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  1. The nitrogen cycle needed for scaled agriculture relies on energy- and carbon-intensive processes and generates nitrate-containing wastewater. Here we focus on an alternative approach—the electrified co-electrolysis of nitrate and CO2 to synthesize urea. When this is applied to industrial wastewater or agricultural runoff, the approach has the potential to enable low-carbon-intensity urea production while simultaneously providing wastewater denitrification. We report a strategy that increases selectivity to urea using a hybrid catalyst: two classes of site independently stabilize the key intermediates needed in urea formation, *CO2NO2 and *COOHNH2, via a relay catalysis mechanism. A Faradaic efficiency of 75% at wastewater-level nitrate concentrations (1,000 ppm NO3− [N]) is achieved on Zn/Cu catalysts. The resultant catalysts show a urea production rate of 16 µmol h−1 cm−2. Life-cycle assessment indicates greenhouse gas emissions of 0.28 kg CO2e per kg urea for the electrochemical route, compared to 1.8 kg CO2e kg−1 for the present-day route. 
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    Free, publicly-accessible full text available October 1, 2024
  2. Abstract Road profile information can be utilized to enhance vehicle control performance, passenger ride comfort, and route planning and optimization. Existing road-profile estimation algorithms are mainly based on one single vehicle, which are usually susceptible to modeling uncertainties and measurement noises. This technical brief proposes a new cascaded learning framework that utilizes multiple heterogeneous vehicles to achieve enhanced estimation. In this framework, each individual vehicle first performs a local estimation via a standard disturbance observer (DOB) while traversing a considered road segment. Then learning filters are designed to dynamically connect the vehicles, and the preliminary estimates from one vehicle are utilized to generate the learning signal for another. For each vehicle, a heterogeneous learning signal is produced and added to its estimation loop for estimating enhancement, through which the estimations are improved over multiple iterations. Extensive numerical studies are carried out to validate the effectiveness of the proposed method with promising results demonstrated. 
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