The development of methods to valorise rice husk to produce value-added products
Bibliographic record
Abstract
In this work, the development of sustainable routes to valorise rice husk waste biomass of Thai origin for production of value-added products (bio-derived mesoporous materials and cellulose fibres) were successfully prepared through microwave-assisted methods in almost all steps with mild conditions.Microwave heating has been found to be an energyefficient and cost-effective method to fabricate a range of bio-derived materials from rice husk without sacrificing the quality of the materials.Due to rice husk is a silica-rich material that can be extracted for preparation of silicate solution to use as a source of silica to produce mesoporous silica material (SiO2) and ordered mesoporous silica material (SBA-15).Moreover, bio-oil derived from rice husk can be also used as carbon source for carbon-silica composite material (CSC).All as-prepared materials exhibit type IV isotherms typical for mesoporous structure which is beneficial for methylene blue removal.The removal efficiency demonstrated that SBA-15 can remove nearly 100% of methylene blue (MB) within an hour (98.15%),CSC can remove 78.30% of MB while SiO2 can remove only 43.22% of MB.The adsorption isotherms of all materials were fitted with the Langmuir isotherm.SBA-15 possesses the highest qe,cal value of 126.58 mg/g, suggesting that mesoporous SBA-15 holds the most promising adsorption property for cationic dye.In addition, rice husk-derived cellulose microfibres/nanofibres were also successfully prepared by the development of greener procedures via organosolv microwave-assisted pretreatment followed by mild chemical processing for using as reinforcing agent to develop biodegradable composite film preparations.Bio-composite films possess good mechanical and optical properties (transparency) and thermal stability which offer an advantage for food packaging, film packaging or sustainable alternative materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".