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    Preparation of Chitin Nanofibers and Natural Rubber Composites and Their Triboelectric Nanogenerator Applications
    (2024-02-01)
    Petchnui, Kattaliya
    ;
    Uwanno, Teerayut
    ;
    Phonyiem Reilly, Mayuree
    ;
    Pinming, Chinathun
    ;
    Treetong, Alongkot
    Triboelectric nanogenerators (TENGs) have gained significant attention as promising energy-harvesting devices that convert mechanical energy into electrical energy through charge separation induced by friction and electrostatic induction. In this study, we explore the utilization of biowaste shrimp shell-extracted chitin nanofiber (ChNF) as a viable eco-friendly material for TENG applications. Composite materials were prepared by incorporating ChNF into natural rubber (NRL) at loading levels of 0.1 and 0.2 wt% (NRL/ChNF) to form the TENG triboelectric layer. ChNFs with a uniform width of approximately 10–20 nm were successfully extracted from the shrimp shells through a simple mechanical procedure. The NRL/ChNF composites exhibited enhanced mechanical properties, as evidenced by a higher Young’s modulus (3.4 GPa) compared to pure NRL. Additionally, the NRL/ChNF composites demonstrated an increased dielectric constant of 3.3 at 0.1 MHz. Moreover, the surface potential difference of NRL increased from 0.182 V to 1.987 V in the NRL/ChNF composite. When employed as the triboelectric layer in TENG, the NRL/ChNF composites exhibited significant improvement in their output voltage, with it reaching 106.04 ± 2.3 V. This enhancement can be attributed to the increased dielectric constant of NRL/ChNF, leading to enhanced charge exchange and charge density. This study presents a straightforward and environmentally friendly technique for preparing sustainable natural materials suitable for energy-harvesting devices.
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    Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data
    (2024-01-01)
    Jongyingcharoen, Jiraporn Sripinyowanich
    ;
    Howimanporn, Suppakit
    ;
    Sitorus, Agustami
    ;
    Phanomsophon, Thitima
    ;
    Posom, Jetsada
    Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80–88% TSI, and more than 88% TSI. The 80–88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance.
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