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.

Owing to harsh working environments and complex industrial requirements, traditional gas sensors are prone to deformation damage, possess a limited detection range, require a high working temperature, and display low reliability, thereby necessitating the development of flexible and low-temperature gas sensors. In this study, we developed a low-temperature polyimide (PI)-based flexible gas sensor comprising a reduced graphene oxide (rGO)/MoS₂ composite. The micro-electro-mechanical system technology was used to fabricate Au electrodes on a flexible PI sheet to form a “sandwiched” sensor structure. The rGO/MoS₂ composites were synthesized via a one-step hydrothermal method. The gas-sensing response was the highest for the composite comprising 10% rGO. The structure of this material was characterized, and a PI-based flexible gas sensor comprising rGO/MoS₂ was fabricated. The optimal working temperature of the sensor was 141 °C, and its response-recovery time was significantly short upon exposure to 50–1500 ppm NH₃. Thus, this sensor exhibited high selectivity and a wide NH₃ detection range. Furthermore, it possessed the advantages of low power consumption, a short response-recovery time, a low working temperature, flexibility, and variability. Our findings provide a new framework for the development of pollutant sensors that can be utilized in an industrial environment.

Despite the numerous available treatments for cancer, many patients succumb to side effects and reoccurrence. Zinc oxide (ZnO) quantum dots (QDs) are inexpensive inorganic nanomaterials with potential applications in photodynamic therapy. To verify the photoluminescence of ZnO QDs and determine their inhibitory effect on tumors, we synthesized and characterized ZnO QDs modified with polyvinylpyrrolidone. The photoluminescent properties and reactive oxygen species levels of these ZnO/PVP QDs were also measured. Finally, in vitro and in vivo experiments were performed to test their photodynamic therapeutic effects in SW480 cancer cells and female nude mice. Our results indicate that the ZnO QDs had good photoluminescence and exerted an obvious inhibitory effect on SW480 tumor cells. These findings illustrate the potential applications of ZnO QDs in the fields of photoluminescence and photodynamic therapy.

The cost-reducing potential of intelligent supply chains (ISCs) has been recognized by companies and researchers. This paper investigates a two-echelon steel supply chain scheduling problem that considers the parallel-batching processing and deterioration effect in the production stage and sufficient vehicles in the port delivery stage. To solve this problem, we first analyze several sufficient and necessary conditions of the optimal scheme. We then propose a heuristic algorithm based on a dynamic programming algorithm to obtain the optimal solution for a special case where the assignment of all ingots to the soaking pits is known. Based on the results of this special case, we develop a modified biased random-key genetic algorithm (BRKGA), which incorporates genetic operations based on the flower pollination algorithm (FPA) to obtain joint production and distribution schedules for the general problem. Finally, we conduct a series of computational experiments, the results of which indicate that BRKGA-FPA has certain advantages in solving quality and convergence speed issues.

An improved method for fluctuation-enhanced sensing (FES) is introduced. We enhanced the old binary fingerprinting method, where the fingerprint bit values were ±1, by introducing ternary fingerprint bits utilizing a reference odor. In the ternary method, the fingerprint bit values are −1, 0, and +1, where the 0 value stands for the situation where the slope of the spectrum is identical to that of the reference odor. The application of the reference odor spectrum makes the fingerprint relative to the reference. The ternary nature and the reference feature increase the information entropy of the fingerprints. The method is briefly illustrated by sensing bacterial odor in cow manure isolates.