Introduction and Advancements in Room-Temperature Ferromagnetic Metal Oxide Semiconductors for Enhanced Photocatalytic Performance

Recent advancements in the field of room-temperature ferromagnetic metal oxide semiconductors (RTFMOS) have revealed their promising potential for enhancing photocatalytic performance. This review delves into the combined investigation of the photocatalytic and ferromagnetic properties at room temperature, with a particular focus on metal oxides like TiO2, which have emerged as pivotal materials in the fields of magnetism and environmental remediation. Despite extensive research efforts, the precise mechanism governing the interplay between ferromagnetism and photocatalysis in these materials remains only partially understood. Several crucial factors contributing to magnetism, such as oxygen vacancies and various metal dopants, have been identified. Numerous studies have highlighted the significant role of these factors in driving room-temperature ferromagnetism and photocatalytic activity in wide-bandgap metal oxides. However, establishing a direct correlation between magnetism, oxygen vacancies, dopant concentration, and photocatalysis has posed significant challenges. These RTFMOS hold immense potential to significantly boost photocatalytic efficiency, offering promising solutions for diverse environmental- and energy-related applications, including water purification, air pollution control, and solar energy conversion. This review aims to offer a comprehensive overview of recent advancements in understanding the magnetism and photocatalytic behavior of metal oxides. By synthesizing the latest findings, this study sheds light on the considerable promise of RTFMOS as effective photocatalysts, thus contributing to advancements in environmental remediation and related fields

Role of Adipose-Derived Mesenchymal Stem Cells in Bone Regeneration

Bone regeneration involves multiple factors such as tissue interactions, an inflammatory response, and vessel formation. In the event of diseases, old age, lifestyle, or trauma, bone regeneration can be impaired which could result in a prolonged healing duration or requiring an external intervention for repair. Currently, bone grafts hold the golden standard for bone regeneration. However, several limitations hinder its clinical applications, e.g., donor site morbidity, an insufficient tissue volume, and uncertain post-operative outcomes. Bone tissue engineering, involving stem cells seeded onto scaffolds, has thus been a promising treatment alternative for bone regeneration. Adipose-derived mesenchymal stem cells (AD-MSCs) are known to hold therapeutic value for the treatment of various clinical conditions and have displayed feasibility and significant effectiveness due to their ease of isolation, non-invasive, abundance in quantity, and osteogenic capacity. Notably, in vitro studies showed AD-MSCs holding a high proliferation capacity, multi-differentiation potential through the release of a variety of factors, and extracellular vesicles, allowing them to repair damaged tissues. In vivo and clinical studies showed AD-MSCs favoring better vascularization and the integration of the scaffolds, while the presence of scaffolds has enhanced the osteogenesis potential of AD-MSCs, thus yielding optimal bone formation outcomes. Effective bone regeneration requires the interplay of both AD-MSCs and scaffolds (material, pore size) to improve the osteogenic and vasculogenic capacity. This review presents the advances and applications of AD-MSCs for bone regeneration and bone tissue engineering, focusing on the in vitro, in vivo, and clinical studies involving AD-MSCs for bone tissue engineering

Stability and rheological study on carbon-based nanofluids

In this study, an organic derived nanofluid has been developed from bio-origin resources. Carbon nanopowder (CNP) is obtained from derived rice husk and was prepared via a simple two-steps thermal process with minimum energy (low temperature and reaction time) using solar assisted plasma furnace. Nanofluids comprised of CNP and EG/water binary mixture has been prepared at various concentration such as 0.02 – 0.10 vol% of CNP. Flow curve of nanofluids showed that at minimum inclusion of CNP improved the stress of the fluid significantly. More to the addition, dynamic viscosity measure possesses that addition of CNP stabilized the properties of the fluid compared to virgin base fluid. Moreover, the stability results showed that the nanofluids stabilized starting from 1 week onwards as evidenced by UV-Visible spectrophotometer analysis. Furthermore, little to no precipitate noticed even after 8 weeks. This work offers greener approach for nanofluids which organic derived and environmentally friendly (very low percentage of nanoparticle, 0.02 vol%

Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses

Research Summary We create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level, which we call the AI Industry Exposure (AIIE) and a measure of AI exposure at the county level, which we call the AI Geographic Exposure (AIGE). We also describe several ways in which the AIOE can be used to create firm level measures of AI exposure. We validate the measures and describe how they can be used in different applications by management, organization and strategy scholars. Managerial Summary Although artificial intelligence (AI) promises to spur economic growth, there is widespread concern that it could displace workers, alter industry trajectories, and reshape organizations. Despite the interest in this area, we have limited ability to study the effects of AI on occupations, firms, industries, and geographies because of limited availability of data that measures exposure to AI. To address this limitation, we create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level (AIIE) and county level (AIGE). We describe how our measures can be useful to scholars and policy-makers interested in identifying the effect of AI on markets