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During wastewater treatment, microbial electrochemical technologies (METs) are a promising means for in situ energy harvesting and resource recovery. The primary constraint for such systems is scaling them up from the laboratory to practical applications. Currently, most research (∼90%) has been limited to benchtop models because of bioelectrochemical, economic, and engineering design limitations. Field trials, i.e., 1.5 m3 bioelectric toilet, 1000 L microbial electrolysis cell and industrial applications of METs have been conducted, and their results serve as positive indicators of their readiness for practical applications. Multiple startup companies have invested in the pilot-scale demonstrations of METs for industrial effluent treatment. Recently, advances in membrane/electrode modification, understanding of microbe-electrode interaction, and feasibility of electrochemical redox reactions have provided new directions for realizing the practical application. This study reviews the scaling-up challenges, success stories for onsite use, and readiness level of METs for commercialization that is inexpensive and sustainable.

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Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400-1000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities.

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The recent observation of extremely large magnetoresistance (MR) in the transition-metal dichalcogenide MoTe2 has attracted considerable interest due to its potential technological applications as well as its relationship with novel electronic states predicted for a candidate type-II Weyl semimetal. In order to understand the origin of the MR, the electronic structure of MoTe2-x (x = 0.08) is systematically tuned by application of pressure and probed via its Hall and longitudinal conductivities. With increasing pressure, a monoclinic-to-orthorhombic (1 T' to Td) structural phase transition temperature (T*) gradually decreases from 210 K at 1 bar to 58 K at 1.1 GPa, and there is no anomaly associated with the phase transition at 1.4 GPa, indicating that a T = 0 K quantum phase transition occurs at a critical pressure (Pc) between 1.1 and 1.4 GPa. The large MR observed at 1 bar is suppressed with increasing pressure and is almost saturated at 100% for P > Pc. The dependence on magnetic field of the Hall and longitudinal conductivities of MoTe2-x shows that a pair of electron and hole bands are important in the low-pressure Td phase, while another pair of electron and hole bands are additionally required in the high-pressure 1 T' phase. The MR peaks at a characteristic hole-to-electron concentration ratio (nc) and is sharply suppressed when the ratio deviates from nc within the Td phase. These results establish the comprehensive temperature-pressure phase diagram of MoTe2-x and underscore that its MR originates from balanced electron-hole carrier concentrations.

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A mediator-less microbial fuel cell was optimized in terms of various operating conditions. Current generation was dependent on several factors such as pH, resistance, electrolyte used, and dissolved oxygen concentration in the cathode compartment. The highest current was generated at pH 7. Under the operating conditions, the resistance was the rate-determining factor at over 500 omega. With resistance lower than 500 omega, proton transfer and dissolved oxygen (DO) supply limited the cathode reaction. A high strength buffer reduced the proton limitation to some extent. The DO concentration was around 6 mg l(-1) at the DO limited condition. The fact that oxygen limitation was observed at high DO concentration is believed to be due to the poor oxygen reducing activity of the electrode used, graphite. The current showed linear relationship with the fuel added at low concentration, and the electronic charge was well correlated with substrate concentration from up to 400 mg l(-1) of COD(cr). The microbial fuel cell might be used as a biochemical oxygen demand (BOD) sensor.

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