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

Oxidation of Alkenes: Syn Dihydroxylation with Osmium Tetraoxide02:44

Oxidation of Alkenes: Syn Dihydroxylation with Osmium Tetraoxide

11.1K
Alkenes are converted to 1,2-diols or glycols through a process called dihydroxylation. It involves the addition of two hydroxyl groups across the double bond with two different stereochemical approaches, namely anti and syn. Dihydroxylation using osmium tetroxide progresses with syn stereochemistry.
11.1K

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

Dual-targeting LILRB3/LILRB4 CAR-T cells for the treatment of monocytic acute myeloid leukemia.

Biochemical pharmacology·2026
Same author

TOPS-CRISPR: Thermally-regulated and oligonucleotide-mediated one-pot CRISPR-Cas12a assay for ultra-sensitive and rapid on-site diagnostics.

Biosensors & bioelectronics·2026
Same author

Guiding the De Novo Synthesis of Open Metal Frameworks Through Low-Symmetry Bent Linker for Hydrogen Isotope Separation.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Nitrogen-Tuned Cationic Au Sites on TiO<sub>2</sub> Enable Selective Methane Oxidation to Methanol.

Journal of the American Chemical Society·2026
Same author

Genomic Analysis of Ocular Pseudomonas aeruginosa Isolates: Insights From a Predominantly Asian, Multi-Regional Dataset.

Investigative ophthalmology & visual science·2026
Same author

Piperidine-functionalized formononetin derivatives: Design, synthesis and evaluation of antibacterial activity, modes and phenotypes.

Pest management science·2026

相关实验视频

Updated: May 5, 2026

Experimental Strategies to Bridge Large Tissue Gaps in the Injured Spinal Cord after Acute and Chronic Lesion
09:14

Experimental Strategies to Bridge Large Tissue Gaps in the Injured Spinal Cord after Acute and Chronic Lesion

Published on: April 5, 2016

8.3K

人工智能预测OSDAs能够直接合成层间扩展的热石.

Jilong Wang1, Yaqi Fan2,3, Zheng Wan1

  • 1Shanghai Key Laboratory of Green Chemistry and Chemical Processes, State Key Laboratory of Petroleum Molecular & Process Engineering, School of Chemistry and Molecular Engineering, East China Normal University, North Zhongshan Rd. 3663, Shanghai 200062, China.

Journal of the American Chemical Society
|March 4, 2026
PubMed
概括

研究人员开发了一种机器学习模型,用于预测有机结构指导剂 (OSDAs) 对于酸盐合成. 这种方法成功地发现了三种新型热带,克服了传统查方法的局限性.

更多相关视频

Synthesis of Zeolites Using the ADOR Assembly-Disassembly-Organization-Reassembly Route
08:26

Synthesis of Zeolites Using the ADOR Assembly-Disassembly-Organization-Reassembly Route

Published on: April 3, 2016

15.3K
Organic Structure-directing Agent-free Synthesis for *BEA-type Zeolite Membrane
08:49

Organic Structure-directing Agent-free Synthesis for *BEA-type Zeolite Membrane

Published on: February 22, 2020

13.5K

相关实验视频

Last Updated: May 5, 2026

Experimental Strategies to Bridge Large Tissue Gaps in the Injured Spinal Cord after Acute and Chronic Lesion
09:14

Experimental Strategies to Bridge Large Tissue Gaps in the Injured Spinal Cord after Acute and Chronic Lesion

Published on: April 5, 2016

8.3K
Synthesis of Zeolites Using the ADOR Assembly-Disassembly-Organization-Reassembly Route
08:26

Synthesis of Zeolites Using the ADOR Assembly-Disassembly-Organization-Reassembly Route

Published on: April 3, 2016

15.3K
Organic Structure-directing Agent-free Synthesis for *BEA-type Zeolite Membrane
08:49

Organic Structure-directing Agent-free Synthesis for *BEA-type Zeolite Membrane

Published on: February 22, 2020

13.5K

科学领域:

  • 材料科学 材料科学 材料科学
  • 化学 化学 化学
  • 晶体学 晶体学是指结晶学.

背景情况:

  • 石结晶是一个复杂的,变态稳定的过程.
  • 针对特定的热带石框架的定向合成是具有挑战性的,因为人们对其机制的理解不足.
  • 有机结构指导剂 (OSDA) 对于控制石框架形成至关重要,但它们的发现严重依赖于低效的试错选.

研究的目的:

  • 开发一种新的,基于领域知识的机器学习模型,用于预测OSDA.
  • 克服传统的基于描述器的机器学习模型在OSDA选新型热利特框架方面的局限性.
  • 为了使新的热带石材料的高效和有针对性的合成.

主要方法:

  • 开发一个基于域知识的机器学习模型 (ECNU-Zeoformer).
  • 集成一个端到端的架构与积极的学习策略.
  • 为了有效的OSDA选择,预测OSDA-地石结合能.

主要成果:

  • 成功合成了三种新型热石:ECNU-30,ECNU-34和ECNU-40.
  • 与传统方法相比,ECNU-Zeoformer模型表现出优异的预测性能.
  • 该模型在不同的热带石框架拓学中表现出极好的通用性.

结论:

  • 开发的机器学习模型有效地预测了OSDAs,使得发现新的热石成为可能.
  • 这种方法通过用精确的计算预测取代试错选,显著提升了石的定向合成.
  • 在ECNU-Zeoformer代表了在新型热带石框架的材料发现的突破.