Research Topic

Material and Composition Screening Approaches in Electrocatalysis and Battery Research

About this Research Topic

On the way toward a sustainable energy economy, major challenges still need to be resolved. On the one hand, technologies beyond lithium-ion batteries are required in order to replace scarce lithium in electrolyte and electrode materials. On the other hand, sustainable catalysts for electrocatalytic reactions, such as oxygen or hydrogen evolution and reduction, need to be identified and developed. Material screening is a promising strategy to recognize potential electrode and electrolyte compositions.

The introduction of the computational hydrogen electrode (CHE) led to a renaissance in theoretical electrocatalysis. This approach facilitates conducting conventional Volcano analyses based on binding energies, thus establishing a simple and straightforward tool to screen electrocatalysts. Meanwhile, the Volcano concept has reached the field of battery science to scrutinize electrode and electrolyte compositions.

However, the simple Volcano analysis is not free of uncertainties and errors, which is mainly traced to the fact that the entire analysis relies on thermodynamic considerations only, whereas the kinetics as well as the effect of the applied overpotential on the energetics is omitted. Here, advanced approaches enclosing overpotential, kinetics, or machine-learning techniques in the analysis are called for, enabling material screening beyond a conventional Volcano scheme. The utilization of such extended frameworks may be beneficial to account for a thorough sorting of electrode materials and electrode compositions, before the most promising configurations are further optimized toward developing catalysts with an enhanced electrochemical performance.

This Research Topic is dedicated to Original Research articles, Perspectives, and Review articles that report improved material-screening techniques to identify electrode or electrolyte compositions with applications in electrocatalysis or batteries. In the recent literature combined experiment-theory approaches are emerging so that this Research Topic does not entirely address theoretical work, but combined experiment-theory approaches are also welcome. Themes of interest may include, but are not limited to, the following:
• Material-screening approaches to determine potential electrocatalysts
• Material-screening approaches to determine potential electrode materials for batteries
• Material-screening approaches to determine potential electrolyte compositions for electrocatalytic processes
• Material-screening approaches to determine potential electrolyte compositions for batteries
• Material-screening approaches including kinetics and applied overpotential into the analysis
• Material-screening approaches based on a combination of experiment and theory
• Material-screening approaches comprising machine-learning techniques


Keywords: material screening, electrocatalysis, solid-state batteries, electrolyte composition, machine learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

On the way toward a sustainable energy economy, major challenges still need to be resolved. On the one hand, technologies beyond lithium-ion batteries are required in order to replace scarce lithium in electrolyte and electrode materials. On the other hand, sustainable catalysts for electrocatalytic reactions, such as oxygen or hydrogen evolution and reduction, need to be identified and developed. Material screening is a promising strategy to recognize potential electrode and electrolyte compositions.

The introduction of the computational hydrogen electrode (CHE) led to a renaissance in theoretical electrocatalysis. This approach facilitates conducting conventional Volcano analyses based on binding energies, thus establishing a simple and straightforward tool to screen electrocatalysts. Meanwhile, the Volcano concept has reached the field of battery science to scrutinize electrode and electrolyte compositions.

However, the simple Volcano analysis is not free of uncertainties and errors, which is mainly traced to the fact that the entire analysis relies on thermodynamic considerations only, whereas the kinetics as well as the effect of the applied overpotential on the energetics is omitted. Here, advanced approaches enclosing overpotential, kinetics, or machine-learning techniques in the analysis are called for, enabling material screening beyond a conventional Volcano scheme. The utilization of such extended frameworks may be beneficial to account for a thorough sorting of electrode materials and electrode compositions, before the most promising configurations are further optimized toward developing catalysts with an enhanced electrochemical performance.

This Research Topic is dedicated to Original Research articles, Perspectives, and Review articles that report improved material-screening techniques to identify electrode or electrolyte compositions with applications in electrocatalysis or batteries. In the recent literature combined experiment-theory approaches are emerging so that this Research Topic does not entirely address theoretical work, but combined experiment-theory approaches are also welcome. Themes of interest may include, but are not limited to, the following:
• Material-screening approaches to determine potential electrocatalysts
• Material-screening approaches to determine potential electrode materials for batteries
• Material-screening approaches to determine potential electrolyte compositions for electrocatalytic processes
• Material-screening approaches to determine potential electrolyte compositions for batteries
• Material-screening approaches including kinetics and applied overpotential into the analysis
• Material-screening approaches based on a combination of experiment and theory
• Material-screening approaches comprising machine-learning techniques


Keywords: material screening, electrocatalysis, solid-state batteries, electrolyte composition, machine learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

About Frontiers Research Topics

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

Topic Editors

Loading..

Submission Deadlines

14 July 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..

Topic Editors

Loading..

Submission Deadlines

14 July 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

Loading..
Loading..

total views article views article downloads topic views

}
 
Top countries
Top referring sites
Loading..