Effect of Reduced Graphene Oxide on the Ta<sub>2</sub>O<sub>5</sub>-IrO<sub>2</sub> Electrocatalyst for Water Splitting
Bibliographic record
Abstract
There is a great interest in the development of advanced electrocatalysts for efficient water splitting. A tantalum iridium oxide (Ta2O5-IrO2) coating is considered to be one of the best electrocatalysts for the oxygen evolution reaction (OER) in acidic media. In the present study, novel Ta2O5-IrO2-rGO coatings with varying loads of reduced graphene oxide (rGO) were designed to investigate the effects of rGO on the catalytic activity and stability of the Ta2O5-IrO2 coating for the OER. Five different electrodes comprised of Ta2O5-IrO2-rGO on a titanium substrate were fabricated with incremental weight percentages of rGO (0.0 wt.%, 1.0 wt.%, 2.0 wt.%, 5.0 wt.% and 7.5 wt.%) using a facile thermal decomposition method. Scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and energy dispersive X-ray spectroscopy (EDS) were employed to characterize the morphology and composition of the prepared Ta2O5-IrO2-rGO coatings. Longevity tests revealed that the incorporation of rGO into the oxide layer strongly affected the stability of the Ta2O5-IrO2-rGO electrodes. The electrochemical activities of the prepared Ta2O5-IrO2-rGO electrodes were characterized by cyclic voltammetry (CV), linear sweep voltammetry (LSV), and electrochemical impedance spectroscopy (EIS). The Ta2O5-IrO2-rGO coating containing 1.0 wt.% rGO exhibited the greatest stability, along with enhanced OER activity.
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".