Optimization of purity and yield of amorphous bio‐silica nanoparticles synthesized from bamboo leaves
Bibliographic record
Abstract
Abstract The fallen yellowish bamboo leaves around bamboo crops are always overlooked even though they contain high silica in their ash. Bamboo leaf valorization consequently has the potential to be a green process for synthesizing amorphous silica. Unfortunately, the optimum process parameters have not been widely disclosed. Hence, this study intends to optimize the synthesis of amorphous bio‐silica nanoparticles from bamboo leaves using Box–Behnken design (BBD). Bamboo leaves were initially washed with HCl, followed by combustion at 700°C, and then ash washing, extraction of silica with NaOH (sol–gel method), gelation, and drying. According to the results, optimum conditions occur under no acid washing of leaves, solvent‐to‐feed ratio = 5 mL/g, and extraction duration = 1.5 h. The optimum conditions give the highest purity (94.1 wt.%) and yield (42.33%) as well as the highest surface area (328.61 m 2 /g), smallest pore diameter (8.69 nm), and largest pore volume (0.71 cc/g) of bio‐silica nanoparticles. Furthermore, bio‐silica nanoparticles are amorphous, spherical‐shaped aggregates, and have a white powdered colour.
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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 | 0.000 | 0.001 |
| 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 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".