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 (NH 3 ), as a green energy carrier, potential transportation fuel and chemical for fertilizer synthesis, plays an indispensable role in the agricultural, plastic, pharmaceutical and textile industries 1 . Industrially, NH 3 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 (CO 2 ), per 1 ton of NH 3 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 N 2 fixation under milder conditions. Recently, a worldwide gold rush has been triggered, and many pioneering methods are being investigated to convert N 2 to NH 3 , including biological catalysis 4 , photocatalysis 5,6 and electrocatalysis 2,7 . Particularly, electrochemical reduction of N 2 to NH 3 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 H 2 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 N 2 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 NH 3 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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".