Self-Assembly of Alkylamido Isophthalic Acids toward the Design of a Supergelator: Phase-Selective Gelation and Dye Adsorption
Bibliographic record
Abstract
A new series of 5-alkylamido isophthalic acid (ISA) derivatives with varying single and twin alkyl chain lengths were designed and synthesized as potential supramolecular organogelators. 5-alkylamido ISAs with linear or branched alkyl tail-groups of different lengths were effective gelators for low polarity solvents. In particular, among the presented series, a derivative with a branched, 24 carbon atom tail-group behaves as a "supergelator" with up to twenty organic solvents forming gels that are highly stable over time. The gelation behavior was analyzed using Hansen solubility parameters, and the thermal stability and viscoelastic properties of select gels were characterized. Microscopy, spectroscopy, powder X-ray diffraction, and computer modeling studies were consistent with a hierarchical self-assembly process involving the formation of cyclic H-bonded hexamers via the ISA carboxylic acid groups, which stack into elementary fibers stabilized by H-bonding of the amide linker groups and π-π stacking of the aromatic groups. These new nanomaterials exhibited potential for the phase-selective gelation of oil from oil-water mixtures and dye uptake from contaminated water. The work expands upon the design and synthesis of supramolecular self-assembled nanomaterials and their application in water purification/remediation.
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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".