Modification of rice husk with ultrasound-assisted Inorganic treatment and application in the catalytic hydrolysis of NaBH4
Bibliographic record
Abstract
Abstract Rice husk is a massive byproduct of the agricultural sector, but less effort has been paid for its recycling. In this study, ultrasound-assisted inorganic treatment with HNO3, H2SO4, and H2O2 was adopted to modify rice husk as a biomass catalyst. The activity of produced biomass catalyst was assessed in hydrogen production through the hydrolysis of sodium borohydride in an alkaline solution. The characterization of as-prepared biomass catalysts revealed that HNO3 was an efficient agent to protonate the surface of rice husk and make active sites available for the hydrolysis reactions. In addition, ultrasound-assisted HNO3 treatment caused a constructive effect on the structural property of rice husk and increased the surface area from 1.9 m2.gr− 1 to 17 m2.gr− 1 and pore volume from 0.45 cm3.gr− 1 to 3.9 cm3.gr− 1. According to optimum synthesizing conditions (45 wt% HNO3 and 10 min ultrasonication), ARH-N45-10 could produce 745 ml.gr− 1 hydrogen from the alkaline solution of NaBH4 at ambient conditions. Based on the kinetic study, the catalytic hydrolysis of NaBH4 by modified rice husk followed first-order kinetic concerning the NaBH4 per fixed NaOH ratio. Moreover, the thermodynamic analysis indicated that hydrolysis of NaBH4 and formation of hydrogen on the modified rice husk is an endothermic and spontaneous reaction, where the hydrogen generated at 55˚C (5280 ml.gr− 1) was seven times more than that at ambient temperature and also activation energy was calculated 57.68 kJ.mol− 1 from Arrhenius plot.
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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) | 0.000 | 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 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".