Morphology evolution of CoNi‐LDHs synergistically engineered by precipitant and variable cobalt for asymmetric supercapacitor with superior cycling stability
Bibliographic record
Abstract
Abstract Cobalt–nickel layered double hydroxides (CoNi‐LDHs) have been extensively synthesized through precipitation methods for their application in supercapacitors (SC). However, the influence of precipitant quantity on both morphology evolution and SC performance has been an underexplored area. This study systematically examines the morphological changes in CoNi‐LDHs by varying the alkaline quantity and evaluates the performance of asymmetric SC. The findings reveal a progressive transformation in the morphology of CoNi‐LDHs with an increase in alkaline content, starting from nanorod (Co 1 Ni 2 (OH) 2 ‐1HMA), progressing to nanorod/nansosheet composite (Co 1 Ni 2 (OH) 2 ‐4HMA), and ultimately evolving into nanosheet (Co 1 Ni 2 (OH) 2 ‐8HMA). This evolution is attributed to the synergetic effect of the precipitant and variable cobalt, which provides multiple valences and induces morphology evolution. The resulting LDHs demonstrate different SC performances: (1) Co 1 Ni 2 (OH) 2 ‐1HMA exhibits a maximum capacitance of 1764 F/g, while Co 1 Ni 2 (OH) 2 ‐4HMA and Co 1 Ni 2 (OH) 2 ‐8HMA show values of 1460 F/g and 1676 F/g, respectively; (2) rate capabilities showcase percentages of 60.5% for Co 1 Ni 2 (OH) 2 ‐1HMA, 83.1% for Co 1 Ni 2 (OH) 2 ‐4HMA, and 66.3% for Co 1 Ni 2 (OH) 2 ‐8HMA; (3) maximum energy densities are recorded at 72.1 Wh/kg for Co 1 Ni 2 (OH) 2 ‐1HMA, 41.3 Wh/kg for Co 1 Ni 2 (OH) 2 ‐4HMA, and 62.8 Wh/kg for Co1Ni 2 (OH) 2 ‐8HMA. Particularly, Co 1 Ni 2 (OH) 2 ‐8HMA exhibits superlong cycling stability, retaining approximately 99% capacitance after 25000 consecutive charge/discharge cycles at 7.0 A/g. This result underscores its significant potential for efficient 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".