Capillarity-based preparation system for optical colorimetric sensor arrays.

In recent years, optical colorimetric sensor arrays have demonstrated beneficial features, including rapid response, high selectivity, and high specificity; as a result, it has been extensively applied in food inspection and chemical studies, among other fields. There are instruments in the current market available for the preparation of an optical colorimetric sensor array, but it lacks the corresponding research of the preparation mechanism. Therefore, in connection with the main features of this kind of sensor array such as consistency, based on the preparation method of contact spotting, combined with a capillary fluid model, Washburn equation, Laplace equation, etc., this paper develops a diffusion model of an optical colorimetric sensor array during its preparation and sets up an optical colorimetric sensor array preparation system based on this diffusion model. Finally, this paper compares and evaluates the sensor arrays prepared by the system and prepared manually in three aspects such as the quality of array point, response of array, and response result, and the results show that the performance index of the sensor array prepared by a system under this diffusion model is better than that of the sensor array of manual spotting, which meets the needs of the experiment.

A novel device based on a fluorescent cross-responsive sensor array for detecting lung cancer related volatile organic compounds.

In this paper, a novel, simple, rapid, and low-cost detection device for lung cancer related Volatile Organic Compounds (VOCs) was constructed. For this task, a sensor array based on cross-responsive mechanism was designed. A special gas chamber was made to insure sensor array exposed to VOCs sufficiently and evenly, and FLUENT software was used to simulate the performance of the gas chamber. The data collection and processing system was used to detect fluorescent changes of the sensor arrays before and after reaction, and to extract unique patterns of the tested VOCs. Four selected VOCs, p-xylene, styrene, isoprene, and hexanal, were detected by the proposed device. Unsupervised pattern recognition methods, hierarchical cluster analysis and principal component analysis, were used to analyze data. The results showed that the methods could 100% discriminate the four VOCs. What is more, combined with artificial neural network, the correct rate of quantitative detection was up to 100%, and the device obtained responses at concentrations below 50 ppb. In conclusion, the proposed detection device showed excellent selectivity and discrimination ability for the VOCs related to lung cancer. Furthermore, our preliminary study demonstrated that the proposed detection device has brilliant potential application for early clinical diagnosis of lung cancer.