Autonomous Electrochemical Platform for Mechanistic Investigation

By Dr Silpaja Chandrasekar, PhD

In a paper published in the journal Nature Communications, researchers described an autonomous electrochemical platform that implemented an adaptive, closed-loop workflow for investigating molecular electrochemistry mechanistically.

Study: Autonomous Electrochemical Platform for Mechanistic Investigation . Image Credit: Sontaya Bongprom/Shutterstock

As a proof-of-concept, the platform autonomously identified and explored an electrochemical (EC) mechanism involving interfacial electron transfer (E step) followed by a solution reaction (C step) for cobalt tetraphenylporphyrin when exposed to a library of organohalide electrophiles.

The workflow, empowered by artificial intelligence (AI), accurately discerned the presence of the EC mechanism amid negative controls and outliers, adaptively designed desired experimental conditions, and quantitatively extracted kinetic information of the C step spanning over seven orders of magnitude. It facilitated gaining mechanistic insights into oxidative addition pathways. The work demonstrated opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories, eliminating the need for manual intervention.

Related Work

In recent years, molecular and organic electrochemistry has advanced, necessitating the exploration of numerous experimental parameters. However, manual exploration of this complexity poses challenges in elucidating reaction mechanisms efficiently.

Integrating automated electroanalytical tools with closed-loop decision-making has been proposed to address this. This approach, empowered by deep learning models, enables autonomous mechanistic investigation from cyclic voltammetry data. Many studies introduced a deep learning model capable of probabilistically classifying electrochemical mechanisms, paving the way for autonomous closed-loop processes.

Building upon this foundation, researchers developed an autonomous electrochemical platform that successfully identified an interfacial electron transfer mechanism, showcasing the potential for autonomous mechanistic discoveries in electrochemistry.

Experiment Setup & Platform

Researchers utilized cobalt (II) tetraphenylporphyrin (CoIITPP), recrystallized before experimentation, along with tetrabutylammonium hexafluorophosphate (NBu4PF6) and dimethylformamide (DMF), stored within a glove box. Various organic electrophiles were studied, acquired from different suppliers, and handled according to specific protocols for solid and liquid chemicals.

Electrochemical experiments were conducted in a glovebox using a three-electrode cell setup with a glassy carbon working electrode, a non-aqueous Ag/Ag+ reference electrode, and a platinum wire counter electrode. Cyclic voltammetry measurements were performed with automatic iR compensation for three cycles, with specific scan ranges and conditions tailored for the system under study.

Researchers constructed the autonomous electrochemical platform and deployed and designed it to mechanistically investigate an EC mechanism between cobalt tetraphenylporphyrin (CoTPP) and organohalide substrates. The supplementary information provides detailed specifications, operating procedures, and rationale behind the closed-loop workflow, divided into two stages for mechanism discernment and electrokinetic analysis. It also contains additional information about the platform, including hardware and software specifications, the rationale behind the design choices for autonomous investigation, and standard operating procedures for conducting experiments and analysis.

Autonomous Electrochemical Platform Study

In this study, researchers developed an autonomous electrochemical platform for investigating molecular mechanisms and optimizing experimental conditions. The platform comprised five key modules: flow chemistry for automated electrolyte formulation and disposal, automated electrochemical testing with deep learning-based analysis, and adaptive exploration of large parameter space using Bayesian optimization.

Researchers constructed an autonomous electrochemical platform, housing it within a glovebox for compatibility with sensitive chemistry. This platform consisted of five key modules: flow chemistry for automated electrolyte formulation and disposal, automated electrochemical testing with automatic iR compensation, DL-based automated CV analysis, adaptive parameter space exploration using Bayesian optimization, and a conventional three-electrode cell setup.

The authors deployed the platform for a proof-of-concept study, investigating the oxidative addition of RX electrophiles to electrogenerated low-valent metal complexes. They selected this step because it is pivotal in numerous metal-catalyzed transformations, demonstrating the platform’s capabilities.

Through autonomous closed-loop decision-making, the platform successfully probed the parameter space to discern the presence of an EC mechanism and identify suitable experimental conditions for kinetic analysis. Researchers underscored the scarcity of suitable parameter combinations for electrokinetic analysis, emphasizing the necessity for an adaptive approach to expedite mechanistic studies.

Autonomous studies involving various organohalide substrates further demonstrated the platform’s adaptability. Measured kinetic rate constants (k0) for various substrates showed sensitivity to halogen leaving group, alkyl chain length, and electron-withdrawing functional groups. Additionally, the platform facilitated a Hammett study of para-substituted primary benzyl bromide derivatives, shedding light on the reaction mechanism and providing valuable insights into organic electrosynthesis.

Notably, the platform’s sensitivity allowed mechanistic outliers, such as 4-chlorobutyronitrile, to be detected, which exhibited unexpected behavior. The platform’s ability to discern alternative mechanisms was validated through manual confirmation experiments, highlighting its robustness and reliability in identifying subtle differences in reaction pathways.

Overall, this study presents a pioneering autonomous platform for fundamental electrochemistry, offering a versatile tool for mechanistic discovery and investigation. Researchers expect future advancements in machine learning models and robotic handling to enhance the platform’s capabilities further, paving the way for general-purpose self-driving electrochemistry laboratories.

Conclusion

To summarize, developing an autonomous electrochemical platform represented a significant advancement in the field, offering a versatile tool for fundamental electrochemistry research. By integrating various modules and autonomous decision-making capabilities, the platform enabled efficient investigation of molecular mechanisms and optimization of experimental conditions.

The successful deployment of the platform in a proof-of-concept study highlighted its effectiveness in probing complex reaction mechanisms and identifying subtle differences in reaction pathways. Moreover, the platform’s adaptability to different substrates underscored its potential for broad organic synthesis and mechanistic studies applications. Researchers anticipated that further advancements in machine learning and robotic handling would enhance the platform’s capabilities, thereby paving the way for developing general-purpose self-driving electrochemistry laboratories.

Journal reference: