Sustainable Synthesis of Functional Materials Assisted by Deep Eutectic Solvents for Biomedical, Environmental, and Energy Applications
Bibliographic record
Abstract
Abstract The rapid expansion of the global economy has led to a surge in energy demand, resulting in significant environmental pollution and energy scarcity due to the concomitant increase in greenhouse gas emissions. The advancement of deep eutectic solvents (DESs) has introduced a viable substitute for traditional solvents and processing methods, boasting numerous intrinsic benefits, such as superior eco‐compatibility, outstanding thermal stability, and desirable electrochemical properties. Consequently, DESs have garnered significant attention from the research community, demonstrating a broad spectrum of prospective applications in a variety of fields for instance energy, biomass degradation, materials synthesis, and biomedicine. This review aims to offer a comprehensive and methodical overview of DESs, encompassing their historical development, classification, preparation methodologies, and fundamental physicochemical properties. Furthermore, this review explores the applications of DESs in the synthesis of various functional materials and examines their multifunctional roles. Crucially, the economic viability of DESs for environmental and energy applications is thoroughly examined, including an assessment of their cost‐effectiveness and market potential. Finally, the review concludes by outlining future research directions for DESs development and the challenges that remain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".