Role of Hydrogen Bonds in Controlling the Morphology of Self-Assembling Carbamate Systems
Bibliographic record
Abstract
With a view to understand the role of hydrogen bonding in controlling the morphology of self-assembling carbamate systems, N-octadecylcarbamate dodecyl ester was blended individually with a low molecular weight polyethylene and two commercial clarifiers, namely Kemamide S and Kemamide E 180. The effect of blending on the morphology of this long chain carbamate was investigated using optical microscopy, differential scanning calorimetry, and X-ray diffraction. The crystal structure of the carbamate was not affected by the addition of polyethylene or Kemamide S. The heterogeneous nucleation of the carbamate by the polyethylene or Kemamide S resulted in the reduction of the spherulite size of the carbamate, but it did not improve the transparency of the sample due to phase separation. On the other hand, significant improvement of transparency was achieved when the carbamate was blended with Kemamide E 180. Blending reduced the crystallite and spherulite size, heat of fusion, and crystallinity. An exchange of hydrogen bonds between the carbamate and Kemamide E is indicated in the IR spectra, and this affects the packing of the alkyl chains of the carbamates. This heterogeneous blending shows similar effects on the morphology as was achieved by blending two homologous carbamates in our previous study (J. Phys. Chem. B 2003, 107, 8416).
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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) | 0.001 | 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".