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For several decades, forming blend and composite of polyaniline (PANI) with insulating polymers has been a widely studied research area because of the potential applications of such blends, which have a unique combination of mechanical properties, the processability of conventional polymers and the electrical property of conducting polymers. The current review paper will emphasize PANI composites or blends with thermosetting polymer matrices. The enhanced electro-mechanical properties of the blends and composites depend on the uniform dispersion of the PANI particle in polymer matrix. Therefore, considerable studies have focused on improving the distribution of PANI particles within the thermoset matrices. In this review paper, all the parameters and conditions that influence the surface morphology and application of PANI thermoset blends and composites will be described systematically. Recent progress on PANI based thermoset system with multifunctional ternary composites research will be highlighted in this paper. Furthermore, encouraging applications of different PANI thermoset composites and blends are discussed, such as flame-retardant materials, lightning damage suppression, metal ion removal, anticorrosive coating, electromagnetic shielding, conductive adhesives, and sensing materials.

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The future photovoltaic technologies based on perovskite materials are aimed to build low tech, truly economical, easily fabricated, broadly deployable, and trustworthy solar cells. Hole transport material (HTM) free perovskite solar cells (PSCs) are among the most likely architectures which hold a distinctive design and provide a simple way to produce large-area and cost-effective manufacture of PSCs. Notably, in the monolithic scheme of the HTM-free PSCs, all layers can be printed using highly reproducible and morphology-controlled methods, and this design has successfully been demonstrated for industrial-scale fabrication. In this review article, we comprehensively describe the recent advancements in the different types of mesoporous (nanostructured) and planar HTM-free PSCs. In addition, the effect of various nanostructures and mesoporous layers on their performance is discussed using the electrochemical impedance spectroscopy (EIS) technique. We bring together the different perspectives that researchers have developed to interpret and analyze the EIS data of the HTM-free PSCs. Their analysis using the EIS tool, the limitations of these studies, and the future work directions to overcome these limitations to enhance the performance of HTM-free PSCs are comprehensively considered.

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Excessive daytime sleepiness (EDS) is highly prevalent among medical students and can have serious negative outcomes for both students and their patients. Little is known about the magnitude and predictors of EDS among medical college students. A meta-regression analysis was conducted to achieve these two targets. A systematic search was performed for English-language studies that reported the prevalence of EDS among medical students using the Epworth sleepiness scale (ESS), age, sex, sleep duration and sleep quality as predictive variables. A total of nine observational studies (K = 9, N = 2587) were included in the analyses. Meta-regression analyses were performed using mean age (years), sex (proportion of male subjects), sleep duration (hours/night) and sleep quality index score (continuous scale) as moderators for EDS—with the prevalence of EDS as an outcome variable. An interaction term of sleep duration X sleep quality was created to assess if these two variables simultaneously influenced the outcome variable. Utilizing the ESS, the pooled prevalence of EDS among medical students was 34.6% (95% Confidence Interval (CI) 18.3–50.9%). Meta-regression models of age, sex, sleep duration and sleep quality alone revealed poor predictive capabilities. Meta-regression models of sleep duration–sleep quality interaction revealed results with high statistical significance. The findings from this review contribute supporting evidence for the relationship between sleep duration and sleep quality scores (i.e., sleep duration X sleep quality score) in predicting EDS in medical students.

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