A New Class of Selective Low-Molecular-Weight Gelators Based on Salts of Diaminotriazinecarboxylic Acids
Bibliographic record
Abstract
New low-molecular-weight gelators can be discovered by an approach that integrates classical methods for identifying potential gelators with strategies recently developed by crystal engineers to build porous molecular networks. This hybrid approach has yielded a potent new class of selective gelators based on salts of 4,6-bis(arylamino)-1,3,5-triazine-2-carboxylic acids. These compounds lack the high degree of conformational flexibility and long alkyl chains typical of classical gelators, and Na + and DMSO play specific roles in the mechanism of gelation. Scanning electron microscopy and atomic force microscopy showed that the resulting gels consist of elemental nanofibers that are approximately 30−100 nm in width, and X-ray diffraction yielded the structure of needle-shaped crystals of a gelator obtained directly from its gel. The crystals are constructed from bilayers, with the hydrophobic aryl groups of the gelators interacting intermolecularly to form the core of the bilayers and polar triazinecarboxylate headgroups aligned on the surface. The polar surfaces then stack in a process directed by the formation by multiple intermolecular hydrogen bonds and chelation of Na + . The hybrid approach that led to the discovery of these gels promises to yield other new molecular materials at the boundary between gels and crystalline solids.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".