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相关概念视频

Ladder Diagrams: Redox Equilibria01:30

Ladder Diagrams: Redox Equilibria

447
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+...
447
Redox Equilibria: Overview01:23

Redox Equilibria: Overview

558
A reduction-oxidation reaction is commonly called a redox reaction. In a redox reaction, electrons are transferred from one species to another rather than being shared between or among atoms. The reducing agent or reductant is the species that loses electrons and gets oxidized in the process. The species that gains electrons and gets reduced in the process is the oxidizing agent or oxidant. Redox reactions are represented as two separate equations called half-reactions, where one equation...
558
Redox Titration: Overview01:21

Redox Titration: Overview

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Redox titration is a chemical analysis technique used to determine the concentration of an unknown substance by measuring the electron transfer in a redox (reduction-oxidation) reaction. The process involves gradually adding a titrant with a known concentration of an oxidizing or reducing agent, to the analyte, the solution with an unknown concentration, until reaching the endpoint, which indicates the completion of the reaction between the two substances. Ensuring the analyte is in a single...
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Standard Electrode Potentials03:02

Standard Electrode Potentials

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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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Balancing Redox Equations02:58

Balancing Redox Equations

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Electrochemistry is the science involved in the interconversion of electrical and chemical reactions. Such reactions are called reduction-oxidation, or redox reactions. These important reactions are defined by changes in oxidation states for one or more reactant elements and include a subset of reactions involving the transfer of electrons between reactant species. Electrochemistry as a field has evolved to yield sufficient insights on the fundamental principles of redox chemistry and multiple...
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Electromotive Force02:36

Electromotive Force

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Electricity is generated by either electrons or ions flowing through a solution or a conducting medium. This flow of electrons or specifically electrical charge is defined as an electric current. When electrons move through a wire, they generate an electric current. It can be recalled  that in a redox reaction, electrons are lost and gained. In the spontaneous redox reaction of zinc  with copper, when zinc is immersed in a copper ion solution, a transfer of electrons from one...
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Updated: Jun 23, 2025

EPR Monitored Redox Titration of the Cofactors of Saccharomyces cerevisiae Nar1
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通过基于图形的机器学习方法预测氧化还原潜力.

Linlin Jia1, Éric Brémond2, Larissa Zaida2

  • 1The PRG Group, Institute of Computer Science, University of Bern, Bern, Switzerland.

Journal of computational chemistry
|June 26, 2024
PubMed
概括
此摘要是机器生成的。

机器学习,特别是基于图形的方法,加速了氧化和还原潜力的预测. 这项研究引入了ORedOx159数据库,并证明了在电化学系统设计中提高了准确性.

关键词:
ORedOx159 数据库的数据库.重氧化潜在预测预测密度函数理论密度函数理论基于图形的机器学习方法.

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科学领域:

  • 计算化学计算化学
  • 电化学 电化学 电化学
  • 机器学习 机器学习

背景情况:

  • 精确预测氧化和还原潜力在化学中至关重要.
  • 理论计算通常是资源密集型和耗时的.
  • 机器学习为高效的潜在预测提供了一个有希望的替代方案.

研究的目的:

  • 应用机器学习,专注于基于图形的方法,用于预测氧化和还原潜力.
  • 引入ORedOx159数据库来评估这些方法.
  • 为了证明计算电化学的提高准确性和效率.

主要方法:

  • 开发ORedOx159数据库,包括318种反应和159种有机化合物.
  • 基于图形的机器学习技术的审查 (图形编辑距离,内核,神经网络).
  • 使用快速计算描述器评估机器学习模型性能.

主要成果:

  • 机器学习模型实现了潜在的显著预测准确性.
  • 平均绝对误差 (MAE) 为降解电位的5.6 kcal/mol,氧化电位的7.2 kcal/mol.
  • 快速描述器计算显著改善了预测性能.

结论:

  • 机器学习,特别是基于图形的方法,为预测电化学潜力提供了有效的途径.
  • ORedOx159数据库是开发方法的宝贵资源.
  • 这项工作促进了新型电化学系统的in silico设计.