Perovskite solar cells (PSCs), since their inception in 2009, have experienced a meteoric rise in power conversion efficiencies (PCEs), challenging established photovoltaic technologies. However, their commercial deployment is hindered by stability and performance issues related to the presence of defects at the perovskite surface and grain boundaries. This study focused on the exploration of Morpholinium tetrafluoroborate (MOT) as a post-treatment additive to mitigate these challenges. Comprehensive characterization techniques revealed that the synergistic action of Morpholine and BF₄⁻ ions in MOT substantially improved the quality of the perovskite films and passivates surface and bulk defects, yielding notable enhancements in device PCE and stability. MOT-doped PSCs exhibited a PCE of 23.83% and retain 92% of the initial PCE after 2000 h of continuous illumination under one sun condition. The findings underscore the significance of additive engineering in advancing perovskite solar cell technology, opening up prospects for high-performing and durable perovskite photovoltaic devices.

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.