Scalable Synthesis of High-Quality Graphene Quantum Dots by Reductive Intercalation/Exfoliation of Coal
Bibliographic record
Abstract
Coal, historically a low-cost and abundant energy resource, is emerging as a promising carbon-rich precursor for advanced nanomaterials. In this work, we introduce a reductive intercalation strategy to synthesize reduced (electron-rich) graphene quantum dots (GQDs) directly from anthracite coal. Potassium intercalation transforms the rigid graphenic framework of anthracite coal into a stage-I polyelectrolyte salt that spontaneously dissolves in N -methyl-2-pyrrolidone (NMP), yielding uniform (2.5–3.5 nm), reduced GQDs without the need for sonication or oxidative processing. The method achieves an isolated yield of <28% based on the starting mass of anthracite coal. Practically, this means that 3.6 kg of coal can yield up to 1 kg of graphene quantum dots, highlighting the scalability and efficiency of this approach. The resulting GQDs exhibit a direct bandgap of 3.4 eV and strong excitation-dependent photoluminescence. Thermo-optical characterization of GQDs in NMP reveals a thermal diffusivity of (6.4 ± 0.3) × 10 –8 m 2 /s and a nonlinear refractive index of −4.69 × 10 –9 cm 2 /W, demonstrating their potential for photothermal conversion and nonlinear optical applications. Notably, the GQDs can be precipitated and collected as slurries or powders that are readily dispersible in a variety of other solvents, including water, ethanol, isopropanol, facilitating their integration into diverse solution-processable systems. This scalable, oxidation-free approach positions coal as a viable feedstock for high-performance quantum nanomaterials with potential applications in sustainable sensing, and thermal management technologies.
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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 | 0.000 | 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) | 0.000 | 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".