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

Solvents01:12

Solvents

71.3K
A solvent is a substance, most often a liquid, that can dissolve other substances. Here, the substance being dissolved is called a solute. When a solvent and a solute combine, they form a solution - a homogenous mixture of both the solvent and the solute. Water is a universal biological solvent. Its polar structure allows it to dissolve many other polar compounds. The ability of water to dissolve is governed by a balance between water molecules binding to each other and binding to the solute.
A...
71.3K
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

792
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
792
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

611
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
611
Titration in Nonaqueous Solvents01:16

Titration in Nonaqueous Solvents

1.4K
Most acid-base titrations are performed in an aqueous medium. In aqueous titrations, water competes with weaker acids or bases for proton donation or acceptance, leading to ambiguous endpoints in the titration curve. Water also affects the partial ionization of weak acids or bases. For example, water accepts a proton from acetic acid to form hydronium and acetate ions. The hydronium ion formed is a stronger acid than acetic acid, and the acetate ion is a stronger base than water. As a result,...
1.4K
Machines01:19

Machines

581
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...
581
Group Design02:01

Group Design

10.7K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
10.7K

You might also read

Related Articles

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

Sort by
Same author

Machine learning-based prediction of polyvinyl alcohol product viscosity and design of optimal process conditions.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2026
Same author

Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization.

Journal of chemical information and modeling·2026
Same author

Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models.

Journal of chemical information and modeling·2026
Same author

A general framework for extrapolation-aware prediction reliability in forward and inverse analyses of Gaussian mixture regression models.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2026
Same author

Correction: Xu et al. Nucleosome Clustering as a Biomarker and Mechanistic Switch for Reprogramming Cells. <i>Cells</i> 2026, <i>15</i>, 113.

Cells·2026
Same author

Preoperative Factors Associated With Postoperative Delirium in Older Patients With Cancer Undergoing Surgery: Retrospective Cohort Study.

Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society·2026
Same journal

Biobased Lignin as a Sustainable Flame Retardant Additive for Recycled Polyester Fabrics.

ACS omega·2026
Same journal

Practical Microwave-Assisted Recirculated Flow Esterification at Atmospheric Pressure.

ACS omega·2026
Same journal

Effects of Fiber Inclusion on Pervious Concrete: A Multiscale Experimental and Imaging Study.

ACS omega·2026
Same journal

Investigation on the Influence Mechanisms and Graded Monitoring of CO<sub>2</sub> Leakage on Surface Water Environmental Indicators.

ACS omega·2026
Same journal

The N‑Glycoproteomic Landscape of the Lung in Monocrotaline-Induced Pulmonary Arterial Hypertension.

ACS omega·2026
Same journal

Immobilized Copper-Substituted Keggin POM on Graphene Oxide for Highly Selective Heterogeneous Catalysis of Starch Hydrolysis.

ACS omega·2026
See all related articles
  1. Home
  3. Design Of Gold Extraction Solvents Using Machine Learning Models.
  1. Home
  3. Design Of Gold Extraction Solvents Using Machine Learning Models.

Related Experiment Video

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K

Design of Gold Extraction Solvents Using Machine Learning Models.

Takuto Tsunemi1, Tatsuya Oshima2, Hiroki Yokota2

  • 1Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.

ACS Omega
|February 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Researchers developed predictive models to find new solvents for gold extraction. These models identified (-)-fenchone and 4-phenyl-2-butanone as effective alternatives to Dibutyl carbitol (DBC) with lower water solubility, reducing aqueous contamination.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K

Related Experiment Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K

Area of Science:

  • * Computational chemistry and materials science.
  • * Focus on solvent extraction and chemical process optimization.

Background:

  • * Dibutyl carbitol (DBC) is a conventional solvent for extracting gold (Au-III) after chlorination, but its high water solubility causes aqueous phase contamination.
  • * The need for efficient gold extraction solvents with minimal environmental impact is critical in hydrometallurgy.

Purpose of the Study:

  • * To develop predictive models for gold extractability and solvent water solubility based on molecular descriptors.
  • * To identify novel solvents with high gold extraction efficiency and low water solubility, overcoming the limitations of DBC.

Main Methods:

  • * Construction of a quantitative structure-property relationship (QSPR) model using molecular descriptors (COSMO-RS charge density profiles) and experimental data.
  • * Application of genetic algorithm-based wavelength selection for variable selection in the predictive models.
  • * Inverse analysis of the established models to screen for optimal solvent candidates.

    • Main Results:

    • * Predictive models for gold extractability and water solubility demonstrated high accuracy (correlation coefficients > 0.9).
    • * (-)-Fenchone and 4-phenyl-2-butanone were identified as promising solvents.
    • * These novel solvents exhibit gold extractability comparable to DBC but with significantly lower water solubility.

    Conclusions:

    • * Predictive modeling is a powerful tool for designing efficient and environmentally friendly solvents for gold extraction.
    • * (-)-Fenchone and 4-phenyl-2-butanone represent viable alternatives to DBC, offering reduced aqueous contamination.
    • * The study provides a framework for discovering new solvents in hydrometallurgical applications.