Dual Zn<sub>5</sub>−NiS<sub>4</sub> Sites in a Redox‐Active Metal–Organic Framework Enables Efficient Cascade Catalysis for Nitrate‐to‐Ammonia Conversion
Bibliographic record
Abstract
Abstract Electrocatalytic Nitrate Reduction to Ammonia (NO 3 RR) offers a promising solution to both environmental pollution and the sustainable energy conversion. Here we propose an efficient cascade catalytic mechanism based on a dual Zn 5 −NiS 4 sites, orderly assembled in a redox‐active metal–organic framework structure, which separately promotes the reaction kinetics of nitrate‐to‐nitrite and nitrite‐to‐ammonia conversions. Specifically, the Zn 5 clusters adsorb and selectively reduce the NO 3 − to NO 2 − , whereas [NiS 4 ] acts as an analogue to the ferredoxins, subsequently boosts the reduction of NO 2 − to produce NH 3 . To this end, the bimetallic Zn 5 −NiS 4 TP MOF was synthesized based on the redox‐active ligand [Ni(C 2 S 2 (TPCOOH) 2 ) 2 ]. A maximum ammonia production rate of 23477.59 μg ⋅ h −1 ⋅ mg −1 cat. and faradaic efficiency 92.87 % was achived by Zn 5 −NiS 4 TP MOF under neutral conditions. To validate the critical role of dual Zn 5 −NiS 4 sites, Mn 5 −NiS 4 TP and Cd 2 −NiS 4 TP were synthesized as control samples, together with Zn‐TTFTB, Zn−NiS 4 Ph and other Zn 5 ‐cluster‐based MOFs applied for the investigation of electrocatalytic nitrate reduction. Our results indicated that substitution by ‐thienyl instead of ‐phenyl group increases the S‐heteroatom content, improves the conductivity and facilitates electron transfer. Furthermore, Density Functional Theory (DFT) calculations of the energy changes for the reduction of each species could rationalize experimental results.
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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".