LiMnBO<sub>3</sub> Nanobeads As an Innovative Anode Material for High Power Lithium Ion Capacitor Applications
Bibliographic record
Abstract
A novel approach was made to fabricate lithium ion hybrid capacitor (Li-HC) having LiMnBO3 nanobead (LMB-NB) anode and polyaniline nanofiber (PANI) cathode in 1 M LiPF6 organic electrolyte. LMB-NB and PANI nanofibers were synthesized using urea assisted microwave-solvothermal method and chemical polymerization process, respectively. The PANI/LMB-NB cell showed improved electrochemical capacitive behavior as compared to activated carbon (AC)/LMB-NB cell due to the characteristic conductivity and the morphological feature of PANI as well as LMB-NB electrodes. A discharge capacitance (DCcell) of ∼125 F g(-1) was obtained at a current density of 1 A g(-1) between the potential range 0 and 3 V for PANI/LMB-NB cell, while AC/LMB-NB cell delivered only 77 F g(-1) at the same current density. Moreover, PANI/LMB-NB cell exhibited excellent rate performance with the DCcell of about 55 F g(-1) at 2.25 A g(-1) and still retained 94% of the initial value after 30 000 charge-discharge cycles. In addition, maximum energy and power densities of 42 Wh kg(-1) and 5350 W kg(-1), respectively, were achieved from PANI/LMB-NB cell. The obtained DCcell, energy, and power densities along with prolonged cyclic life for PANI/LMB-NB cell are some of the best ever reported values for Li-HC as compared to the cells constructed with various lithium intercalating 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".