Optimized Double Manganese Oxide Deposition for Enhanced Electrochemical Capacitor Performance
Bibliographic record
Abstract
Manganese oxide pseudocapacitive materials are deposited using a novel procedure involving first depositing a heat-treated base layer followed by a hydrous top layer. The ratio of heat-treated to hydrous film is optimized to generate films that excel across a wide range of electrochemical capacitor (EC) properties and to elucidate the mechanisms underpinning the film performance. We show that a thin heat-treated base layer imparts low resistance, high energy efficiency and power-capability and enhanced film stability in a large potential window. These benefits are due to an improved oxide-current collector connection; however, if the layer is too thin (<25 nm), the stability is lost. Conversely, heat-treatment causes more parasitic oxidation reactions during initial film cycling, though these reactions are mitigated with a thick hydrous top layer. This hydrous film also affords high capacitance, capacity, coulombic efficiency and energy density due to an abundance of hydrated sites in the oxide to facilitate the cation insertion/removal needed for pseudocapacitance. The double-deposited films also show less self-discharge. We find that a dry:wet film ratio of 5:95 results in optimal film performance. While this novel dry-wet double-deposition has been demonstrated with manganese oxide, we anticipate similar performance benefits with other pseudocapacitive 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 | 0.000 |
| 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".