AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture.

The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long short-term memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO2, NO2, and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO2 gas, which is a noteworthy achievement. The determination coefficient (R2) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.

Paper-Based Sandwich-Structured Wearable Sensor with Sebum Filtering for Continuous Detection of Sweat pH.

Wearable sweat sensors, a product of the development of flexible electronics and microfluidic technologies, can continuously and noninvasively monitor abundant biomarkers in human sweat; however, sweat interferences, such as sebum, can reduce sensor reliability and accuracy. Herein, for the first time, the influence of sebum on the potentiometric response of an all-solid-state pH sensor was studied, and the obtained experimental results show that sebum mixed in sweat can decrease the potential response of the sensor and the slope of its calibration curve. A paper-based sandwich-structured pH sensor that can filter the sebum mixed in sweat was proposed based on commonly used oil-control sheets. Moreover, the hydrophilic properties, microstructure, and microfluidic performance of the sensor were investigated. The detection performance of the paper-based sandwich-structured pH sensor was comprehensively evaluated in terms of calibration in the presence of sebum and potentiometric response upon the addition of sebum. Furthermore, the anti-interference ability of the sensor was evaluated using different analytes under various deformation conditions. On-body trials were conducted to verify the performance, and their results showed that the proposed sensor can filter over 90% of the sebum in sweat, significantly enhancing sensor reliability and accuracy. Additionally, microfluidic channels could be simply fabricated using a scissor and paper, obviating the need for complex micromachining processes, such as photolithography and laser engraving. Overall, this work illustrates the influence of sebum on the detection performance of traditional potentiometric wearable sensors and paves the way for their development for real-world applications.

Estimation of pollutant sources in multi-zone buildings through different deconvolution algorithms.

Effective identification of pollution sources is particularly important for indoor air quality. Accurate estimation of source strength is the basis for source effective identification. This paper proposes an optimization method for the deconvolution process in the source strength inverse calculation. In the scheme, the concept of time resolution was defined, and combined with different filtering positions and filtering algorithms. The measures to reduce effects of measurement noise were quantitatively analyzed. Additionally, the performances of nine deconvolution inverse algorithms under experimental and simulated conditions were evaluated and scored. The hybrid algorithms were proposed and compared with single algorithms including Tikhonov regularization and iterative methods. Results showed that for the filtering position and algorithm, Butterworth filtering performed better, and different filtering positions had little effect on the inverse calculation. For the calculation time step, the optimal Tr (time resolution) was 0.667% and 1.33% in the simulation and experiment, respectively. The hybrid algorithms were found to not perform better than the single algorithms, and the SART (simultaneous algebraic reconstruction technique) algorithm from CAT (computer assisted tomography) yielded better performances in the accuracy and stability of source strength identification. The relative errors of the inverse calculation for source strength were typically below 25% using the optimization scheme.

Inertia coupling analysis of a self-decoupled wheel force transducer under multi-axis acceleration fields.

Wheel force transducer (WFT), which measures the three-axis forces and three-axis torques applied to the wheel, is an important instrument in the vehicle testing field and has been extremely promoted by researchers with great interests. The transducer, however, is typically mounted on the wheel of a moving vehicle, especially on a high speed car, when abruptly accelerating or braking, the mass/inertia of the transducer/wheel itself will have an extra effect on the sensor response so that the inertia/mass loads will also be detected and coupled into the signal outputs. The effect which is considered to be inertia coupling problem will decrease the sensor accuracy. In this paper, the inertia coupling of a universal WFT under multi-axis accelerations is investigated. According to the self-decoupling approach of the WFT, inertia load distribution is solved based on the principle of equivalent mass and rotary inertia, thus then inertia impact can be identified with the theoretical derivation. The verification is achieved by FEM simulation and experimental tests. Results show that strains in simulation agree well with the theoretical derivation. The relationship between the applied acceleration and inertia load for both wheel force and moment is the approximate linear, respectively. All the relative errors are less than 5% which are within acceptable and the inertia loads have the maximum impact on the signal output about 1.5% in the measurement range.

[Surface plasmon resonance spectral based fiber optic sensor for detection of total dissolved solids in water quality analysis].

As surface plasmon resonance (SPR) spectrum is sensitive to refractive index of the mediums, we explored its sensitivity characteristic of ions composition detection in a solution so as to measure the total dissolved solid value in water. Seven ionic (NaCl, KCl, CaCl2, MgCl2, Na2CO3, MgSO4 and ZnSO4) and 3 non-ionic (glucose, glycerol and sucrose) liquid samples were studied experimentally with a fiber optic SPR sensor. The influence of ion concentration on the resonance wavelength shift in SPR spectrum was investigated and discussed, and with that, the response curves were established to realize the detection of total dissolved solid in water quality analysis. The FO-SPR sensor spectral curve for TDS measurement was in conformity with ionic conductivity in 8 different water samples, and results show that the spectral method was better in high ion concentration detection of water. This research will broaden the SPR application in the field of water quality monitoring.