Solvent–Less Environmentally Benign Amino Ester, Amide, and Peptide Synthesis Enabled by Resonant Acoustic Mixing
Bibliographic record
Abstract
The demand for sustainable methods for peptide synthesis grows imperatively with their escalating use in therapeutic and biomedical applications. The cornerstone technology of solid-phase peptide synthesis has unsustainable environmental impact due to high process mass intensity and reliance on hazardous solvents. The reported study presents pioneering use of a resonant acoustic mixer (RAM) for sustainable peptide bond formation at elevated concentration with minimal solvent waste. Effective amide and ester bond formation were achieved rapidly under mild conditions using the RAM in “green” solvents such as ethyl acetate and 1,2-dimethoxyethane (DME), with minimal levels of epimerization. Peptide coupling was systematically studied under various conditions, indicating influences of solvent viscosity and base solubility on conversion efficiency. Comparisons with conventional and ultrasonic stirring confirmed the superiority of resonant acoustic mixing at and above 60 g0 for promoting higher conversions. Assessment in different peptide model systems prone to racemization and fragment couplings confirmed comparable conversion and minimal epimerization to that observed using solution-phase and mechanochemical methods. The RAM approach proved to be robust and scalable providing an environmentally friendly platform for the synthesis of amino esters, amides, and peptides, thereby presenting novel opportunities for the sustainable production of peptide-based therapeutics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".