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A glut of dinitrogen‐derived ammonia (NH₃) over the past century has resulted in a heavily imbalanced nitrogen cycle and consequently, the large‐scale accumulation of reactive nitrogen such as nitrates in our ecosystems has led to detrimental environmental issues. Electrocatalytic upcycling of waste nitrogen back into NH₃holds promise in mitigating these environmental impacts and reducing reliance on the energy‐intensive Haber–Bosch process. Herein, we report a high‐performance electrolyzer using an ultrahigh alkalinity electrolyte, NaOH−KOH−H₂O, for low‐cost NH₃electrosynthesis. At 3,000 mA/cm², the device with a Fe−Cu−Ni ternary catalyst achieves an unprecedented faradaic efficiency (FE) of 92.5±1.5 % under a low cell voltage of 3.83 V; whereas at 1,000 mA/cm², an FE of 96.5±4.8 % under a cell voltage of only 2.40 V was achieved. Techno‐economic analysis revealed that our device cuts the levelized cost of ammonia electrosynthesis by ~40 % ($30.68 for Fe−Cu−Ni vs. $48.53 for Ni foam per kmol‐NH₃). The NaOH−KOH−H₂O electrolyte together with the Fe−Cu−Ni ternary catalyst can enable the high‐throughput nitrate‐to‐ammonia applications for affordable and scalable real‐world wastewater treatments.

 

We describe work in progress on detecting and understanding the moral biases of news sources by combining framing theory with natural language processing. First we draw connections between issue-specific frames and moral frames that apply to all issues. Then we analyze the connection between moral frame presence and news source political leaning. We develop and test a simple classification model for detecting the presence of a moral frame, highlighting the need for more sophisticated models. We also discuss some of the annotation and frame detection challenges that can inform future research in this area.