Alteration of Electronic Band Structure <i>via </i>a Metal-Semiconductor Interfacial Effect Enables High Faradaic Efficiency for Electrochemical Nitrogen Fixation
Bibliographic record
Abstract
The interface engineering strategy has been an emerging field in terms of material improvisation that not only alters the electronic band structure of a material but also induces beneficial effects on electrochemical performances. Particularly, it is of immense importance for the environmentally benign electrochemical nitrogen reduction reaction (NRR), which is potentially impeded by the competing hydrogen evolution reaction (HER). The main problem lies in the attainment of the desired current density at a negotiable potential where the NRR would dominate over the HER, which in turn hampers the Faradaic efficiency for the NRR. To circumvent this issue, catalyst development becomes necessary, which would display a weak affinity for H-adsorption suppressing the HER at the catalyst surface. Herein, we have adopted the interfacial engineering strategy to synthesize our electrocatalyst NPG@SnS 2, which not only suppressed the HER on the active site but yielded 49.3% F.E. for the NRR. Extensive experimental work and DFT calculations regarded that due to the charge redistribution, the Mott–Schottky effect, and the band bending of SnS 2 across the contact layer at the interface of NPG, the d-band center for the surface Sn atoms in NPG@SnS 2 lowered, which resulted in favored adsorption of N 2 on the Sn active site. This phenomenon was driven even forward by the upshift of the Fermi level, and eventually, a decrease was seen in the work function of the heterostructure that increased the conductivity of the material as compared to pristine SnS 2 . This strategy thus provides a field to methodically suppress the HER in the realm of improving the Faradaic efficiency for the NRR.
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 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) | 0.003 | 0.001 |
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".