Increasing the Efficiency of Electrochemical Nitrogen Fixation by Using a Direct Nitrate-to-Urea Pathway
Bibliographic record
Abstract
With the increasing demand and application of nitrogen fertilizers, electrochemical urea synthesis from CO2 and NO3- is seen as a great alternative to bypass heavily fossil fuel-based production and remediate agricultural leaching. However, urea synthesis remains underdeveloped with a handful of existing literature due to the lack of understanding of reaction conditions. This thesis summarizes my efforts to understand nitrogen electrochemical reduction (Chapter 2), carbon dioxide reduction (Chapter 3), and the combination of two reactions to synthesize urea (Chapter 4). Chapter 2 describes a strategy to improve nitrogen conversion to ammonia catalytic performance via catalyst facet design. We challenged conventional wisdom and found that smooth low-energy facets outcompete irregular high-energy facets on nickel sulfide catalysts. Chapter 3 describes a carbon dioxide conversion project with molecular catalysts to understand CO2 reduction in aqueous systems. We showed adjusting terminal functional groups on metalloporphyrins could affect the electron density on the metal center, thus further altering the reaction pathway. Chapter 4 initially describes our experience in reproducing early studies results, the electrolyte impacts on urea electrosynthesis, and limitations on previous urea detection methods. Initially, we concluded urea synthesis is achievable on a copper catalyst with small current densities and proper electrolyte conditions. NO3- concentration is the most impactful factor and can vary urea selectivity from 0% to 50%. NO2- is observed as the major competing product under small bias and shares a scaling relationship with urea. In the second part of Chapter 4, we demonstrate that limitations in the current colorimetric and 13C NMR methods can lead to false conclusions, which overthrow our previous results. We provide guidelines for reliable urea detection and advocate liquid chromatography-mass spectrometry (LC-MS) as the ultimate detection method due to its high sensitivity for ppm-level urea and isotope-coupled resolving ability. By using a simple LC-MS protocol, ppm-level urea concentrations can be firmly proven with/without isotope labeling reactants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".