Light-harvesting microelectronic devices for wireless electrosynthesis

Nature, Год журнала: 2025, Номер 637(8045), С. 354 - 361

Опубликована: Янв. 8, 2025

Язык: Английский

---

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Март 30, 2024

Abstract 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 electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous platform implements adaptive, closed-loop workflow mechanistic investigation molecular electrochemistry. As a proof-of-concept, this autonomously identifies investigates EC mechanism, interfacial electron transfer ( E step) followed by solution reaction C step), cobalt tetraphenylporphyrin exposed library organohalide electrophiles. The generally applicable accurately discerns mechanism’s presence amid negative controls outliers, adaptively designs desired conditions, quantitatively extracts kinetic information step spanning over 7 orders magnitude, from which insights into oxidative addition pathways gained. This work opens opportunities discoveries self-driving electrochemistry laboratories without manual intervention.

Язык: Английский

---

Interfacing High-Throughput Electrosynthesis and Mass Spectrometric Analysis of Azines

Analytical Chemistry, Год журнала: 2024, Номер 96(21), С. 8249 - 8253

Опубликована: Май 8, 2024

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

Язык: Английский

---

Nanomaterials Synthesis Discovery via Parallel Electrochemical Deposition

Chemistry of Materials, Год журнала: 2024, Номер 36(6), С. 3034 - 3041

Опубликована: Март 14, 2024

Electrodeposition of nanoparticles is investigated with a multichannel potentiostat in electrochemical and chemical arrays. De novo deposition shape control palladium are explored arrays two-stage strategy. Initial conditions for electrodeposition materials discovered first stage then used second to logically expand parameters. Shape analyzed primarily scanning electron microscopy. Using this approach, optimized the cubic were identified from set previously untested conditions. The parameters through array format successfully extrapolated traditional bulk three-electrode cell. Electrochemical also explore reported previous studies, further demonstrating correspondence between systems. These results broadly highlight opportunities arrays, both discovery investigations nanomaterials synthesis.

Язык: Английский

---

Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides

Journal of the American Chemical Society, Год журнала: 2025, Номер unknown

Опубликована: Май 22, 2025

When developing machine learning models for yield prediction, the two main challenges are effectively exploring condition space and substrate space. In this article, we disclose an approach mapping Ni/photoredox-catalyzed cross-electrophile coupling of alkyl bromides aryl in a high-throughput experimentation (HTE) context. This model employs active (in particular, uncertainty querying) as strategy to rapidly construct model. Given vastness space, focused on that builds initial then uses minimal data set expand into new chemical spaces. built virtual 22,240 compounds using less than 400 points. We demonstrated can be expanded 33,312 by adding information around 24 building blocks (<100 additional reactions). Comparing learning-based one constructed randomly selected showed was significantly better at predicting which reactions will successful. A combination density function theory (DFT) difference Morgan fingerprints employed random forest Feature importance analysis indicates key DFT features related reaction mechanism (e.g., radical LUMO energy) were crucial performance predictions outside training set. anticipate combining featurization uncertainty-based querying help synthetic organic community build predictive data-efficient manner other feature large diverse scopes.

Язык: Английский

---

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Опубликована: Окт. 16, 2023

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 electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous platform implements adaptive, closed-loop workflow mechanistic investigation molecular electrochemistry. As a proof-of-concept, this autonomously identifies investigates EC mechanism, interfacial electron transfer (E step) followed by solution reaction (C step), cobalt tetraphenylporphyrin exposed library organohalide electrophiles. The generally applicable accurately discerns mechanism’s presence amid negative controls outliers, adaptively designs desired conditions, quantitatively extracts kinetic information C step spanning over 7 orders magnitude, from which insights into oxidative addition pathways gained. This work opens opportunities discoveries self-driving electrochemistry laboratories without manual intervention.

Язык: Английский

---

A Well‐Advanced High‐Throughput Test System for Electrocatalytic Screening Applications Under Industrial Relevant Conditions – A Perspective to Accelerate Electrolysis Research and Development

Electrochemical Science Advances, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 3, 2024

ABSTRACT Electrolysis is a dynamic research area in which both mature and new promising processes, such as alkaline water electrolysis electrochemical CO 2 reduction, are under enormous development pressure due to their high relevance for the energy sector. High‐throughput (HT) technologies efficient screening platforms that can accelerate activities significantly shorten times. Over past 25 years, various HT have found way into research. These typically one or more major disadvantages: they characterized by abstract experimental conditions, designed specific application process, generate insufficiently comparable data. In this publication, we present newly developed test system enables parallel operation of 16 bench‐scale flow cells industry‐relevant conditions. The specially modular cell be operated variably fully automated allows most common applications electrochemistry many different processes with focus on all relevant variants reduction. Both generation reliably reproducible data comparability order strengthen scientific exchange. process control, online analysis programmable feedback loops provide great potential design experiment strategies. implementation Design Experiment strategies will maximize testing efficiency innovative system.

Язык: Английский

---

Electrochemical Organic Synthesis

Synthesis, Год журнала: 2023, Номер 55(18), С. 2797 - 2798

Опубликована: Авг. 29, 2023

Received: 03 August 2023 Accepted after revision: Article published online:29

Язык: Английский

---