Core–Shell Structured NiFeSn@NiFe (Oxy)Hydroxide Nanospheres from an Electrochemical Strategy for Electrocatalytic Oxygen Evolution Reaction
Bibliographic record
Abstract
Abstract Efficient electrocatalysts for the oxygen evolution reaction (OER) are highly desirable because of the intrinsically sluggish kinetics of OER. Herein, core–shell structured nanospheres of NiFexSn@NiFe (oxy)hydroxide (denoted as NiFexSn‐A) are prepared as active OER catalysts by a facile electrochemical strategy, which includes electrodeposition of NiFexSn alloy nanospheres on carbon cloth (CC) and following anodization. The alloy core of NiFexSn could promote charge transfer, and the amorphous shell of NiFe (oxy)hydroxide is defect‐rich and nanoporous due to the selective electrochemical etching of Sn in alkaline medium. The optimized catalyst of NiFe0.5Sn‐A displays a remarkable OER performance with a low overpotential of 260 mV to reach the current density of 10 mA cm−2, a small Tafel slope of 50 mV dec−1, a high turnover frequency of 0.194 s−1 at an overpotential of 300 mV, and a robust durability. Further characterizations indicate that the superior OER performance of the core–shell structured NiFe0.5Sn‐A nanospheres might originate from abundant active sites and small charge transfer resistance. This work brings a new perspective to the design and synthesis of core–shell structured nanospheres for electrocatalysis through a facile electrochemical strategy.
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".