Vacancy-Activated Surface Reconstruction of Perovskite Nanofibers for Efficient Lattice Oxygen Evolution
Bibliographic record
Abstract
Inducing the surface reconstruction of perovskites to promote the oxygen evolution reaction (OER) has garnered increasing attention due to the enhanced catalytic activities caused by the self-reconstructed electroactive species. However, the high reconstruction potential, limited electrolyte penetration, and accessibility to the perovskite surface greatly hindered the formation of self-reconstructed electroactive species. Herein, trace Ce-doped La 0.95 Ce 0.05 Ni 0.8 Fe 0.2 O 3−δ nanofibers (LCNF-NFs) were synthesized via electrospinning and postcalcination to boost surface reconstruction. The upshift of the O 2p band center induced by the rich oxygen vacancies lowered the reconstruction potential, and the specific one-dimensional nanostructure effectively enabled enhanced electrolyte accessibility and permeation to the LCNF-NFs. These collectively caused massive in situ generation of self-reconstructed electroactive Ni/FeO(OH) species on the surface. As a result, the surface-reconstructed LCNF-NFs exhibited accelerated lattice kinetics with a comparatively lower Tafel slope of 50.12 mV dec –1, together with an overpotential of only 342.3 mV to afford a current density of 10 mA cm –2 in 0.1 M KOH, which is superior to that of pristine LaNi 0.8 Fe 0.2 O 3−δ nanoparticles (NPs) and the same stoichiometric La 0.95 Ce 0.05 Ni 0.8 Fe 0.2 O 3−δ NPs, commercial IrO 2, and most of the state-of-the-art OER electrocatalysts. This study provided deep insights into the surface reconstruction behaviors induced by oxygen defects and an intellectual approach for constructing electroactive species in situ on perovskites for various energy storage and conversion devices.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".