Amino Acid-Containing Phase-Selective Organogelators: A Water-Based Delivery System for Oil Spill Treatment
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The simple structural modification of replacing a terminal carboxylic acid with a primary amide group was found to lower the minimum gelation concentration (MGC), by at least an order of magnitude, for a series of N -lauroyl- l -amino acid phase-selective organogelators in decane. The amide-functionalized analogue N -lauroyl- l -alanine-CONH 2 was demonstrated to gel a broad range of solvents from diesel to THF at MGCs of 2.5% w/v or less, as well as to produce gels with a higher thermal stability ( ca. 30 °C) and enhanced mechanical properties (5 times increase in complex modulus), compared to the carboxylic acid analogue, N -lauroyl- l -alanine-COOH. These improved properties may be due to the additional hydrogen bonding in the primary amide analogue as revealed by SCXRD. Most significantly for this study, the introduction of the primary amide functionality enabled N -lauroyl- l -alanine-CONH 2 to form a self-assembled fibrillar network in water. The aqueous network could then actively uptake and rapidly gel decane, diesel, and diluted bitumen (“dilbit”) with MGCs of 2.5% w/v or less. This aqueous delivery method is advantageous for oil-remediation applications as no harmful carrier solvents are required and the gel can be easily separated from the water, allowing the oil to be recovered and the gelator recycled.
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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) | 0.000 | 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".