Scalable Production of Few-Layer Boron Sheets by Liquid-Phase Exfoliation and Their Superior Supercapacitive Performance
Bibliographic record
Abstract
Although two-dimensional boron (B) has attracted much attention in electronics and optoelectronics due to its unique physical and chemical properties, in-depth investigations and applications have been limited by the current synthesis techniques. Herein, we demonstrate that high-quality few-layer B sheets can be prepared in large quantities by sonication-assisted liquid-phase exfoliation. By simply varying the exfoliating solvent types and centrifugation speeds, the lateral size and thickness of the exfoliated B sheets can be controllably tuned. Additionally, the exfoliated few-layer B sheets exhibit excellent stability and outstanding dispersion in organic solvents without aggregates for more than 50 days under ambient conditions, owing to the presence of a solvent residue shell on the B sheet surface that provides excellent protection against air oxidation. Moreover, we also demonstrate the use of the exfoliated few-layer B sheets for high-performance supercapacitor electrode materials. This as-prepared device exhibits impressive electrochemical performance with a wide potential window of up to 3.0 V, excellent energy density as high as 46.1 Wh/kg at a power density of 478.5 W/kg, and excellent cycling stability with 88.7% retention of the initial specific capacitance after 6000 cycles. This current work not only demonstrates an effective strategy for the synthesis of the few-layer B sheets in a controlled manner but also makes the resulting materials promising for next-generation optoelectronics and energy storage applications.
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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".