Upon compression, large close-packed aggregates in the mixed LB films split into small uniform ones. Hysteresis degree can be interpreted with chain entanglement and block mobility.

A non-dispersive infrared (NDIR) gas sensor with an integrated optical gas chamber was designed to accurately detect multiple gas concentrations in a complex polluted environment. This chamber consists of a hexagonal prism shell and a reflection plate. In particular, six dual-channel infrared detectors for sensing different gases and one collimated infrared light source are integrated in the gas chamber. The IR light emitted by the collimated IR light source arrives at these detectors after four reflections. The result of simulation and data collection for optical path length and luminous flux shows that the average optical path length in the chamber is 63 mm, and the effective utilization rate of the luminous flux can reach 78.8%. The highly compact NDIR sensor can detect the concentrations of a mixture of gases (CO, CO2, CH4, H2CO, NH3, and NO with a volume fraction ranging from 0 to 4%).

Prolonged life expectancy in humans has been accompanied by an increase in the prevalence of cancers. Breast cancer (BC) is the leading cause of cancer-related deaths. It accounts for one-fourth of all diagnosed cancers and affects one in eight females worldwide. Given the high BC prevalence, there is a practical need for demographic screening of the disease. In the present study, we re-analyzed a large microRNA (miRNA) expression dataset (), with the goal of optimizing miRNA biomarker selection using neural network cascade (NNC) modeling. Our results identified numerous candidate miRNA biomarkers that are technically suitable for BC detection. We combined three miRNAs (miR-1246, miR-6756-5p, and miR-8073) into a single panel to generate an NNC model, which successfully detected BC with 97.1% accuracy in an independent validation cohort comprising 429 BC patients and 895 healthy controls. In contrast, at least seven miRNAs were merged in a multiple linear regression model to obtain equivalent diagnostic performance (96.4% accuracy in the independent validation set). Our findings suggested that suitable modeling can effectively reduce the number of miRNAs required in a biomarker panel without compromising prediction accuracy, thereby increasing the technical possibility of early detection of BC.

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A sensor operating at room temperature has low power consumption and is beneficial for the detection of environmental pollutants such as ammonia and benzene vapor. In this study, polyaniline (PANI) is made from aniline under acidic conditions by chemical oxidative polymerization and doped with tin dioxide (SnO2) at a specific percentage. The PANI/SnO2 hybrid material obtained is then ground at room temperature. The results of scanning electron microscopy show that the prepared powder comprises nanoscale particles and has good dispersibility, which is conducive to gas adsorption. The thermal decomposition temperature of the powder and its stability are measured using a differential thermo gravimetric analyzer. At 20 °C, the ammonia gas and benzene vapor gas sensing of the PANI/SnO2 hybrid material was tested at concentrations of between 1 and 7 ppm of ammonia and between 0.4 and 90 ppm of benzene vapor. The tests show that the response sensitivities to ammonia and benzene vapor are essentially linear. The sensing mechanisms of the PANI/SnO2 hybrid material to ammonia and benzene vapors were analyzed. The results demonstrate that doped SnO2 significantly affects the sensitivity, response time, and recovery time of the PANI material.