Harvesting Bacterial Cellulose from Kitchen Waste to Prepare Superhydrophobic Aerogel for Recovering Waste Cooking Oil toward a Closed-Loop Biorefinery
Bibliographic record
Abstract
Bacterial cellulose (BC), harvested from the hydrolysate of starch-rich solid in kitchen waste (KW), was employed to prepare a superhydrophobic stearic acid (SA)-modified BC aerogel (S-BCA) for cooking oil adsorption. S-BCA achieved some distinct differences in the porous structure, surface wettability, mechanical property, and oil-adsorption capacity as affected by the modifier of SA concentration. 3% SA for BC modification can be a suitable strategy because the SA-coated S-BCA surface can be formed properly without excessive blocking of internal pores. The “thermal switch” mechanism by thermal activation at 90 °C achieved an efficient oil desorption through breaking the hydrogen bonds between BC and SA. S-BCA displayed a rapid oil-adsorption equilibrium within ∼30 s, a considerable saturated-oil-adsorption capacity of 48.2 g/g, and a superior recyclability for at least 10 cycles with the remaining 89% initial adsorption capacity. Herein, this work provides a new insight to construct a more sustainable biorefinery on KW through valorizing the starch-rich solid for the superhydrophobic BC preparation to efficiently recover the waste cooking oil.
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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".