Model Operational Matrix for the Betterment of Ruthenium As a Catalyst for the Electrochemical Nitrogen Reduction Reaction to Ammonia in Aqueous Electrolytes
Bibliographic record
Abstract
Ammonia (NH3), as a green energy carrier, potential transportation fuel and chemical for fertilizer synthesis, plays an indispensable role in the agricultural, plastic, pharmaceutical and textile industries1. Industrially, NH3 manufacturing is dominated by the Haber–Bosch (HB) process, which consumes more than 2% of the global energy supply, and releases 1.87 tons of greenhouse gas, carbon dioxide (CO2), per 1 ton of NH3 2. This energy-intensive process is also inefficient and relatively low conversion ratio are achieved due to unfavorable chemical equilibrium 3. Hence, it is of great significance to develop alternative routes for more efficient N2 fixation under milder conditions. Recently, a worldwide gold rush has been triggered, and many pioneering methods are being investigated to convert N2 to NH3, including biological catalysis 4, photocatalysis 5,6and electrocatalysis 2,7. Particularly, electrochemical reduction of N2 to NH3 is thermodynamically predicted to be more energy efficient than the HB process by about 20% 7,8 . An electrochemical process could also provide the benefit of reducing greenhouse gas emission as the source of H2 is the electrolysis of water molecules instead of natural gas. With this scenario, ammonia would be synthesized in a carbon-neutral manner if renewable electrical energy is used. In the electrochemical N2 reduction reaction (NRR) system, though electrocatalysts are the paramount components, a rational cell design, synthesize and operation conditions are very vital 4. Most of recent studies have looked on a single parameter (or two) such as catalyst morphology, catalyst deposition and loading, nitrogen reduction potential or type, temperature, pressure and components of cell and electrolytes. However, the fact that NRR and catalyst deposition is a multistep process, sampled parameters study might not provide sufficient information about the actual electrocatalytic process. Therefore, our group tried to determine the best working conditions and ways of designing NRR experiments in order to draw conclusions on the process efficiency in terms of charge used and NH3 yield. In the present study, electrochemically deposited Ru metal catalysts have been investigated. It is found that in ambient reaction conditions and in highly concentrated electrolytes, a Faradic Efficiency as high as 1.2 % can be reached by optimizing the Ru deposition morphology and deposition time (loading), as well as the NRR potential, nature of cation/anion exchange membranes and size of the counter cations in the electrolyte. This is a 4-fold improvement compared to the maximum efficiency reported 4 with the same catalyst (< 0.3 %). References: 1. H. Wang et al., Angew. Chemie Int. Ed., 57, 12360–12364 (2018) https://doi.org/10.1002/anie.201805514. 2. C. J. M. van der Ham, M. T. M. Koper, and D. G. H. Hetterscheid, Chem. Soc. Rev., 43, 5183–5191 (2014) http://dx.doi.org/10.1039/C4CS00085D. 3. H. Cheng, P. Cui, F. Wang, L.-X. Ding, and H. Wang, Angew. Chemie, 131, 15687–15693 (2019) https://doi.org/10.1002/ange.201910658. 4. X. Guo, H. Du, F. Qu, and J. Li, J. Mater. Chem. A, 7, 3531–3543 (2019) http://dx.doi.org/10.1039/C8TA11201K. 5. B. M. Comer et al., J. Am. Chem. Soc., 140, 15157–15160 (2018) https://doi.org/10.1021/jacs.8b08464. 6. Y. Wan, J. Xu, and R. Lv, Mater. Today, 27, 69–90 (2019) https://www.sciencedirect.com/science/article/pii/S136970211930001X#f0015. 7. V. Smil, Enriching the earth : Fritz Haber, Carl Bosch and the transformation of world food production, Cambridge (Mass.) : MIT press, (2004) http://lib.ugent.be/catalog/rug01:000891228. 8. M. Wang et al., Nat. Commun., 10, 341 (2019) https://doi.org/10.1038/s41467-018-08120-x.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".