Fragmentation of chitosan by ultrasonic irradiation
Bibliographic record
Abstract
Kinetics of chitosan fragmentation by ultrasonic irradiation at frequency of 20 kHz, and the effects of experimental variables (power of ultrasound, chitosan concentration and solution temperature) on fragmentation were investigated. The kinetics studies were followed by measuring solution viscosity of the original and its fragments, and determining average number of chain scission of the fragments. The effects of ultrasonic power, chitosan concentration and solution temperature on fragmentation process were followed by viscometry and size exclusion chromatography. The chemical structure of the original chitosan and its fragments were examined by (1)H NMR spectroscopy and elemental analysis. The experimental results showed that the rate of fragmentation increased with an increase in power of ultrasound. Chain scission increased with an increase in power of ultrasound; and solution temperature, but a decrease in chitosan concentration. The chemical structure and polydispersity of the original and the fragments were nearly identical. A model based on experimental data to describe the relationship between chain scission and experimental variables (power of ultrasound; irradiation time; reduced concentration, c[eta]; and solution temperature) was proposed. It was concluded that ultrasonic irradiation is a suitable method to perform partial depolymerization and to obtain moderate macromolecules from large ones.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".