Formation of highly quaternized <i>N</i>,<i>N</i>,<i>N</i>-trimethylchitosan: a chemoselective methodology in aqueous media
Bibliographic record
Abstract
Abstract N,N,N-Trimethylchitosan (TMC) represents a rare example of cationic polysaccharides and numerous studies have shown its potential in biological and biomedical applications. TMC with high degrees of quaternization (DQ) were synthesized from N-methylation of N,N-dimethylchitosan (DMC), which was obtained by reductive alkylation of high molecular weight chitosan in a simple step process and in good yields. The effects of base and solvents were evaluated on the quaternization reaction. The N-methylation of DMC was performed selectively by CH3I and carbonate in water where quaternization was achieved quantitatively with a low degree of O-methylation (17 %). Moreover, the greener procedure allows easy recovery and purification by conventional filtration as a carbonate salt, in which the anion can be exchanged by an acid-base reaction. Quantification of DQ involving 1H NMR integration of methyl peaks must be performed on protonated TMC. High field NMR spectra of TMC showed two specific chemical shifts for anomeric peaks (5.0 and 5.4 ppm) that can also be used for the determination of DQ. This latter method avoids the superimposition problems with other pyranosyl peaks.
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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".