Study of the Effect of Calcination Temperature on the Morphology and Activity of Iridium Oxide Electrocatalyst Supported on Antimony Tin Oxide (ATO) for PEM Electrolyser Technology
Bibliographic record
Abstract
Iridium oxide nanoparticles supported on Antimony Tin oxide (ATO) have been widely used for the oxygen evolution reaction in polymer electrolyte membrane (PEM) electrolysis. In this paper the morphology, crystallinity, catalyst-support interaction and performance for the oxygen evolution reaction (OER) activity of the catalyst iridium oxide supported on ATO at various calcination temperatures will be reported. The supported catalyst was synthesized using a modified Polyol method and calcined at temperatures of 200, 350, 500 and 700ºC. The electrocatalysts were physically characterized by Differential Scanning Calorimetry, Thermogravimetric Analysis, X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM) and Brunauer, Emmett and Teller (BET) surface area measurements. The catalysts were also electrochemically characterized by cyclic voltammetry in a typical three electrode system. The main focus of this work is to study the effect of calcination temperatures on the morphology of the iridium oxide nano particles as a function of temperature and degree of crystallization. We find that iridium oxide nano particles synthesized with Polyol method has amorphous structure and calcining at temperatures higher than 400ºC increases the particle size and changes its structure from amorphous to crystalline. It also was found that the amorphous iridium nano particles are highly active and efficient catalyst toward OER, while calcining the catalyst decreases the electrochemically active surface area and its activity.
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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) | 0.000 | 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".