Harnessing deep eutectic solvents for the extraction of biomolecules from plant waste: Their industrial applications, optimization, and mechanistic structure
4 affiliations
- 1Anton de Kom Universiteit van Suriname, Universiteits campus Leysweg 86, Paramaribo 9212, Suriname
- 2College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou, Jiangsu Province 215123, PR China
- 3Department of Chemical Sciences, Faculty of Natural Sciences, Redeemer’s University P.M.B 230, Ede, Osun State, Nigeria
- 4Co-Innovation Center for Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry University, Nanjing, Jiangsu 210037, PR China
Abstract
Deep eutectic solvents (DES) are emerging as enabling media for valorizing plant waste into high-value biomolecules, yet industrial translation requires mechanistic clarity and an integrated design logic that connects solvent selection, process intensification, and deployment constraints. This review synthesizes recent evidence on how DES composition and controlled water addition tune polarity, hydrogen bonding capacity, and microheterogeneity to disrupt lignocellulosic barriers and solubilize phenolics, flavonoids, proteins, betalains, terpenoids, and related targets. Hydrogen bonding contributions are delineated using molecular dynamics observables such as hydrogen bond populations and lifetimes, alongside spectroscopy fingerprints from infrared and nuclear magnetic resonance that track solvent structuring and polymer association. Dispersion-dominated solvation is distinguished as the governing interaction regime for aromatic and lipid-like fractions in hydrophobic eutectic systems, while viscosity-governed mass transfer is positioned as the dominant transport limitation that couple’s temperature, water activity, and diffusion. To convert these mechanistic descriptors into an actionable extraction design, contemporary predictive workflows are evaluated as a mature baseline, combining COSMO RS screening, response surface optimization, and machine learning models for nonlinear parameter spaces. A unified translation framework is proposed that links in-silico ranking to statistically efficient experimentation, mechanistic validation, recyclability, and product quality checks, and regulatory readiness through standardized reporting of composition, physicochemical properties, and safety endpoints. The resulting agenda defines measurable decision criteria for solvent formulation and intensified operations that support reproducible, scalable, and safer bioresource valorization.
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BibTeX
@article{ejeromedoghene2026harnessing,
title = {{Harnessing deep eutectic solvents for the extraction of biomolecules from plant waste: Their industrial applications, optimization, and mechanistic structure}},
author = {Onome Ejeromedoghene and Ephraim Akor and Siëda Bont and Anthony Druiventak and Abiodun Oladipo},
journal = {Next Sustainability},
year = {2026},
month = jun,
doi = {10.1016/j.nxsust.2026.100363},
url = {https://doi.org/10.1016/j.nxsust.2026.100363},
note = {CC BY},
}
RIS
TY - JOUR
TI - Harnessing deep eutectic solvents for the extraction of biomolecules from plant waste: Their industrial applications, optimization, and mechanistic structure
AU - Onome Ejeromedoghene
AU - Ephraim Akor
AU - Siëda Bont
AU - Anthony Druiventak
AU - Abiodun Oladipo
PY - 2026
DA - 2026/06/01
JO - Next Sustainability
DO - 10.1016/j.nxsust.2026.100363
UR - https://doi.org/10.1016/j.nxsust.2026.100363
AB - Deep eutectic solvents (DES) are emerging as enabling media for valorizing plant waste into high-value biomolecules, yet industrial translation requires mechanistic clarity and an integrated design logic that connects solvent selection, process intensification, and deployment constraints. This review synthesizes recent evidence on how DES composition and controlled water addition tune polarity, hydrogen bonding capacity, and microheterogeneity to disrupt lignocellulosic barriers and solubilize phenolics, flavonoids, proteins, betalains, terpenoids, and related targets. Hydrogen bonding contributions are delineated using molecular dynamics observables such as hydrogen bond populations and lifetimes, alongside spectroscopy fingerprints from infrared and nuclear magnetic resonance that track solvent structuring and polymer association. Dispersion-dominated solvation is distinguished as the governing interaction regime for aromatic and lipid-like fractions in hydrophobic eutectic systems, while viscosity-governed mass transfer is positioned as the dominant transport limitation that couple’s temperature, water activity, and diffusion. To convert these mechanistic descriptors into an actionable extraction design, contemporary predictive workflows are evaluated as a mature baseline, combining COSMO RS screening, response surface optimization, and machine learning models for nonlinear parameter spaces. A unified translation framework is proposed that links in-silico ranking to statistically efficient experimentation, mechanistic validation, recyclability, and product quality checks, and regulatory readiness through standardized reporting of composition, physicochemical properties, and safety endpoints. The resulting agenda defines measurable decision criteria for solvent formulation and intensified operations that support reproducible, scalable, and safer bioresource valorization.
SN - 2949-8236
ER -