Ecofriendly synthesis of selenium nanoparticles using agricultural <scp><i>Citrus fortunella</i></scp> waste and decolourization of crystal violet from aqueous solution
Bibliographic record
Abstract
Abstract In this study, the reuse of Citrus fortunella (CF) plant waste, an agricultural product, was evaluated within the scope of sustainability. In this context, selenium nanoparticles (CF‐Se NPs) were synthesized from CF waste extracts and crystal violet (CV) dye was removed. The characteristic structure of the synthesized CF‐Se NPs was determined by X‐ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X‐ray (EDX), Fourier transform infrared spectroscopy (FTIR), UV–vis spectroscopy, and point of zero charge (pHpzc). Batch adsorption tests were applied to determine the effect of the synthesized CF‐Se NPs on CV removal. Four different kinetic and isotherm models were examined using error analysis functions. While the particle size of CF‐Se NPs was determined as 27.58 nm, the pHpzc value was calculated as 9.40, the average surface charge distribution was −24.1 mV, and mass losses were 9.03% and 13.42% at 334.99 and 739.21°C, respectively. The most suitable kinetic and isotherm model for CV removal with CF‐Se NPs was determined to be pseudo‐second‐order with a R2‐value of 0.999 and Freundlich with R2‐value of 0.993, and the qmax was calculated as 23.55 mgCV/gCF‐SeNPs. The effectiveness of CF‐Se NPs synthesized from waste in CV removal is a remarkable issue in terms of sustainable production.
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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".