Recognizing frequency characteristics of gas sensor array

A novel method based on independent component analyzing (ICA) in frequency domain to distinguish the frequency characteristics of multi-sensor system is presented. The conditions of this type of ICA are considered and each step of resolving the problem is discussed. For a two gas sensor array, the frequency characteristics including amplitude-frequency and phase-frequency are recognized by this method, and cross-sensitivity between them is also eliminated. From the principle of similarity, the recognition mean square error is no more than 0.085.

Real-Time Tunable Gas Sensing Platform Based on SnO_(2) Nanoparticles Activated by Blue Micro-Light-Emitting Diodes

Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power consumption.However,despite these benefits,challenges still exist such as a limited range of detectable gases and slow response.In this study,we present a blueμLED-integrated light-activated gas sensor array based on SnO_(2)nanoparticles(NPs)that exhibit excellent sensitivity,tunable selectivity,and rapid detection with micro-watt level power consumption.The optimal power forμLED is observed at the highest gas response,supported by finite-difference time-domain simulation.Additionally,we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO_(2)NPs.The noble metals induce catalytic interaction with reducing gases,clearly distinguishing NH3,H2,and C2H5OH.Real-time gas monitoring based on a fully hardwareimplemented light-activated sensing array was demonstrated,opening up new avenues for advancements in light-activated electronic nose technologies.

On the Study of Multi-Gas Data Acquisition System Based on PCI1711 Card

A data acquisition system for testing gas sensor array response to multi-gas is presented.The testing system is based on the character of the gas response of metal oxide semiconductor gas sensor array.The data acquisition is realized automatically through the real time controlling of the data acquisition card PCI1711.This system is highly attractive for electronic nose,which is a powerful tool for the discrimination of gases.

Method for detecting soil total nitrogen contents based on pyrolysis and artificial olfaction

Soil nitrogen is an essential nutrient element for crop growth and development,and an important indicator of soil fertility characteristics.This study proposed a method based on pyrolysis and artificial olfaction to quickly and accurately determine the soil total nitrogen(STN)content.A muffle furnace was used to pyrolyze the soil samples,and ten different types of oxide semiconductor gas sensors were used to construct a sensor array to detect the soil samples’pyrolysis gas.The response curves of the sensors were tested at pyrolysis temperatures of 200℃,300℃,400℃,and 500℃ and at pyrolysis times of 1 min,3 min,5 min,and 10 min to obtain the optimal pyrolysis state of the soil samples.The optimal pyrolysis temperature was 400℃,and the pyrolysis time was 3 min.The response area,maximum value,average differential coefficient,variance value,maximum gradient value,average value,and 8th-second transient value of the sensor response curve were extracted to construct an artificial olfactory feature space of 121×10×7(121 soil samples,ten sensor numbers,seven extracted eigenvalues).Back-propagation neural network algorithm(BPNN),partial least squares regression algorithm(PLSR),and partial least squares regression combined with back-propagation neural network algorithm(PLSR-BPNN)were used to establish a prediction model of artificial olfactory feature space and STN content.Moreover,coefficient of determination(R2),root mean square error(RMSE),and the ratio of performance to deviation(RPD)were used as the performance indicators of the prediction results.The test results showed that the R2 of the PLSR,BPNN,and PLSR-BPNN models were 0.89033,0.81185,and 0.92186,and the RMSE values were 0.24297,0.37370,and 0.21781,and the RPD were 2.9964,1.9482,and 3.3426,respectively.The model established by the PLSR-BPNN algorithm has the highest R2 and RPD and the smallest RMSE,can achieve the accurate prediction of STN content,and therefore the model is rated as“excellent”.The detection method in this study achieves a low-cost,rapid,and accurate determination of STN content,and provides a new reference for the measurement of STN.