Introduction Nowadays, precision farming is a key topic. Because of the steady increase in the world's population, which is expected to reach 9.6 billion by 2050, it is vital to increase the productivity of agricultural land while reducing waste of water, fertilizers, pesticides and pesticide. To achieve these goals, precision agriculture aims to provide farmers with a wealth of information to optimize field management, by matching farming practices more closely to crop needs. This information is obtained exploiting satellite and weather data, and wireless sensor arrays, which combined with the use of GPS, Internet of Things (IoT) and machine learning allow the farmer to operate with both a control and predictive approach [1]. For the best use of precision agriculture, it is therefore essential to collect as much data as possible on the crop status. On the one hand, various technologies have been developed or improved to collect information directly in the field, in particular to measure the pH, the nitrogen compound concentrations and the humidity amount in the soil [2,3]. On the other hand, there is not yet a well-structured system for monitoring crop gas emissions, which together with the control of soil parameters, can lead to a comprehensive evaluation of the effective health status and growth of the crop [4]. In this work, a sensor array composed of four different sensing materials, i.e. SnO2 decorated with Ag, Pd, Pt and Au nanoclusters, were developed and investigated to selective detect five different gases commonly present or emitted by crops. Material and Method SnO2 nanoparticles were synthesized by means of sol-gel technique, by dissolving Sn(II) ethylhexanoate in a hydroalcoholic solution of water and 2-propanol. Afterwards, it was adopted a simple impregnation method to add the metal nanoclusters. For instance, to obtain the decoration with Au nanoclusters, SnO2 nanoparticles were kept stirred in a water solution together with AuBr3, at room temperature. The nanopowder was then calcined at 650°C for 2 hours. The four sensing materials were then screen printed onto alumina substrates, which were equipped with a Pt heater and Au interdigitated electrodes. The gas sensors were then tested with five different gases commonly present or emitted by crops: ethylene, isoprene, CO, methanol and ammonia. They were analyzed five different concentrations of ethylene, methanol and CO and three concentrations of isoprene and ammonia, respectively. The sensing responses were collected by thermo-activating the sensing materials at four different temperatures: 200, 250, 300 and 350°C. The response values from the sensors, combined in a 3-dimensional point for each gas concentration, were processed through a Support Vector Machine (SVM) with a linear kernel to improve sensors selectivity. A first set was used to train the system, and a second one was used to optimize and test its performance. Results and Conclusions The Scanning Electron Microscope (SEM) analysis highlighted that the average size of the synthesized SnO2 nanoparticles was about 30-40 nm (Figure 1), while the X-ray Diffraction (XRD) highlighted a single crystalline phase (cassiterite). The SEM images showed that the metal nanoclusters were growth over the SnO2 nanoparticles surface, without modifying the SnO2 morphology, average size and crystalline structure (XRD) of SnO2. The weight concentration of metals in decorated samples was lower than 1% (Figure 2). The gas sensing characterization was performed in a sealed aluminum gas chamber, by exploiting certified cylinders of target gases and mass flow controllers. The Figure 3 shows the Principle Component Analysis (PCA) for the SnO2/Pt gas sensors vs. tested gases. As it can be observed, the PCA clearly discriminates the different concentrations of gases. The Figure 4 a) and b) show the results obtained in the test set of gas sensing responses with SnO2/Pt gas sensor, after the training of the SVM with linear kernels fit, carried out to build the classification model. The position of each point of the test set was compared with the trained model and thus classified. Figure 4a) highlights that there was a perfect SVM classification (100%) of the different concentration of the tested gases (21 points) for the SnO2/Pt, with a relative low error in the estimated concentrations compared to the real ones (Figure 4b). The same data analysis was then performed for the other sensing materials: Ag, Au and Pd/SnO2. Finally, the SVM classification was also carried out by combining all the gas sensing responses of the four different gas sensors, by achieving a perfect classification of the gases analyzed together with a very low error in the estimated concentrations, compared with the concentrations injected in the gas chamber. References [1] Maia, R.F., Netto, I., Tran, A.L.H. Precision agriculture using remote monitoring systems in Brazil (2017) GHTC 2017 - IEEE Global Humanitarian Technology Conference, Proceedings, 2017-January, pp. 1-6. doi: 10.1109/GHTC.2017.8239290. [2] Ruiz-Garcia, L., Lunadei, L., Barreiro, P., Robla, J.I. A review of wireless sensor technologies and applications in agriculture and food industry: State of the art and current trends (2009) Sensors (Switzerland), 9 (6), pp. 4728-4750. doi: 10.3390/s90604728. [3] El-Shikha, D.M., Waller, P., Hunsaker, D., Clarke, T., Barnes, E. Ground-based remote sensing for assessing water and nitrogen status of broccoli (2007) Agricultural Water Management, 92 (3), pp. 183-193. doi: 10.1016/j.agwat.2007.05.020. [4] Fabbri, B., Valt, M., Parretta, C., Gherardi, S., Gaiardo, A., Malagù, C., Mantovani, F., Strati, V., Guidi, V. Correlation of gaseous emissions to water stress in tomato and maize crops: From field to laboratory and back (2020) Sensors and Actuators, B: Chemical, 303. doi: 10.1016/j.snb.2019.127227. Figure 1

Tin dioxide (SnO2) is the most-used semiconductor for gas sensing applications. However, lack of selectivity and humidity influence limit its potential usage. Antimony (Sb) doped SnO2 showed unique electrical and chemical properties, since the introduction of Sb ions leads to the creation of a new shallow band level and of oxygen vacancies acting as donors in SnO2. Although low-doped SnO2:Sb demonstrated an improvement of the sensing performance compared to pure SnO2, there is a lack of investigation on this material. To fill this gap, we focused this work on the study of gas sensing properties of highly doped SnO2:Sb. Morphology, crystal structure and elemental composition were characterized, highlighting that Sb doping hinders SnO2 grain growth and decreases crystallinity slightly, while lattice parameters expand after the introduction of Sb ions into the SnO2 crystal. XRF and EDS confirmed the high purity of the SnO2:Sb powders, and XPS highlighted a higher Sb concentration compared to XRF and EDS results, due to a partial Sb segregation on superficial layers of Sb/SnO2. Then, the samples were exposed to different gases, highlighting a high selectivity to NO2 with a good sensitivity and a limited influence of humidity. Lastly, an interpretation of the sensing mechanism vs. NO2 was proposed.

Introduction Gas sensors are widely applied to monitor the combustible and harmful gases which can be detrimental to safety, health and environmental protection. SnO2, a wide gap semiconductor, is one of the most promising gas sensor material because of high chemical stability and low fabrication cost[1]. While due to the drawbacks of SnO2 working as gas sensing material, such as low selectivity and high working temperature, doping by noble or transition metals is a particularly efficient way to improve its sensing properties[2]. SnO2 doped by antimony (SnO2/Sb) is a typical n-type oxide material, which has high electrical conductivity and thermal stability. Materials and Method The impacts of Sb doping (10 wt.% doping content) on morphology, nanostructure of commercial SnO2 were investigated by scanning electron microscopy (SEM) and x-ray diffraction (XRD) techniques. The samples were added into a composed solution mixed with glycol ether as wetting agent and an acrylic resin, and then screen printed onto alumina substrates, where Au was applied as interdigitated electrodes on front-side, and the back-side was equipped with a heater to provide with working temperature. In the last, the screen-printed films were treated at 180 ˝C in a muffle oven for 12 h in air to obtain the thermal stabilization[3]. The response values (R) were obtained by the equation (1), hence Ra is the resistance while exposed to the dry air, and Rg is the resistance while exposed to target gas, which were measured by home-made gas sensing system. The samples under different temperatures were named as SnO2-x and SnO2/Sb-x (x=300°C, 350°C, 400°C and 450°C). The gas sensing properties dependence of samples on different temperatures under acetone gas condition was clarified, and the relationship between doping and selectivity of samples under different target gases at 350°C were demonstrated. The results about high antimony doping content at different working temperatures and target gas situations were investigated in this work. Results and Conclusions The SEM reveled that antimony doping SnO2 contributed to harshen the surface of SnO2, which is beneficial to increase the specific surface area and the adsorption efficiency to target gases, as shown in Fig. 1.[4]. XRD characterization shows that no other phases such as SnO and Sn3O4 were detected, which means the high purity of the SnO2[5]. Meanwhile, Sb doping did not change the tetragonal structure of SnO2, but it distinctly affected the preferred (110), (101) and (211) orientation growth, which can be detected in Fig.2. Furthermore, the gas sensing properties between pure SnO2 and SnO2/Sb were compared at different working temperatures and under different target gases environment. The measurement results in Fig.3 (a) and (b) demonstrated that the response of SnO2 and SnO2/Sb reached highest value at 350°C. While SnO2 and SnO2/Sb showed abnormal trend at 450°C that is conducive to oxygen atoms insertion into the crystal structure[6]. Specifically, SnO2/Sb showed p-type shape response at 450°C, maybe it is because antimony doping decreased the grain size of SnO2 so that the whole grains were fully depleted. In the beginning of reaction with target gas, the concentration of O2 on the surface of SnO2/Sb decreased and the conductance increased. Whilst, when the depletion zone was smaller than the grain size, the conductance decreased a lot. When stopping injection of target gases, the trend processed reversely [6]. Otherwise, this p-type shape response of SnO2/Sb can only be observed under acetone environment at 450°C, which can be used to distinguish acetone among other gases. From Fig.4 (a) and (b), it can be found that Sb doping facilitated SnO2 the highest response to acetone among H2S, ethylene, ethanol, and CO environments, which means antimony doping can improve the selectivity of SnO2. References [1] Korotcenkov, G. and B.K. Cho, Metal oxide composites in conductometric gas sensors: Achievements and challenges. Sensors and Actuators B: Chemical, 2017. 244: p. 182-210. [2] Degler, D., U. Weimar, and N. Barsan, Current Understanding of the Fundamental Mechanisms of Doped and Loaded Semiconducting Metal-Oxide-Based Gas Sensing Materials. ACS Sens, 2019. 4(9): p. 2228-2249. [3] Gaiardo, A., et al., Metal Sulfides as Sensing Materials for Chemoresistive Gas Sensors. Sensors (Basel), 16 (2016) 296. [4] Zhang, R., et al., Improvement of gas sensing performance for tin dioxide sensor through construction of nanostructures. J Colloid Interface Sci, 2019. 557: p. 673-682. [5] Zeng, W., et al., Hierarchical SnO2–Sn3O4 heterostructural gas sensor with high sensitivity and selectivity to NO2. Sensors and Actuators B: Chemical, 2019. 301: p. 127010. [6] Al-Hashem, M., S. Akbar, and P. Morris, Role of Oxygen Vacancies in Nanostructured Metal-Oxide Gas Sensors: A Review. Sensors and Actuators B: Chemical, 2019. 301: p. 126845. [7] C. M. Aldao, D. A. Mirabella, M. A. Ponce, Giberti, C. Malagu, Role of Intragrain Oxygen Diffusion in Polycrystalline Tin Oxide Conductivity, Journal of Applied Physics. 063723 (2011) 109-112. doi:10.1063/1.3561375. Figure 1