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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Decoding active sites in high-entropy catalysts via attention-enhanced model.

Liang Yin1,2,3, Tiantian Ma4, Zibo Zhu1,5

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Researchers developed a predictive model to find active sites in catalysts. This led to the discovery of TiFeNiZn-CoOOH, a high-performance catalyst for the oxygen evolution reaction (OER).

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Area of Science:

  • Materials Science and Engineering
  • Catalysis
  • Computational Chemistry

Background:

  • Identifying active sites is crucial for catalyst optimization, particularly in complex high-entropy materials.
  • Predicting catalytic activity and overpotential in these materials presents significant challenges due to numerous random sites.

Purpose of the Study:

  • To develop an advanced predictive model for accurately identifying active sites and their associated overpotentials.
  • To screen a large number of high-entropy catalysts for optimal oxygen evolution reaction (OER) performance.
  • To identify specific elemental compositions and coordination environments that enhance catalytic activity.

Main Methods:

  • Developed an attention-enhanced, multiobjective predictive model.
  • Applied the model to predict OER overpotentials and doping formation energies in high-entropy CoOOH materials.
  • Screened 17,500 potential catalysts, followed by automated synthesis and experimental validation.

Main Results:

  • Identified 8 catalysts with optimal catalytic activity from the screening.
  • Discovered TiFeNiZn-CoOOH, exhibiting an exceptional OER overpotential of 263 mV at 100 mA/cm².
  • Confirmed Zn's high active site occupation probability and the [CoNiZn] coordination's role in minimizing overpotential.

Conclusions:

  • The developed predictive model precisely identifies active sites and overpotentials in high-entropy catalysts.
  • Zn incorporation and specific coordination environments significantly enhance OER catalytic activity by activating gap states.
  • This approach provides a powerful framework for discovering high-performance catalysts with predictable structures.