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

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

Non-point source pollution prediction and dynamics simulation in urban runoff: a physics-informed neural network approach.

Water research·2026
Same author

Multi-omics analysis reveals propanol is superior electron donor for odd-chain elongation.

Bioresource technology·2026
Same author

Machine learning-integrated multi-objective application and optimization framework for sulfur-based reactive filler towards nutrient removal.

Water research·2026
Same author

Knowledge distillation enables prediction of ring-class polycyclic aromatic hydrocarbons concentration using underwater drone.

Journal of hazardous materials·2026
Same author

Interpretable causal machine learning elucidates performance regulation via atomic Fe‑S synergistic pathways in Sulfidated zero‑valent Iron.

Water research·2026
Same author

Global data-water symbiosis reduces AI infrastructure's carbon and water footprint.

Environmental science and ecotechnology·2026
Same journal

In-sewer microplastics drive microbial metabolic shifts toward enhanced methanogenesis.

Environmental science and ecotechnology·2026
Same journal

From sustainable development to enduring development: Renewing ecological, social, and human foundations.

Environmental science and ecotechnology·2026
Same journal

Cross-sector deep learning scales life cycle assessment using unified textual descriptions.

Environmental science and ecotechnology·2026
Same journal

Decade-scale contrasts in sediment metal(loid)s across Qinghai-Xizang Plateau lakes.

Environmental science and ecotechnology·2026
Same journal

Reply to: Beyond carbon sequestration: The critical oversight of emission avoidance in restoration of wetland ecosystems.

Environmental science and ecotechnology·2026
Same journal

A culturomics biobank decodes extremophile evolution and metabolism in acid mine drainage.

Environmental science and ecotechnology·2026
查看所有相关文章

相关实验视频

Updated: Jan 13, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

587

通过数据驱动的反向设计加速生物电解.

Zhiling Li1, Tianyi Huang1, Fan Chen2

  • 1State Key Laboratory of Urban-rural Water Resources and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, PR China.

Environmental science and ecotechnology
|October 29, 2025
PubMed
概括
此摘要是机器生成的。

机器学习加速微生物电气呼吸,以生物修复化有机污染物 (COP). 这种数据驱动的方法优化了环境清洁的条件,使环境清洁更快,更具成本效益,而不需要大量的实验室工作.

关键词:
数据驱动的方法学.机器学习是机器学习.微生物的电气呼吸.降低性去化是一种减少性去化.

更多相关视频

Characterizing Electron Transport through Living Biofilms
08:52

Characterizing Electron Transport through Living Biofilms

Published on: June 1, 2018

8.9K
Characterizing Mediated Extracellular Electron Transfer in Lactic Acid Bacteria with a Three-Electrode, Two-Chamber Bioelectrochemical System
10:23

Characterizing Mediated Extracellular Electron Transfer in Lactic Acid Bacteria with a Three-Electrode, Two-Chamber Bioelectrochemical System

Published on: August 23, 2024

1.7K

相关实验视频

Last Updated: Jan 13, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

587
Characterizing Electron Transport through Living Biofilms
08:52

Characterizing Electron Transport through Living Biofilms

Published on: June 1, 2018

8.9K
Characterizing Mediated Extracellular Electron Transfer in Lactic Acid Bacteria with a Three-Electrode, Two-Chamber Bioelectrochemical System
10:23

Characterizing Mediated Extracellular Electron Transfer in Lactic Acid Bacteria with a Three-Electrode, Two-Chamber Bioelectrochemical System

Published on: August 23, 2024

1.7K

科学领域:

  • 环境微生物学 环境微生物学
  • 生物修复是一种生物修复.
  • 机器学习应用 机器学习应用

背景情况:

  • 化有机污染物 (COP) 污染环境,对生态系统和人类健康构成风险.
  • 微生物电气呼吸为COP补救提供了一个可持续的解决方案,通过使用细菌来驱动减少性脱.
  • 水库条件带来了复杂的挑战,包括空间变化和缓慢的反应速度,阻碍了传统的修复工作.

研究的目的:

  • 开发一个机器学习框架,以快速优化生物电增强的降解脱.
  • 确定影响脱率的关键环境变量,微生物群落和电化学特性.
  • 为了实现反向设计,以确定有效的COP补救的最佳条件.

主要方法:

  • 综合实验设计与使用机器学习模型 (例如,极端梯度增强,随机森林,多层感知子) 的阴极生物膜数据分析.
  • 在文献衍生数据集上训练模型,以发现变量和脱动力学之间的相互关系.
  • 采用反向设计来预测特定COP脱的最佳参数.

主要成果:

  • 确定温度和阴极潜力作为实验设计的主要驱动因素.
  • 突出了关键的微生物属 (例如, *Clostridium*, *Dehalococcoides*, *Geobacter*) 参与脱.
  • 对四乙烯和三乙烯等代表性COP实现了反应速率预测,误差低于6%.

结论:

  • 机器学习框架显著加速了生物电解过程的优化.
  • 与传统方法相比,这种数据驱动的战略提高了效率,降低了成本,并加快了生物修复的速度.
  • 该方法促进了微生物电呼吸的可扩展应用,用于COP污染水的整治和更广泛的生物电化学应用.