Towards High Capacitive Performance of Chemically Deposited β-Ni(OH)2 Nanolamellae Electrode Films
Bibliographic record
Abstract
Nickel hydroxide β-Ni(OH)2 nanolamellae with high aspect ratios were grown via chemical bath deposition (CBD) on both smooth and textured nickel foil. Depending on bath composition and/or the presence of an additive, thin foam-like nanolamellae to stacked lamellae were obtained. The used CBD method is highly cost-effective, as it is faster and requires less chemicals than typical hydrothermal methods, and it is readily implementable for large-scale production. The influence of surface texture on the final morphology and its effect on capacitive performance was investigated. Herein, we show how subtle changes in the concentration can drastically influence the morphology, which, in turn, drastically impacts the supercapacitive performance of the electrode. Also, the use of a textured surface significantly impacts the morphology, with vastly better cycling performance than samples made on a relatively smooth substrate. The measured specific capacitance values of the best sample were 1961 Fg−1 at 5 mVs−1 and 1998 Fg−1 at 1 Ag−1 under potentiostatic and galvanostatic conditions, respectively. This sample also retained 100% of its initial specific capacitance when discharged at a very high current density of 40 Ag−1. These values are substantially enhanced compared to previously reported data using a nearly analogous method (CBD with higher reagent conc.), with our method, cost-wise, offering economic advantages relative to results obtained with similar materials and other methods (e.g., hydrothermal).
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".