Improved oxygen evolution activity of IrO<sub>2</sub> by <i>in situ</i> engineering of an ultra-small Ir sphere shell utilizing a pulsed laser
Bibliographic record
Abstract
Noble metal-based catalysts are vital electrocatalysts for the oxygen evolution reaction (OER), which is a half reaction among multiple renewable energy-related reactions. To fully exploit their potential as efficient OER catalysts, we developed a fast one-step strategy to engineer a unique nanostructure for the benchmark catalyst IrO2 utilizing an ultra-fast pulse laser, through which a shell of ultra-small Ir spheres with a diameter of ca. 2 nm is in situ engineered around the IrO2 core. The creation of the Ir sphere shell not only increases the electrochemical surface area, but also improves the electrical conductivity of the electrocatalyst. The as-engineered IrO2@Ir architecture exhibits extremely high electrocatalytic activity towards the OER, revealing an overpotential of 255 mV at 10 mA cm-2 and Tafel slope of 45 mV dec-1. These values are much lower than those observed for the unmodified structure. Furthermore, the catalytic performance is the best among all the noble metal-based OER catalysts. This work may open a new avenue to efficiently improve the catalytic activity of noble metal-based catalysts and significantly advance the development in the energy industry.
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".