Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Atomic Emission Spectroscopy: Overview

Atomic Emission Spectroscopy: Overview

Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...

You might also read

Related Articles

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

Sort by

Same author

CoRE 2D-HOIP DB: Computation-Ready, Experimental Database of Two-Dimensional Hybrid Organic-Inorganic Perovskites.

Journal of chemical information and modeling·2026

Same author

Rigidified cyclic diglycolamides: coordination and extraction of trivalent f-elements.

Dalton transactions (Cambridge, England : 2003)·2026

Same author

Symmetry-Sensitive Analysis of Molecular Graph Neural Network Models.

Journal of chemical information and modeling·2026

Same author

A Fast and Accurate Semi-Empirical Approach for Hydrogen-Exchange Kinetic Isotope Effect Evaluation.

Journal of computational chemistry·2026

Same author

The role of neptunium oxidation states and coordination in shaping XANES spectra at the Np L<sub>3</sub> absorption edge.

Physical chemistry chemical physics : PCCP·2026

Same author

From database to prediction: Machine learning for 5-f elements coordination using actinide x-ray experimental spectra (AXES) collection.

The Journal of chemical physics·2025

Same journal

Solvent Coordination-Induced Synergistic Phase, Facet, and Defect Engineering of CdS for Photocatalytic Hydrogen Evolution.

Inorganic chemistry·2026

Same journal

Tailoring the Electron-Enriched Microenvironment of UiO-66 via Thiol Functionalization to Boost Non-Thermal Plasma CO<sub>2</sub> Conversion.

Inorganic chemistry·2026

Same journal

Nonporous Self-Assembled Pd(II) Coordination Cage Enabling Dual Capture of Iodine and Methyl Iodide.

Inorganic chemistry·2026

Same journal

A Three-Dimensional Organic-Inorganic Hybrid Perovskite-Type Molecular Ferroelectric Material [3.2.2-H<sub>2</sub>dabcn]Rb(NO<sub>3</sub>)<sub>3</sub>.

Inorganic chemistry·2026

Same journal

Nonlinear Optical-Active NaAlP<sub>2</sub>S<sub>6</sub> Synthesized by the MOBQ Method: Synthesis, Structure, and Optical Properties.

Inorganic chemistry·2026

Same journal

Ligand-Controlled Redox and Photophysical Properties in Photoluminescent Tris-Heteroleptic Ru(II) Pyridyl-Phosphonium Ylide Complexes.

Inorganic chemistry·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 13, 2025

Activated Cross-linked Agarose for the Rapid Development of Affinity Chromatography Resins - Antibody Capture as a Case Study

Activated Cross-linked Agarose for the Rapid Development of Affinity Chromatography Resins - Antibody Capture as a Case Study

Published on: August 16, 2019

On F-Element Extractant Design Using HSAB Theory.

Kirill V Karpov¹, Artem A Mitrofanov^1,2, Stepan N Kalmykov¹

¹Chemistry department, Moscow State University, Leninskie Gory 1, Moscow 119991, Russia.

Inorganic Chemistry

|June 11, 2025

Summary

This summary is machine-generated.

Investigating the hard and soft acid and base (HSAB) theory, this study found that chemical species hardness is crucial for selective liquid-liquid extraction. Combining hardness with other parameters aids predictive modeling for metal complexation.

More Related Videos

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

Quantification of Humic and Fulvic Acids in Humate Ores, DOC, Humified Materials and Humic Substance-Containing Commercial Products

Quantification of Humic and Fulvic Acids in Humate Ores, DOC, Humified Materials and Humic Substance-Containing Commercial Products

Published on: March 18, 2022

Related Experiment Videos

Last Updated: Jun 13, 2025

Activated Cross-linked Agarose for the Rapid Development of Affinity Chromatography Resins - Antibody Capture as a Case Study

Activated Cross-linked Agarose for the Rapid Development of Affinity Chromatography Resins - Antibody Capture as a Case Study

Published on: August 16, 2019

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

Quantification of Humic and Fulvic Acids in Humate Ores, DOC, Humified Materials and Humic Substance-Containing Commercial Products

Quantification of Humic and Fulvic Acids in Humate Ores, DOC, Humified Materials and Humic Substance-Containing Commercial Products

Published on: March 18, 2022

Area of Science:

Nuclear chemistry
Separation science
Coordination chemistry

Background:

Liquid-liquid extraction relies on metal-ligand complex formation at phase interfaces.
The hard and soft acid and base (HSAB) theory is recognized for its role in complexation selectivity.
A systematic analysis of the quantitative relationship between hardness and selectivity is lacking.

Purpose of the Study:

To investigate the correlation between chemical species hardness and selectivity in liquid-liquid extraction.
To compare the influence of hardness with other ligand parameters on lanthanide and actinide separation.

Main Methods:

Utilized the hard and soft acid and base (HSAB) theory framework.
Analyzed selectivity in the context of metal-ligand complexation during extraction.
Compared hardness as a descriptor against other common ligand parameters.

Main Results:

Hardness is a significant factor influencing selectivity in liquid-liquid extraction.
No single descriptor, including hardness, fully explains complexation selectivity.
The combination of multiple descriptors shows promise for predictive modeling.

Conclusions:

Extractant design for selective metal separation requires consideration of multiple factors beyond just hardness.
A combined approach using hardness and other ligand descriptors can enhance predictive modeling for separation processes.