<sup>2</sup> H NMR Studies on Two‐Homopolypeptide Lyotropic Enantiodiscriminating Mesophases: Experimental Quantification of Solute–Fiber Affinities
Bibliographic record
Abstract
The analytical potential and enantioselective properties of lyotropic mesophases made by mixing two chemically different chiral polypeptides are described. Here we examine the case of a mixture of poly-gamma-benzyl-L-glutamate (PBLG) and poly-epsilon-carbobenzyloxy-L-lysine (PCBLL). We demonstrate that 2H NMR spectroscopy on these chiral oriented mixtures can discriminate both enantiomers and enantiotopic directions in prochiral molecules. Moreover, in such systems, degree of enantiodiscrimination, resolution, and sensitivity can be conjointly optimized by changing the relative proportion of the two polypeptides. Therefore, these new enantiodiscriminating media provide a favorable alternative to single-polypeptide mesophases with respect to stereochemical applications. At a more fundamental level, the present work points out that solute distribution in the vicinity of each polypeptide partly governs the degree of enantiodiscrimination and NMR relaxation rates. To this end, the experimental trends of solute NMR observables (Delta nu Q, T1) versus the fraction of peptide units of each polymer were analyzed by using a "mean-field" model derived from that proposed for mixtures of thermotropic nematic solvents, and based on the separation of intermolecular interactions between the solute and both polypeptides. This approach allows the relative solute-fiber affinities in these lyotropic systems to be determined. To identify the factors controlling solute-polypeptide affinities, we investigated various solutes (polar/apolar, rigid/flexible, achiral/prochiral/chiral molecules) using 2H NMR at natural abundance or on isotopically enriched solutes.
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 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".