Effect of Surface Morphology and Pore Structure on the Water Electrolysis, O<sub>2</sub> Evolution Activity of Sb-Doped SnO<sub>2</sub> Supported IrO<sub>2</sub>
Bibliographic record
Abstract
Nanoparticle IrO2 supported on Antimony doped Tin Oxide (ATO) is a very active electrocatalyst for the oxygen evolution in polymer electrolyte water electrolysis. Using the modified polyol method [A. Marshal and et al. /Material Chemistry and Physics, 94 (2005) 226] nanoparticles of IrO2 were synthesized and deposited on commercial antimony doped tin oxide (ATO). This synthesis involves the reduction of a metal precursors (IrCl2) in ethylene glycol containing a suspension of ATO. Nanoparticle catalysts were characterized by cyclic voltammetry (CV) and steady state polarization analysis in 0.5 M H2SO4 solution. The CV voltammograms for IrO2/ATO catalyst showed clear broad peaks for oxidation and reduction of IrO2, with peaks at 0.72 V and 0.82 V respectively. The catalyst morphology, surface area and the pore structure were studied using BET nitrogen physisorption, mercury porosimetry, Scanning Electron Microscope (SEM), Transmission Electron Microscopy (TEM). The BET surface area was found to be 59.4 m2/gr and the total porosity (interparticle and intraparticle porosity) of the catalyst was found to be 4.45 %. A narrow particle size distribution for the IrO2 phase in the range of 1 to 3 nm was determined by examining and analyzing the TEM images. X-Ray Diffraction (XRD) and Differential Scanning Calorimetry (DSC) were also done to provide information on the crystal structure and thermal properties. Polarization curves for membrane electrode assemblies (MEAs) fabricated using this catalyst will be presented. The importance of the pore structure in providing good catalytic activity and mass transport in the two-phase flow process will be discussed.
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".