Over the last few decades, the development of the electronic nose (E-nose) for detection and quantification of dangerous and odorless gases, such as methane (CH4) and carbon monoxide (CO), using an array of SnO2 gas sensors has attracted considerable attention. This paper addresses sensor cross sensitivity by developing a classifier and estimator using an artificial neural network (ANN) and least squares regression (LSR), respectively. Initially, the ANN was implemented using a feedforward pattern recognition algorithm to learn the collective behavior of an array as the signature of a particular gas. In the second phase, the classified gas was quantified by minimizing the mean square error using LSR. The combined approach produced 98.7% recognition probability, with 95.5 and 94.4% estimated gas concentration accuracies for CH4 and CO, respectively. The classifier and estimator parameters were deployed in a remote microcontroller for the actualization of a wireless E-nose system.
It is time for industry to pay a serious heed to the application and quality-dependent research on the most important solution growth methods for ZnO, namely, aqueous chemical growth (ACG) and microwave-assisted growth (MAG) methods. This study proffers a critical analysis on how the defect density and formation behavior of ZnO nanostructures (ZNSs) are growth method-dependent. Both antithetical and facile methods are exploited to control the ZnO defect density and the growth mechanism. In this context, the growth of ZnO nanorods (ZNRs), nanoflowers, and nanotubes (ZNTs) are considered. The aforementioned growth methods directly stimulate the nanostructure crystal growth and, depending upon the defect density, ZNSs show different trends in structural, optical, etching, and conductive properties. The defect density of MAG ZNRs is the least because of an ample amount of thermal energy catered by high-power microwaves to the atoms to grow on appropriate crystallographic planes, which is not the case in faulty convective ACG ZNSs. Defect-centric etching of ZNRs into ZNTs is also probed and methodological constraints are proposed. ZNS optical properties are different in the visible region, which are quite peculiar, but outstanding for ZNRs. Hall effect measurements illustrate incongruent conductive trends in both samples.
High-power microwave-assisted gallium (Ga) -doped ZnO nanorods (MGZRs) are grown on p-Si substrates, and their optoelectronic characteristics are reported. Gallium nitrate hydrate is mixed with zinc nitrate hexahydrate and hexamethylenetetramine to make 1, 2, and 5% MGZRs in a domestic microwave oven. The MGZR diameter decreased when doping increased from 1 to 2%, but the diameter of the highly doped (5%) sample significantly increased. The EDS results confirm the incorporation of Ga atoms in the ZnO crystal lattice, where an increase in the dopant concentration in growth solution increase the probability of Ga ion incorporation into ZnO crystal lattice. However, exact values for EDS quantification are not found because of Si peaks from the substrate. The high-intensity photoluminescence UV peaks associated to exciton recombination are blue-shifted, and some defects are incorporated by Ga, which respond to the visible and near-IR regions in MGZRs. Furthermore, the n-MGZR/p-Si heterostructures show a diode-like I-V response, where the current levels increase when the doping concentration increase because of an increase in carrier concentration in MGZRs, which is confirmed by Hall-effect measurements. The MGZRs address the low carrier transport issues in undoped microwave-assisted nanorods and are notably effective in altering their optoelectronic characteristics. status solidi physica a Gallium Doping www.pss-a.com
Hitherto, most research has primarily focused on improving the UV sensor efficiency via surface treatments and by stimulating the ZnO nanorod (ZNR) surface Schottky barriers. However, to the best of our knowledge, no study has yet probed the intrinsic crystal defect generation and its effects on UV sensor efficiency. In this study, we undertake this task by fabricating an intrinsic defect-prone hydrothermally grown ZNRs (S1), Ga-doped ZNRs (S2), and defect-free microwave-assisted grown ZNRs (S3). The defect states were recognized by studying X-ray diffraction and photoluminescence characteristics. The large number of crystal defects in S1 and S2 had two pronged disadvantages. (1) Most of the UV light was absorbed by the defect traps and the e–h pair generation was compromised. (2) Mobility was directly affected by the carrier–carrier scattering and phonon scattering processes. Hence, the overall UV sensor efficiency was compromised based on the defect-induced mobility-response model. Considering the facts, defect-free S3 exhibited the best UV sensor performance with the highest on/off ratio, the least impulse response time, the highest recombination time, and highest gain-induced responsivity to 368 nm UV light, which was desired of an efficient passive metal oxide-based UV sensor. Our results were compared with the recently published results.
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