Hydrous Ir Oxide Film Properties at Sol-Gel Derived Ir Nanoparticles
Bibliographic record
Abstract
Ir oxide (IrOx) films, formed on Au substrates using the sol‐gel (SG) technique, have been characterized electrochemically in sulfuric acid solution. The highest film charge densities can be reached by using higher withdrawal rates of the substrate electrode from the Ir‐containing sol solution, as well as by applying multiple successive coats. Allowing films to dry at ca. 100°C also yields higher charge densities. The kinetics of the Ir(III)/(IV) redox process are excellent, significantly higher, on a mole or mass IrOx basis, than for IrOx films formed by potential cycling/pulsing of standard bulk Ir electrodes in acidic solutions. The SG‐derived films are electrochromic, highly capacitive, appear to be particularly catalytic toward the oxygen evolution reaction, and have excellent adhesive properties at Au. It appears that small area planar substrates are preferred over curved ( e.g. , wire) electrodes in terms of SG‐formed IrOx film uniformity and the maximum achievable charge density. The measurement of the mass of fresh SG‐formed Ir films and the maximum IrOx charge density which can be obtained indicates that one out of every 2 to 3 Ir atoms are used in IrOx formation. © 2000 The Electrochemical Society. All rights reserved.
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".