Molecular Nuances Governing the Self-Assembly of 1,3:2,4-Dibenzylidene-<scp>d</scp>-sorbitol
Bibliographic record
Abstract
1,3:2,4-Dibenzylidene- d -sorbitol (DBS) is the gold-standard for low-molecular-weight organogelators (LMOGs). DBS gels a wide array of solvents, as illustrated by the large Hansen sphere representing gels (2δ d = 33.5 MPa 1/2, δ p = 7.5 MPa 1/2, and δ h = 8.7 MPa 1/2; radius = 11.2 MPa 1/2 ). Derivatives of DBS have been synthesized to isolate and determine molecular features essential for organogelation. In this work, π–π stacking and hydrogen bonding are the major noncovalent interactions examined. The importance of π–π stacking was studied using 1,3:2,4 dicyclohexanecarboxylidene- d -sorbitol (DCHS), which eliminates possible π–π stacking while still conserving the other structural aspects of DBS. The replacement of the benzyl groups with cyclohexyl groups led to a very a poor gelator; only one of the several solvents examined, carbon tetrachloride, formed a gel. 1,3:2,4-Diethylidene- d -sorbitol (DES), another DBS analogue incapable of π–π stacking but with very different polarity, gelated a large Hansen space (2δ d = 34.0 MPa 1/2, δ p = 10.9 MPa 1/2, and δ h = 10.8 MPa 1/2; radius = 9.2 MPa 1/2 ). DES gels solvents with higher δ p and δ h values than DBS. To assess the role of hydrogen bonding, DBS was acetalated (A-DBS), and it was found that the Hansen space gelated by A-DBS shifted to less polar solvents with higher hydrogen-bonding Hansen solubility parameters (HSPs) (2δ d = 33.8 MPa 1/2, δ p = 6.3 MPa 1/2, and δ h = 9.6 MPa 1/2; radius = 11.1 MPa 1/2 ) than for DBS. These systematic structural modifications are the first step in exploring how specific intermolecular features alter aspects of Hansen space corresponding to positive gelation outcomes.
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".