Ir Cluster-Decorated Carbon Composite as Bifunctional Electrocatalysts for Acidic Stable Overall Water Splitting
Bibliographic record
Abstract
In this paper, the bifunctional Ir cluster-decorated carbon Composite (Ir/C-1) electrocatalysts for both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) are synthesized through simple in-situ growth of bimetallic Zn-Ir-MOF, followed by Zn reduction and evaporation for creating pore structure and uniform particle distribution. The prepared catalyst of Ir/C-1 with Ir cluster ( d = 3 ± 0.1 nm) highly dispersed and closely attached to pore carbon through Ir–O–C bonds shows both excellent OER and HER performance. At a current density of 10 mA cm −2 , the catalyst shows overpotentials of only 50.4 mV for HER and 359 mV for OER in 0.5 M H 2 SO 4 , respectively, which are better than commercial Ir/C and Pt/C catalysts. Density functional theory (DFT) calculations further revealed that the exposure of Ir (110) in Ir/C-1 can facilitate the thermodynamic process of HER and OER. This paper also gives a detailed discussion about the enhancement mechanism. To validate the catalyst, a single electrolysis cell is assembled using the prepared Ir/C-1 catalyst as both the anode and cathode catalysts. The result shows that the cell only needs a cell voltage of 1.653 V to obtain a current density of 10 mA cm −2 , which is better than commercial Ir/C and Pt/C catalysts.
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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 | 0.001 | 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".