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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".