Molybdenum Sulfide Nanoflowers as Electrodes for Efficient and Scalable Lithium‐Ion Capacitors
Bibliographic record
Abstract
Abstract Hybrid supercapacitors (HSCs) bridge the unique advantages of batteries and capacitors and are considered promising energy storage devices for hybrid vehicles and other electronic gadgets. Lithium‐ion capacitors (LICs) have attained particular interest due to their higher energy and power density than traditional supercapacitor devices. The limited voltage window and the deterioration of anode materials upsurged the demand for efficient and stable electrode materials. Two‐dimensional (2D) molybdenum sulfide (MoS 2 ) is a promising candidate for developing efficient and durable LICs due to its wide lithiation potential and unique layer structure, enhancing charge storage efficiency. Modifying the extrinsic features, such as the dimensions and shape at the nanoscale, serves as a potential path to overcome the sluggish kinetics observed in the LICs. Herein, the MoS 2 nanoflowers have been synthesized through a hydrothermal route. The developed LIC exhibited a specific capacitance of 202.4 F g −1 at 0.25 A g −1 and capacitance retention of >90 % over 5,000 cycles. Using an ether electrolyte improved the voltage window (2.0 V) and enhanced the stability performance. The ex‐situ material characterization after the stability test reveals that the storage mechanism in MoS 2 ‐LICs is not diffusion‐controlled. Instead, the fast surface redox reactions, especially intercalation/deintercalation of ions, are more prominent for charge storage.
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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".