Co-designing Electrocatalytic Systems with Separations To Improve the Sustainability of Reactive Nitrogen Management
Bibliographic record
Abstract
Humans have altered the nitrogen cycle to produce nitrogen commodities like fertilizers and chemicals while releasing anthropogenic reactive nitrogen (N r ) contaminants into the environment. These contaminants endanger human and environmental health, but nitrogen commodities are necessary for quality of life. One approach to solving this global challenge is to remove and recover N r contaminants as commodities; this approach has caught the attention of the electrocatalysis and separations communities alike. In this perspective we propose co-design, or the integration of typically disparate N r separations and electrocatalytic technologies. We consider real N r contaminant waste streams and N r commodity purity requirements. Considering these criteria in electrocatalytic system design reveals fundamental gaps in understanding as well as opportunity for developing co-designed technology that is uniquely tailored to address a challenge in nitrogen management. We focus on three representative challenges in nitrogen management (nitrate, nitrogen oxides, and nitrous oxide), identify their sources and conditions, highlight accomplishments in the fields of electrocatalysis and separations, and explore ways to address each challenge with a co-design approach. We note that this approach will benefit from advancements in related fields such as nitrogen sensing and environmental policy, especially because transformative solutions for the nitrogen challenge lie at the confluence of multiple fields. The final goal is to transition to a circular nitrogen economy that secures a food-safe, environmentally friendly future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".