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相关概念视频

Ionic Strength: Effects on Chemical Equilibria01:19

Ionic Strength: Effects on Chemical Equilibria

1.5K
The addition of an inert ionic compound increases the solubility of a sparingly soluble salt. For example, adding potassium nitrate to a saturated solution of calcium sulfate significantly enhances the solubility of calcium sulfate. Le Châtelier's principle cannot predict this shift in the equilibrium. Instead, this could be explained in terms of changes in the effective concentration of the ions in solution in the presence of added inert salt.
In this solution, the primary...
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Ionic Strength: Overview01:12

Ionic Strength: Overview

1.4K
The ionic strength of a solution is a quantitative way of expressing the total electrolyte concentration of a solution. This concept was first introduced in 1921 by two American physical chemists, Gilbert N. Lewis and Merle Randall, while describing the activity coefficient of strong electrolytes. During the calculation of ionic strength (I or μ), all the cations and anions are considered. However, the concentration (c) of an ion with a greater charge number (z) has a greater contribution...
1.4K

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相关实验视频

Updated: Jul 11, 2025

Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques
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彻底改变固态纳西康电池:通过多变量实验参数进行增强的离子导电率估计,利用机器学习.

Yuyao Zhang1,2,3, Tingjie Zhan4, Yang Sun1,2

  • 1Department of Environmental Science, Zhejiang University, Hangzhou, Zhejiang, 310058, China.

ChemSusChem
|November 7, 2023
PubMed
概括
此摘要是机器生成的。

机器学习准确地预测固态电解质的超离子导体 (NASICON) 材料中的离子导电性. 神经网络确定了含量和合成条件是提高电池性能的关键因素.

关键词:
离子导电性 离子导电性机器学习 机器学习这里是纳西康尼.离子电池是一种离子电池.固态电解质是一种固态电解质.

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Extending the Lifespan of Soluble Lead Flow Batteries with a Sodium Acetate Additive
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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
  • 计算化学的计算化学

背景情况:

  • 超离子导体 (NASICON) 材料是离子电池的有希望的固态电解质,因为它们的稳定性.
  • 目前NASICON材料的有限离子导电性限制了它们的实际应用.
  • 了解影响导电性的因素的复杂相互作用对于材料设计至关重要.

研究的目的:

  • 探索机器学习的应用,用于预测纳西康材料中的离子导电性.
  • 确定控制离子导电性的关键材料描述符和合成参数.
  • 加速纳西康电解质的发现和优化,以提高离子电池的性能.

主要方法:

  • 编制了一个包含211个数据集的综合数据库,涵盖160个NASICON材料.
  • 使用简单的描述符,包括合成参数,结构属性和电子属性.
  • 开发和优化机器学习模型,特别是随机森林 (RF) 和神经网络 (NN),用于导电性预测.

主要成果:

  • 神经网络 (NN) 模型显示出优异的预测性能 (R2=0.820),特别是在有限的数据下.
  • 识别了Na的静电计数作为影响离子导电性的关键因素.
  • 突出了合成参数和结构因素的显著影响,而兴奋剂的电负性影响较小.

结论:

  • 机器学习提供了一种强大的方法来预测和理解纳西康材料中的离子导电性.
  • 这项研究为导电性的驱动因素提供了关键的见解,指导了先进的固态电解质的合理设计.
  • 这些发现为更有效地优化NASICON材料用于下一代离子电池铺平了道路.