Advanced Electrode Materials Comprising of Structure‐Engineered Quantum Dots for High‐Performance Asymmetric Micro‐Supercapacitors
Bibliographic record
Abstract
Abstract Micro‐supercapacitors (MSCs) as a new class of energy storage devices have attracted great attention due to their unique merits. However, the narrow operating voltage, slow frequency response, and relatively low energy density of MSCs are still insufficient. Therefore, an effective strategy to improve their electrochemical performance by innovating upon the design from various aspects remains a huge challenge. Here, surface and structural engineering by downsizing to quantum dot scale, doping heteroatoms, creating more structural defects, and introducing rich functional groups to two dimensional (2D) materials is employed to tailor their physicochemical properties. The resulting nitrogen‐doped graphene quantum dots (N‐GQDs) and molybdenum disulfide quantum dots (MoS2‐QDs) show outstanding electrochemical performance as negative and positive electrode materials, respectively. Importantly, the obtained N‐GQDs//MoS2‐QDs asymmetric MSCs device exhibits a large operating voltage up to 1.5 V (far exceeding that of most reported MSCs), an ultrafast frequency response (with a short time constant of 0.087 ms), a high energy density of 0.55 mWh cm−3, and long‐term cycling stability. This work not only provides a novel concept for the design of MSCs with enhanced performance but also may have broad application in other energy storage and conversion devices based on QDs materials.
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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".