Reinforcement of Electrocatalytic Oxygen Evolution Activity Enabled by Constructing Silver‐Incorporated NiCo‐PBA@NiFe‐LDH Hierarchical Nanoboxes
Bibliographic record
Abstract
Abstract Complicated oxygen evolution reaction (OER) poses the bottleneck in improving the efficiency of hydrogen production through water electrolysis. Herein, an integrated strategy to modulate the electronic structure of NiFe layered double hydroxide (NiFe‐LDH) is reported by constructing Ag‐incorporated NiCo‐PBA@NiFe‐LDH heterojunction with a hierarchical hollow structure. This “double heterojunction” facilitates local charge polarization at the interface, thereby promoting electron transfer and reducing the adsorption energy of intermediates, ultimately enhancing the intrinsic activity of the catalyst. It is noteworthy that an exchange bias field is observed between NiCo‐PBA and NiFe‐LDH, which will be conducive to regulating the electron spin states of metals and facilitating the production of triplet oxygen. Additionally, the unique hierarchical nanoboxes provide a large specific surface area that ensures adequate exposure to adsorption sites and active sites. Profiting from the synergistic advantages, the overpotential is as low as 190 mV at a current density of 10 mA cm −2 , with a low Tafel slope of 21 mV dec −1 . Moreover, density functional theory (DFT) calculation further substantiated that the incorporation of Ag in the heterojunction can effectively reduce the adsorption energy of reactant intermediates and enhance the conductivity.
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".