Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring.Gas sensors based on conventional bulk materials often suffer from their poor surfa...Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring.Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites,leading to a very low gas adsorption ability.Moreover,the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior.In this work,a gas sensing structure model based on CuS quantum dots/Bi_(2)S_(3) nanosheets(CuS QDs/Bi_(2)S_(3) NSs)inspired by artificial neuron network is constructed.Simulation analysis by density functional calculation revealed that CuS QDs and Bi_(2)S_(3) NSs can be used as the main adsorption sites and charge transport pathways,respectively.Thus,the high-sensitivity sensing of NO_(2) can be realized by designing the artificial neuron-like sensor.The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable,which can enhance the NO_(2) sensitivity due to the rich sensitive sites and quantum size effect.The Bi_(2)S_(3) NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission.The neuron-like sensor that simulates biological smell shows a significantly enhanced response value(3.4),excellent responsiveness(18 s)and recovery rate(338 s),low theoretical detection limit of 78 ppb,and excellent selectivity for NO_(2).Furthermore,the developed wearable device can also realize the visual detection of NO2 through real-time signal changes.展开更多
An array composed of sixteen gas sensors was constructed to analyze gas mixtures quantitatively. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP alg...An array composed of sixteen gas sensors was constructed to analyze gas mixtures quantitatively. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP algorithms and PLS. The analytical results indicated that the concentration predicted with ANN is better than that with PLS. The average prediction errors for ethane, propane and propylene were 5.11%, 8.28%, 2.64%, respectively.展开更多
基金supported by the National Natural Science Foundation of China(61971284)the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(SL2020ZD203 and SL2020MS031)+2 种基金Scientific Research Fund of Second Institute of Oceanography,Ministry of Natural Resources of P.R.China(SL2003)Shanghai Sailing Program(21YF1421400)Startup Fund for Youngman Research at Shanghai Jiao Tong University.
文摘Real-time rapid detection of toxic gases at room temperature is particularly important for public health and environmental monitoring.Gas sensors based on conventional bulk materials often suffer from their poor surface-sensitive sites,leading to a very low gas adsorption ability.Moreover,the charge transportation efficiency is usually inhibited by the low defect density of surface-sensitive area than that in the interior.In this work,a gas sensing structure model based on CuS quantum dots/Bi_(2)S_(3) nanosheets(CuS QDs/Bi_(2)S_(3) NSs)inspired by artificial neuron network is constructed.Simulation analysis by density functional calculation revealed that CuS QDs and Bi_(2)S_(3) NSs can be used as the main adsorption sites and charge transport pathways,respectively.Thus,the high-sensitivity sensing of NO_(2) can be realized by designing the artificial neuron-like sensor.The experimental results showed that the CuS QDs with a size of about 8 nm are highly adsorbable,which can enhance the NO_(2) sensitivity due to the rich sensitive sites and quantum size effect.The Bi_(2)S_(3) NSs can be used as a charge transfer network channel to achieve efficient charge collection and transmission.The neuron-like sensor that simulates biological smell shows a significantly enhanced response value(3.4),excellent responsiveness(18 s)and recovery rate(338 s),low theoretical detection limit of 78 ppb,and excellent selectivity for NO_(2).Furthermore,the developed wearable device can also realize the visual detection of NO2 through real-time signal changes.
文摘An array composed of sixteen gas sensors was constructed to analyze gas mixtures quantitatively. The data of responses from the sensor array to ethane, propane and propylene were treated by three-layer ANN with BP algorithms and PLS. The analytical results indicated that the concentration predicted with ANN is better than that with PLS. The average prediction errors for ethane, propane and propylene were 5.11%, 8.28%, 2.64%, respectively.
