An automated electrochemistry platform for studying pH-dependent molecular electrocatalysis†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-08-05 DOI:10.1039/D4DD00186A
Michael A. Pence, Gavin Hazen and Joaquín Rodríguez-López
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Abstract

Comprehensive studies of molecular electrocatalysis require tedious titration-type experiments that slow down manual experimentation. We present eLab as an automated electrochemical platform designed for molecular electrochemistry that uses opensource software to modularly interconnect various commercial instruments, enabling users to chain together multiple instruments for complex electrochemical operations. We benchmarked the solution handling performance of our platform through gravimetric calibration, acid–base titrations, and voltammetric diffusion coefficient measurements. We then used the platform to explore the TEMPO-catalyzed electrooxidation of alcohols, demonstrating our platforms capabilities for pH-dependent molecular electrocatalysis. We performed combined acid–base titrations and cyclic voltammetry on six different alcohol substrates, collecting 684 voltammograms with 171 different solution conditions over the course of 16 hours, demonstrating high throughput in an unsupervised experiment. The high versatility, transferability, and ease of implementation of eLab promises the rapid discovery and characterization of pH-dependent processes, including mediated electrocatalysis for energy conversion, fuel valorization, and bioelectrochemical sensing, among many applications.

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研究 pH 依赖性分子电催化的自动电化学平台
对分子电催化的全面研究需要进行繁琐的滴定型实验,从而降低了手动实验的速度。我们介绍的 eLab 是专为分子电化学设计的自动化电化学平台,它使用开源软件模块化地连接各种商用仪器,使用户能够将多台仪器串联起来进行复杂的电化学操作。我们通过重量校准、酸碱滴定和伏安扩散系数测量,对平台的溶液处理性能进行了基准测试。然后,我们利用该平台探索了 TEMPO 催化的醇类电氧化,展示了我们的平台在 pH 依赖性分子电催化方面的能力。我们对六种不同的醇类底物进行了酸碱滴定和循环伏安测定,在 16 个小时的时间里收集了 684 张伏安图,涉及 171 种不同的溶液条件,展示了无监督实验的高吞吐量。eLab 的多功能性、可移植性和易实施性使其有望快速发现和表征 pH 依赖性过程,包括用于能量转换、燃料价值化和生物电化学传感等多种应用的介导电催化。
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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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