Supraparticle Assembly of La<sub>0.8</sub>Sr<sub>0.2</sub>CoO<sub>3</sub> Nanoparticles for Enhanced Lattice Oxygen Oxidation in Alkaline Electrolysis
Bibliographic record
Abstract
Developing effective non-noble metal electrocatalysts for the oxygen evolution reaction (OER) remains challenging due to limited active sites, poor electronic conductivity, and high overpotentials associated with the conventional adsorbate evolution mechanism (AEM). To address these limitations, a one-step spray drying method is employed to assemble high-surface-area La 0.8 Sr 0.2 CoO 3 nanoparticles (LSCO-NP) into hierarchical supraparticles with ≈65% porosity and interconnected meso-/macropore networks. This architecture not only accelerates ion diffusion and interparticle electron transfer but also induces a mechanistic switch from the AEM to the lattice oxygen oxidation mechanism (LOM). La 0.8 Sr 0.2 CoO 3 supraparticles (LSCO-SP) demonstrate significantly enhanced OER performance, requiring ∼300 mV lower overpotential at 100 mA cm –2 after 1 h compared to LSCO-NP. Moreover, LSCO-SP exhibit faster catalytic kinetics, evidenced by a smaller Tafel slope of 76.2 mV dec –1 versus 82.5 mV dec –1 and lower charge transfer resistance of 1.11 Ω versus 1.31 Ω for LSCO-NP. Structural analyses confirmed that the LSCO-SP maintained their integrity under OER conditions. Furthermore, post-mortem X-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR) analyses reveal an increased formation of oxygen vacancies (O vac ) in LSCO-SP, confirming that the supraparticle design tunes the lattice oxygen-mediated mechanism–oxygen vacancy site mechanism (LOM–OVSM), enhancing OER performance. The hierarchical structure of LSCO-SP highlights their potential as a novel building block for catalyst layers in renewable energy applications.
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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".