Room Temperature, Ambient Pressure Synthesis of Urea By Electrolysis and Its Accurate and Consistent Measurement
Bibliographic record
Abstract
There is rapidly growing interest in the electrochemical reduction of both nitrite and carbon dioxide to mitigate environmental concerns and produce fuels and commodity chemicals (1). Urea is an important target in this area since it is the most widely used nitrogen fertilizer, and can also be used as a fuel and source of hydrogen. It can be produced simultaneously by electrochemical coreduction of NO2 - and CO2 (2-7). Various TiO2 based catalysts such as Cu-doped TiO2 (7) and FeTiO3 (4) have been reported to be effective for co-reduction of CO2 and NO2 -. Metallophthalocyanine catalysts have also been found to be effective in a gas-diffusion electrode configuration under ambient conditions (3). The primary objective of this research is to develop fast and straightforward methodologies that can be routinely used to comprehensively evaluate and compare commercial and new catalysts for co-electrolysis of CO2 and NO2 - in an anion exchange membrane multi-cathode electrolysis cell under ambient conditions. The second objective is using this methodology to synthesize urea and develop reliable and consistent urea measurement methods using various techniques including spectrophotometric, 1H-NMR, mass spectrometry, and enzyme-based methods. It was found that NO2 - and the electrolyte can cause interference during urea measurement. In this research, commercial catalysts such as iron (II) phthalocyanine were used for co-electrolysis of CO2 and NO2 -. Acknowledgements This project is funded in part by the Government of Canada. / Ce projet est financé en partie par le gouvernement du Canada, and by Memorial University References C. Tang, Y. Zheng, M. Jaroniec and S. Z. Qiao, Angew. Chem. Int. Ed., 60, 19572 (2021). M. Shibata, K. Yoshida and N. Furuya, J. Electrochem. Soc., 145, 2348 (1998). M. Shibata and N. Furuya, Electrochim. Acta, 48, 3953 (2003). P. Siva, P. Prabu, M. Selvam, S. Karthik and V. Rajendran, Ionics, 23, 1871 (2017). Y. G. Feng, H. Yang, Y. Zhang, X. Q. Huang, L. G. Li, T. Cheng and Q. Shao, Nano Lett., 20, 8282 (2020). N. N. Meng, Y. M. Huang, Y. Liu, Y. F. Yu and B. Zhang, Cell Reports Physical Science, 2 (2021). N. Cao, Y. L. Quan, A. X. Guan, C. Yang, Y. L. Ji, L. J. Zhang and G. F. Zheng, J. Colloid Interface Sci., 577, 109 (2020).
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".