Synergistic Effect of MoO<sub>2</sub>–Ni<sub>3</sub>(PO<sub>4</sub>)<sub>2</sub> Heterostructures In Situ Grown on Nickel Foam Enhances the Efficiency of Hydrogen Evolution Reaction in Simulated Seawater
Bibliographic record
Abstract
Hydrogen (H 2 ) with a high gravimetric energy density (142 MJ/kg) and zero carbon emissions is a green energy source. The electrocatalytic hydrogen evolution reaction (HER) is a prominent strategy for hydrogen production, and the essential technology of electrocatalysts focuses on creating catalysts that are highly efficient, cost-effective, and excellently stable. Herein, a heterostructure electrode/catalyst consisting of MoO 2 –Ni 3 (PO 4 ) 2 /NF (where NF = nickel foam) was fabricated using (NH 4 ) 6 [NiMo 9 O 32 ]·6H 2 O as precursor via a two-step method utilizing hydrothermal synthesis and chemical vapor deposition (CVD). Thanks to the remarkable synergistic effect occurring at the interfaces of the heterostructure, the catalytic efficiency of MoO 2 –Ni 3 (PO 4 ) 2 /NF can outperform that of other catalyst materials. In particular, the MoO 2 –Ni 3 (PO 4 ) 2 /NF electrode exhibits overpotentials of 66 and 258 mV at 10 mA cm –2, along with low Tafel slopes of 56.03 and 85.32 mV/dec in 1 mol/L KOH and simulated seawater electrolyte, respectively. Density functional theory calculations (DFT) validate that the Gibbs free energy (Δ G H* ) values for hydrogen adsorption of MoO 2 (110)/Ni 3 (PO 4 ) 2 (−222) with 0.033 eV are much closer to zero, similar to Pt/C. In situ FTIR spectra indicate that the synergistic effect of MoO 2 (110)/Ni 3 (PO 4 ) 2 (−222) can further create more catalytic active sites and modulate intermediate H* adsorption to promote the HER process. Overall, this study highlights the potential of nanostructured MoO 2 –Ni 3 (PO 4 ) 2 heterostructures for application in efficient hydrogen production under seawater conditions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".