Ultrasound-assisted esterification of cotton cellulose with long chain free fatty acids
Bibliographic record
Abstract
• Ultrasound decreased the esterification time from 24 h to 30 min. • Ultrasound produced cellulose esters with a glass transition at 47 °C. • Ultrasound at 20°C required five times less power density than conventional process. • Attenuation due to acoustic cavitation bubbles attenuated the acoustic pressure by 100 %. • Simulation predicted the highest streaming velocity for power density 842 W L -1 . This work covers the production of cellulose esters with varying degrees of substitution ( DS ) using ultrasound (US) power input, leveraging free fatty acids as esterification agent ( EA ) as a bio-based alternative to traditional chlorides, anhydrides and vinyl esters. . The best conditions without US were achieved with oleic acid, with an EA /cellulose molar ratio of 6 and a temperature of 80°C for 24h, producing esters with a DS of 1.44. Applying US at 20 kHz and 4.39 W at room temperature, , required less than 30min to produce cellulose esters with a DS of 0.38. Then, the effects of the US input power, reaction volume and properties of cellulose solutions on the cavitation activity were investigated by simulation. The density, viscosity and speed of sound in the cellulose esters solutions were measured and defined in the simulations as 936.2 kg m -3 , 23.3·10 -3 Pa.s, and 1495.8 m s -1 for 25 g L -1 . Simulations with conditions resulting in the highest DS with US were characterized by the smallest acoustic cavitation volume and the lowest u : 9.60·10 -8 m 3 and 40.06 m s -1 . US-assisted esterification produced thermoplastic esters with an energy input of 18 W g -1 of cellulose against 93 W g -1 required by conventional esterification.
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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".