Selective Mass Accumulation at the Metal–Polymer Bridging Interface for Efficient Nitrate Electroreduction to Ammonia and Zn-Nitrate Batteries
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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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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 | 0.000 | 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) | 0.000 | 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 it