Electrodeposition of Pluronic F127 assisted rod-like EMD/carbon arrays for efficient energy storage
Bibliographic record
Abstract
In the traditional Duracell battery, the results obtained to date remain marginal in terms of cyclability. The development of the existing Zn-MnO2 with superior electrochemical performance for use in alkaline rechargeable batteries is reported. Electrolytic manganese dioxide (EMD) was synthesized from a conventional manganese sulphate bath but having a unique non-ionic surfactant (Pluronic F127), and activated carbon, in an electrolytic cell. The surface areas and morphologies of the as-prepared EMDs were influenced by the presence of these novel additives in the solution while the X-ray data revealed that there was no noticeable change in the crystal orientations thus all the EMDs were structurally similar. The synergistic effect of the optimal ratio of surfactant to carbon powder produced rod-like arrays exhibiting a larger surface area, which facilitates ion transport for better energy storage. It is interesting to note that EMD deposited in the presence of F127 showed better cyclability whereas in the presence of carbon, although it showed better storage capability, it was endowed with poor efficiency when compared with the surfactant added sample, nevertheless the results are better than the existing Zn-MnO2 technology (additive free EMD). Therefore, both the surfactant (50 mg dm(-3)) and the activated carbon (5 g dm(-3)) have been added together in the bath and the resultant EMD exhibits a high specific capacity and an excellent cycling stability. Moreover, the presence of surfactant and activated carbon improved the discharge capacity and its retention thus making this alkaline technology feasible for storing renewable energy for future use. The synergistic effect and the mechanism involved have been discussed.
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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".