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Solvents

Solvents

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...

Predicting Products: Substitution vs. Elimination

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

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Updated: Jan 11, 2026

High-Throughput Measurement and Classification of Organic P in Environmental Samples

High-Throughput Measurement and Classification of Organic P in Environmental Samples

Published on: June 8, 2011

Machine Learning for Green Solvents: Assessment, Selection and Substitution.

Rohan Datta¹, Janhavi Nistane², Abhishek Sose²

¹School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|November 17, 2025

Summary

This summary is machine-generated.

A new data-driven pipeline assesses solvent sustainability, creating the largest green solvent database. This accelerates the discovery of environmentally friendly solvents that match hazardous ones in performance.

Keywords:

green solvents machine learning

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Area of Science:

Green Chemistry
Computational Chemistry
Environmental Science

Background:

Growing demand for sustainable solvents due to strict environmental regulations.
Existing Solvent Selection Guides (SSGs) are limited in scope and data availability.
Identifying greener alternatives requires balancing complex criteria like sustainability, cost, and performance.

Purpose of the Study:

To develop a data-driven pipeline for assessing solvent sustainability and identifying greener substitutes.
To expand the pool of evaluated solvents beyond the limited scope of current guides.
To create a comprehensive database of green solvent metrics.

Main Methods:

Trained and evaluated Gaussian Process Regression (GPR) and GPT models on the GlaxoSmithKline Solvent Sustainability Guide (GSK SSG).
Utilized GPR to predict greenness metrics for 10,189 solvents, creating the GreenSolventDB.
Combined predicted metrics with Hansen solubility parameters to find suitable greener alternatives.

Main Results:

GPR model slightly outperformed language-based GPT models in predicting solvent greenness.
GreenSolventDB was established as the largest public database of green solvent metrics.
Predicted alternatives for hazardous solvents like benzene and diethyl ether showed good alignment with known substitutes.

Conclusions:

The developed framework effectively quantifies solvent sustainability and identifies viable greener substitutes.
This approach significantly accelerates the discovery and adoption of environmentally friendly solvents.
The GreenSolventDB provides a valuable resource for researchers and industry.