Synthesis of Chiral Triazine Frameworks for Enantiodiscrimination
Bibliographic record
Abstract
Manipulation of the structure of covalent organic frameworks at the molecular level is an efficient strategy to shift their biological, physicochemical, optical, and electrical properties in the desired windows. In this work, we report on a new method to construct chiral triazine frameworks using metal-driven polymerization for enantiodiscrimination. The nucleophilic substitution reaction between melamine and cyanuric chloride was performed in the presence of PdCl 2, ZnCl 2, and CuCl 2 as chirality-directing agents. Palladium, with the ability of planar complex formation, was able to assemble monomers in two-dimensions and drive the reaction in two directions, leading to a two-dimensional triazine network with several micrometers lateral size. Nonplanar arrangements of monomers in the presence of ZnCl 2 and CuCl 2, however, resulted in calix and bouquet structures, respectively. While 2D and bouquet structures showed strong negative and positive bands in the CD spectra, respectively, their calix counterparts displayed long-range weak negative bands. In spite of the ability of both calix and bouquet networks to load l -histidine 35 and 50% more than d -histidine from pure enantiomers, respectively, only calix counterparts were able to take up this enantiomer (78%) from the racemic mixture. The two-dimensional polytriazine network did not show any specific interactions with pure enantiomers or their racemic mixtures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".