Orange peel derived biochar assisted rGO@MoS₂ composite for visible light driven ciprofloxacin degradation and hydrogen evolution
Bibliographic record
Abstract
In the context of global sustainability and clean water initiatives, developing multifunctional catalysts for hydrogen production and pharmaceutical pollutant removal is crucial. Herein, a ternary reduced graphene oxide@MoS₂/orange peel-derived biochar (rGO@MoS₂/ODB) nanocomposite was synthesized via a facile hydrothermal method to enhance photocatalytic and electrocatalytic efficiencies through synergistic assembly. The composite exhibited a hierarchical morphology, confirming successful integration of MoS₂, ODB, and rGO. Photoluminescence analysis revealed significantly reduced charge recombination, with oxygen vacancy concentration increasing to 33.86 % from 14.17 % in pristine MoS₂. The rGO@MoS₂/ODB showed superior hydrogen evolution reaction activity, with a Tafel slope of 107 mV/dec and overpotential of 425 mV at 5 mA/cm 2 , indicating efficient charge transfer. In photocatalysis, it achieved 97.4 % ciprofloxacin degradation under visible light in 100 min, outperforming binary and pristine MoS₂. Performance depended on catalyst dosage, pollutant concentration, and irradiation time; optimum conditions were 25 mg catalyst, 20 ppm CIP, and 150 W lamp. Response surface methodology confirmed high statistical correlation (R 2 > 0.98). The catalyst maintained 93.4 % degradation efficiency after 10 cycles, demonstrating excellent reusability. These findings underscore the novelty of this sustainable, multifunctional composite as a robust platform for clean hydrogen production and wastewater treatment. • Novel rGO@MoS₂/ODB from orange peel biochar enables HER and CIP degradation. • Composite showed 97.4 % CIP removal and 107 mV/dec Tafel slope for HER activity. • RSM optimized degradation; catalyst retained >93 % efficiency after 10 cycles.
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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".