Hydrotalcite precursors of La modified Ni-Al2O3 and Ni-SiO2 catalysts prepared by co-precipitation method and the catalytic activities were examined for the production of COx-free H2 by CH4 decomposition. Physico-chem...Hydrotalcite precursors of La modified Ni-Al2O3 and Ni-SiO2 catalysts prepared by co-precipitation method and the catalytic activities were examined for the production of COx-free H2 by CH4 decomposition. Physico-chemical characteristics of fresh, reduced and used catalysts were evaluated by XRD, TPR and O2 pulse chemisorptions, TEM and BET-SA techniques. XRD studies showed phases due to hydrotalcite-like precursors in oven dried form produced dispersed NiO species upon calcination in static air above 450 C. Raman spectra of deactivated samples revealed the presence of both ordered and disordered forms of carbon. Ni-La-Al2O3catalyst with a mole ratio of Ni : La : Al = 2 : 0.1 : 0.9 exhibited tremendously high longevity with a hydrogen production rate of 1300 molH2 mol 1 Ni. A direct relationship between Ni metal surface area and hydrogen yields was established.展开更多
Smart,low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications.Traditionally,electrochemical and nondispersive infrared(IR...Smart,low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications.Traditionally,electrochemical and nondispersive infrared(IR)gas sensors are designed to detect a single specific analyte.Although IR spectroscopy-based sensors provide superior performance,their deployment is limited due to their large size and high cost.In this study,a smart,low-cost,multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm.The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free,compact(~1 cm^(3))and lightweight(~1 g).The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present.The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100%with 100%accuracy.It also detects hazardous gases at low concentrations with an accuracy of 98.4%.Ammonia can be detected at a concentration of 100 ppm.Additionally,methyl-ethyl-ketone can be detected at its permissible exposure limit(200 ppm);this concentration is considered low and nonhazardous.This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.展开更多
文摘Hydrotalcite precursors of La modified Ni-Al2O3 and Ni-SiO2 catalysts prepared by co-precipitation method and the catalytic activities were examined for the production of COx-free H2 by CH4 decomposition. Physico-chemical characteristics of fresh, reduced and used catalysts were evaluated by XRD, TPR and O2 pulse chemisorptions, TEM and BET-SA techniques. XRD studies showed phases due to hydrotalcite-like precursors in oven dried form produced dispersed NiO species upon calcination in static air above 450 C. Raman spectra of deactivated samples revealed the presence of both ordered and disordered forms of carbon. Ni-La-Al2O3catalyst with a mole ratio of Ni : La : Al = 2 : 0.1 : 0.9 exhibited tremendously high longevity with a hydrogen production rate of 1300 molH2 mol 1 Ni. A direct relationship between Ni metal surface area and hydrogen yields was established.
基金supported in part by the Department of Defence’s“Operating in CBRN Environments”STaR Shot and the Defence Science Institute under the Hazardous Agents Challengesupported in part by the Australian Research Council(ARC)Centre of Excellence for Transformative Meta-Optical Systems(TMOS,CE200100010)supported in part by the MCN Technology Fellow Ambassador program.
文摘Smart,low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications.Traditionally,electrochemical and nondispersive infrared(IR)gas sensors are designed to detect a single specific analyte.Although IR spectroscopy-based sensors provide superior performance,their deployment is limited due to their large size and high cost.In this study,a smart,low-cost,multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm.The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free,compact(~1 cm^(3))and lightweight(~1 g).The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present.The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100%with 100%accuracy.It also detects hazardous gases at low concentrations with an accuracy of 98.4%.Ammonia can be detected at a concentration of 100 ppm.Additionally,methyl-ethyl-ketone can be detected at its permissible exposure limit(200 ppm);this concentration is considered low and nonhazardous.This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.
