Effects of Additives on the Morphology of Thiamine Nitrate: The Great Difference of Two Kinds of Similar Additives
Bibliographic record
Abstract
The growth of thiamine nitrate, in supersaturated aqueous solutions in the absence and presence of sodium alkyl sulfates CH 3 (CH 2 ) n SO 4 Na and sodium alkyl sulfonates CH 3 (CH 2 ) n SO 3 Na was studied by a single seed crystal growth experiment. It was surprisingly found that the growth of the a -axis of thiamine nitrate is significantly inhibited by CH 3 (CH 2 ) n SO 4 Na, thus reducing the aspect ratio of thiamine nitrate, while the aspect ratio of thiamine nitrate in the presence of CH 3 (CH 2 ) n SO 3 Na remains almost constant. Furthermore, the mechanism of additives to modify the crystal morphology is proposed: both additives can inhibit the growth of thiamine nitrate by hindering the solute diffusion. However, their unusual behavior is due to the selective adsorption, which was caused by electrostatic and hydrogen bond interactions between solute and additive molecules. In particular, the anionic groups exposed at the end of the additives demonstrate an interesting case in which a small variation in the charge density and hydrogen bonding ability can lead to a marked difference in modifying the crystal growth behavior. The results obtained from this study should be helpful in the performance evaluation and selection of the morphology modifiers for thiamine nitrate crystals.
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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 | 0.001 |
| 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) | 0.001 | 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".