Electrosynthesis of Ammonia Using Porous Bimetallic Pd–Ag Nanocatalysts in Liquid- and Gas-Phase Systems
Bibliographic record
Abstract
Cost-effective production of ammonia via electrochemical nitrogen reduction reaction (NRR) hinges on N 2 electrolysis at high current densities with suitable selectivity and activity. Here, we report our findings in electrochemical NRR for ammonia synthesis using porous bimetallic Pd–Ag nanocatalysts in both gas-phase and liquid-phase electrochemical cells at current densities above 1 mA cm –2 under ambient conditions. While the gas-phase cell has lower Ohmic losses and higher energy efficiency, the liquid-phase cell achieved higher selectivity and Faradaic efficiency, attributed to the presence of concentrated N 2 molecules dissolved in an aqueous electrolyte and the hydration effects. The liquid cell demonstrated notable performance for electrocatalytic NRR, achieving an NH 3 production rate of 45.6 ± 3.7 μg cm –2 h –1 at a cell voltage of −0.6 V (vs RHE) and current density of 1.1 mA cm –2, corresponding to a Faradaic efficiency of ∼19.6% and an energy efficiency of ∼9.9%. Similarly, the gas-phase cell achieved a NH 3 yield rate of 19.4 ± 2.1 μg cm –2 h –1 at −0.07 V (vs RHE) and 1.15 mA cm –2 with a Faradaic efficiency of 7.9% and an energy efficiency of 27.1%. Further, operando surface-enhanced Raman spectroscopy and density functional theory (DFT) are used to identify intermediate species relevant to the NRR at the electrode–electrolyte interfaces to provide insights into the NRR mechanism on Pd–Ag nanoparticles. This work highlights the importance of design and optimization of cell configuration in addition to the modification of the catalyst to achieve high-performance N 2 electrolysis for ammonia synthesis.
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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".