A holistic review on the recent trends, advances, and challenges for high-precision room temperature liquefied petroleum gas sensors

High versatility of polyethylene terephthalate (PET) waste for the development of batteries, biosensing and gas sensing devices

Continuously growing adoption of electronic devices in energy storage, human health and environmental monitoring systems increases demand for cost-effective, lightweight, comfortable, and highly efficient functional structures. In this regard, the recycling and reuse of polyethylene terephthalate (PET) waste in the aforementioned fields due to its excellent mechanical properties and chemical resistance is an effective solution to reduce plastic waste. Herein, we review recent advances in synthesis procedures and research studies on the integration of PET into energy storage (Li-ion batteries) and the detection of gaseous and biological species. The operating principles of such systems are described and the role of recycled PET for various types of architectures is discussed. Modifying the composition, crystallinity, surface porosity, and polar surface functional groups of PET are important factors for tuning its features as the active or substrate material in biological and gas sensors. The findings indicate that conceptually new pathways to the study are opened up for the effective application of recycled PET in the design of Li-ion batteries, as well as biochemical and catalytic detection systems. The current challenges in these fields are also presented with perspectives on the opportunities that may enable a circular economy in PET use.

Gas Graph Convolutional Transformer for Robust Generalization in Adaptive Gas Mixture Concentration Estimation

Gas concentration estimation has a tremendous research significance in various fields. However, existing methods for estimating the concentration of mixed gases generally depend on specific data-preprocessing methods and suffer from poor generalizability to diverse types of gases. This paper proposes a graph neural network-based gas graph convolutional transformer model (GGCT) incorporating the information propagation properties and the physical characteristics of temporal sensor data. GGCT accurately predicts mixed gas concentrations and enhances its generalizability by analyzing the concentration tokens. The experimental results highlight the GGCT's robust performance, achieving exceptional levels of accuracy across most tested gas components, underscoring its strong potential for practical applications in mixed gas analysis.