Manipulating the Self‐Assembly of Multicomponent Low Molecular Weight Gelators (LMWGs) through Molecular Design
Bibliographic record
Abstract
Multicomponent low molecular weight gelators (LMWGs) may self-assemble by co-assembly (CA), social self-sorting (SSS), or narcissistic self-sorting (NSS). Understanding the nuances of the self-assembly processes is important to predict the behavior of multicomponent organogels. Here, we investigate the effect of molecular structure on self-assembly in a series of amino-acid based bicomponent LMWGs that differ in headgroup and alkyl chain length. Packing preference of the organogels was determined using differential scanning calorimetry, nuclear magnetic resonance spectroscopy and small angle X-ray scattering. From 66 bicomponent samples we found 50 CA, 14 SSS and 2 NSS. Furthermore, we performed statistical analysis to investigate the role of hydrophobicity and chain length on the overall pathway of self-assembly for these systems. We found the hydrophobicity of the headgroup strongly affected the assembly preference of the organogel, but alkyl chain length only played a small role.
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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".