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Abstract: The sensing properties of ZnO nanorods which were immobilized with CA15.3 antibody (anti-CA15.3) for CA15.3 detection were studied by using quartz crystal microbalance (QCM) sensors in this work. ZnO nanorods were synthesized at 90°C by wet chemical route which was used to grow the ZnO nanorods directly on the QCM. The sensors were constructed by binding anti-CA15.3 on ZnO nanorods surface using 3-aminopropyltrietoxy silane (APTES) and glutaraldehyde and were used for CA15.3 detection. The morphology of ZnO nanorods was examined by scanning electron microscopy (SEM). The SEM results indicated the grown ZnO nanorods were well-aligned and the diameter was around 100nm. These QCM sensors showed high sensitivity (25.34±0.67Hz/scale (1U/mL)) and good linearity (0.99) in the concentration range of CA15.3 from 0.5 to 26U/mL by the experiment. The response time of these sensors were observed less than 10s with good reproducibility and stability. All the results indicated that one-dimensional ZnO nanostructure is a good material for biosensor application. [Copyright &y& Elsevier]

Abstract: We reported a facile and controllable route to prepare nest-like ZnO without any complicate experimental equipments, pressure and gas control procedures. The nest-like ZnO was made within a muffle furnace in air atmosphere under normal pressure through a simple carbon thermal reduction reaction. The product was characterized by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), field emission scanning electron microscope (FESEM) and photoluminescence (PL). The results showed that the product was wurtzite ZnO and had an interesting hollow nest-like microstructure with a prickly porous shell. The shell consisted of ZnO nanospheres, and a few ZnO nanorods grew on the shell surface. The gas sensing test indicated that the nest-like ZnO had an excellent gas sensing behavior toward ethanol. The response of the sensor to 50ppm ethanol was about 100, which was at least four times more than other gases. Moreover, even down to 1ppm ethanol, the response still reached 3.8. Both the response time and recovery time of the sensor were short. All of these proved that the nest-like ZnO is a promising gas sensing material for ethanol detecting. [Copyright &y& Elsevier]

Abstract: A series of SnO2/V2O5 composites were designed and developed for selectively sensing BTEX (benzene, toluene, ethylbenzene, and xylol) based on a catalytic oxidation reaction. These composites were prepared with a facile solid phase reaction using sol–gel synthesized SnO2 and commercial V2O5 as raw materials. XRD and TEM were employed to characterize these composites. The results revealed forming process of the SnO2/V2O5 composites. Gas sensing tests of side-heating sensors showed that the SnO2/V2O5 composites had a much better response to BTEX rather than other VOCs such as ethanol, acetone, methanal, methanol and acetic acid compared with pure SnO2. Among these composites, the one containing 10wt% V2O5 has the best selectivity and response (R a/R g) toward BTEX. At 270°C, an optimal working temperature, the values of gas response to 50ppm (parts per million) BTEX are more than 5 times, meanwhile the limit of detection is as low as 0.5ppm. The influence of V2O5 on BTEX sensing of SnO2-based gas sensor was also discussed. [Copyright &y& Elsevier]

Abstract: Nanocrystalline In2O3 gas sensing material was prepared by sintering a precursor In(OH)3 at 600°C which was hydrothermally synthesized at 250°C for 24h by using InCl3·4H2O as a starting material. The nanopowder was characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), thermogravimetry–differential scanning calorimetry (TG–DSC) and X-ray photoelectron spectrometer (XPS). The results indicated that the precursor of indium oxide was cubic indium hydroxide with range size of 50–80nm, and indium oxide was composed of In and O. Gas sensing properties of the sensors were tested by mixing a gas in air at static state, the tested results showed that the sensor based on In2O3 nanocrystals had satisfying H2S gas sensing properties at rather low temperature. [Copyright &y& Elsevier]

Hydrogen (H 2) is the cleanest energy but also dangerous, thus the real-time detection of H 2 leakage is crucially important. Herein, we develop ultra-large micro-sized Sn 3 O 4 nanosheet hierarchies via a facile hydrothermal method using polyvinyl pyrrolidone as a regulating agent. Structural analyses reveal the as-prepared Sn 3 O 4 nanosheets are single-crystalline with {010}-facet exposure and Sn2+-deficiency. When examined as gas sensing materials, the {010}-faceted Sn2+-deficient Sn 3 O 4 nanosheets demonstrate highly efficient H 2 sensing with excellent selectivity/response (two times higher than those for NO 2 , SO 2 , H 2 S, and H 2 O) and stability upon long-term testing (over 37 days). The sensor displays fast response /recovery times of 9.4/24 s to 10 ppm H 2 at a low working temperature of 150 °C, and even well responds to 50 ppb H 2 with a theoretical limit of detection (LOD) of 0.54 ppb. The excellent H 2 sensing performance of the Sn 3 O 4 nanosheets has been verified by the first-principle calculation based on density functional theory (DFT), suggesting that the Sn2+-deficient {010} surface of Sn 3 O 4 provides abundant adsorption sites for hydrogen molecules. Moreover, we demonstrate the practical application of Sn 3 O 4 sensor embedded into a miniaturized deployable module, which realizes the real-time on-line H 2 monitoring with excellent repeatability and selectivity for long-term operation. [Display omitted] • We predicted that Sn 3 O 4 NSs with Sn2+ defect were sensitive to H 2 by DFT. • The Sn 3 O 4 NSs have been successfully synthesized by hydrothermal method. • H 2 -sensitive properties were tested comprehensively to verify the DFT calculation. • We fabricated a miniaturized deployable module to monitor the hydrogen leak. [ABSTRACT FROM AUTHOR]

Owing to the high toxicity of lead (II) (Pb 2+ ) and silver (I) ions (Ag + ) to aquicolous organisms, it is highly desirable to develop a sensitive method for the simultaneous detection of Pb 2+ and Ag + . In this work, a novel and sensitive single-labeled fluorescent oligonucleotide (OND) probe is designed to simultaneously detect based on the specific complexation of Ag + to cytosines, the special induction capacity of Pb 2+ to guanine-rich OND to form G-quadruplex and the inherent quenching ability of G-quadruplex to the hexachloro fluorescein (HEX). The Ag + -induced hairpin-link structure makes HEX labeled at the 5′-termini close to the Pb 2+ -induced G-quadruplex connected at the 3′-terminin, resulting in a remarkable fluorescence quenching owing to photoinduced electron transfer (PET) process from G-quadruplex to HEX. At 298 K, the apparent associating constant between them is 1.85 × 10 8 (L/mol). Using this system, we can simultaneously detect the Pb 2+ and Ag + over the linear response range of 1–100 nmol/L with a detection limits of 96 pmol/L and 21 pmol/L, respectively. Through the assay in real samples, it is known that this system could be applied to accurately monitor the trace Pb 2+ and Ag + in aqueous solution. [ABSTRACT FROM AUTHOR]

Measuring the concentration of oxynitride NO x (including NO and NO 2 ) can provide a scientific basis for determining its potential harm to the human body. The existing metal oxide semiconductor sensor arrays are greatly limited by high operating temperature and low integration. In this article, we report on a high-integration carbon nanotubes sensor array with two different electrode separations sensitive to oxynitride mixture at low temperature. The triple-electrode sensor array is comprised of a large carbon nanotube array cathode, two extracting electrodes and two collecting electrodes and worked at non-self-sustaining discharge state, which was determined by the study on current-voltage characteristic of a double-electrode structure sensor. Through studying the relationship between gas concentration and discharge current at 1 atm, we obtained the distinct sensitivities of the binary mixture in the concentration ranges of 0–1166 ppm NO and 0–712 ppm NO 2 using the sensor array at low operating voltages and 60 °C. Collecting currents of the two sensors in the array decreased with increasing NO and NO 2 concentration in the gas mixture. The repeatable characteristics and dynamical response tests of the sensors were also conducted. The proposed sensor array has potential for the direct detection of a NO-NO 2 mixture without separating the mixed gases. [ABSTRACT FROM AUTHOR]

NO 2 is the representative pollutant in air quality monitoring and power industry. Although numerous gas sensors have been employed for the accurate detection of NO 2 , a universal method sieving for the optimum sensor is still lacked. Herein, a strategy to acquire the optimal NO 2 -sensitive material has been proposed, and a series of tests verified its effectiveness. First, the serious noise would exist in the gas sensors with the high resistivity, so an appropriate baseline resistance is the basis for the practical application. Furthermore, the performances of SnCl 2 -In 2 O 3 doped with six metal oxides (CuO, NiO, TiO 2 , SnO 2 , WO 3 , ZnO) on NO 2 were investigated. The quantitative evaluation method that integrated the variation coefficient method (VCM), the technique for order preference by similarity to ideal solution (TOPSIS), and the grey correlation analysis (GRA) has been proposed to solve the imbalance of sensors' indices. Due to the highest comprehensive score, 10 wt% WO 3 -doped SnCl 2 -In 2 O 3 with high response value (5962.20%) was regarded as the optimum for NO 2 detection. Ultimately, an additional comparative test among the as-prepared sensors, previous investigations and commercial devices has demonstrated the great potential of the method in practice. • Verifying the gas-sensitive material needs proper resistance for practical use. • Providing a comprehensive evaluation method. [ABSTRACT FROM AUTHOR]

Monitoring the gas composition around power cables using gas sensors is a promising method for detecting their overheating. In this paper we screened the sensing performance in dry and humid conditions of a variety of commercial and homemade sensors. The target gases were 2-Ethylhexanol (2-EH), Dioctyl terephthalate (DOTP), and Benzene; they are the main gases evaporated from overheated polyvinyl chloride (PVC), which is the material of choice for the external insulation of power cables. We found that the tested sensors show different sensing performances to the three target gases, generally with a fast response and slow recovery, the latter especially for 2-EH and DOTP. We also found that the responses to DOTP and 2-EH are correlated and, generally, larger than the ones to benzene. We also showed that by combining the sensors into a sensor array and applying PCA it is possible to distinguish between 2-EH and DOTP, on the one hand, and benzene, on the other hand. However, a more refined pattern recognition method is needed to distinguish between 2-EH and DOTP. • Sensing performance of various sensors was examined for diagnosing the overheating of PVC insulators. • Sensing patterns for DOTP and 2-EH are quite similar and different from the ones corresponding to benzene. • Common functional groups in DOTP and 2-EH are vital. Benzene likely reacts with the surface material initially via π bond. • Best candidates seem to be commercially availables TGS822 and TS2620 and homemade sensors based on Pd-In 2 O 3 and Au-WO 3. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes an application of quartz tuning forks (QTFs) coated with nanocrystalline ZnO films used as relative humidity sensors. The nanocrystalline ZnO thin films were deposited on the QTFs by a sol–gel method. The film was characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) to obtain the information on the structural and morphological properties. And the humidity sensing characteristics of the sensors have been investigated. Ethanol and acetone interference on the sensor performance have been also tested. Sensing experiments were examined by measuring the resonance frequency shift of QTFs due to the additional mass loading. The results showed that the sensors had high humidity sensitivity, good stability, fast response time and well reproducibility. We demonstrated here a simple and low-cost humidity sensor using the QTFs. [Copyright &y& Elsevier]

• A sensor array comprising four gas sensors has been utilized to detect 11 types of mixtures of NO 2 and CO, whose concentration varies from 0 to 50ppm. The range of mixed gas concentration is wide enough, and the theoretical LOD could reach dozens of ppb levels. • Features were extracted by principal component analysis (PCA), and then C-means clustering and back propagation neural network (BPNN) were employed to identify gases. Also, we have verified the optimized effect of genetic algorithm (GA) for BPNN. Through transforming response curves into gray image, CNN could be used to directly recognize various gases. • To investigate which feature of input sample is most influential to BPNN model, a random variable substitution method has been proposed. • Experiments for sensing 6 kinds of CO and NO 2 mixtures under 4 different relatively humidity (25%, 33%, 50%, 75%) have been implemented to evaluate the effect of environmental humidity on our sensor array. The results demonstrated our method could eliminate the effects of humidity. • Even though this work stressed on the identification of mixtures of NO 2 and CO, the approach presented here is generic, and could in principle, be extended to other relevant gas species. In this work, a sensor array comprised four sensors has been employed to detect 11 types of mixtures of nitrogen dioxide (NO 2) and carbon monoxide (CO), with concentration varying from 0 to 50 ppm. To reduce the effect of sensor noise and ensure high recognition accuracy, average resistance over a period of time was introduced. Then, 12 features including response value, response time and recovery time were extracted from each sample. After that, C-means clustering and back propagation neural network (BPNN) were performed to identify various gases, with classification accuracy of 94.55 % and 100 %, respectively. Genetic algorithm (GA) was also employed to further improve BPNN's performance. Moreover, a random variable substitution method has been introduced to study which feature of the input sample influence the BPNN model most. Through gray processing, dynamic curves have been transformed into gray images, from which convolutional neural network (CNN) was introduced to automatically extract high-level features, and an identification accuracy of 100 % has been realized. Finally, experiments for sensing gas mixtures of CO and NO 2 under various humidity levels have been done to test the impact of humidity on the sensor array. The results demonstrated the proposed method could eliminate the effects of humidity. [ABSTRACT FROM AUTHOR]