Graphene Quantum Dots‐Based Advanced Electrode Materials: Design, Synthesis and Their Applications in Electrochemical Energy Storage and Electrocatalysis
Bibliographic record
Abstract
Abstract Graphene quantum dots (GQDs) have aroused great interest in the scientific community in recent years due to their unique physicochemical properties and potential applications in different fields. To date, much research has been conducted on the ingenious design and rational construction of GQDs‐based nanomaterials used as electrode materials and/or electrocatalysts. Despite these efforts, research on the efficient synthesis and application of GQDs‐based nanomaterials is still in the early stages of development and timely updates of recent research progress on new design concepts, synthetic strategies, and significant breakthroughs in GQDs‐based nanomaterials are highly desired. In light of the above, the effect of synthetic methods on the final product of the GQDs, the GQDs synthesis mechanism, and specific perspectives regarding the effect of the unique surface and structural properties of GQDs (e.g., defects, heteroatom doping, surface/edge state, size, conductivity) on the electrochemical energy‐related systems are discussed in‐depth in this review. Additionally, this review also focuses on the design of GQDs‐based composites and their applications in the fields of electrochemical energy storage (e.g., supercapacitors and batteries) and electrocatalysis (e.g., fuel cell, water splitting, CO 2 reduction), along with constructive suggestions for addressing the remaining challenges in the field.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".