Lattice‐Strain Engineering of Homogeneous NiS<sub>0.5</sub>Se<sub>0.5</sub> Core–Shell Nanostructure as a Highly Efficient and Robust Electrocatalyst for Overall Water Splitting
Bibliographic record
Abstract
Abstract Developing highly‐efficient non‐noble‐metal electrocatalysts for water splitting is crucial for the development of clean and reversible hydrogen energy. Introducing lattice strain is an effective strategy to develop efficient electrocatalysts. However, lattice strain is typically co‐created with heterostructure, vacancy, or substrate effects, which complicate the identification of the strain‐activity correlation. Herein, a series of lattice‐strained homogeneous NiS x Se 1− x nanosheets@nanorods hybrids are designed and synthesized by a facile strategy. The NiS 0.5 Se 0.5 with ≈2.7% lattice strain exhibits outstanding activity for hydrogen and oxygen evolution reaction (HER/OER), affording low overpotentials of 70 and 257 mV at 10 mA cm −2 , respectively, as well as excellent long‐term durability even at a large current density of 100 mA cm −2 (300 h), significantly superior to other benchmarks and the precious metal catalysts. Experimental and theoretical calculation results reveal that the generated lattice strain decreases the metal d‐orbital overlap, leading to a narrower bandwidth and a closer d‐band center toward the Fermi level. Thus, NiS 0.5 Se 0.5 possesses favorable H* adsorption kinetics for HER and lower energy barriers for OER. This work provides a new insight to regulate the lattice strain of advanced catalyst materials and further improve the performance of energy conversion technologies.
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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) | 0.000 | 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".