Microwave-assisted synthesis of biomass-derived N-doped carbon dots for metal ion sensing
Bibliographic record
Abstract
Abstract Biomass-derived carbon dots (CDs) have gained significant research interest for environmental monitoring applications thanks to their cost-effectiveness and sustainability. Using eco-friendly biowastes as precursors for CDs production offers an alternative to expensive and unsustainable inorganic and chemically synthesized CDs. This study presents the findings regarding the successful synthesis of biomass-based nitrogen-doped carbon dots (N-CDs) via a rapid, cost-effective, and environmentally friendly microwave-assisted method. Carboxymethyl cellulose (CMC) and glycine were used as carbon precursors and nitrogen dopants for the first time. The N-CDs exhibited a moderately high quantum yield of 31.6 ± 1.5% with an optimal fluorescence excitation wavelength of 400 nm. FTIR, CHNS, and SEM–EDX analyses characterized the N-CDs' surface functional groups and elemental composition. The optical stability of the N-CDs was validated across varying pH levels and NaCl concentrations. The N-CDs displayed notable selectivity and sensitivity for Fe 3 ⁺, Cu 2 ⁺, and Hg 2 ⁺ ions. The primary quenching mechanisms involve electrostatic interactions, π–π interactions, inner filter effects, and energy transfer. Stern–Volmer analysis revealed strong linear quenching for Fe 3 ⁺, Cu 2 ⁺, and Hg 2 ⁺ ions within the 0–10 µM range concentrations, with detection limits (LOD) of 6.0 µM, 1.41 µM and 1.36 µM for Fe 3 ⁺, Cu 2 ⁺, and Hg 2 ⁺, respectively. The fluorescence quenching for Fe 3 ⁺ ions enhanced sensitivity at higher concentrations, while selectivity decreased at lower concentrations. These findings highlight the potential of these N-CDs as a cost-effective and sustainable tool for environmental monitoring, offering a promising approach to addressing critical water contamination issues. Graphical Abstract
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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".