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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...

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Related Experiment Video

Updated: Jul 15, 2026

Ion Mobility-Mass Spectrometry Techniques for Determining the Structure and Mechanisms of Metal Ion Recognition and Redox Activity of Metal Binding Oligopeptides
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Homologous-Constrained Machine Learning Enables Amino Acid Additive Screening for Water-Structure-Regulated Aqueous

Shuchang Wei1, Zinan Wang1, Peng Wang1,2

  • 1School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding071000, China.

ACS Applied Materials & Interfaces
|July 13, 2026
PubMed
Summary

Machine learning identified amino acid additives to improve aqueous zinc-ion batteries (AZIBs). These additives prevent hydrogen evolution and zinc dendrites, enabling over 4900 hours of stable battery operation.

Keywords:
Zn anode interfaceelectrolyte additiveshomologous-constraint strategymachine learning screeningwater-structure regulation

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Published on: January 26, 2024

Area of Science:

  • Electrochemistry
  • Materials Science
  • Computational Chemistry

Background:

  • Aqueous zinc-ion batteries (AZIBs) offer safe and cost-effective grid-scale energy storage.
  • Challenges include hydrogen evolution and zinc dendrite formation, hindering practical application.

Purpose of the Study:

  • To develop a machine learning-assisted strategy for designing electrolyte additives for AZIBs.
  • To identify additives that suppress water-induced side reactions and promote uniform zinc deposition.

Main Methods:

  • Employed a machine learning paradigm with a homologous-constraint strategy.
  • Utilized linear-kernel support vector regression and leave-one-out cross-validation to screen additives.
  • Investigated the effect of dl-glutamic acid and dl-histidine on electrolyte properties and zinc deposition.

Main Results:

  • Identified dl-glutamic acid and dl-histidine as effective water-structure-regulating additives.
  • Additives suppressed hydrogen evolution and reduced Zn2+ desolvation energy.
  • Achieved uniform 2D zinc nucleation and deposition, preventing dendrites.
  • Demonstrated stable Zn||Zn symmetric cell operation for over 4900 hours.

Conclusions:

  • Established a data-driven strategy for designing AZIB electrolyte additives.
  • Provided a framework for molecular engineering in complex battery systems.
  • Highlighted the potential of amino acid derivatives for enhancing AZIB performance and safety.