In-situ formation of SrCO3 microcrystals-decorated Fe3O4 nanosheets as an efficient and long-lasting catalyst for overall water splitting
Bibliographic record
Abstract
• Hydrothermal synthesis enabled in-situ Fe 3 O 4 @SrCO 3 /NF formation with enhanced surface roughness and stability. • Catalytic performance surpassed commercial IrO 2 /NF with 243 mV OER overpotential and a low Tafel slope of 38 mV dec −1 . • DFT calculations revealed in-situ formed SrCO 3 lowers the OER energy barrier to 0.73 eV, optimizing charge redistribution. • Fe 3 O 4 @SrCO 3 /NF demonstrated outstanding stability, maintaining OER for 300 h and OWS for 125 h at 10 mA cm −2 . A novel Fe 3 O 4 @SrCO 3 /NF electrocatalyst material was developed via hydrothermal synthesis, creating a composite structure with Fe 3 O 4 nanosheets and SrCO 3 crystals on nickel (Ni) foam (NF) that enhances surface roughness and stability, in turns optimizing catalytic performance in water-splitting applications. In 1.0 M KOH, the as -prepared Fe 3 O 4 @SrCO 3 /NF achieved a low OER overpotential of 243 mV at 10 mA cm −2 , surpassing commercial IrO 2 /NF, with a Tafel slope of 38 mV dec −1 indicating rapid reaction kinetics. Electrochemical Impedance Spectroscopy (EIS) confirmed a low charge transfer resistance ( R ct ) of 1.49 Ω, indicating efficient electron mobility. For HER, Fe 3 O 4 @SrCO 3 /NF displayed a moderate overpotential of 172 mV at − 10 mA cm −2 . In a two-electrode setup with Pt/C (cathode), Fe 3 O 4 @SrCO 3 /NF (anode) demonstrated efficient overall water splitting, requiring only 1.55 V at 10 mA cm −2 , underscoring its viability for sustainable energy applications. Density Functional Theory (DFT) calculations revealed that SrCO 3 in-situ formation not only shifted the OER rate-determining step, lowering the energy barrier to 0.73 eV, but also optimized the d -band center and facilitated interfacial charge redistribution, enhancing intermediate adsorption and catalytic activity. Notably, Fe 3 O 4 @SrCO 3 /NF demonstrated exceptional stability, sustaining OER activity for over 300 h and delivering stable overall water-splitting performance for 125 h, significantly outperforming many state-of-the-art OER catalysts. This durability, combined with high catalytic efficiency, establishes Fe 3 O 4 @SrCO 3 /NF as a promising candidate in non-precious metal electrocatalysts for water-splitting 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) | 0.001 | 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".