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

Benzene to 1,4-Cyclohexadiene: Birch Reduction Mechanism01:18

Benzene to 1,4-Cyclohexadiene: Birch Reduction Mechanism

2.8K
Birch reduction uses solvated electrons as reducing agents. The reaction converts benzene to 1,4-cyclohexadiene. The reaction proceeds by the transfer of a single electron to the ring to form a benzene radical anion. This anion is highly basic—it abstracts a proton from the alcohol to form a cyclohexadienyl radical. Another single electron transfer gives the cyclohexadienyl anion. A proton transfer from the alcohol forms 1,4-cyclohexadiene. Since this reduction occurs via radical anion...
2.8K
Cycloaddition Reactions: MO Requirements for Photochemical Activation01:12

Cycloaddition Reactions: MO Requirements for Photochemical Activation

2.9K
Some cycloaddition reactions are activated by heat, while others are initiated by light. For example, a [2 + 2] cycloaddition between two ethylene molecules occurs only in the presence of light. It is photochemically allowed but thermally forbidden.
2.9K
Reduction of Benzene to Cyclohexane: Catalytic Hydrogenation01:28

Reduction of Benzene to Cyclohexane: Catalytic Hydrogenation

6.4K
Unlike the easy catalytic hydrogenation of an alkene double bond, hydrogenation of a benzene double bond under similar reaction conditions does not take place easily. For example, in the reduction of stilbene, the benzene ring remains unaffected while the alkene bond gets reduced. Hydrogenation of an alkene double bond is exothermic and a favorable process. In contrast, to hydrogenate the first unsaturated bond of benzene, an energy input is needed; that is, the process is endothermic. This is...
6.4K
Structure of Benzene: Molecular Orbital Model01:18

Structure of Benzene: Molecular Orbital Model

13.8K
According to the molecular orbital (MO) model, benzene has a planar structure with a regular hexagon of six sp2 hybridized carbons. As shown in Figure 1, each carbon is bonded to three other atoms with C–C–C and H–C–C bond angles of 120°. The C–H bond length is 109 pm, and the C–C bond length is 139 pm which is midway between the single bond length of sp3 hybridized carbons (154 pm) and sp2 hybridized carbons (133 pm).
13.8K
Benzene to Phenol via Cumene: Hock Process01:27

Benzene to Phenol via Cumene: Hock Process

4.5K
The synthesis of phenol from benzene via cumene and cumene hydroperoxide is called the Hock process. First, a Friedel–Crafts alkylation reaction of benzene with propene gives cumene. Then cumene forms cumene hydroperoxide via a radical chain reaction. In the chain initiation step, the benzylic hydrogen is abstracted to give a benzylic radical. In the chain propagation step, the benzylic radical reacts with an oxygen diradical to form a cumene hydroperoxide radical. The cumene...
4.5K
Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

4.3K
Ziegler–Natta polymerization is another form of addition or chain‐growth polymerization used for synthesizing linear polymers over branched polymers. The catalyst used for polymerization is the Ziegler–Natta catalyst, named after Karl Ziegler and Giulio Natta, who developed it in 1953. This catalyst is an organometallic complex of titanium tetrachloride and triethyl aluminum, with the active form of the catalyst being an alkyl titanium compound. Using the Ziegler–Natta...
4.3K

You might also read

Related Articles

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

Sort by
Same author

Quantitative Structure-Property Relationships for Fluorescence Quantum Yield of [6]Helicene Derivatives: Kinetic, Electronic, and Vibrational Descriptors Exploration.

The journal of physical chemistry letters·2026
Same author

A peripheral subpopulation of retinal pigment epithelium resists oxidative damage through SERPINE3-mediated Caspase-1 inhibition.

The Journal of clinical investigation·2026
Same author

Boosting Photocatalytic Overall Water Splitting Activity of Phosphorene Through Five-Coordinate Passivation Enabled by Carbene Addition.

Angewandte Chemie (International ed. in English)·2026
Same author

Endowing Metal Oxychloride Solid Electrolytes with Improved Li Compatibility.

Journal of the American Chemical Society·2026
Same author

Unveiling microbial communities and biogeochemical cycles in Antarctic colored snow.

BMC microbiology·2026
Same author

Synergy of Spin States, Active Centers, and H Adsorption Sites on <i>d</i>-<i>p</i> Hybridized Fe-Sn-N<sub>6</sub>-C Dual-Atom Catalysts for Enhanced Oxygen Reduction Reaction.

Journal of the American Chemical Society·2026

Related Experiment Video

Updated: Apr 2, 2026

Determination of the Photoisomerization Quantum Yield of a Hydrazone Photoswitch
09:33

Determination of the Photoisomerization Quantum Yield of a Hydrazone Photoswitch

Published on: February 7, 2022

4.0K

Unlocking azobenzene isomerization mechanisms via an LLM agent-driven workflow integrating simulation, experiment,

Yixi Shen1, Ledu Wang1, Yan Huang1

  • 1State Key Laboratory of Precision and Intelligent Chemistry, Hefei National Research Center for Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China Hefei 230026 China hyang@ustc.edu.cn jiangj1@ustc.edu.cn zyzhuq@ustc.edu.cn linjiangchen@ustc.edu.cn.

Chemical Science
|April 1, 2026
PubMed
Summary

Large language model (LLM) agents automated a study of photo-responsive azobenzene switches. This AI-driven approach successfully linked molecular dynamics, spectroscopy, and machine learning to reveal isomerization mechanisms.

More Related Videos

Light-driven Molecular Motors on Surfaces for Single Molecular Imaging
08:40

Light-driven Molecular Motors on Surfaces for Single Molecular Imaging

Published on: March 13, 2019

12.2K
Synthesis of 1,2-Azaborines and the Preparation of Their Protein Complexes with T4 Lysozyme Mutants
08:56

Synthesis of 1,2-Azaborines and the Preparation of Their Protein Complexes with T4 Lysozyme Mutants

Published on: March 25, 2017

8.1K

Related Experiment Videos

Last Updated: Apr 2, 2026

Determination of the Photoisomerization Quantum Yield of a Hydrazone Photoswitch
09:33

Determination of the Photoisomerization Quantum Yield of a Hydrazone Photoswitch

Published on: February 7, 2022

4.0K
Light-driven Molecular Motors on Surfaces for Single Molecular Imaging
08:40

Light-driven Molecular Motors on Surfaces for Single Molecular Imaging

Published on: March 13, 2019

12.2K
Synthesis of 1,2-Azaborines and the Preparation of Their Protein Complexes with T4 Lysozyme Mutants
08:56

Synthesis of 1,2-Azaborines and the Preparation of Their Protein Complexes with T4 Lysozyme Mutants

Published on: March 25, 2017

8.1K

Area of Science:

  • Photochemistry
  • Molecular Switches
  • Artificial Intelligence in Chemistry

Background:

  • Bridged azobenzene derivatives are crucial photo-responsive molecular switches.
  • Understanding their Z ↔ E isomerization mechanisms is difficult due to challenges in structure-spectrum relationships.
  • Current spectroscopic and computational methods have limitations in providing comprehensive mechanistic insights.

Purpose of the Study:

  • To develop and demonstrate an integrated, AI-driven workflow for studying the microscopic isomerization mechanisms of bridged azobenzenes.
  • To establish clear structure-spectrum relationships for azobenzene photoisomerization.
  • To provide a generalizable blueprint for AI-driven investigations of dynamic molecular systems.

Main Methods:

  • Utilized a large-language-model (LLM) agent-driven workflow for literature-guided planning, ab initio molecular dynamics (AIMD) sampling, and density functional theory (DFT) spectral calculations.
  • Employed robotic infrared/Raman measurements and interpretable machine learning, specifically an attention-based convolutional neural network (ATT-CNN).
  • The ATT-CNN predicted the C-N[double bond, length as m-dash]N-C dihedral angle from vibrational spectra, achieving high accuracy (r = 0.99, MAE = 5°).

Main Results:

  • The ATT-CNN accurately predicted the dihedral angle, demonstrating a strong correlation between vibrational spectra and molecular structure.
  • Attention maps highlighted key spectral bands, enabling holistic interpretation of the isomerization mechanism.
  • Transfer learning successfully extended the model's performance across different chemical environments and experimental datasets.
  • LLM agents successfully planned and coordinated automated simulations and experiments, while human researchers focused on model development and interpretation.

Conclusions:

  • The study presents the first LLM-agent-planned and orchestrated mechanistic investigation of azobenzene photoisomerization, integrating literature, theory, experiment, and machine learning.
  • The developed workflow provides quantitative insights into azobenzene photoisomerization mechanisms.
  • This AI-driven strategy offers a generalizable blueprint for future investigations of dynamic molecular systems.