Fluorescent Chitosan Hydrogels Based on Biomass‐Derived Carbon Dots for Toxic Aromatic Detection
Bibliographic record
Abstract
Abstract This study presents a sustainable method for synthesizing anionic carbon dots (CDs) from food waste, specifically almond peels (AP) and butternut peels (BP) and seeds (BS), using a microwave‐ultrasound process. The novelty of this work lies in the systematic investigation of thermal pretreatment (torrefaction) and its influence on the photoluminescence (PL) properties of biomass‐derived CDs. Torrefaction enhances PL intensity in CDs synthesized from almond peels and butternut seeds, while having a limited effect on those from butternut peels. The resulting CDs exhibit excitation‐dependent, blue‐emitting fluorescence and maintain over 85% PL stability across the environmentally relevant pH range of 5–9. To enable pollutant detection, the optimized CDs are embedded into chitosan‐based hydrogels, forming water‐stable, reusable fluorescent sensors. These composites detect aromatic contaminants, including pentachlorophenol (PCP) and 4‐chloro‐2‐methylphenoxyacetic acid (MCPA), with detection limits of 1.45 ± 0.08 and 4.10 ± 0.10 n m , respectively. The combination of waste valorization, surface‐state modulation, and soft‐material integration supports the development of cost‐effective, environmentally friendly sensors. This initiave contributes to Sustainable Development Goals focused on clean water and responsible consumption by transforming food waste into functional materials for environmental monitoring.
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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".