Designing Ultrasmall Carbon Nanospheres with Tailored Sizes and Textural Properties for High-Rate High-Energy Supercapacitors
Bibliographic record
Abstract
The present work demonstrates the efficient design of ultrasmall porous carbon nanospheres with tailored sizes (5–40 nm in diameter) and optimized intrasphere textural properties for high-rate high-energy supercapacitor application. The carbon nanospheres are synthesized via a miniemulsion polymerization technique followed by KOH activation. It is shown that dual-step activation renders enlarged intrasphere micropores/mesopores, facilitating enhanced ion transports. Meanwhile, a decrease in nanosphere size from 40 to 5 nm significantly improves the rate performance, demonstrating the pronounced size effects due to enhanced intrasphere ion transport. The optimum dual-step-activated carbon nanosphere sample with an average sphere size of 5 nm, ACNS5-2, shows the high specific capacitances along with outstanding high-rate capabilities in both aqueous (272 F g–1 at 0.5 A g–1 and 81.6% of retention at 200 A g–1) and EMIMBF4 (223 F g–1 at 0.5 A g–1 and 67.2% of retention at 100 A g–1) electrolytes in symmetrical two-electrode cells. In EMIMBF4, ACNS5-2 displays a high energy density of 48 Wh kg–1 at a high power density of 14 kW kg–1, suggesting excellent energy storage efficiency. Moreover, the performance of ACNS5-2 competes well with or is superior to some best-performing porous carbon-based materials reported in the literature for supercapacitor applications even at lowered temperatures (at −20 °C: 150 F g–1 at 0.5 A g–1 with a capacitance retention of 64% at 10 A g–1) and high mass loading (8 mg cm–2: 205 F g–1 at 0.5 A g–1 with a capacitance retention of 64.5% at 20 A g–1). Our results, combined with structure–performance relationships, offer valuable guidelines for the rational design of carbon nanomaterials of optimum supercapacitive performances.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".