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

Ion Exchange01:17

Ion Exchange

1.1K
Ion exchange chromatography separates charged molecules from a solution by reversibly exchanging them with mobile, or 'active', ions associated with the oppositely charged stationary phase. This method can be used to separate ions, soften and deionize water, and purify solutions. The polymers comprising the ion-exchange column are high-molecular-weight and chemically stable polymers, crosslinked to be porous and essentially insoluble. They are also functionalized with either acidic or...
1.1K
Multi-Step Reactions02:31

Multi-Step Reactions

8.6K
Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
8.6K
The Two-State Receptor Model01:29

The Two-State Receptor Model

3.0K
The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
3.0K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.7K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.7K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.7K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.7K
E2 Reaction: Kinetics and Mechanism02:45

E2 Reaction: Kinetics and Mechanism

12.2K
SN2 substitutions and E2 eliminations of alkyl halides proceed via a concerted pathway. While the nucleophile attacks the alpha carbon in SN2 reactions, it functions as a strong base and abstracts a beta hydrogen in the E2 mechanism. The rate-limiting transition state in E2 elimination reactions is characterized by partially broken carbon–hydrogen and carbon–halogen bonds and a partially formed pi bond between the alpha and beta carbons. The beta hydrogen and halide are eliminated...
12.2K

You might also read

Related Articles

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

Sort by
Same author

Where Machine Learning Fails in Predicting Emerging Contaminant Adsorption: A Decision-Oriented Framework for Model Credibility and Transferability.

Environmental science & technology·2026
Same author

Improving Kinetic Prediction and Structural-Electronic Mechanistic Coherence in the Fenton Process via a Cross-Scale Machine-Learning Framework.

Environmental science & technology·2026
Same author

MXene membrane with directionally functionalized channel entrances for enhanced ion selectivity and permeability.

Nature communications·2026
Same author

Understanding and Steering the Surface-Pollutant Interaction over Nanoscale Zero-Valent Iron toward Proton-Coupled Electron Transfer.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Ultralow Dose Protoporphyrin IX-Mediated Ferrate(VI) System for Rapid Degradation of Phenolic Pollutants: Key Roles of Fe(V/IV/III) Complexes.

Environmental science & technology·2026
Same author

Unveiling the Size Effects of Oxygen Vacancy Clusters in Toluene Combustion under Humid Conditions.

Environmental science & technology·2025

Related Experiment Video

Updated: Jan 13, 2026

A Dual-Functional Electroactive Filter Towards Simultaneously SbIII Oxidation and Sequestration
08:34

A Dual-Functional Electroactive Filter Towards Simultaneously SbIII Oxidation and Sequestration

Published on: December 5, 2019

6.0K

Revealing Key Mechanisms in Multimechanism Interplay for ReO4- Removal: A Knowledge-Data Dual-Driven Framework for

Ling Yuan1, Han Zhang1, Chen Chen1

  • 1State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, China.

ACS Applied Materials & Interfaces
|January 9, 2026
PubMed
Summary

A novel knowledge-data dual-driven machine learning framework accurately identified electrostatic interactions as the key mechanism for perrhenate adsorption. This insight enabled the design of a covalent organic framework with record-breaking capacity for radioactive technetium removal.

Keywords:
adsorbent designadsorptioncovalent organic frameworkdomain-knowledge embeddingmachine learning

More Related Videos

Removal of Arsenic Using a Cationic Polymer Gel Impregnated with Iron Hydroxide
08:01

Removal of Arsenic Using a Cationic Polymer Gel Impregnated with Iron Hydroxide

Published on: June 28, 2019

7.8K
In situ FTIR Spectroscopy as a Tool for Investigation of Gas/Solid Interaction: Water-Enhanced CO2 Adsorption in UiO-66 Metal-Organic Framework
11:38

In situ FTIR Spectroscopy as a Tool for Investigation of Gas/Solid Interaction: Water-Enhanced CO2 Adsorption in UiO-66 Metal-Organic Framework

Published on: February 1, 2020

16.8K

Related Experiment Videos

Last Updated: Jan 13, 2026

A Dual-Functional Electroactive Filter Towards Simultaneously SbIII Oxidation and Sequestration
08:34

A Dual-Functional Electroactive Filter Towards Simultaneously SbIII Oxidation and Sequestration

Published on: December 5, 2019

6.0K
Removal of Arsenic Using a Cationic Polymer Gel Impregnated with Iron Hydroxide
08:01

Removal of Arsenic Using a Cationic Polymer Gel Impregnated with Iron Hydroxide

Published on: June 28, 2019

7.8K
In situ FTIR Spectroscopy as a Tool for Investigation of Gas/Solid Interaction: Water-Enhanced CO2 Adsorption in UiO-66 Metal-Organic Framework
11:38

In situ FTIR Spectroscopy as a Tool for Investigation of Gas/Solid Interaction: Water-Enhanced CO2 Adsorption in UiO-66 Metal-Organic Framework

Published on: February 1, 2020

16.8K

Area of Science:

  • Materials Science
  • Machine Learning
  • Environmental Chemistry

Background:

  • Efficient removal of pertechnetate (TcO4-) from nuclear waste is crucial for safe disposal.
  • Current adsorbents for TcO4- exhibit limited capacity and unclear adsorption mechanisms due to interfering factors.
  • Covalent organic frameworks (COFs) show promise as adsorbents but require mechanistic understanding for optimization.

Purpose of the Study:

  • To develop a machine learning (ML) framework integrating domain knowledge and data to elucidate adsorption mechanisms.
  • To identify the dominant mechanism for perrhenate (ReO4-) adsorption on COFs using a knowledge-data dual-driven (DKD) approach.
  • To design and synthesize a novel COF with enhanced adsorption capacity for ReO4- based on mechanistic insights.

Main Methods:

  • A knowledge-data dual-driven (DKD) machine learning framework was developed, incorporating mathematical descriptors for five adsorption mechanisms.
  • The DKD model's predictive accuracy and interpretability were compared against a purely data-driven model.
  • SHAP analysis was employed to quantify the contribution of different mechanisms to ReO4- uptake.
  • Density Functional Theory (DFT) calculations and spectral analyses were used to confirm adsorption mechanisms.

Main Results:

  • The DKD-ML model achieved higher predictive accuracy (R2 = 0.93) compared to the data-driven model (R2 = 0.91).
  • Electrostatic interaction was identified as the dominant adsorption mechanism, contributing 66.7% to ReO4- uptake.
  • A novel imine-linked COF, Tb-APDC-M, with ultrahigh charge density was synthesized, achieving a record ReO4- adsorption capacity of 1689.78 mg g-1.
  • The enhanced performance was attributed to the high charge density introduced at the iminium linkage.

Conclusions:

  • The DKD-ML framework is effective in elucidating complex adsorption mechanisms in porous materials.
  • Targeted adsorbent design, guided by mechanistic insights, can significantly enhance adsorption capacity for critical contaminants like TcO4-.
  • This work provides a pathway for developing advanced adsorbents for efficient radioactive waste remediation.