Selective Mass Accumulation at the Metal–Polymer Bridging Interface for Efficient Nitrate Electroreduction to Ammonia and Zn-Nitrate Batteries
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The electrochemical conversion of nitrate (NO 3 – ), a common nitrogen source in industrial wastewater and contaminated groundwater, into ammonia (NH 3 ), signifies an approach to wastewater treatment and NH 3 production. Nevertheless, its selectivity and activity at low NO 3 – concentrations and industrial current densities are constrained by limited mass transfer around the electrode. Here, we report a metal–polymer bridging interface constructed by anchoring Cu/Cu 2 O nanoparticles onto a two-dimensional (2D) Cu-based benzene dicarboxylate (CuBDC) coordination polymer via in situ electroreduction (denoted as E-CuBDC). This interface weakens the electrostatic repulsion and regulates the distribution/migration of NO 3 – and H 2 O, creating a Janus NO 3 – -rich and H 2 O-poor domain near the catalyst surface. Operando characterizations and theoretical simulations indicate that the metal–polymer bridging interface selectively accumulates NO 3 – and reduces the energy barrier toward the reduction of *NH 2 OH to *NH 2, overcoming the mass transfer limitations at a low NO 3 – concentration. E-CuBDC exhibits a high Faradaic efficiency (FE) of over 90% across wide NO 3 – concentrations (7.1–100 mM NO 3 – ) and high applied voltages. Additionally, it achieved stable NH 3 production over 100 h at ampere-level current densities. When applied in a Zn–NO 3 – system, this newly developed E-CuBDC catalyst demonstrates an outstanding power density and FE for NH 3 production, showcasing its great potential for large-scale electrochemical conversion and storage systems. This study presents a generalizable strategy for constructing metal–polymer interfaces to regulate interfacial mass transport.
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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".