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

Block Diagram Reduction01:22

Block Diagram Reduction

492
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
492
Alcohols from Carbonyl Compounds: Reduction02:23

Alcohols from Carbonyl Compounds: Reduction

12.0K
Reduction is a simple strategy to convert a carbonyl group to a hydroxyl group. The three major pathways to reduce carbonyls to alcohols are catalytic hydrogenation, hydride reduction, and borane reduction.
Catalytic hydrogenation is similar to the reduction of an alkene or alkyne by adding H2 across the pi bond in the presence of transition metal catalysts like Raney Ni, Pd–C, Pt, or Ru. Aldehydes and ketones can be reduced by this method, often under mild to moderate heat (25–100°C) and...
12.0K
Carboxylic Acids to Primary Alcohols: Hydride Reduction01:17

Carboxylic Acids to Primary Alcohols: Hydride Reduction

4.7K
Carboxylic acids, upon reaction with strong reducing agents such as lithium aluminum hydride followed by hydrolysis, undergo reduction to form primary alcohols.
4.7K
Acid Halides to Alcohols: LiAlH4 Reduction01:19

Acid Halides to Alcohols: LiAlH4 Reduction

3.8K
Acid halides are reduced to alcohols in the presence of a strong reducing agent like lithium aluminum hydride.
The mechanism proceeds in three steps. First, the nucleophilic hydride ion attacks the carbonyl carbon of the acid halide to form a tetrahedral intermediate. Next, the carbonyl group is re-formed, and the halide ion departs as a leaving group, generating an aldehyde. A second nucleophilic attack by the hydride yields an alkoxide ion, which, upon protonation, gives a primary alcohol as...
3.8K
Preparation of Aldehydes and Ketones from Nitriles and Carboxylic Acids01:24

Preparation of Aldehydes and Ketones from Nitriles and Carboxylic Acids

4.3K
Although it is possible to reduce a carboxylic acid to an aldehyde, strong reducing agents, like lithium aluminum hydride (LAH), prohibit a controlled reduction, instead causing the generated aldehyde to instantly over-reduce to a primary alcohol.
Reducing carboxylic acid derivatives like acyl chlorides (RCOCl), esters (RCO2R′), and nitriles (RCN) using milder aluminum hydride agents like lithium tri-tert-butoxyaluminum hydride [LiAlH(O-t-Bu)3] and diisobutylaluminum hydride [DIBAL-H]...
4.3K
Reduction of Alkenes: Catalytic Hydrogenation02:13

Reduction of Alkenes: Catalytic Hydrogenation

13.9K
Alkenes undergo reduction by the addition of molecular hydrogen to give alkanes. Because the process generally occurs in the presence of a transition-metal catalyst, the reaction is called catalytic hydrogenation.
Metals like palladium, platinum, and nickel are commonly used in their solid forms — fine powder on an inert surface. As these catalysts remain insoluble in the reaction mixture, they are referred to as heterogeneous catalysts.
The hydrogenation process takes place on the...
13.9K

You might also read

Related Articles

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

Sort by
Same author

Information theory for hypergraph similarity.

Science advances·2026
Same author

The microscale organization of directed hypergraphs.

Communications physics·2026
Same author

Reducibility of higher-order networks from dynamics.

Nature communications·2026
Same author

Higher-order shortest paths in hypergraphs.

Physical review. E·2025
Same author

Transfer entropy for finite data.

Physical review. E·2025
Same author

Higher-order interactions shape collective human behaviour.

Nature human behaviour·2025
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

Scalable Syntheses of Graphene Oxide and Reduced Graphene Oxide using Cascade Design Oxidation and Highly Basic Reduction Reactions
08:57

Scalable Syntheses of Graphene Oxide and Reduced Graphene Oxide using Cascade Design Oxidation and Highly Basic Reduction Reactions

Published on: July 3, 2025

1.8K

Structural Reducibility of Hypergraphs.

Alec Kirkley1,2,3, Helcio Felippe4, Federico Battiston4

  • 1University of Hong Kong, Institute of Data Science, Hong Kong SAR, China.

Physical Review Letters
|January 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an information-theoretic framework to simplify complex system analysis. It identifies and removes redundant higher-order interactions in networks, preserving essential structures for clearer understanding.

More Related Videos

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.5K
Visible-light Induced Reduction of Graphene Oxide Using Plasmonic Nanoparticle
07:24

Visible-light Induced Reduction of Graphene Oxide Using Plasmonic Nanoparticle

Published on: September 22, 2015

14.8K

Related Experiment Videos

Last Updated: Jan 7, 2026

Scalable Syntheses of Graphene Oxide and Reduced Graphene Oxide using Cascade Design Oxidation and Highly Basic Reduction Reactions
08:57

Scalable Syntheses of Graphene Oxide and Reduced Graphene Oxide using Cascade Design Oxidation and Highly Basic Reduction Reactions

Published on: July 3, 2025

1.8K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.5K
Visible-light Induced Reduction of Graphene Oxide Using Plasmonic Nanoparticle
07:24

Visible-light Induced Reduction of Graphene Oxide Using Plasmonic Nanoparticle

Published on: September 22, 2015

14.8K

Area of Science:

  • Network science
  • Information theory
  • Complex systems analysis

Background:

  • Traditional pairwise interactions offer limited insight into complex systems.
  • Higher-order network analysis provides deeper understanding but faces interpretation and computational challenges.

Purpose of the Study:

  • To develop a framework for assessing structural redundancy in hypergraph representations of complex systems.
  • To identify critical higher-order interactions for simplifying network analysis.
  • To enable the removal of redundancies while preserving essential network structures.

Main Methods:

  • Utilizing an information-theoretic framework.
  • Analyzing hypergraph representations of networked systems.
  • Quantifying structural redundancy and identifying critical interaction orders.

Main Results:

  • A method to determine the extent of structural redundancy in higher-order network representations.
  • Identification of key higher-order interactions that are crucial for maintaining network integrity.
  • A pathway to simplify complex network analysis by reducing non-essential interactions.

Conclusions:

  • The proposed framework effectively quantifies redundancy in higher-order network structures.
  • It facilitates the identification of essential interactions, simplifying complex system analysis.
  • This approach enhances the interpretability and computational efficiency of higher-order network studies.