Polylactic acid (PLA) is derived from renewable resources and can be composted under controlled industrial conditions, but its flammability and brittleness limit wider use. This study combined Ti₃C₂T_x MXene with surface terminations and an intumescent flame retardant (IFR) system based on ammonium polyphosphate and pentaerythritol (4:1, w/w). A styrene–butadiene–styrene (SBS) elastomer was then added to improve toughness. At a total flame retardant loading of 10 wt%, PLA/8.5IFR/1.5MXene reached a limiting oxygen index (LOI) of 30.32% and a UL-94 V-0 rating without dripping. Compared with PLA/10IFR, the peak heat release rate (PHRR) and total heat release (THR) decreased by 18.6% and 28.8%, respectively, and the residue at 600 °C increased from 5.88 to 6.75 wt%. The composite containing MXene formed a denser char and showed a slightly lower Raman D/G intensity ratio. Adding 10 wt% SBS increased notched impact strength by 48% for PLA/IFR and 31% for PLA/IFR/MXene, but reduced tensile strength and modulus. MXene therefore improved the barrier in the condensed phase at a lower IFR content, while SBS partly recovered toughness.

Hydration level (HL) is a critical physiological indicator of human health and functional status, and accurate HL monitoring is essential for applications in healthcare, sports, and wellness assessment. However, existing methods are either invasive and inconvenient or noninvasive but limited by system complexity and insufficient accuracy. To address these limitations, this study proposes a methodological approach for noninvasive computerized HL estimation based on galvanic skin response (GSR) signals, termed the PSAML approach, which integrates principal component analysis (PCA), successive decomposition index (SDI), and machine learning (ML) classifiers. A representative GSR dataset was collected from three healthy subjects under dehydrated, normal, and overhydrated states in sitting, standing, and posture-independent scenarios. After preprocessing, including outlier removal, Butterworth filtering, and time-window segmentation, conventional time-domain features were extracted and compared with PCA- and SDI-based representations. Six ML algorithms were used for classification. The results show that the conventional feature method achieved a maximum accuracy of 63.97%, whereas PCA-based feature reduction significantly improved performance, with PCA+SVM, PCA+LR, and PCA+LDA achieving accuracies above 99% in most cases. SDI-based features also demonstrated strong performance with suitable classifiers under smaller time windows. These findings demonstrate that the proposed PSAML approach provides an accurate and efficient solution for wearable noninvasive HL monitoring.