Optimizing the Performance of Sunflower (Helianthus annuus L.) Seed Shell-Derived Biochar for Lead Ion Adsorption
Bibliographic record
Abstract
Sunflower seed shells are agricultural waste that is abundant and often underutilized.Converting these shells into biochar for lead ion adsorption not only provides a valueadded use for the waste material but also addresses the issue of agricultural residue management.Sunflower seed shell biochar is produced through a pyrolysis process, transforming the agricultural waste into a carbon-rich material with a high surface area and porous structure.The study aims to determine the effect of biochar derived from sunflower seed shells (BSFL) as an adsorbent by determining the optimum pH and optimum weight in absorbing lead ions.Water content and ash content were determined using the drying and dusting method.The research results showed that the characteristics of sunflower seed shell biochar were 45%, the water content was 3.6% and the ash content was 7.2%.Determination of the optimum pH varied from 3, 4, 5, and 6 while determination of optimum weight was carried out with weight variations of 30 mg, 60 mg, 90 mg, 120 mg, and 150 mg respectively.The research results showed that the optimal pH of sunflower seed shell biochar for adsorbing lead was 5 with the percentage of adsorbed was 99.58%.Meanwhile, the optimum weight of biochar to adsorb Lead ions is 120 mg with a percentage of Lead adsorbed is 99.71%.The pH influences the surface charge of the biochar and the speciation of lead ions, while the weight determines the available surface area for adsorption.Biochar morphology pore and the content of the element characterized by SEM-EDS indicated the macropores and the carbon content of 62.65%.The finding contributes to reducing environmental pollution, addressing waste management challenges, and leveraging biochar's efficiency for the removal of lead ions from contaminated water sources, providing insights into the key factors influencing its adsorption capacity.
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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 | 0.001 |
| 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".