Marie Juramy, Eric Besson, Stephane Gastaldi, Fabio Ziarelli, Stéphane Viel, Giulia Mollica, Pierre Thureau
In this study, nuclear magnetic resonance (NMR) is used to investigate the crystallisation behaviour of aspirin within a mesoporous SBA-15 silica material. The potential of dynamic nuclear polarisation (DNP) experiments is also investigated using specifically designed porous materials that incorporate polarising agents within their walls. The formation of the metastable crystalline form II is observed when crystallisation occurs within the pores of the mesoporous structure. Conversely, bulk crystallisation yields the most stable form, namely form I, of aspirin. Remarkably, the metastable form II remains trapped within the pores of mesoporous SBA-15 silica material even 30 days after impregnation, underscoring its persistent stability within this confined environment.
在这项研究中，核磁共振 (NMR) 被用来研究阿司匹林在介孔 SBA-15 硅材料中的结晶行为。此外，还利用专门设计的多孔材料（其壁内含有极化剂）研究了动态核极化（DNP）实验的潜力。当结晶发生在介孔结构的孔隙中时，可观察到 "可转移结晶形式 II "的形成。相反，块状结晶会产生最稳定的阿司匹林形态，即形态 I。值得注意的是，即使在浸渍 30 天后，析晶形式 II 仍被困在介孔 SBA-15 二氧化硅材料的孔隙中，这表明它在这种密闭环境中具有持久的稳定性。
{"title":"Exploring the crystallisation of aspirin in a confined porous material using solid-state nuclear magnetic resonance","authors":"Marie Juramy, Eric Besson, Stephane Gastaldi, Fabio Ziarelli, Stéphane Viel, Giulia Mollica, Pierre Thureau","doi":"10.1039/d4fd00123k","DOIUrl":"https://doi.org/10.1039/d4fd00123k","url":null,"abstract":"In this study, nuclear magnetic resonance (NMR) is used to investigate the crystallisation behaviour of aspirin within a mesoporous SBA-15 silica material. The potential of dynamic nuclear polarisation (DNP) experiments is also investigated using specifically designed porous materials that incorporate polarising agents within their walls. The formation of the metastable crystalline form II is observed when crystallisation occurs within the pores of the mesoporous structure. Conversely, bulk crystallisation yields the most stable form, namely form I, of aspirin. Remarkably, the metastable form II remains trapped within the pores of mesoporous SBA-15 silica material even 30 days after impregnation, underscoring its persistent stability within this confined environment.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719552","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}
Platinum-black (Pt-B) has been demonstrated as an excellent electrocatalytic material for the electrochemical oxidation of hydrogen peroxide (H2O2). As Pt-B films can be deposited electrochemically, micro- and nano-sized conductive transducers can be modified with Pt-B. Here, we present the potential of Pt-B micro- and sub-micro-sized sensors for the detection and quantification of hydrogen (H2) in solution. Using these microsensors, no sampling step for H2 determination is required and e.g., in photocatalysis, the onset of H2 evolution can be monitored in situ. We present Pt-B- based H2 micro- and sub-micro-sized sensors based on different electrochemical transducers such as microelectrodes and atomic force microscopy (AFM)- scanning electrochemical microscopy (SECM) probes, which enable local measurements e.g., at heterogenized photocatalytically active samples. The microsensors are characterized in terms of limits of detection (LOD), which ranges from 4.0 µM to 30 µM depending on the size of the sensors and the experimental conditions such as type of electrolyte and pH. The sensors were tested for the in situ H2 evolution by light-driven water-splitting, i.e., using ascorbic acid or triethanolamine, showing a wide linear concertation range, good reproducibility, and high sensitivity. Proof-of-principle experiments using Pt-B-modified cantilever-based sensors were performed using a model sample like platinum substrate to map the electrochemical H2 evolution along with the topography using AFM-SECM.
{"title":"Scanning electrochemical probe microscopy: towards the characterization of micro-and nanostructured photocatalytic materials","authors":"Giada Caniglia, Sarah Horn, Christine Kranz","doi":"10.1039/d4fd00136b","DOIUrl":"https://doi.org/10.1039/d4fd00136b","url":null,"abstract":"Platinum-black (Pt-B) has been demonstrated as an excellent electrocatalytic material for the electrochemical oxidation of hydrogen peroxide (H2O2). As Pt-B films can be deposited electrochemically, micro- and nano-sized conductive transducers can be modified with Pt-B. Here, we present the potential of Pt-B micro- and sub-micro-sized sensors for the detection and quantification of hydrogen (H2) in solution. Using these microsensors, no sampling step for H2 determination is required and e.g., in photocatalysis, the onset of H2 evolution can be monitored in situ. We present Pt-B- based H2 micro- and sub-micro-sized sensors based on different electrochemical transducers such as microelectrodes and atomic force microscopy (AFM)- scanning electrochemical microscopy (SECM) probes, which enable local measurements e.g., at heterogenized photocatalytically active samples. The microsensors are characterized in terms of limits of detection (LOD), which ranges from 4.0 µM to 30 µM depending on the size of the sensors and the experimental conditions such as type of electrolyte and pH. The sensors were tested for the in situ H2 evolution by light-driven water-splitting, i.e., using ascorbic acid or triethanolamine, showing a wide linear concertation range, good reproducibility, and high sensitivity. Proof-of-principle experiments using Pt-B-modified cantilever-based sensors were performed using a model sample like platinum substrate to map the electrochemical H2 evolution along with the topography using AFM-SECM.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719554","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}
We describe the problems of quantum chemistry, the intuition behind classical heuristic methods used to solve them, a conjectured form of the classical com- plexity of quantum chemistry problems, and the subsequent opportunities for quantum advantage. This article is written for both quantum chemists and quan- tum information theorists. In particular, we attempt to summarize the domain of quantum chemistry problems as well as the chemical intuition that is applied to solve them within concrete statements (such as a classical heuristic cost conjec- ture) in the hope that this may stimulate future analysis.
{"title":"Quantum chemistry, classical heuristics, and quantum advantage","authors":"Garnet Kin-Lic Chan","doi":"10.1039/d4fd00141a","DOIUrl":"https://doi.org/10.1039/d4fd00141a","url":null,"abstract":"We describe the problems of quantum chemistry, the intuition behind classical heuristic methods used to solve them, a conjectured form of the classical com- plexity of quantum chemistry problems, and the subsequent opportunities for quantum advantage. This article is written for both quantum chemists and quan- tum information theorists. In particular, we attempt to summarize the domain of quantum chemistry problems as well as the chemical intuition that is applied to solve them within concrete statements (such as a classical heuristic cost conjec- ture) in the hope that this may stimulate future analysis.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613544","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}
Daniel Speckhard, Tim Bechtel, Luca M. Ghiringhelli, Martin Kuban, Santiago Rigamonti, Claudia Draxl
Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what {it big} means in the context of typical materials-science machine-learning problems. This concerns not only data volume, but also data quality and veracity as much as infrastructure issues. With selected examples, we ask (i) how models generalize to similar datasets, (ii) how high-quality datasets can be gathered from heterogenous sources, (iii) how the feature set and complexity of a model can affect expressivity, and (iv) what infrastructure requirements are needed to create larger datasets and train models on them. In sum, we find that big data present unique challenges along very different aspects that should serve to motivate further work.
{"title":"How big is Big Data?","authors":"Daniel Speckhard, Tim Bechtel, Luca M. Ghiringhelli, Martin Kuban, Santiago Rigamonti, Claudia Draxl","doi":"10.1039/d4fd00102h","DOIUrl":"https://doi.org/10.1039/d4fd00102h","url":null,"abstract":"Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what {it big} means in the context of typical materials-science machine-learning problems. This concerns not only data volume, but also data quality and veracity as much as infrastructure issues. With selected examples, we ask (i) how models generalize to similar datasets, (ii) how high-quality datasets can be gathered from heterogenous sources, (iii) how the feature set and complexity of a model can affect expressivity, and (iv) what infrastructure requirements are needed to create larger datasets and train models on them. In sum, we find that big data present unique challenges along very different aspects that should serve to motivate further work.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613550","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}
MoS2 is a promising electrocatalytic material for replacing noble metals. Nanoelectrochemistry studies, such as using nanoelectrochemical cell confinement, have particularly helped in demonstrating the preferential electrocatalytic activity of MoS2 edges. These findings have been accompanied by considerable research efforts to synthetize edge-abundant nanomaterials. However, to fully apprehend their electrocatalytic performance, at the single particle level, new instrumental developments are also needed. Here, we feature a highly sensitive refractive index optical microscopy technique, namely interferometric scattering microscopy (iSCAT), for monitoring local electrochemistry at single MoS2 petal-like sub-microparticles. This work focuses on the oxygen reduction reaction (ORR), which operates at low current densities and thus requires high-sensitivity imaging techniques. By employing a precipitation reaction to reveal the ORR activity and utilizing the high spatial resolution and contrast of iSCAT, we achieve the sensitivity required to evaluate the ORR activity at single MoS2 particles.
{"title":"Seeing nanoscale electrocatalytic reactions at individual MoS2 particles under an optical microscope: probing sub-mM oxygen reduction reaction","authors":"Nikan Afsahi, Zhu Zhang, Sanli Faez, Jean-Marc Noël, Manas Ranjan Panda, Mainak Majumder, Naimeh Naseritaheri, Jean-François Lemineur, Frederic Kanoufi","doi":"10.1039/d4fd00132j","DOIUrl":"https://doi.org/10.1039/d4fd00132j","url":null,"abstract":"MoS2 is a promising electrocatalytic material for replacing noble metals. Nanoelectrochemistry studies, such as using nanoelectrochemical cell confinement, have particularly helped in demonstrating the preferential electrocatalytic activity of MoS2 edges. These findings have been accompanied by considerable research efforts to synthetize edge-abundant nanomaterials. However, to fully apprehend their electrocatalytic performance, at the single particle level, new instrumental developments are also needed. Here, we feature a highly sensitive refractive index optical microscopy technique, namely interferometric scattering microscopy (iSCAT), for monitoring local electrochemistry at single MoS2 petal-like sub-microparticles. This work focuses on the oxygen reduction reaction (ORR), which operates at low current densities and thus requires high-sensitivity imaging techniques. By employing a precipitation reaction to reveal the ORR activity and utilizing the high spatial resolution and contrast of iSCAT, we achieve the sensitivity required to evaluate the ORR activity at single MoS2 particles.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141578049","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}
Victor Trinquet, Matthew Evans, Cameron Hargreaves, Pierre-Paul De Breuck, Gian-Marco Rignanese
Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction that has been shown to perform especially well for small materials datasets, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.
{"title":"Optical materials discovery and design via federated databases and machine learning","authors":"Victor Trinquet, Matthew Evans, Cameron Hargreaves, Pierre-Paul De Breuck, Gian-Marco Rignanese","doi":"10.1039/d4fd00092g","DOIUrl":"https://doi.org/10.1039/d4fd00092g","url":null,"abstract":"Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction that has been shown to perform especially well for small materials datasets, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573174","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}
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.
{"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}
We describe a single-molecule electrochemical imaging strategy to study the electrocatalytic (EC') mechanism. Using the fluorescent molecule ATTO647N at extremely low concentrations as the substrate, we confirmed its catalytic reduction to a nonfluorescence form in the presence of the mediator phenazine methosulfate (PMS) by imaging and counting fluorescent molecules. Conventional electrochemical current in cyclic voltammetry would not have allowed us to infer the existence of an EC’ process or the PMS-mediated ATTO647N reduction. Additionally, we observed shifts in the catalytic reduction potential of ATTO647N at various mediator concentrations, which agree with the theoretical predictions by Savéant. Our work offers a new perspective on connecting single-molecule EC’ behaviors with the conventional ensemble EC’ mechanism, both practically and theoretically.
{"title":"Single-molecule electrochemical imaging of 'split waves' in the electrocatalytic (EC') mechanism","authors":"Wandong Zhao, Jin Lu","doi":"10.1039/d4fd00126e","DOIUrl":"https://doi.org/10.1039/d4fd00126e","url":null,"abstract":"We describe a single-molecule electrochemical imaging strategy to study the electrocatalytic (EC') mechanism. Using the fluorescent molecule ATTO647N at extremely low concentrations as the substrate, we confirmed its catalytic reduction to a nonfluorescence form in the presence of the mediator phenazine methosulfate (PMS) by imaging and counting fluorescent molecules. Conventional electrochemical current in cyclic voltammetry would not have allowed us to infer the existence of an EC’ process or the PMS-mediated ATTO647N reduction. Additionally, we observed shifts in the catalytic reduction potential of ATTO647N at various mediator concentrations, which agree with the theoretical predictions by Savéant. Our work offers a new perspective on connecting single-molecule EC’ behaviors with the conventional ensemble EC’ mechanism, both practically and theoretically.","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":"141573176","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}
Single-entity electrochemistry (SEE) is an emerging field within electrochemistry focused on investigating individual entities such as nanoparticles, bacteria, cells, or single molecules. Accurate identification and analysis of SEE signals require effective data processing methods for unbiased and automated feature extraction. In this study, we apply and compare two approaches for step detection in SEE data: discrete wavelet transforms (DWT) and convolutional neural networks (CNN).
单实体电化学（SEE）是电化学中的一个新兴领域，重点研究纳米粒子、细菌、细胞或单分子等单个实体。要准确识别和分析 SEE 信号，需要有效的数据处理方法，以实现无偏的自动特征提取。在本研究中，我们应用并比较了 SEE 数据中阶跃检测的两种方法：离散小波变换 (DWT) 和卷积神经网络 (CNN)。
{"title":"Advanced Algorithm for Step Detection in Single-Entity Electrochemistry: A Comparative Study of Wavelet Transforms and Convolutional Neural Networks","authors":"Ziwen Zhao, Arunava Naha, Nikolaos Kostopoulos, Alina Sekretareva","doi":"10.1039/d4fd00130c","DOIUrl":"https://doi.org/10.1039/d4fd00130c","url":null,"abstract":"Single-entity electrochemistry (SEE) is an emerging field within electrochemistry focused on investigating individual entities such as nanoparticles, bacteria, cells, or single molecules. Accurate identification and analysis of SEE signals require effective data processing methods for unbiased and automated feature extraction. In this study, we apply and compare two approaches for step detection in SEE data: discrete wavelet transforms (DWT) and convolutional neural networks (CNN).","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141552191","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}
Local electrochemical impedance spectroscopy (LEIS) has emerged to characterize local electrochemical processes on heterogeneous surfaces. However, the current LEIS heavily relies on lock-in amplifier that has a poor gain effect for weak current, limiting the achievement of high-spatial imaging. Herein, an integrated scanning electrochemical cell microscopy is developed by directly collecting the alternating current (AC) current signal through a preamplifier. The recorded local current (sub nA-level) is compared with the initial excitation signal to get the parameters for Nyquist plotting. By integrating this method into a scanning electrochemical cell microscopy (SECCM), an image of LEIS at the Indium Tin Oxide/gold (ITO/Au) electrode is obtained with a spatial resolution of 180 nm. The established SECCM platform is integrated that could be positioned into the limited space (e.g. glove box) for real characterization of electrodes.
局部电化学阻抗光谱法（LEIS）是为描述异质表面的局部电化学过程而出现的。然而，目前的局部电化学阻抗光谱主要依赖于锁相放大器，该放大器对微弱电流的增益效果不佳，限制了高空间成像的实现。在此，我们开发了一种集成扫描电化学细胞显微镜，通过前置放大器直接采集交流电流信号。记录的局部电流（亚 nA 级）与初始激励信号进行比较，以获得奈奎斯特绘图参数。通过将此方法集成到扫描电化学电池显微镜（SECCM）中，可获得铟锡氧化物/金（ITO/Au）电极的 LEIS 图像，空间分辨率为 180 nm。已建立的 SECCM 平台可集成到有限的空间（如手套箱）中，用于电极的实际表征。
{"title":"Integrated Scanning Electrochemical Cell Microscopy Platform with Local Electrochemical Impedance Spectroscopy using Preamplifier","authors":"Ancheng Wang, Rong Jin, Dechen Jiang","doi":"10.1039/d4fd00122b","DOIUrl":"https://doi.org/10.1039/d4fd00122b","url":null,"abstract":"Local electrochemical impedance spectroscopy (LEIS) has emerged to characterize local electrochemical processes on heterogeneous surfaces. However, the current LEIS heavily relies on lock-in amplifier that has a poor gain effect for weak current, limiting the achievement of high-spatial imaging. Herein, an integrated scanning electrochemical cell microscopy is developed by directly collecting the alternating current (AC) current signal through a preamplifier. The recorded local current (sub nA-level) is compared with the initial excitation signal to get the parameters for Nyquist plotting. By integrating this method into a scanning electrochemical cell microscopy (SECCM), an image of LEIS at the Indium Tin Oxide/gold (ITO/Au) electrode is obtained with a spatial resolution of 180 nm. The established SECCM platform is integrated that could be positioned into the limited space (e.g. glove box) for real characterization of electrodes.","PeriodicalId":76,"journal":{"name":"Faraday Discussions","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501891","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}
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