Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Batteries and Fuel Cells03:12

Batteries and Fuel Cells

27.0K
A battery is a galvanic cell that is used as a source of electrical power for specific applications. Modern batteries exist in a multitude of forms to accommodate various applications, from tiny button batteries such as those that power wristwatches to the very large batteries used to supply backup energy to municipal power grids. Some batteries are designed for single-use applications and cannot be recharged (primary cells), while others are based on conveniently reversible cell reactions that...
27.0K
Electrogravimetric Analysis: Overview01:30

Electrogravimetric Analysis: Overview

206
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...
206
Voltammetry: Factors Affecting Measurements01:21

Voltammetry: Factors Affecting Measurements

133
A current produced due to the redox reactions of the analyte at the working and auxiliary electrodes is called a faradaic current. The reaction can be divided into two types. The current generated due to the reduction of the analyte is called cathodic current, and it carries a positive charge. In contrast, the current produced by analyte oxidation is known as an anodic current, and it has a negative charge. The applied potential at the working electrode determines the faradaic current flow, and...
133
Voltammetry: Stripping Methods01:13

Voltammetry: Stripping Methods

184
Anodic Stripping Voltammetry (ASV), Cathodic Stripping Voltammetry (CSV), and Adsorptive Stripping Voltammetry (AdSV) are electrochemical techniques used to determine trace amounts of analytes in solution. These methods involve applying a potential to an electrode and measuring the resulting current.
Anodic Stripping Voltammetry (ASV)
ASV is used to determine metals and metalloids at trace levels. It involves two steps: deposition and stripping. First, a negative potential is applied to the...
184

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Nitrile-Assisted Hybrid-Solvation Electrolyte Enables Wide-Temperature, High-Voltage, and Ultrafast-Charging Lithium-Metal Batteries.

Angewandte Chemie (International ed. in English)·2026
Same author

Finite-Time Intermittent Anti-Disturbance Control for Discrete-Time Switched Systems With Stochastic Gain Fluctuations: Partial Information Loss Case.

IEEE transactions on cybernetics·2026
Same author

Gradient fluorination strategy to screen asymmetric ether for high-voltage and low-temperature lithium metal batteries.

Science bulletin·2026
Same author

Structural Origin of Morphotropic Phase Boundary in Advanced Perovskite Ferroelectric Oxides.

Journal of the American Chemical Society·2026
Same author

Machine Learning-Guided Coordination Engineering of M-N-C Single-Atom Electrocatalysts for Superior Oxygen Reduction.

Journal of the American Chemical Society·2026
Same author

A Low-Concentration All-Phosphate Electrolyte for High-Voltage and High-Safety Lithium-Ion Batteries.

Advanced materials (Deerfield Beach, Fla.)·2025

相关实验视频

Updated: Jun 7, 2025

Non-aqueous Electrode Processing and Construction of Lithium-ion Coin Cells
12:28

Non-aqueous Electrode Processing and Construction of Lithium-ion Coin Cells

Published on: February 1, 2016

21.5K

电池材料的解决方案 机器学习中的数据问题:概述和未来展望

Pengcheng Xue1, Rui Qiu1, Chuchuan Peng2

  • 1School of Chemistry, Guangzhou Key Laboratory of Materials for Energy Conversion and Storage, South China Normal University, Guangzhou, 510006, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|November 18, 2024
PubMed
概括

机器学习 (ML) 通过解决数据挑战来增强电池开发. 策略改善数据质量,可靠地发现电池材料和预测性能.

关键词:
数据处理策略数据处理策略域名知识域名知识域名知识电池材料是电池的材料.机器学习是机器学习.

更多相关视频

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
11:25

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Published on: March 7, 2022

4.5K
Failure Analysis of Batteries Using Synchrotron-based Hard X-ray Microtomography
08:11

Failure Analysis of Batteries Using Synchrotron-based Hard X-ray Microtomography

Published on: August 26, 2015

8.8K

相关实验视频

Last Updated: Jun 7, 2025

Non-aqueous Electrode Processing and Construction of Lithium-ion Coin Cells
12:28

Non-aqueous Electrode Processing and Construction of Lithium-ion Coin Cells

Published on: February 1, 2016

21.5K
Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
11:25

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Published on: March 7, 2022

4.5K
Failure Analysis of Batteries Using Synchrotron-based Hard X-ray Microtomography
08:11

Failure Analysis of Batteries Using Synchrotron-based Hard X-ray Microtomography

Published on: August 26, 2015

8.8K

科学领域:

  • 材料科学 材料科学 材料科学
  • 电化学 电化学 电化学
  • 数据科学数据科学数据科学

背景情况:

  • 机器学习 (ML) 在电池研究中的应用正在出现.
  • 电池材料数据带来了挑战:多来源,异质,高维,小样本大小.
  • ML的准确性在很大程度上取决于数据质量.

研究的目的:

  • 系统地审查和提出处理电池材料数据的策略.
  • 提高电池ML应用中的数据质量,模型可靠性和可解释性.
  • 为其他科学领域的类似数据挑战提供参考.

主要方法:

  • 对ML数据处理技术的系统文献审查.
  • 拟议的策略包括:分类,提取,选,探索,缩小维度,生成,建模,评估和域知识整合.
  • 重点是数据库管理和数据分析方法.

主要成果:

  • 确定有效的数据处理策略,以克服电池研究中的数据挑战.
  • 方法旨在提高ML模型的准确性和可靠性.
  • 建议的策略不仅适用于电池,还适用于相关领域.

结论:

  • 解决数据质量问题对于在电池科学中推进ML至关重要.
  • 拟议的方法提供了一个可靠数据处理的框架.
  • 这些策略对具有复杂数据问题的领域具有广泛的适用性.