Superior Micro-Supercapacitors Based on Graphenequantum Dots
Bibliographic record
Abstract
Graphene quantum dots (GQDs) have attracted tremendous research interest due to the unique properties associated with both graphene and quantum dots. Here, a new application of GQDs as ideal electrode materials for supercapacitors is reported. To this end, a GQDs//GQDs symmetric microsupercapacitor is prepared using a simple electro-deposition approach, and its electrochemical properties in aqueous electrolyte and ionic liquid electrolyte are systematically investigated. The results show that the as-made GQDs micro-supercapacitor has superior rate capability up to 1000 V s −1 , excellent power response with very short relaxation time constant ( τ0 = 103.6 μs in aqueous electrolyte and τ0 = 53.8 μs in ionic liquid electrolyte), and excellent cycle stability. Additionally, another GQDs//MnO2 asymmetric supercapacitor is also built using MnO2 nanoneedles as the positive electrode and GQDs as the negative electrode in aqueous electrolyte. Its specific capacitance and energy density are both two times higher than those of GQDs//GQDs symmetric micro-supercapacitor in the same electrolyte. The results presented here may pave the way for a new promising application of GQDs in micropower suppliers and microenergy storage devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".