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相关概念视频

ATP Energy Storage and Release01:31

ATP Energy Storage and Release

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ATP is a highly unstable molecule. Unless quickly used to perform work, ATP spontaneously dissociates into ADP and inorganic phosphate (Pi), and the free energy released during this process is lost as heat. The energy released by ATP hydrolysis is used to perform work inside the cell and depends on a strategy called energy coupling. Cells couple the exergonic reaction of ATP hydrolysis with endergonic reactions, allowing them to proceed.
One example of energy coupling using ATP involves a...
14.6K
Sugars as Energy Storage Molecules01:10

Sugars as Energy Storage Molecules

9.9K
Sugar (a simple carbohydrate) metabolism (chemical reactions) is a classic example of the many cellular processes that use and produce energy. Living things consume sugar as a major energy source because sugar molecules have considerable energy stored within their bonds. Consumed carbohydrates have their origins in photosynthesizing organisms like plants. During photosynthesis, plants use the energy of sunlight to convert carbon dioxide gas into sugar molecules, like glucose. Because this...
9.9K
Fats as Energy Storage Molecules01:06

Fats as Energy Storage Molecules

27.1K
Triglycerides are a form of long-term energy storage molecules. They are made of glycerol and three fatty acids. To obtain energy from fat, triglycerides must first be broken down by hydrolysis into their two principal components, fatty acids and glycerol. This process, called lipolysis, takes place in the cytoplasm. The resulting fatty acids are oxidized by β-oxidation into acetyl-CoA, which is used by the Krebs cycle. The glycerol that is released from triglycerides after lipolysis...
27.1K
Storage01:23

Storage

418
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
418
What is Energy?04:10

What is Energy?

59.4K
The universe is composed of matter in different forms, and all forms of matter contain energy.  The different forms of energy on Earth originate from the Sun — the ultimate energy source. Plants capture light energy from the Sun, and, via the process of photosynthesis, convert it into chemical energy. This stored energy from plants can be harnessed in many ways. For example, eating plant products as food provides energy for our body to function, and burning wood or coal (fossilized...
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Free Energy Changes for Nonstandard States03:25

Free Energy Changes for Nonstandard States

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The free energy change for a process taking place with reactants and products present under nonstandard conditions (pressures other than 1 bar; concentrations other than 1 M) is related to the standard free energy change according to this equation:
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相关实验视频

Updated: Feb 11, 2026

Fabrication and Design of Wood-Based High-Performance Composites
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Fabrication and Design of Wood-Based High-Performance Composites

Published on: November 9, 2019

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由机器学习实现的优越储能性能,为无抗铁电材料的加速组合设计提供了优越的储能性能.

Feng Li1, Liang Sun1, Zongyuan Zhang1

  • 1Information Materials and Intelligent Sensing Laboratory of Anhui Province, Leibniz International Joint Research Center of Materials Sciences of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, China.

Small (Weinheim an der Bergstrasse, Germany)
|February 10, 2026
PubMed
概括

机器学习加速了无AgNbO3基反铁电的发现,用于储能. 一个新的框架预测了具有超高能量储存密度和效率的组合,优于传统方法.

关键词:
抗铁电气制品 抗铁电气制品介电电容器的介电电容器储能密度 储能密度 储能密度没有的无材料.机器学习是机器学习.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 陶工程 陶工程 陶工程
  • 计算材料科学科学 计算材料科学

背景情况:

  • 基于AgNbO3 (AN) 的高性能无抗铁电 (AFEs) 对先进的储能电容器至关重要.
  • 为了优化储能密度 (Wrec) 和效率 (η),传统的试错方法是耗时且低效的,因为构成空间庞大.

研究的目的:

  • 开发一个数据驱动的方法来设计高性能AN-based AFEs.
  • 通过机器学习克服传统选方法的局限性.
  • 识别具有优越能量储存能力的新型组合物.

主要方法:

  • 实施一个双层堆叠机器学习 (ML) 框架,SS-PAN (用于预测基于AN的陶的堆叠策略).
  • 交叉验证以评估模型性能,达到0.82.2的高R2得分.
  • 沙普利添加式扩张 (SHAP) 分析以确定关键预测特征 (耐受性因子,B位电子亲和力).
  • 使用STEM和DFT计算来解码局部结构的实验验证.

主要成果:

  • 在预测基于AN的AFEs方面,SS-PAN框架的表现优于单个ML模型.
  • 一个预测的组成,Li0.01Ag0.99Nb0.5Ta0.5O3,显示出一个近乎线性的PE循环.
  • 在MLCC中,在108kV mm−1时实现了16.6 J cm−3的超高Wrec和92.6%的 η,功率为224.3 J cm−3.
  • SHAP分析强调了耐受性因子和B位电子亲和力对预测准确性的重要性.

结论:

  • 数据驱动的SS-PAN框架为发现新出现的放松器AFEs提供了一种强有力的方法.
  • /陶联合剂诱导极极的短距离反铁电纳米域,增强介电性质.
  • 这项工作为通过ML引导的材料设计为先进的介电电容器应用铺平了道路.