Green and Sustainable Solvents for Solid-Phase Peptoid Synthesis
Bibliographic record
Abstract
Peptoids are a class of sequence-controlled polymers that provide a versatile platform for the design of bioinspired materials. Solid-phase synthetic methods offer absolute control over the polypeptoid sequence and have been optimized to improve reaction efficiency and versatility. However, these solid-phase strategies rely on the use of reprotoxic and restricted solvents, N, N -dimethylformamide (DMF) and N -methyl-2-pyrrolidone (NMP), resulting in significant hazardous solvent consumption and waste generation. Here, we report the solid-phase synthesis of peptoids with complete elimination of DMF and NMP and their replacement with greener solvents and binary mixtures to minimize the environmental impact and improve the sustainability of peptoid synthesis. We investigate the resin swelling performance of the green solvents (gamma-valerolactone, dimethyl sulfoxide, ethyl acetate, and binary mixtures) and show that the purity profile and yield of the final peptoids are not adversely affected when compared to those synthesized in traditional solid-phase solvents. Furthermore, we adapt these greener methods for use in automated synthesizers for the synthesis of peptoids with different sequences and long chain lengths. The replacement of hazardous solvents in solid-phase peptoid synthesis represents an advancement in the sustainability of peptoid research, which could improve the translation of peptoids from academic labs to industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".