Synthesis of Porous Super-Capacitor Electrodes using the SPPS Deposition Technique
Bibliographic record
Abstract
Abstract Electrical double-layer capacitors (EDLCs) owe their large capacitance to high specific surface area carbon-based electrode materials adhered to a current collector via an adhesive. However, recent studies attribute greater electrical energy storage capacity to transition metal oxides/nitrides: a new generation of electrode materials for use in super-capacitors with mixed double-layer and pseudo-capacitive properties. Solution Precursor Plasma Spray (SPPS) deposition is a technique that allows coatings to be fabricated with fine grain sizes, high porosity levels, and high surface area; characteristics ideal for application as transition metal oxide super-capacitor electrodes. A liquid injection apparatus was designed to inject the liquid into the DC-arc plasma and to investigate the effects of various operating parameters such as spray distance, solution concentration and solution flow rate on the chemistry and surface topography of the deposits. Understanding and controlling the evolution of the precursor solution in the DC-arc plasma jet is crucial in producing coatings of the desired structures. DTA/TGA, SEM, XRD, and electrochemical analyses performed to characterize the coatings will be discussed, and the potential of the deposits for use in super-capacitors will be assessed.
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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".