Effects of silico-tungstic acid on the pseudo capacitive properties of manganese oxide for electrochemical capacitors applications
Bibliographic record
Abstract
In this work manganese oxide (MnO2) is modified with silico-tungstic acid (STA). Three samples are synthesized by the co-precipitation method. The powders obtained after elaboration are characterized by X-ray diffraction (XRD), Scanning Electron Microscopy (SEM) imaging coupled to Electron Dispersive Spectroscopy (EDS) analysis and Brunauer-Emmet-Teller (BET) for their surface area determination. The effect of the modification of the manganese oxide with STA on its surface is determined. It is shown that MnO2 modified with STA exhibits better cumulative high specific surface areas and mesoporous volumes areas. For example, the sample fabricates with 10% STA (MnO2 – 10% STA) has a BET surface area of 153.6 m2 g−1 and volume arear 0.92 cm3 g−1 whereas the sample without STA (MnO2 – 0% STA) has a surface area of 132.57 m2 g−1 and a mesoporous area of 0.26 cm3 g−1 . The electrochemical performance analysis of the different working electrodes prepared for super-capacitors applications is carried out using cyclic voltammetry (CV)., using a solution of 0.5 MK2SO4 as an electrolyte in potential range of -0.4 and 0.9 Volt at a sweep speed of 10 mV/s. The CV results are correlated to those of the BET surface and mesoporous areas values Accordingly, it is shown that samples spiked with STA exhibit higher electrochemical double layer capacitance than those of none modified with STA. These measurements respectively give 38 F g-1, and 181 F g-1 for MnO2 without STA (MnO2 – 0% STA), and MnO2 modified with 10% STA (MnO2 – 5% STA).
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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".