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Supercapacitors are energy storage devices with high power density, rapid charge/discharge rate, and excellent cycle stability. Carbon-based supercapacitors are increasingly attracting attention because of their large surface area and high porosity. Carbon-based materials research has been recently centered on biomass-based materials due to the rising need to maintain a sustainable environment. Cellulose and lignin constitute the major components of lignocellulose biomass. Since they are renewable, sustainable, and readily accessible, lignin and cellulose-based supercapacitors are economically viable and environmentally friendly. This review aims to systematically analyze published research findings on electrospun lignin, cellulose, and lignin/cellulose nanofibers for use as supercapacitor electrode materials. A rigorous scientific approach was employed to screen the eligibility of relevant articles to be included in this study. The research questions and the inclusion criteria were clearly defined. The included articles were used to draw up the research framework and develop coherent taxonomy of literature. Taxonomy of research literature generated from the included articles was classified into review papers, electrospun lignin, cellulose, and lignin/cellulose nanofibers for use as supercapacitor electrode materials. Furthermore, challenges, recommendations, and research directions for future studies were equally discussed extensively. Before this study, no review on electrospun lignin/cellulose nanofiber-based supercapacitors has been reported. Thus, this systematic review will provide a reference for other researchers interested in developing biomass-based supercapacitors as an alternative to conventional supercapacitors based on petroleum products.

Composite polymer electrolyte (CPE) based on polyvinyl alcohol (PVA) polymer, potassium carbonate (K2CO3) salt, and silica (SiO2) filler was investigated and optimized in this study for improved ionic conductivity and potential window for use in electrochemical devices. Various quantities of SiO2 in wt.% were incorporated into PVA-K2CO3 complex to prepare the CPEs. To study the effect of SiO2 on PVA-K2CO3 composites, the developed electrolytes were characterized for their chemical structure (FTIR), morphology (FESEM), thermal stabilities (TGA), glass transition temperature (differential scanning calorimetry (DSC)), ionic conductivity using electrochemical impedance spectroscopy (EIS), and potential window using linear sweep voltammetry (LSV). Physicochemical characterization results based on thermal and structural analysis indicated that the addition of SiO2 enhanced the amorphous region of the PVA-K2CO3 composites which enhanced the dissociation of the K2CO3 salt into K+ and CO32− and thus resulting in an increase of the ionic conduction of the electrolyte. An optimum ionic conductivity of 3.25 × 10−4 and 7.86 × 10−3 mScm−1 at ambient temperature and at 373.15 K, respectively, at a potential window of 3.35 V was observed at a composition of 15 wt.% SiO2. From FESEM micrographs, the white granules and aggregate seen on the surface of the samples confirm that SiO2 particles have been successfully dispersed into the PVA-K2CO3 matrix. The observed ionic conductivity increased linearly with increase in temperature confirming the electrolyte as temperature-dependent. Based on the observed performance, it can be concluded that the CPEs based on PVA-K2CO3-SiO2 composites could serve as promising candidate for portable and flexible next generation energy storage devices.

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

Successful synthesis of ZnO-chitosan nanocomposites was conducted for the removal of methylene blue from an aqueous medium. Remarkable performance of the nanocomposites was demonstrated for the effective uptake of the dye, thereby achieving 83.77, 93.78 and 97.93 mg g−1 for the chitosan, 5 wt.% ZnO-Chitosan and 10 wt.% ZnO-Chitosan, respectively. The corresponding adsorption efficiency was 88.77, 93.78 and 97.95 for the chitosan, 5 wt.% ZnO-Chitosan and 10 wt.% ZnO-Chitosan, respectively. Upon regeneration, good reusability of the nanocomposites was manifested for the continuous removal of the dye up to six consecutive cycles. The adsorption process was kinetically described by a pseudo-first order model, while the isotherms were best fitted by the Langmuir model.