Flexible Strain Sensors Based on Thermoplastic Polyurethane Fabricated by Electrospinning: A Review.

Over recent years, thermoplastic polyurethane (TPU) has been widely used as a substrate material for flexible strain sensors due to its remarkable mechanical flexibility and the ease of combining various conductive materials by electrospinning. Many research advances have been made in the preparation of flexible strain sensors with better ductility, higher sensitivity, and wider sensing range by using TPU in combination with various conductive materials through electrospinning. However, there is a lack of reviews that provide a systematic and comprehensive summary and outlook of recent research advances in this area. In this review paper, the working principles of strain sensors and electrospinning technology are initially described. Subsequently, recent advances in strain sensors based on electrospun TPU are tracked and discussed, with a focus on the incorporation of various conductive fillers such as carbonaceous materials, MXene, metallic materials, and conductive polymers. Moreover, the wide range of applications of electrospun TPU flexible strain sensors is thoroughly discussed. Finally, the future prospects and challenges of electrospun TPU flexible strain sensors in various fields are pointed out.

Preparation of a novel DNA-imprinted sensor based on chitosan and its highly sensitive detection of Pb2.

Lead ion is very harmful to the environment, so it is very important to study its detection methods. In this study, a novel electrochemical sensor was constructed by modifying deoxyribonucleic acid (DNA) on the electrode, which can be used for the detection of Pb2+ in the environment. Part of the mixed solution of chitosan (CS) and Pb2+ template ions was dropped onto the surface of a glassy carbon electrode. CS-Pb2+ film was cross-linked through sodium tripolyphosphate. And a novel DNA-imprinted sensor was prepared by electrodepositing CS-Pb2+ thin film with gold nanoparticles (AuNPs), removing Pb2+ templates, and immobilizing specific double-stranded DNA. The electroactive area, surface morphology, sensitivity, and electrochemical reaction mechanism of the DNA-imprinted sensor were analyzed. The elementary reaction steps were studied through electrochemical reaction kinetics analysis. The experimental results indicate that the DNA-imprinted electrochemical biosensor can quantitatively detect Pb2+ in the range of 10-100 µM (R2 = 0.9935), and its detection limit is 6.5074 µM (3σ/slope). The sensitivity of the electrochemical biosensor is 1.55233 × 10-6 A/µM, and its active areas is 6.233 cm2. The desorption mechanism and adsorption mechanism have been explored through dynamic parameter analysis. The novel DNA imprinted electrochemical biosensor developed in this paper provides a robust method for detecting lead ions in solution. Additionally, it establishes a solid groundwork for detecting other metal ions.

Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques.

Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in the global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes the segmentation threshold combination by accelerating convergence and diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination of optimal thresholds for final segmentation. The efficacy of DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM), and compared with six contemporary swarm intelligence algorithms. The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm's practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.