This study presents a sustainable and low-cost approach for fabricating micro-supercapacitors using a carbon electrode material derived from plant waste, an abundant and renewable resource. Curcuma longa (turmeric), Carica papaya (papaya), Musa acuminata (banana), and Macaranga peltata (macaranga) leaves were collected, carbonized, and evaluated for their electrochemical performance to determine the optimal carbonization temperature and specific capacitance of the bio-derived carbon. Among these, Curcuma longa leaves required the lowest carbonization temperature and exhibited the highest specific capacitance. Consequently, it was selected for further activation to enhance performance. Interdigital micro-supercapacitors were fabricated on a paper substrate using a stencil-assisted method with the biomass-derived carbon electrode material. The supercapacitor demonstrated an excellent specific capacitance of 64.15 mF/cm2 at 0.25 mA/cm2 and a cyclic stability of 98.3
Lead-free inorganic perovskite material, such as Cs2AgBiBr6, must be developed to address the toxicity and stability issues associated with conventional lead halide perovskite solar cells (PSCs). Nevertheless, the Cs2AgBiBr6 film’s broad bandgap diminishes its capacity to absorb light, leading to generally lower power conversion efficiency (PCE) than its competitors. Anionic alloying (halide mixture) can effectively modify the optoelectronic properties of lead-free halide double perovskites (DPs), including the band gap. The potential of lead-free DPs that have been air-processed, specifically Cs2AgBiBr5.5I0.5, has been examined in this study using both theoretical and experimental methodologies. The material was facile to fabricate in an ambient environment and exhibited exceptional thermal stability. In both theoretical and experimental investigations, the Cs2AgBiBr5.5I0.5 perovskites have been demonstrated to possess a reduced bandgap and an increased light-absorbing capacity. The performance of Cs2AgBiBr5.5I0.5 PSCs was substantially influenced by the metal work function, defect density, acceptor density, and temperature of the charge transporting layers (CTLs) as demonstrated by the SCAPS-1D simulations. The optimized device, which utilized an absorber layer of Cs2AgBiBr5.5I0.5 perovskite, exhibited an exceptional PCE of 16.9%. Our research suggests that the halide alloying procedure may be a viable method for enhancing the stability and performance of lead-free DPs.
Artificial intelligence (AI)-enabled diagnostics in maxillofacial pathology require structured, high-quality multimodal datasets. However, existing resources provide limited ameloblastoma coverage and lack the format consistency needed for direct model training. We present a newly curated multimodal dataset specifically focused on ameloblastoma, integrating annotated radiological, histopathological, and intraoral clinical images with structured data derived from case reports. Natural language processing techniques were employed to extract clinically relevant features from textual reports, while image data underwent domain specific preprocessing and augmentation. Using this dataset, a multimodal deep learning model was developed to classify ameloblastoma variants, assess behavioral patterns such as recurrence risk, and support surgical planning. The model is designed to accept clinical inputs such as presenting complaint, age, and gender during deployment to enhance personalized inference. Quantitative evaluation demonstrated substantial improvements; variant classification accuracy increased from 46.2 percent to 65.9 percent, and abnormal tissue detection F1-score improved from 43.0 percent to 90.3 percent. Benchmarked against resources like MultiCaRe, this work advances patient-specific decision support by providing both a robust dataset and an adaptable multimodal AI framework.
This study presents a comparative tribological evaluation of A356-15 wt.
Optimizing the placement of fifth generation (5G) base station requires techniques that can handle complex geographical challenges. This paper presents a comparative analysis of three metaheuristic algorithms, Genetic Algorithms (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) for positioning 5G base stations optimally using real-world geographical data. The analysis accounts for terrain models, landuse patterns and constraints including slope limitations, human exposure and spacing between base stations. Our experimental results indicate that SA consistently produced the most reliable solution and PSO provides rapid scenario analysis, whereas GA offers the most practical choice for balanced deployment planning.
