A steadily growing need for efficient energy storage systems has positioned supercapacitors as an innovative technology. Conventional supercapacitor materials have low energy density compared to batteries, limited electrolyte stability and capacitance loss over prolonged operation. Polymer nanocomposites have gained attention as a solution to these drawbacks to a certain extent but still face challenges like poor filler dispersion and weak interfacial bonding. One efficient way to get around these obstacles is to employ functionalized nanofillers in polymer nanocomposites. This review briefly discusses various classes of nanofillers, their functionalization, along with fabrication techniques of functionalized polymer nanocomposites. A detailed discussion is presented on the effect of functionalized nanofillers in polymer matrices on supercapacitor performance parameters, such as specific capacitance, power density, and long-term cyclic stability. Among the reported nanofiller systems, graphene and its derivatives were the most extensively investigated, followed by carbon nanotubes and carbon quantum dots, while polyaniline and polypyrrole dominate as the preferred polymer matrices. A wide range of emerging nanomaterials and polymer matrices have received comparatively limited attention. This review provides a comprehensive overview of the performance of functionalized polymer nanocomposites for advanced supercapacitor applications and provide an outline for future research and development strategies.
While ferromagnetism and antiferromagnetism are well-established classes of magnetic order, a third class of collinear magnetic order, termed altermagnetism, has recently attracted scientific interest. We measured magnetic circular dichroism (MCD) in core-level photoemission (XPS) at the Ru 2p3/2 and 2p1/2 core levels in epitaxial RuO2(110)/TiO2(110) films using circularly polarized x rays at 6 keV, as well as x-ray magnetic circular dichroism (XMCD) in resonant x-ray absorption at the Ru M3,2 (3p3/2 and 3p1/2) edges. Charge transfer multiplet calculations show that the MCD-XPS and the XMCD can be explained by an altermagnetic locking of Ru magnetic moments and a distorted crystal field orientation. The distortion is caused by the epitaxial strain. The collinear magnetic moments in RuO2 occupy sublattice sites with distorted octahedral crystal fields that are rotated by 90 degrees with respect to each other. A change in the sign of the MCD-XPS at different sample positions indicates the presence of altermagnetic domains with the size of around hundreds of micrometers.
This study explores generational differences in employee perceptions of virtual reality (VR) training within corporate settings. As immersive technologies become increasingly integrated into organizational learning, understanding how diverse age cohorts respond to VR is critical for effective implementation. Based on a quantitative survey of 121 employees who participated in VR-based training programs, the research investigates perceived benefits, limitations, and barriers across Generation Z, Millennials (Generation Y), Generation X, and Baby Boomers. The results reveal statistically significant differences between age groups in terms of perceived usefulness, intuitiveness, and physical comfort. Younger employees (especially Millennials) showed higher acceptance, technological confidence, and stronger engagement, whereas older participants, particularly Baby Boomers, reported lower confidence, greater discomfort, and higher skepticism. Key advantages identified include improved engagement, safe practice in realistic simulations, and betterknowledge transfer. However, barriers such as physical side effects, low digital literacy, limited prior exposure, and reduced personal interaction with trainers remain prominent, especially among older cohorts. Correlation analysis further demonstrated strong links between supervisor support, digital readiness, and positive training outcomes. Findings also highlight that while VR can enhance job performance and motivation, successful implementation requires addressing generational expectations, providing sufficient onboarding, and offering targeted support. This study contributes to the growing body of research on immersive learning by emphasizing the role of intergenerational dynamics in technology acceptance and training effectiveness. The insights offer practical implications for HR professionals, trainers, and instructional designers aiming to develop inclusive, adaptive VR-based training that meets the needs of a multigenerational workforce.
This study examines the design, manufacturing, and testing of planar PCB inductors (spiral and toroid), including multilayer PCB toroid configurations. These inductors are intended for environments with strong magnetic fields, such as high-energy physics experiments and medical applications, where traditional inductors with ferromagnetic cores are unsuitable. Twelve inductor samples were manufactured and tested. The focus was on maximizing inductance and evaluating performance in a high-frequency DC-DC step-down converter. Key parameters measured included inductance, resistance, thermal performance, electromagnetic interference (EMI), and frequency-dependent behavior in multilayer PCB implementations. The results showed that planar spiral inductors handled higher currents and achieved better efficiency, reaching up to 74.86%. Planar toroid inductors were more tolerant of added shielding, maintaining their inductance, while multilayer toroid designs exhibited reduced DC resistance but increased frequency dependence and sensitivity to parasitic effects. Overall, planar inductors were found to be viable for applications where ferromagnetic cores are unsuitable. Further optimization of geometry, layer configuration, and manufacturing processes could enhance their performance.
Inductive thermography provides a non-destructive approach for detecting and characterising cracks in metallic components. This study introduces a method to assess crack geometry - depth and inclination angle - by combining inductive thermography with machine learning. Thermographic sequences from inductively heated cracked specimens were processed using various techniques, including the Fourier transform, to generate phase images. A comparative analysis revealed that the fast Fourier transform (FFT) outperformed other methods, achieving the highest contrast-to-noise ratio (CNR) and effectively suppressing non-uniform heating effects. Phase profiles perpendicular to the crack, extracted at its midpoint, were used as input features. Two machine learning models were developed: one trained on simulated phase profiles to predict crack inclination angle, and a second to estimate crack depth based on the known angle and phase data. Validated against simulated datasets, the models demonstrated high accuracy, advancing the quantitative evaluation of crack geometry for structural integrity and predictive maintenance applications.
