A handheld food freshness monitoring device is fabricated using a SnO₂–Au 0.5 wt% nanostructure-based gas sensor, showing good sensitivity, selectivity, and repeatability for H₂S gas.

Heavy metals cause various fetal diseases in humans. Heavy metals from factory wastewater can contaminate drinking water, fish, and crops. Inductively coupled plasma-mass spectrometry (ICP-MS) and atomic absorption spectrometry (AAS) are commonly used to analyze heavy metal contents; however, these methods require pre-treatment processes and are expensive and complex. To overcome these limitations, three metal-sensing materials using a whole-cell biosensor in Escherichia coli (E. coli) were developed. Strains were engineered to harbor three kinds of plasmids containing the copA, zntA, and mer promoters for sensing copper, cadmium, and mercury, respectively. The luciferase (lux) gene was inserted as a reporter into the plasmid, which was later replaced with a fused protein sequence containing OmpA (1–159) and mCherry for optical detection. The constructed strains could detect mercury, cadmium, and copper at 0.1–0.75 ppm, 0.2–0.75 ppm, and 2–7.5 ppm, respectively, with linearity values of 0.99030, 0.99676, and 0.95933, respectively. The immobilization linearity value was 0.99765. Notably, these three heavy metals could be detected by visual analysis of the strains. Overall, these findings establish this novel sensor as a potential approach for heavy metal detection in biological samples and foods.

Herein, a facile fabrication process of ZnO-ZnFe₂O₄ hollow nanofibers through one-needle syringe electrospinning and the following calcination process is presented. The various compositions of the ZnO-ZnFe₂O₄ nanofibers are simply created by controlling the metal precursor ratios of Zn and Fe. Moreover, the different diffusion rates of the metal oxides and metal precursors generate a hollow nanostructure during calcination. The hollow structure of the ZnO-ZnFe₂O₄ enables an enlarged surface area and increased gas sensing sites. In addition, the interface of ZnO and ZnFe₂O₄ forms a p-n junction to improve gas response and to lower operation temperature. The optimized ZnO-ZnFe₂O₄ has shown good H₂S gas sensing properties of 84.5 (S = R_a/R_g) at 10 ppm at 250 °C with excellent selectivity. This study shows the good potential of p-n junction ZnO-ZnFe₂O₄ on H₂S detection and affords a promising sensor design for a high-performance gas sensor.

Advancement of precision agriculture increasingly relies on cost-effective and scalable technologies for real-time environmental management, particularly in greenhouse environments where vertical and spatial microclimate heterogeneity influences crop performance. This study presents the design, implementation, and experimental validation of an Android-based smartphone application edge supervisory monitoring system integrated with multi-layer wireless sensing and control nodes for real-time monitoring in a smart greenhouse. The system combined multi-layer wireless sensor nodes, wireless control nodes, a Long-Range Wide Area Network (LoRaWAN) gateway, Message Queuing Telemetry Transport (MQTT) communication, and a cloud-synchronized smartphone-based supervisory interface for visualizing environmental data, detecting defined abnormal events, and controlling actuators remotely. For feasibility tests, 54 sensing nodes and 12 actuator nodes were deployed across three vertical layers in two sections, measuring temperature, humidity, CO₂ concentration, and light intensity. Abnormality was defined as environmental threshold violations, statistical signal deviations, actuator power inconsistencies, and communication timeout events. Experimental results revealed vertical and spatial environmental variability across greenhouse sections, while real-time time-series and 3D spatial maps enabled the rapid detection of abnormal conditions. The rule-based abnormality detection engine identified out-of-range environmental values and sensor-related inconsistencies and generated immediate notifications. Smartphone profiling revealed that display and system-level processes accounted for energy consumption, with battery power reaching a peak of 3.5 W and application CPU utilization ranging from 40% to 70% during active monitoring. The results demonstrate system-level feasibility, responsiveness, and scalability under commercial greenhouse workloads, supporting future integration of predictive control and energy-efficient operation.

Amino acid arrays comprising bioluminescent amino acid auxotrophic Escherichia coli are effective systems to quantitatively determine multiple amino acids. However, there is a need to develop a method for convenient long-term preservation of the array to enable its practical applications. Here, we reported a potential strategy to efficiently maintain cell viability within the portable array. The method involves immobilization of cells within agarose gel supplemented with an appropriate cryoprotectant in individual wells of a 96-well plate, followed by storage under freezing conditions. Six cryoprotectants, namely dimethyl sulfoxide, glycerol, ethylene glycol, polyethylene glycol, sucrose, and trehalose, were tested in the methionine (Met) auxotroph-based array. Carbohydrate-type cryoprotectants (glycerol, sucrose, and trehalose) efficiently preserved the linearity of determination of Met concentration. In particular, the array with 5% trehalose exhibited the best performance. The Met array with 5% trehalose could determine Met concentration with high linearity (R² value = approximately 0.99) even after storage at −20 °C for up to 3 months. The clinical utilities of the Met and Leu array, preserved at −20 °C for 3 months, were also verified by successfully quantifying Met and Leu in spiked blood serum samples for the diagnosis of the corresponding metabolic diseases. This long-term preservation protocol enables the development of a ready-to-use bioluminescent E. coli-based amino acid array to quantify multiple amino acids and can replace the currently used laborious analytical methods.