The high cost, low heat resistance, and brittleness of poly(L-lactide) (PLLA) is a significant drawback that inhibits its diffusion into many industrial applications. These weaknesses were solved by forming a polylactide stereocomplex (ST) and blending it with thermoplastic starch (TPS). We blended poly (L-lactide)(PLLA), up to 30% thermoplastic starch, and a chain extender (2%) in an internal mixer, which was then hand-mixed with poly (D-lactide)(PDLA) and injection molded to form specimens, in order to study mechanical, thermal, and crystallization behavior. Differential scanning calorimetry (DSC) and wide-angle X-ray diffraction (XRD) demonstrated that the stereocomplex structures were still formed despite the added TPS and showed melting points ~55 °C higher than neat PLLA. Furthermore, stereocomplex crystallinity decreased with the increased TPS content. Dynamic mechanical analysis revealed that ST improved PLLA heat resistance, and tensile testing suggested that the TPS improved the elongation-at-break of ST. Moreover, the chain extender reduced the degradation of ST/TPS blends and generally improved ST/TPS composites’ mechanical properties.

Low-cost sensors (LCS) have been increasingly deployed to monitor PM_2.5 concentrations. More than 1500 LCS have been installed in Thailand to increase public awareness of air quality. However, performance of these sensors has not been systematically investigated. In this study, PM_2.5 LCS were co-located next to a PM_2.5 federal equivalent method (FEM) reference instrument at three Thai locations—in the north, center and northeast. We evaluated the performance of a PM_2.5 LCS (PMS7003, Plantower) to understand the key factors affecting performance, including emission sources, relative humidity, temperature and PM_2.5 concentration. Low PM concentration and high humidity levels had a significant impact on performance. Sensors in a high traffic emission area showed low correlation. The unadjusted PM_2.5 LCS performance varied with locations. Errors were mainly observed at low concentrations. They significantly underestimated concentrations in congested urban environments. After calibration, accuracy was improved with multiple regression models. The performance of sensors only at Chiang Mai (CM) during the dry season and Ubon Ratchathani (URT) during the dry and wet seasons were acceptable with coefficient of variation: 5.8 ± 4.7–6.8 ± 5.0%, slope: 0.829–0.945, intercept: 1.12–5.49 µg/m³, R²: 0.880–0.934 and RMSE: 4.3–5.1 µg/m³. In the congested area in Bangkok (BKK), they underestimated concentrations of small particles.