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相关概念视频

Electrodeposition01:08

Electrodeposition

633
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...
633
Standard Electrode Potentials03:02

Standard Electrode Potentials

43.8K
On comparing the reactivity of silver and lead, it is observed that the two ionic species, Ag+ (aq) and Pb2+ (aq), show a difference in their redox reactivity towards copper: the silver ion undergoes spontaneous reduction, while the lead ion does not. This relative redox activity can be easily quantified in electrochemical cells by a property called cell potential. This property is commonly known as cell voltage in electrochemistry, and it is a measure of the energy which accompanies the charge...
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Ladder Diagrams: Redox Equilibria01:30

Ladder Diagrams: Redox Equilibria

457
Ladder diagrams are useful tools for understanding redox equilibrium reactions, especially the effects of concentration changes on the electrochemical potential of the reaction. The vertical axis in the redox ladder diagrams represents the electrochemical potential, E. The area of predominance is demarcated using the Nernst equation.
Consider the Fe3+/Fe2+ half-reaction, which has a standard-state potential of +0.771 V. At potentials more positive than +0.771 V, Fe3+ predominates, whereas Fe2+...
457
Electrogravimetric Analysis: Overview01:30

Electrogravimetric Analysis: Overview

225
Electrogravimetric analysis measures the weight of an analyte deposited electrolytically onto a suitable working electrode. This method involves applying a potential to a pre-weighed electrode submerged in a solution, which results in the desired substance being deposited through reduction at the cathode or oxidation at the anode. The electrode's weight is recorded after deposition, and the difference in weight gives the analyte's weight in the solution.
To test the completeness of the...
225

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Updated: Jun 29, 2025

Precise Electrochemical Sizing of Individual Electro-Inactive Particles
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可解释的机器学习模型用于实用的反子电催化剂性能.

Shyam Deo1,2, Melissa E Kreider1,2, Gaurav Kamat1,2

  • 1Department of Chemical Engineering, Stanford University, Stanford, CA 94305, United States.

Chemphyschem : a European journal of chemical physics and physical chemistry
|March 28, 2024
PubMed
概括
此摘要是机器生成的。

简单的机器学习模型可以准确地预测氧减少反应的催化剂性能. 确定了关键的实验和结构因素,指导催化剂设计以提高效率.

关键词:
电催化剂是一种电催化剂.机器学习是机器学习.设计材料设计材料的设计.

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科学领域:

  • 催化剂是一种催化剂.
  • 材料科学 材料科学 材料科学
  • 计算化学计算化学

背景情况:

  • 在反应条件下预测催化剂性能是复杂的,因为现场表面演变和固体-液体接口.
  • 机器学习为预测催化行为提供了一个潜在的解决方案,即使数据有限.

研究的目的:

  • 开发机器学习模型,用于预测实验观察到的发病潜力.
  • 确定控制过渡金属氧化物的催化性能的关键描述因子,用于氧降解反应.

主要方法:

  • 利用密度函数理论 (DFT) 来获得大量的原子和电子结构描述符.
  • 采用人类可解释的基因编程模型,包括实验条件和DFT描述符.
  • 专注于非贵金属过渡金属氧化物纳米颗粒催化剂的氧化减氧反应.

主要成果:

  • 通过简单的机器学习模型,成功预测了实验观察到的发病潜力.
  • 确定了影响发病潜力的关键实验因素和额外的批量电子/原子描述器.
  • 通过实验证实,氧化中的兴奋剂增加了发病潜力,从而验证了预测.

结论:

  • 机器学习,即使是小数据集,也可以有效地模拟催化性能.
  • 宏观实验因素是预测催化剂行为的关键描述因素.
  • 已识别的描述符为调整催化剂提供了指导,以增强开始潜力.