analysis of particle nucleation in electrochemical systems. Time-dependent kinetic models are presented capable of appropriately analysing the data generated via SECCM to extract meaningful chemical quantities such as surface energies and kinetic rate constants. These results demonstrate a powerful new approach to the analysis of single particle nucleation and growth data which could be leveraged in differentiating behavior within spatially heterogeneous systems.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single-molecule electrochemical imaging of 'split waves' in the electrocatalytic (EC') mechanism 对电催化（EC）机制中的 "分裂波 "进行单分子电化学成像

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-06 DOI: 10.1039/d4fd00126e

Wandong Zhao, Jin Lu

引用次数: 0

Advanced Algorithm for Step Detection in Single-Entity Electrochemistry: A Comparative Study of Wavelet Transforms and Convolutional Neural Networks 单实体电化学中阶跃检测的先进算法：小波变换和卷积神经网络的比较研究

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-04 DOI: 10.1039/d4fd00130c

Ziwen Zhao, Arunava Naha, Nikolaos Kostopoulos, Alina Sekretareva

引用次数: 0

Integrated Scanning Electrochemical Cell Microscopy Platform with Local Electrochemical Impedance Spectroscopy using Preamplifier 利用前置放大器进行局部电化学阻抗光谱分析的集成扫描电化学细胞显微镜平台

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-02 DOI: 10.1039/d4fd00122b

Ancheng Wang, Rong Jin, Dechen Jiang

引用次数: 0

The electrochemical modulation of single molecule fluorescence 单分子荧光的电化学调制

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-02 DOI: 10.1039/d4fd00111g

Ying Yang, Yuanqing Ma, John Justin Gooding

引用次数: 0

High Throughput calculations and machine learning modeling of $^{17}text{O}$ NMR in non-magnetic oxides 非磁性氧化物中 $^{17}text{O}$ NMR 的高通量计算和机器学习建模

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-01 DOI: 10.1039/d4fd00128a

Zhiyuan Li, Bo Zhao, Hongbin Zhang, Yixuan Zhang

{"title":"High Throughput calculations and machine learning modeling of $^{17}text{O}$ NMR in non-magnetic oxides","authors":"Zhiyuan Li, Bo Zhao, Hongbin Zhang, Yixuan Zhang","doi":"10.1039/d4fd00128a","DOIUrl":"https://doi.org/10.1039/d4fd00128a","url":null,"abstract":"The only NMR active oxygen isotope, Oxygen-17($^{17}text{O}$ ), serves as a sensitive probe due to its large chemical shift range, the electric field gradient at the oxygen site, and the quadrupolar interaction. Consequently, $^{17}text{O}$ solid-state NMR offers unique insights into local structures and finds significant applications in the study of disorder, reactivity, and host-guest chemistry. Despite recent advances in sensitivity enhancement, isotopic labeling, and NMR crystallography, the application of $^{17}text{O}$ solid-state NMR is still hindered by low natural abundance, costly enrichment, and challenges in handling spectrum signals. Density functional theory calculations and machine learning techniques offer an alternative approach to mapping the local crystal structures to NMR parameters. However, the lack of high-quality data remains a challenge, despite the establishment of some datasets. In this study, we implement and execute a high-throughput workflow combining AiiDA and Castep to evaluate the NMR parameters. Focusing on non-magnetic oxides, we have collected over 7100 binary, ternary, and quaternary compounds from the Materials Project and performed calculations. Furthermore, using various descriptors for the local crystalline environments, we model the $^{17}text{O}$ NMR using machine learning techniques, further enhancing our ability to predict and understand $^{17}text{O}$ NMR parameters in oxide crystals.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Delivery of Carbon Dioxide to an Electrode Surface Using a Nanopipette 使用纳米管将二氧化碳输送到电极表面

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-07-01 DOI: 10.1039/d4fd00124a

Jaimy Monteiro, Harry Dunne, Kim McKelvey

引用次数: 0

NMR Crystallization: In-Situ NMR Strategies for Monitoring the Evolution of Crystallization Processes NMR 结晶：监测结晶过程演变的原位 NMR 战略

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-06-28 DOI: 10.1039/d4fd00079j

Colan E Hughes, Naomi V. Ratnasingam, Andrew Williams, Erwan Benhenou, Rhian Patterson, Kenneth D M Harris

引用次数: 0

Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes 利用自然语言处理技术整理过渡金属功能复合物的 tmCAT、tmPHOTO、tmBIO 和 tmSCO 数据集

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-06-26 DOI: 10.1039/d4fd00087k

Ilia Kevlishvili, Roland Gerard St. Michel, Aaron Garrison, Jacob Toney, Husain Adamji, Hao-Jun Jia, Yuriy Roman-Leshkov, Heather Kulik

{"title":"Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes","authors":"Ilia Kevlishvili, Roland Gerard St. Michel, Aaron Garrison, Jacob Toney, Husain Adamji, Hao-Jun Jia, Yuriy Roman-Leshkov, Heather Kulik","doi":"10.1039/d4fd00087k","DOIUrl":"https://doi.org/10.1039/d4fd00087k","url":null,"abstract":"The breadth of transition metal chemical space covered by databases such as the Cambridge Structural Database and the derived computational database tmQM is not conducive to application-specific modeling and the development of structure–property relationships. Here, we employ both supervised and unsupervised natural language processing (NLP) techniques to link experimentally synthesized compounds in the tmQM database to their respective applications. Leveraging NLP models, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism. Analyzing the chemical substructures within each dataset reveals common chemical motifs in each of the designated applications. We then use these common chemical structures to augment our initial datasets for each application, yielding a total of 21,631 compounds in tmCAT, 4,599 in tmPHOTO, 2,782 in tmBIO, and 983 in tmSCO. These datasets are expected to accelerate the more targeted computational screening and development of refined structure–property relationships with machine learning.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

First-principles NMR of oxide glasses boosted by machine learning 机器学习促进氧化物玻璃的第一原理 NMR

IF 3.4 3区化学

Faraday Discussions Pub Date : 2024-06-26 DOI: 10.1039/d4fd00129j

Thibault Charpentier

{"title":"First-principles NMR of oxide glasses boosted by machine learning","authors":"Thibault Charpentier","doi":"10.1039/d4fd00129j","DOIUrl":"https://doi.org/10.1039/d4fd00129j","url":null,"abstract":"Solid-State NMR has established itself as a cutting-egde spectroscopy for elucidating the structure of oxide glasses thanks to several decades of methodological and instrumental progresses. First-principles calculations of NMR properties combined with molecular dynamics (MD) simulations provides a powerful complementing approach for the interpretation of the NMR data although they still suffer from limitations in terms of size, time and high consumption of computational resources. We address this challenge by developing a machine-learning framework to boost predictive modelling of NMR spectra. We use kernel ridge regression techniques (least-square support vector regression and linear ridge regression) combined with the smooth overlap of atomic position (SOAP) atom-centered descriptors to efficiently predict NMR interactions: isotropic magnetic shielding and the electric field gradient (EFG) tensor. As illustrated in this work, this approach enables the simulation of MAS and MQMAS NMR spectra of very large models (more than 10000 atoms) and an efficient averaging of NMR properties over MD trajectories of nanoseconds for incorporating finite temperature effects, at computational cost of classical MD simulation. We illustrate these advances on sodium silicate glasses (SiO2-Na2O). NMR parameters (isotropic chemical shift and electric field gradient) could be predicted with an accuracy of 1 to 2% in terms of the total span of the NMR parameter values. To include vibrational effects, an approach is proposed by scaling the EFG tensor in NMR simulations with a factor obtained from the time auto-correlation functions computed on MD trajectory.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0