Ultrafast Synthesis of IrB<sub>1.15</sub> Nanocrystals for Efficient Chlorine and Hydrogen Evolution Reactions in Saline Water
Bibliographic record
Abstract
Abstract The production of storable hydrogen fuel through water electrolysis powered by renewable energy sources such as solar, marine, geothermal, and wind energy presents a promising pathway toward achieving energy sustainability. Nevertheless, state‐of‐the‐art electrolysis requires support from ancillary processes which often incur financial and energy costs. Developing electrolysers capable of directly operating with water that contains impurities can circumvent these processes. Herein, we demonstrate the efficient and durable electrolysis of saline water to produce chlorine gas (Cl 2 ) and hydrogen using structurally ordered IrB 1.15 , synthesized through ultrafast joule heating. IrB 1.15 exhibits remarkable performance, achieving overpotentials of 75 mV for the chlorine evolution reaction (CER) and 12 mV for hydrogen evolution reactions (HER) at current densities of 10 mA cm −2 . Moreover, IrB 1.15 displays a durability of over 90 h towards both CER and HER. Density functional theory reveals that IrB 1.15 has adsorption energies significantly closer to 0 eV for Cl and H, compared to IrO 2 and Pt/C. Furthermore, in situ Raman investigations reveal that Ir in IrB 1.15 serves as the active center for CER, while the introduction of B atoms to Ir lattices mitigates the formation of absorbed hydrogen species on the Ir surface, thereby enhancing the performance of IrB 1.15 in HER.
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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) | 0.001 | 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".