Iridium‐ruthenium‐oxide coatings for supercapacitors
Bibliographic record
Abstract
Abstract Electrochemical, topographical, and morphological properties of thermally‐prepared Irx‐Ru1‐x‐oxide coatings of various compositions (0 < x ≤ 1), formed on a Ti metal substrate, were investigated for their potential application as supercapacitor (SC) electrodes employing scanning electron microscopy and electrochemical techniques of cyclic voltammetry, galvanostatic charge/discharge cycling, and electrochemical impedance spectroscopy. A current state‐of‐the‐art pure ruthenium oxide (RuO2) coating showed relatively low performance compared to other bimetallic IrxRu1‐x‐oxide coatings operated under the same experimental conditions. An electrochemically‐activated Ir0.4Ru0.6‐oxide coating yielded the highest capacitance value (85 mF cm−2). Prolonged electrochemical cycling of the Ir/Ru‐oxide coatings in a corrosive phosphate‐buffered saline pH = 7.4, performed within an extreme potential window of 5 V, revealed an excellent stability of the coatings. In addition, this cycling procedure enabled a significant increase in capacitance for all coating compositions. It was shown that the areal capacitance (CGA) of these coatings is strongly dependent upon the nature of the components of which the metal oxide is composed. The addition of IrO2 to RuO2 improved the stability and capacitive performance of the thermally‐prepared Ir‐Ru‐oxide coatings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".