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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 teacher head, 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".