Automated Measurement of Electrogenerated Redox Species Degradation Using Multiplexed Interdigitated Electrode Arrays

Identifying Reactive Trends in Glycerol Electro-Oxidation Using an Automated Screening Approach: 28 Ways to Electrodeposit an Au Electrocatalyst

Automated, rapid electrocatalyst discovery techniques that comprehensively address the exploration of chemical spaces, characterization of catalyst robustness, reproducibility, and translation of results to (flow) electrolysis operation are needed. Responding to the growing interest in biomass valorization, we studied the glycerol electro-oxidation reaction (GEOR) on gold in alkaline media as a model reaction to demonstrate the efficacy of such methodology introduced here. Our platform combines individually addressable electrode arrays with HardPotato, a Python application programming interface for potentiostat control, to automate electrochemical experiments and data analysis operations. We systematically investigated the effects of reduction potential (E l) and pulse width (PW) on GEOR activity during the electrodeposition (Edep) of gold, evaluating 28 different conditions in triplicate measurements with great versatility. Our findings reveal a direct correlation between E l and GEOR activity. Upon CV cycling, we recorded a 52% increase in peak current density and a -0.25 V shift in peak potential as E l varied from -0.2 to -1.4 V. We also identified an optimal PW of ∼1.0 s, yielding maximum catalytic performance. The swift analysis enabled by our methodology allowed us to correlate performance enhancements with increased electrochemical surface area and preferential deposition of Au(110) and Au(111) sites, even in disparate Edep conditions. We validate our methodology by scaling the Edep process to larger electrodes and correlating intrinsic activity with product speciation via flow electrolysis measurements. Our platform highlights opportunities in automation for electrocatalyst discovery to address pressing needs toward industrial decarbonization, such as biomass valorization.

High-Throughput Single-Entity Electrochemistry with Microelectrode Arrays

We describe micro- and nanoelectrode array analysis with an automated version of the array microcell method (AMCM). Characterization of hundreds of electrodes, with diameters ranging from 100 nm to 2 μm, was carried out by using AMCM voltammetry and chronoamperometry. The influence of solvent evaporation on mass transport in the AMCM pipette and the resultant electrochemical response were investigated, with experimental results supported by finite element method simulations. We also describe the application of AMCM to high-throughput single-entity electrochemistry in measurements of stochastic nanoparticle impacts. Collision experiments recorded 3270 single-particle events from 671 electrodes. Data collection parameters were optimized to enable these experiments to be completed in a few hours, and the collision transient sizes were analyzed with a U-Net deep learning model. Elucidation of collision transient sizes by histograms from these experiments was enhanced due to the large sample size possible with AMCM.

Interfacing High-Throughput Electrosynthesis and Mass Spectrometric Analysis of Azines

Combinatorial electrochemistry has great promise for accelerated reaction screening, organic synthesis, and catalysis. Recently, we described a new high-throughput electrochemistry platform, colloquially named "Legion". Legion fits the footprint of a 96-well microtiter plate with simultaneous individual control over all 96 electrochemical cells. Here, we demonstrate the versatility of Legion when coupled with high-throughput mass spectrometry (MS) for electrosynthetic product screening and quantitation. Electrosynthesis of benzophenone azine was selected as a model reaction and was arrayed and optimized using a combination of Legion and nanoelectrospray ionization MS. The combination of high-throughput synthesis with Legion and analysis via MS proves a compelling strategy for accelerating reaction discovery and optimization in electro-organic synthesis.

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism's presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

Legion: An Instrument for High-Throughput Electrochemistry

Electrochemical arrays promise utility for accelerated hypothesis testing and breakthrough discoveries. Herein, we report a new high-throughput electrochemistry platform, colloquially called "Legion," for applications in electroanalysis and electrosynthesis. Legion consists of 96 electrochemical cells dimensioned to match common 96-well plates that are independently controlled with a field-programmable gate array. We demonstrate the utility of Legion by measuring model electrochemical probes, pH-dependent electron transfers, and electrocatalytic dehalogenation reactions. We consider advantages and disadvantages of this new instrumentation, with the hope of expanding the electrochemical toolbox.

Hard Potato: A Python Library to Control Commercial Potentiostats and to Automate Electrochemical Experiments

Here, we develop and show the use of an open-source Python library to control commercial potentiostats. It standardizes the commands for different potentiostat models, opening the possibility to perform automated experiments independently of the instrument used. At the time of this writing, we have included potentiostats from CH Instruments (models 1205B, 1242B, 601E, and 760E) and PalmSens (model Emstat Pico), although the open-source nature of the library allows for more to be included in the future. To showcase the general workflow and implementation of a real experiment, we have automated the Randles-Ševčı́k methodology to determine the diffusion coefficient of a redox-active species in solution using cyclic voltammetry. This was accomplished by writing a Python script that includes data acquisition, data analysis, and simulation. The total run time was 1 min and 40 s, well below the time it would take even an experienced electrochemist to apply the methodology in a traditional manner. Our library has potential applications that expand beyond the automation of simple repetitive tasks; for example, it can interface with peripheral hardware and well-established third-party Python libraries as part of a more complex and intelligent setup that relies on laboratory automation, advanced optimization, and machine learning.