A study on the adsorptive removal of chromium ( <scp>VI</scp> ) using zerovalent iron nanoparticles prepared from <scp> <i>Aegle marmelos</i> </scp> fruit shell: Kinetic and isotherm insights
Bibliographic record
Abstract
Abstract This study explores a green and sustainable approach for the synthesis of zero‐valent iron nanoparticles using the fruit shell extract of Aegle marmelos (Bael) and their application in the adsorptive removal of chromium (VI) from aqueous systems. The biosynthesized nanoparticles were characterized using XRD and SEM techniques, confirming their porous morphology and predominantly zero‐valent state. Batch adsorption experiments were conducted to evaluate the influence of key parameters such as pH, contact time, adsorbent dosage, and initial chromium (VI) concentration on removal efficiency. Optimal adsorption (∼89%) was achieved at pH 4.0, with a dose of 0.3 g/100 mL and 40 min of contact time. Pseudo‐second‐order kinetic model with R 2 value as 0.93 was the dominant mechanism, suggesting chemisorption. Equilibrium data fit well to the Langmuir isotherm ( R 2 = 0.917), indicating monolayer adsorption with a highest possible capacity ( q m ) of 24.32 mg/g. The process's spontaneous, exothermic, and entropy‐driven nature was validated by thermodynamic characteristics. The dual role of the Bael shell derived zerovalent iron nanoparticles in reducing chromium (VI) to chromium (III) and facilitating its adsorption highlights the potential of this biosorbent for effective and eco‐friendly remediation of chromium‐contaminated water.
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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".