Membranes Matter: Preventing Ammonia Crossover during Electrochemical Ammonia Synthesis.

The electrochemical nitrogen and nitrate reduction reactions (E-NRR and E-NO3RR) promise to provide decentralized and fossil-fuel-free ammonia synthesis, and as a result, E-NRR and E-NO3RR research has surged in recent years. Membrane NH3/NH4+ crossover during E-NRR and E-NO3RR decreases Faradaic efficiency and thus the overall yield. During catalyst evaluation, such unaccounted-for crossover results in measurement error. Herein, several commercially available membranes were screened and evaluated for use in ammonia-generating electrolyzers. NH3/NH4+ crossover of the commonly used cation-exchange membrane (CEM) Nafion 212 was measured in an H-cell architecture and found to be significant. Interestingly, some anion exchange membranes (AEMs) show negligible NH4+ crossover, addressing the problem of measurement error due to NH4+ crossover. Further investigation of select membranes in a zero-gap gas diffusion electrode (GDE)-cell determines that most membranes show significant NH3 crossover when the cell is in an open circuit. However, uptake and crossover of NH3 are mitigated when -1.6 V is applied across the GDE-cell. The results of this study present AEMs as a useful alternative to CEMs for H-cell E-NRR and E-NO3RR electrolyzer studies and present critical insight into membrane crossover in zero-gap GDE-cell E-NRR and E-NO3RR electrolyzers.

Machine Learning Predictions of Oil Yields Obtained by Plastic Pyrolysis and Application to Thermodynamic Analysis.

Chemical recycling via thermal processes such as pyrolysis is a potentially viable way to convert mixed streams of waste plastics into usable fuels and chemicals. Unfortunately, experimentally measuring product yields for real waste streams can be time- and cost-prohibitive, and the yields are very sensitive to feed composition, especially for certain types of plastics like poly(ethylene terephthalate) (PET) and polyvinyl chloride (PVC). Models capable of predicting yields and conversion from feed composition and reaction conditions have potential as tools to prioritize resources to the most promising plastic streams and to evaluate potential preseparation strategies to improve yields. In this study, a data set consisting of 325 data points for pyrolysis of plastic feeds was collected from the open literature. The data set was divided into training and test sub data sets; the training data were used to optimize the seven different machine learning regression methods, and the testing data were used to evaluate the accuracy of the resulting models. Of the seven types of models, eXtreme Gradient Boosting (XGBoost) predicted the oil yield of the test set with the highest accuracy, corresponding to a mean absolute error (MAE) value of 9.1%. The optimized XGBoost model was then used to predict the oil yields from real waste compositions found in Municipal Recycling Facilities (MRFs) and the Rhine River. The dependence of oil yields on composition was evaluated, and strategies for removing PET and PVC were assessed as examples of how to use the model. Thermodynamic analysis of a pyrolysis system capable of achieving oil yields predicted using the machine-learned model showed that pyrolysis of Rhine River plastics should be net exergy producing under most reasonable conditions.