Exploring Surface State and Exciplex Dominated Aggregation Induced Electrochemiluminescence of Graphene Quantum Dots Prepared via Electrochemical Exfoliation
Bibliographic record
Abstract
Abstract Graphene quantum dots (GQDs) have emerged as promising materials for electrochemiluminescence (ECL) applications due to their unique optical and electronic properties. In this study, GQDs were synthesized via electrochemical exfoliation of graphite in a constant current density mode, enabling scalable production with controlled size and surface functionalization. GQDs‐4 and GQDs‐20, synthesized at applied current densities of 4 mA/cm 2 and 20 mA/cm 2 to the graphite electrode, respectively, were investigated on roles of surface states and exciplex dominated aggregation‐induced emission (AIE) in their ECL performance. GQDs‐4 obtained an absolute ECL quantum efficiency of 0.0012 %±0.0002 %. GQDs‐20, with a smaller particle size, achieved an absolute ECL quantum efficiency of 0.028±0.002 %, demonstrating high efficiency in converting electrons into photons. While GQDs‐4 exhibited minor intensity in PL and ECL, they displayed a similar emission spectrum to GQDs‐20 in the ECL process. This finding highlights the significant role of surface states and AIE in influencing the emission properties of GQDs, independent from core‐state transitions. These results provide critical insights into the mechanisms governing GQD‐based ECL and offer pathways for optimizing these materials for use in biosensing, optoelectronics, and imaging applications. Keywords: Electrochemiluminescence, Graphene Quantum Dots, Exciplex, Surface States, Multi‐color Emission.
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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".