Self-Driving Laboratories for Chemistry and Materials Science

Chemical Reviews, Год журнала: 2024, Номер 124(16), С. 9633 - 9732

Опубликована: Авг. 13, 2024

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through automation experimental workflows, along with autonomous planning, SDLs hold potential to greatly accelerate research in chemistry and materials discovery. This review provides in-depth analysis state-of-the-art SDL technology, its applications across various disciplines, implications for industry. additionally overview enabling technologies SDLs, including their hardware, software, integration laboratory infrastructure. Most importantly, this explores diverse range domains where have made significant contributions, from drug discovery science genomics chemistry. We provide a comprehensive existing real-world examples different levels automation, challenges limitations associated each domain.

Язык: Английский

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Closed-Loop Navigation of a Kinetic Zone Diagram for Redox-Mediated Electrocatalysis Using Bayesian Optimization, a Digital Twin, and Automated Electrochemistry

Analytical Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Март 7, 2025

Molecular electrocatalysis campaigns often require tuning multiple experimental parameters to obtain kinetically insightful electrochemical measurements, a prohibitively time-consuming task when performing comprehensive studies across catalysts and substrates. In this work, we present an autonomous workflow that combines Bayesian optimization automated electrochemistry perform fully unsupervised cyclic voltammetry (CV) of molecular electrocatalysis. We developed CV descriptors leveraged the conceptual framework EC' (where denotes step followed by catalytic chemical step) kinetic zone diagram enable efficient optimization. The descriptor's effect on performance was evaluated using digital twin our platform, quantifying accuracy obtained values against known ground truth. demonstrated platform experimentally TEMPO-catalyzed ethanol isopropanol electro-oxidation, demonstrating rapid identification conditions in 10 or less iterations through closed-loop workflow. Overall, work highlights application platforms accelerate mechanistic beyond.

Язык: Английский

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An automated electrochemistry platform for studying pH-dependent molecular electrocatalysis

Digital Discovery, Год журнала: 2024, Номер 3(9), С. 1812 - 1821

Опубликована: Янв. 1, 2024

An automated electrochemistry platform designed for molecular electrocatalysis studies.

Язык: Английский

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Multiple Operando Fields Can Identify a Predictive Mass Transport Theory in Electrolytes

The Journal of Physical Chemistry C, Год журнала: 2025, Номер 129(6), С. 2874 - 2882

Опубликована: Янв. 31, 2025

An electrolyte transport theory connects its properties, evolution of spatiotemporal fields (e.g., concentration), and corresponding macroscopic current voltage responses. Given this interconnection, the properties are typically inferred by analyzing response through lens a chosen theory. Unfortunately, same measurements can be analyzed using different theories to arrive at seemingly dissimilar that inconsistent with each other. We offer resolution dilemma multiple (i.e., operando) for given electrolyte. show predictive analyze operando estimate underlying subsequently predict another field. A passing test identifies meaningful such behavior accurately predicted over wide range excitations, is critical property-based screening discovery efforts.

Язык: Английский

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The Emergence of Automation in Electrochemistry

Current Opinion in Electrochemistry, Год журнала: 2025, Номер unknown, С. 101679 - 101679

Опубликована: Март 1, 2025

Язык: Английский

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A high-throughput experimentation platform for data-driven discovery in electrochemistry

Science Advances, Год журнала: 2025, Номер 11(14)

Опубликована: Апрель 4, 2025

Automating electrochemical analyses combined with artificial intelligence is poised to accelerate discoveries in renewable energy sciences and technologies. This study presents an automated high-throughput characterization (AHTech) platform as a cost-effective versatile tool for rapidly assessing liquid analytes. The Python-controlled combines handling robot, potentiostat, customizable microelectrode bundles diverse, reproducible measurements microtiter plates, minimizing chemical consumption manual effort. To showcase the capability of AHTech, we screened library 180 small molecules electrolyte additives aqueous zinc metal batteries, generating data training machine learning models predict Coulombic efficiencies. Key molecular features governing additive performance were elucidated using Shapley Additive exPlanations Spearman’s correlation, pinpointing high-performance candidates like cis -4-hydroxy- d -proline, which achieved average efficiency 99.52% over 200 cycles. workflow established herein highly adaptable, offering powerful framework accelerating exploration optimization extensive spaces across diverse storage conversion fields.

Язык: Английский

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PANDA: a self-driving lab for studying electrodeposited polymer films

Materials Horizons, Год журнала: 2024, Номер 11(21), С. 5331 - 5340

Опубликована: Янв. 1, 2024

We report the PANDA, a self-driving lab that handles fluids, electrodeposits polymers, and then functionally characterizes result using optics or electrochemistry. As an example application, we perform closed-loop study of electrochromic films.

Язык: Английский

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A Geometric Interpretation of Kinetic Zone Diagrams in Electrochemistry

Journal of the American Chemical Society, Год журнала: 2024, Номер unknown

Опубликована: Дек. 4, 2024

Electrochemical systems with increasing complexity are gaining importance in catalytic energy conversion applications. Due to the interplay between transport phenomena and chemical kinetics, predicting optimization is a challenge, numerous parameters controlling overall performance. Zone diagrams provide way identify specific kinetic regimes track how variations governing translate system either adverse or optimal states. However, current procedures for constructing zone restricted simplified minimal number of parameters. We present computationally based method that maps entire parameter space multidimensional electrochemical automatically identifies regimes. Once output over discrete set interpreted as geometric surface, its geometry encodes all information needed construct diagram. boundaries limiting zones defined by curved flat regions, respectively. This framework enables systematic exploration space, which not readily accessible analytical direct numerical methods. will become increasingly valuable rational design intrinsically high complexity.

Язык: Английский

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An Automated Electrochemistry Platform for Studying pH-dependent Molecular Electrocatalysis

Опубликована: Июнь 25, 2024

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 electrochemistry uses opensource software to modularly interconnect various commercial instruments, enabling users chain together multiple instruments complex operations. benchmarked the solution handling performance our through gravimetric calibration, acid-base titrations, and voltammetric diffusion coefficient measurements. then used explore TEMPO-catalyzed electrooxidation alcohols, demonstrating platforms capabilities pH-dependent electrocatalysis. performed combined titrations cyclic voltammetry on six different alcohol substrates, collecting 684 voltammograms with 171 conditions over course 16 hours, high throughput in unsupervised experiment. The versatility, transferability, ease implementation promises rapid discovery characterization processes, including mediated energy conversion, fuel valorization, bioelectrochemical sensing, among many applications.

Язык: Английский

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Redox-Detecting Deep Learning for Mechanism Discernment in Cyclic Voltammograms of Multiple Redox Events

ACS electrochemistry., Год журнала: 2024, Номер 1(1), С. 52 - 62

Опубликована: Окт. 3, 2024

In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification mechanisms in cyclic voltammetry set foundation for subsequent quantitative evaluation practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists assignment. Herein, we present custom DL architecture dubbed as EchemNet, capable assigning both voltage windows classes events within voltammograms multiple redox events. developed technique detects over 96% all simulated test data shows accuracy up 97.2% with 8 known mechanisms. This newly model, first its kind, proves feasibility redox-event minimal priori knowledge. model will augment human researchers' productivity constitute critical component general-purpose autonomous electrochemistry laboratory.

Язык: Английский

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