Chiral Discrimination of a Proline-Based Stationary Phase: Adhesion Forces and Calculated Selectivity Factors
Bibliographic record
Abstract
As early as 1992, proline was examined as a potential chiral selector for high-performance liquid chromatography. In recent years, brush-type selectors with up to 10 proline units have been examined, and the longer peptides were found to be competitive with commercial chiral stationary phases (CSPs). In this article, we report on a comprehensive examination of a t -butoxycarbonyl- ( t -Boc-) terminated monoproline selector. This selector was grafted through an amide linkage to an aminopropyl siloxane-terminated Si(111) wafer and to a silicon atomic force microscopy tip. Chemical force spectrometry measurements were performed for interaction forces between two d - or l -monoproline monolayers in water and in the presence of various amino acid solutions. When exposed to amino acids, the adhesion forces measured between the proline layers were reduced. Amino acids containing hydrophilic side chains were found to exhibit a selectivity opposite to that seen for those containing hydrophobic side chains. Molecular dynamics simulations of the monoproline interfaces in the presence of racemic alanine and serine identified the importance of hydrogen-bonding interactions between the amino acids and the monoproline selectors. We found that, when amino acids are bound to the proline selector, their side chains protrude into the bulk solution, explaining the strong impact of side-chain hydrophobicity on the selectivity. Taken together, the experiments and simulations show that hydrogen-bonding interactions are key to effective chiral discrimination for proline-based CSPs.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".