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Electrodeposition01:08

Electrodeposition

972
Electrodeposition is a technique used to separate an analyte from interferents by electrochemical processes. Here, the analyte is a metal ion that can be deposited on an electrode immersed in the sample solution. The electrochemical setup consists of an anode and a cathode. When an electric current is applied to the setup, oxidation occurs at the anode. At the cathode, which consists of a large metal surface, metal ions undergo reduction and deposit onto the surface.
Electrodeposition can...
972

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Simple Methods for the Preparation of Non-noble Metal Bulk-electrodes for Electrocatalytic Applications
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Complex-Solid-Solution Electrocatalyst Discovery by Computational Prediction and High-Throughput Experimentation*.

Thomas A A Batchelor1, Tobias Löffler2, Bin Xiao3

  • 1Theoretical Catalysis-Center for High Entropy Alloy Catalysis (CHEAC), Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100, Copenhagen, Kbh, Denmark.

Angewandte Chemie (International Ed. in English)
|December 29, 2020
PubMed
Summary
This summary is machine-generated.

Complex solid solutions, or high entropy alloys, offer novel electrocatalyst designs. A data-driven approach successfully navigates their complexity, predicting optimal materials for reactions like oxygen reduction.

Keywords:
density functional calculationselectrochemistryhigh-entropy alloyshigh-throughput screeningthin films

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Chemistry

Background:

  • Complex solid solutions, or high entropy alloys, feature five or more principal elements.
  • These alloys present a vast number of unique active sites, enabling tunable electronic and geometric effects for catalysis.
  • Traditional catalyst discovery struggles with the complexity of these multi-element systems.

Purpose of the Study:

  • To demonstrate a data-driven cycle for mastering the complexity of high entropy alloy electrocatalysts.
  • To identify optimal complex solid solution materials for electrocatalytic reactions.
  • To showcase the application of this method for the oxygen reduction reaction.

Main Methods:

  • Utilizing a data-driven discovery cycle combining computational modeling and experimental synthesis.
  • Iteratively refining computational models to predict activity trends in complex solid solutions.
  • Synthesizing continuous composition-spread thin-film libraries based on model predictions.
  • Employing high-throughput characterization to gather data for model refinement.

Main Results:

  • Successfully navigated the multidimensionality challenge of high entropy alloy catalyst design.
  • The refined computational model accurately predicted activity trends.
  • Identified optimal composition maxima for the Ag-Ir-Pd-Pt-Ru system in the oxygen reduction reaction.
  • Demonstrated an unprecedented ability to identify optimal complex solid-solution materials.

Conclusions:

  • A data-driven approach is effective for discovering high entropy alloy electrocatalysts.
  • This method allows for the targeted design of materials with enhanced catalytic activity.
  • The approach holds significant promise for accelerating the discovery of novel electrocatalytic materials.