Synthesis And Characterization Of Amorphous Silica From Oil Palm Empty Palm Fruit Bunch
Bibliographic record
Abstract
The valorization of agroindustrial solid wastes, particularly oil palm residues such as empty fruit bunches (EFB), for sustainable and green chemistry initiatives has gained momentum in recent years. This paper explores the extraction of amorphous silica from EFB ash as a means to repurpose this abundant waste material. Characterization of the silica nanoparticles was conducted through scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), particle size and zeta potential analysis, and X-ray diffractometer (XRD) analysis. SEM images revealed irregular shapes and varied compositions of the nanoparticles, while FTIR confirmed the presence of silanol groups and CTAB molecules on the surface. Particle size analysis indicated a size of approximately 962 nm with a zeta potential of -26.9 mV, suggesting negatively charged surfaces. XRD analysis confirmed the amorphous nature of the silica nanoparticles. Overall, this study demonstrates a novel approach to extract silica from EFB ash, highlighting its potential for various applications such as adsorbents, catalysts, and biopolymers, thus contributing to sustainable waste management and green chemistry practices.
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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".