This work presents a detailed study of thin zinc oxide (ZnO) films and ZnO composite films doped with the organic luminescent dye PQX, prepared by the sol-gel method and spin coating. The optical, thermo-optical, structural, and morphological properties of these films were investigated using spectroscopic ellipsometry, X-ray diffraction, thermal analysis, atomic force microscopy, and scanning electron microscopy. The PQX dye showed strong absorption in the blue region of the spectrum with a large Stokes shift, indicating potential for luminescent solar concentrator (LSC) applications. Thermal studies revealed limited stability of PQX in thin films with partial degradation beginning at 100 degrees C and complete decomposition above 300 degrees C, which affected the optical and structural properties of the composite films. Annealing led to an amorphous state transition of the organic component and increased porosity in the ZnO matrix. ZnO:PQX films exhibited increased surface roughness and reduced refractive index compared to pure ZnO. These findings suggest that while PQX enhances certain optical properties, its thermal instability limits the performance of ZnO:PQX composites in LSC devices, highlighting the need for further materials optimization.
Vehicle detection in the road area serves as the basis for various other studies, including vehicle type identification, vehicle counting, and traffic violation detection. Two models that have been successfully implemented were YOLOv2 and YOLOv3, but both still have limitations in detecting small objects and require significant computational resources. YOLO11 displays enhanced accuracy and computational efficiency. Large variant models such as YOLO11m, YOLO11l, and YOLO11x achieve higher accuracy but often come with a substantial decrease in inference speed. Therefore, this study proposes modifying the YOLO11 architecture by removing two convolutional blocks and three C3k2 Blocks to increase detection speed without sacrificing accuracy. The proposed method is applied to two datasets: low-traffic and high-traffic highways. The test results showed that the suggested approach could reduce the inference time by 19% on low-traffic highways and 12% on high-traffic highways, respectively. In addition, the proposed method can increase the Average Precision (AP) by 0.004 on low-traffic highways and 0.003 on high-traffic highways, making it better suited for real-time car detection on highways.
3D-printed concrete (3DPC) cures under nonstandard conditions due to the absence of formwork and external curing, which leads to rapid moisture evaporation. Combined with a unique mix design-characterized by high binder content and reduced water-to-cement ratio-this results in significantly different shrinkage behavior compared to conventional concrete, particularly at early ages. Measuring shrinkage in 3D-printed elements is technically challenging and often technically challenging, which emphasizes the need for reliable predictive models. Since well-established models exist for traditional concrete, this study focuses on estimating updated coefficient values for three widely used models-Eurocode 2 (EC2), RILEM B4, and ACI 209-92-to enable their application to 3DPC. Experimental shrinkage measurements were performed on multi-layer printed specimens consisting of six layers, each with a 40 & times; 10 mm cross-section and a total length of 500 mm. Both noncontact laser sensors and LVDT devices were used. Model parameters were estimated using nonlinear regression techniques. The results show that all three models can accurately describe the shrinkage behavior of 3DPC after recalibration, with coefficients of determination exceeding 0.94. This confirms the potential of adapting existing shrinkage models-originally developed for conventional concrete-to the specific characteristics of 3D-printed materials.
Activated carbon is a widely used adsorbent in wastewater treatment due to its high surface area and tunable physicochemical properties. The adsorption performance of activated carbon is strongly influenced by synthesis parameters, including impregnation ratio, activation time, and activation temperature, which are studied in detail in the present work. An artificial neural network (ANN) model was developed to predict the adsorption capacity of methylene blue based on these three process variables. Activated carbons were prepared using chemical activation with potassium hydroxide (KOH) and sodium hydroxide (NaOH), and their adsorption performance was determined through experimental measurements. The ANN model adopted a multilayer perceptron (MLP) architecture with an input layer consisting of three neurons corresponding to impregnation ratio, activation time, and activation temperature. This was followed by ten hidden layers, each containing fourteen neurons, and a single output neuron representing the adsorption capacity in mg/g. The network was trained using backpropagation and optimized using the Adam optimizer. The architecture and the number of neurons were selected through successive comparison of the loss values on training and validation datasets to ensure high generalization accuracy. The trained ANN model accurately captured the nonlinear relationships between synthesis parameters and adsorption capacity. This work highlights the capability of ANN as an effective predictive tool in materials science, facilitating the design and optimization of activated carbon synthesis with reduced experimental effort. The proposed approach can serve as a foundation for developing intelligent systems for material selection and process optimization in environmental applications.
Reducing the amount of Portland clinker in the cement composition is one of the key aspects of the strategy aiming to reduce carbon dioxide emissions. One solution is to use waste materials for this purpose, for example, stone fine powder, especially limestone, basalt, granite, and melaphyre powder. There is insufficient information about the use of chalcedonite powder as an addition in cement or cement composites. Also, there is no precise information about the impact of this material on the particle size and the resulting changes in the properties of cement pastes. Therefore, this study investigates how the particle size and amount of chalcedony powder affect the hydration processes and rheological properties of cement pastes. The addition was used at 10%, 20%, and 30% by weight of the cement, with grain sizes up to 10 & micro;m, 20 & micro;m, and 36 & micro;m, respectively. The hydration kinetics of the pastes were assessed based on calorimetric measurements, supplemented by DTA-TG analysis and mechanical properties tests. Additionally, the rheological properties were determined. The study demonstrated that chalcedony powder affects the tested parameters to varying degrees. In rheological research, the cement paste containing the largest grain size (CHP36) chalcedonite powder showed the most similar properties to the CEM cement paste (without the addition). In contrast, the pastes modified with chalcedonite powder, in which the cement was replaced by 10% of the additive, exhibited the highest amount of cumulative heat. In terms of 28-day compressive strength, the materials containing CHP20 powder demonstrated the most similar properties to the reference paste, without the addition.
