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Record W4380879252 · doi:10.26434/chemrxiv-2023-sqdkg

Green and Sustainable Solvents for Solid-Phase Peptoid Synthesis

2023· preprint· en· W4380879252 on OpenAlexaff
Abigail Mae Clapperton, Katya Naomi Marín Vera, Jon Babi, Helen Tran

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPeptoidSolventHazardous wasteChemistrySolid-phase synthesisCombinatorial chemistryYield (engineering)Green chemistryPhase (matter)NanotechnologyOrganic chemistryMaterials scienceWaste managementMoleculeEngineeringPeptide

Abstract

fetched live from OpenAlex

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 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 on automated synthesizers for the synthesis of peptoids with different sequences and longer chain lengths. The replacement of hazardous solvents in solid-phase peptoid synthesis represents an advance in the sustainability of peptoid research, which could improve the translation of peptoids from academic labs to industry.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.309
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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