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

Accelerators01:17

Accelerators

291
Accelerators in concrete serve as admixtures to speed up the hardening process, enabling the concrete to achieve early strength faster. Although accelerators do not necessarily impact the time it takes concrete to set, they reduce this time in practice. A common accelerator is calcium chloride, which is particularly useful for hastening early strength development in cold weather or for rapid repair jobs that require quick heat generation after mixing.
The effectiveness of calcium chloride can...
291
Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

21.6K
The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
Chymotrypsin is a pancreatic enzyme that breaks down proteins during digestion....
21.6K
Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

5.1K
The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
5.1K
Machines01:19

Machines

579
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
579
States of Water01:23

States of Water

57.0K
Water exists in any one of the three classical states: solid (ice), liquid (water), and gas (steam or water vapor). The state of water depends on i) the intermolecular forces that draw molecules together and ii) the kinetic energy that leads to movements that pull them apart.
Water freezes when the intermolecular forces are greater than the kinetic energy. Unlike most other substances, water is less dense in its solid state than in its liquid state. This is because each water molecule can form...
57.0K
Average Acceleration01:30

Average Acceleration

14.0K
The importance of understanding acceleration spans our day-to-day experiences, as well as the vast reaches of outer space and the tiny world of subatomic physics. In everyday conversation, to accelerate means to speed up. For instance, we are familiar with the acceleration of our car; the harder we apply our foot to the gas pedal, the faster we accelerate. The greater the acceleration, the greater the change in velocity over a given time. Acceleration is widely seen in experimental physics. In...
14.0K

You might also read

Related Articles

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

Sort by
Same author

Gradient-Guided Graph Contrastive Learning for Mass Spectrometry-Based Proteomics Clustering.

Journal of chemical information and modeling·2026
Same author

Decoupling Processing-Morphology-Stability Relationships Enables 19.65% Organic Solar Cells With Exceptional Photostability.

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

Core-to-Wing Type Hybrid Dimeric Giant Molecule Acceptors With Different-Length Ester-Linked Alkyl Chains Enable 20.25% Efficiency Organic Solar Cells.

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

Structural basis and immunogenic efficacy of porcine circovirus type 3 virus-like particle.

Nature communications·2026
Same author

Autophagy in plant male reproduction: conserved machinery, divergent functions.

The New phytologist·2026
Same author

Designing Biaryl Pyrazole Derivatives for Antibacterial Activity against Plant Pathogenic Bacteria.

Journal of agricultural and food chemistry·2026

Related Experiment Video

Updated: Feb 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.8K

Accelerating catalytic process optimization for water treatment via automated knowledge extraction and machine

Siyuan Jiang1, Ying Yang1, Ziang Liu1

  • 1Key Laboratory for Environmental Pollution Prediction and Control, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, Gansu Province 730000, PR China.

Journal of Hazardous Materials
|February 4, 2026
PubMed
Summary

Automated catalyst discovery for water treatment using Large Language Models is ~270x faster than manual review. This data-driven approach accelerates the identification of effective Advanced Oxidation Processes (AOPs) catalysts.

Keywords:
Advanced oxidation processesCatalyst optimizationData miningMachine learningVirtual high-throughput screening

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
09:28

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Published on: May 18, 2020

9.2K

Related Experiment Videos

Last Updated: Feb 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.8K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
09:28

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Published on: May 18, 2020

9.2K

Area of Science:

  • Environmental Science
  • Materials Science
  • Computational Chemistry

Background:

  • Traditional catalyst discovery for water treatment is hindered by manual literature review, leading to data acquisition bottlenecks.
  • Developing efficient catalysts is crucial for advancing water treatment technologies.

Purpose of the Study:

  • To develop an automated pipeline for efficient catalyst discovery in water treatment.
  • To accelerate materials optimization for environmental catalysis through a data-driven methodology.

Main Methods:

  • An automated pipeline integrating high-fidelity document parsing and a Large Language Model was developed.
  • A comprehensive database of 3276 records was constructed from over 3000 publications on Advanced Oxidation Processes (AOPs).
  • A multi-stage optimized XGBoost model was trained on the curated data.

Main Results:

  • The automated pipeline demonstrated approximately 270 times greater efficiency compared to manual methods.
  • The XGBoost model achieved R² values of 0.6753 for degradation rate constant and 0.8351 for removal efficiency.
  • Three promising catalysts (Co₃O₄@BC, Fe₃O₄@GO, and CoFe₂O₄) were identified and experimentally validated with <4% error.

Conclusions:

  • The data-driven methodology significantly accelerates catalyst discovery and materials optimization in environmental catalysis.
  • The developed pipeline and database provide a valuable resource for researchers in water treatment.
  • Automated approaches can overcome traditional bottlenecks, shifting research focus from data collection to innovation.