MoS<sub>2</sub>@NiFe<sub>2</sub>O<sub>4</sub>/CB Hybrid As a Bifunctional Electrocatalyst for Water Splitting
Bibliographic record
Abstract
Designing effective bifunctional electrocatalysts for OER and HER at high current density with low overpotentials is essential to promote water splitting technology for renewable energy conversion and storage. MoS2 and NiFe2O4 are known functional materials for HER and OER in acidic and alkaline media, respectively. In this work, integrating MoS2 with NiFe2O4/CB in various ratios (1:1, 2:1 and 3:1) has been done to improve the catalytic performance of OER and HER. MoS2@NiFe2O4/CB (2:1) hybrid exhibited enhanced OER performance with a low onset potential of 1.38 V, lower overpotentials of 260 mV at j = 10 mA cm-2 and a low Tafel slope of 46 mV dec-1 in 1 M KOH. The hybrid also displayed excellent stability after 12 hrs OER electrolysis, with a negligible change in overpotentials showing ~ 2 % improvement. Besides the synergistic effects between NiFe2O4/CB and MoS2, the improved performance can be ascribed to the enhanced charge-transfer mechanism and texture properties allowing easy accessibility of electrolytes. Contrary to the OER performance, MoS2@NiFe2O4/CB (1:1) displayed the highest HER performance with a low onset potentials of 264 mV, lower overpotentials of 423 mV at j = 10 mA cm-2 and a low Tafel slope of 104 mV dec-1 in 0.5 M H2SO4, however the performance was lower than that of its individual components (NiFe2O4/CB and MoS2/CB. The low HER results of MoS2@NiFe2O4/CB (1:1) might have resulted from the sluggish kinetics observed. However, the hybrid shows excellent stability affording the similar i-V curves as the initial tests after 1000 cycles, thus MNFC hybrids show considerable potential to facilitate water splitting reactions.
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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".