Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regioselectivity and Stereochemistry of Hydroboration02:36

Regioselectivity and Stereochemistry of Hydroboration

8.0K
A significant aspect of hydroboration–oxidation is the regio- and stereochemical outcome of the reaction.
Hydroboration proceeds in a concerted fashion with the attack of borane on the π bond, giving a cyclic four-centered transition state. The –BH2 group is bonded to the less substituted carbon and –H to the more substituted carbon. The concerted nature requires the simultaneous addition of –H and –BH2 across the same face of the alkene giving syn...
8.0K
ortho–para-Directing Activators: –CH3, –OH, –⁠NH2, –OCH301:11

ortho–para-Directing Activators: –CH3, –OH, –⁠NH2, –OCH3

5.7K
All ortho–para directors, excluding halogens, are activating groups. These groups donate electrons to the ring, making the ring carbons electron-rich. Consequently, the reactivity of the aromatic ring towards electrophilic substitution increases. For instance, the nitration of anisole is about 10,000 times faster than the nitration of benzene. The electron-donating effect of the methoxy group in anisole activates the ortho and para positions on the ring and stabilizes the...
5.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

USP50-mediated NLRP3 deubiquitination enhances NLRP3 inflammasome activation to suppress HCC metastasis.

Journal of pharmaceutical analysis·2025
Same author

MeP Induces Metabolic Disorder and Liver Damage at Human-Relevant Exposure Levels: Role of the Gut Microbiota.

Environment & health (Washington, D.C.)·2025
Same author

From Data to Physics: An Agentic Large Language Model Solves a Competitive Adsorption Puzzle.

Angewandte Chemie (International ed. in English)·2025
Same author

Time-resolved Solvothermal Synthesis for Controlling Lateral Size of 2D Metal-Organic Layers.

Small methods·2025
Same author

Yangzheng mixture reversed EMT against hepatocellular carcinoma metastasis via NF-κB/NLRP3/β-catenin pathway.

Toxicon : official journal of the International Society on Toxinology·2024
Same author

Corrigendum to "Novel Diphenyl urea derivative serves as an inhibitor on human lung cancer cell migration by disrupting EMT via Wnt/β-catenin and PI3K/Akt signaling" [Toxicology in Vitro 69 (2020) 105000].

Toxicology in vitro : an international journal published in association with BIBRA·2024

Related Experiment Video

Updated: May 15, 2025

Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs
08:25

Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs

Published on: January 17, 2020

7.2K

Machine Learning Reveals In-Cavity Versus Surface Activity for Selective C─H Borylation by Metal-Organic Framework

Zhaomin Su1, Bingling Dai1, Xue Wang1

  • 1iChem, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, 422 South Siming Rd., Siming District, Xiamen, Fujian, 361005, P.R. China.

Angewandte Chemie (International Ed. in English)
|May 7, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning identified key factors in metal-organic framework (MOF) catalysts for selective C-H borylation. This enables rational design of MOF-supported nickel catalysts with high sp3 and sp2 selectivity.

Keywords:
Dimension reductionFactor analysisInterpretable machine learningMetal‐organic frameworksSelective C─H borylation

More Related Videos

HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin
11:15

HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin

Published on: July 23, 2016

10.1K
Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

48.0K

Related Experiment Videos

Last Updated: May 15, 2025

Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs
08:25

Development of Heterogeneous Enantioselective Catalysts using Chiral Metal-Organic Frameworks MOFs

Published on: January 17, 2020

7.2K
HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin
11:15

HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin

Published on: July 23, 2016

10.1K
Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

48.0K

Area of Science:

  • Catalysis
  • Materials Science
  • Computational Chemistry

Background:

  • Metal-organic frameworks (MOFs) are versatile platforms for heterogeneous catalysis.
  • Distinguishing between pore-confined and surface catalysis in MOFs is challenging.
  • Understanding structure-activity relationships is crucial for designing efficient MOF catalysts.

Purpose of the Study:

  • To elucidate structure-activity relationships in MOF-supported nickel (Ni) catalysts for selective C-H borylation.
  • To identify critical factors governing sp3 versus sp2 C-H borylation selectivity.
  • To develop a systematic framework for rational MOF catalyst design.

Main Methods:

  • Interpretable machine learning applied to over 470,000 MOF structures.
  • Development of 45 chemically meaningful descriptors for MOF structures.
  • Analysis of distinct activation mechanisms (HAT vs. CMD) for sp3 and sp2 borylation.

Main Results:

  • Identified key structural descriptors influencing MOF catalyst selectivity.
  • Revealed distinct mechanisms for sp3 (radical HAT in pores) and sp2 (CMD on surfaces/defects) borylation.
  • Achieved high selectivity for sp3 (up to 97.8%) and sp2 (up to 88.7%) borylation using designed Ni catalysts.

Conclusions:

  • Established generalizable principles for controlling activity preference in MOF-supported catalysis.
  • Demonstrated the power of interpretable machine learning in catalyst design.
  • Provided a systematic framework for rational design of MOF catalysts for selective C-H functionalization.