Biosynthesis for the preparation of antimicrobial silver nanoparticles (Ag NPs) is a green method without the use of cytotoxic reducing and surfactant agents. Herein, shape-controlled and well-dispersed Ag NPs were biosynthesized using yeast extract as reducing and capping agents. The synthesized Ag NPs exhibited a uniform spherical shape and fine size, with an average size of 13.8 nm. The biomolecules of reductive amino acids, alpha-linolenic acid, and carbohydrates in yeast extract have a significant role in the formation of Ag NPs, which was proved by the Fourier transform infrared spectroscopy analysis. In addition, amino acids on the surface of Ag NPs carry net negative charges which maximize the electrostatic repulsion interactions in alkaline solution, providing favorable stability for more than a year without precipitation. The Ag NPs in combination treatment with ampicillin reversed the resistance in ampicillin-resistant E. coli cells. These monodispersed Ag NPs could be a promising alternative for the disinfection of multidrug-resistant bacterial strains, and they showed negligible cytotoxicity and good biocompatibility toward Cos-7 cells.

Human metabolite moisture detection is important in health monitoring and non-invasive diagnosis. However, ultra-sensitive quantitative extraction of respiration information in real-time remains a great challenge. Herein, chemiresistors based on imine-linked covalent organic framework (COF) films with dual-active sites are fabricated to address this issue, which demonstrates an amplified humidity-sensing signal performance. By regulation of monomers and functional groups, these COF films can be pre-engineered to achieve high response, wide detection range, fast response, and recovery time. Under the condition of relative humidity ranging from 13 to 98%, the COF_TAPB-DHTA film-based humidity sensor exhibits outstanding humidity sensing performance with an expanded response value of 390 times. Furthermore, the response values of the COF film-based sensor are highly linear to the relative humidity in the range below 60%, reflecting a quantitative sensing mechanism at the molecular level. Based on the dual-site adsorption of the (–C=N–) and (C–N) stretching vibrations, the reversible tautomerism induced by hydrogen bonding with water molecules is demonstrated to be the main intrinsic mechanism for this effective humidity detection. In addition, the synthesized COF films can be further exploited to effectively detect human nasal and oral breathing as well as fabric permeability, which will inspire novel designs for effective humidity-detection devices.

Ag-doped TaS₂ monolayers show an obvious change in work function and short recovery time for NO₂, which indicates that TaS₂-based nanomaterials might be a promising candidate for constructing high-performance NO₂ gas sensors.

This work presents an electronic nose-based gas identification and concentration estimation method for detection of food spoilage. The response data of sensors were acquired through a commercial gas sensor array and data acquisition circuit board, and transformed into pictures with the form of the Fourier series. A convolutional neural network model was used to identify the pictures from the conversion of sensor data, thus achieving the purpose of identifying the gases (C ₂ H ₅ OH, NH ₃ and H ₂ S). In order to solve the problem of sample imbalance and to improve the generalization performance of classification models, the synthetic minority oversampling technique and dropout technique were employed. Five-fold cross-validation was used to evaluate the performance of the model, of which the gas identification accuracy rate reached 96.67%. Moreover, a gas concentration regression model with the advantages of simplicity and strong interpretability was further proposed to estimate the concentrations of C ₂ H ₅ OH, NH ₃ and H ₂ S. The mean absolute errors and coefficients of determination for the concentration estimation of C ₂ H ₅ OH, NH ₃ and H ₂ S are (3.71 ppm, 0.968), (0.50 ppm, 0.968) and (0.13 ppm, 0.99), respectively. Furthermore, we used our model to evaluate the freshness of kiwifruit, pork and beef, and it showed satisfactory predictive performance. The method proposed in this work realizes high-precision detection of gases from food spoilage, and has a good application prospect in the rapid judgment of food freshness on the electronic nose system.

An electronic nose (E-nose) based on three metal oxide semiconductor (MOS) gas sensors is designed to quantitatively analyse six types of volatile organic compounds (VOCs). Support vector machine (SVM), extreme learning machine (ELM), and back-propagation (BP) neural network, are used to design different classifiers and regressors for integrating a suitable pattern recognition model of the E-nose system. By using the output of the classifier as one of the input features of the regressor, the models can predict the concentration of different types of VOCs at the same time. The 5-fold cross-validation is applied to search optimal parameters of each model and the independent test is conducted to evaluate the generalization performance. A pipeline is used to connect the best classifier and the best regressor, constructing an integrated model for pattern recognition. The integrated model based on ELM-ELM structure exhibits the best performance. Classification accuracy can be as high as 99% and the R ² score of regression as high as 0.97 in the 5-fold cross-validation. Classification accuracy is up to 93% and the R ² score of regression is up to 0.94 in the independent tests. Furthermore, the integrated model has prominent training and test efficiency, of which time consumptions are both shorter than 0.1 s. This work provides an approach to design an efficient integrated model with high performance for gas type identification and concentration prediction in the E-nose system.

An efficient formaldehyde (HCHO) gas sensor system based on nickel-doped tin oxide (Ni-doped SnO ₂ ) nanoparticle sensor and back-end circuit has been developed. Ni-doped SnO ₂ nanoparticles are prepared by a simple one-step hydrothermal method. To complete the function of detecting HCHO gas in real time, a back-end circuit suitable for resistance measurement of nanoparticle sensors and an organic light emitting diode screen which can display the output of gas concentration data are integrated as a portable device. Compared with the traditional HCHO detection device, this designed system exhibits better stability, excellent selectivity, and high response. Its detection limit is as low as 90 ppb. The gas response to 50 ppm HCHO is 67.63. Further for the quantitative evaluation, we refer to and modify the existing HCHO diffusion model, then carry out a practical test on the HCHO diffusion at the same time. The comparison between the theoretical fitting curve and the actual test curve matched well enough to reflect the potential of this system for the practical HCHO evaluation.