Research Articles

Green synthesis of silver nanoparticles was carried out using leaf extract from Calluna vulgaris. The formation of nanoparticles was confirmed through the emergence of a surface plasmon resonance band in ultraviolet-visible spectroscopy. The characterization conducted using various microscopic techniques revealed that the nanoparticles mostly ranged in size from approximately 20 to 70 nm. Analysis, including Fourier transform infrared spectrometry, X-ray diffraction, and energy-dispersive X-ray spectroscopy, confirmed the chemical, crystalline structure, and presence of silver, respectively. The synthesized nanoparticles exhibited notable stability with an average zeta potential of -23.1 ± 0.6 mV. Evaluation of their antibacterial activity against Staphylococcus aureus and Escherichia coli demonstrated significant efficacy with diameters of inhibition zones measuring 10.23 ± 0.54 mm and 15.38 ± 0.32 mm, respectively. Additionally, the nanoparticles displayed a remarkable inhibition of approximately 88% against E. coli biofilm formation at a concentration of 100 μg/mL. They also exhibited unique photocatalytic performances. This research contributes to the literature in this field by producing new silver nanoparticles with cost-effectiveness, stability, antibacterial, antioxidant, antibiofilm, and photocatalytic properties, while using a previously untapped plant extract for this purpose.

Ultraviolet B (UVB) irradiation causes photoaging, such as wrinkles, roughness and dryness of the skin, and it activates the production of reactive oxygen species (ROS) and inflammatory cytokines. In this study, the anti-photoaging activity of SEP-E (steam-exploded pine extract) was evaluated in HaCaT keratinocytes damaged by UVB irradiation. SEP-E treatment showed cytoprotective effects in HaCaT keratinocytes irradiated with UVB (40 mJ/cm²). SEP-E treatment reduced ROS overproduction and promoted the expression of antioxidant enzymes, such as catalase, superoxide dismutase 1, and superoxide dismutase 2. Additionally, SEP-E treatment suppressed the expression of inflammatory cytokines, including interleukin 6, interleukin 8, and monocyte chemoattractant protein-1. Consequently, SEP-E shows potential as a natural material for photoaging treatment.

Chemical fertilizers used in plant development and differentiation have become a global problem affecting the entire ecosystem, especially soil pollution. Food production demand with the increasing population has encouraged scientists to use biogenic nanoparticles in the agricultural field. Evaluation of growth, development, and differentiation processes of sweet basil (Ocimum basilicum L.) seedlings at gradually increasing concentrations of biogenic iron oxide nanoparticles (BIO-NPs) were identified by morphological and physiological parameters in this study. The results showed that growth parameters reached the maximum value at 100 mg/L but were less at other concentrations. At similar concentration, the stomatal density of the leaf was the maximum, while the stomatal area showed the lowest value. The levels of H₂O₂ and malondialdehyde (MDA) decreased in the treated seedlings. BIO-NPs increased the antioxidant defense and supported its growth by changing the antioxidant enzyme activities, H₂O₂, and MDA contents. The BIO-NP treatment provided positive improvements in phytochemical content in parallel with the growth and development of sweet basil seedlings. Different growth parameters, physiological results, supporting enzyme activities, and biochemical data revealed the contribution of the BIO-NP treatments to the growth and development of sweet basil seedlings. BIO-NPs improved higher phytochemical production of sweet basil, which may be suitable for its propagation on a commercial scale.

This research investigated and compared the pitting type, pit number (PN), and pit diameter (PD) in the cross-field of compression wood (CWD), lateral wood (LWD), and opposite wood (OWD) in stem wood of Pinus merkusii and Agathis loranthifolia growing in Indonesia. Identification and quality evaluation were done using optical and scanning electron microscopy. A piceoid pit type was observed in the CWD of both species. The LWD and OWD of P. merkusii showed window-like and pinoid pits, whereas those of A. loranthifolia showed taxodioid and cupressoid pits. The PN and PD were the smallest in CWD of both species. In P. merkusii, LWD and OWD showed similar PN values, and PN in all parts increased from the pith to the bark. In A. loranthifolia, LWD had higher PN than in OWD, and PN in CWD and LWD decreased from near the pith to the bark, whereas in OWD, it increased. All parts of P. merkusii and CWD and OWD of A. loranthifolia showed a positive correlation between PN and radial tracheid diameter, whereas LWD showed a negative correlation. In P. merkusii, the PD of LWD approximated that of OWD, whereas, in A. loranthifolia, LWD had a larger PD than that exhibited by OWD.

Mechanical properties (tensile strength (TS), modulus of elasticity in tensile (MET), flexural strength (FS), modulus of elasticity (MOE)) of the material to be obtained depending on the production parameters in the production of high-density polyethylene (HDPE) wood-polymer composites with Scots pine wood flour additive were predicted using Artificial Neural Networks (ANN) model and without destructive testing. In the first stage of the study, an ANN model was developed using data from 56 different studies in the literature on the mechanical properties of wood polymer composites. In the second stage, in order to determine the reliability of the model, output values were estimated using input parameters that had not been used in training and testing of the model. Based on the same input parameters, test specimens were produced and mechanical tests were performed. The results obtained from the experiments and ANN model were compared by considering the mean absolute percentage error (MAPE) value. The coefficient of determination (R²) values obtained in the training and testing phase of the ANN models were all higher than 0.90. In this way, the mechanical properties of the wood polymer composite were successfully predicted by the ANN model. Because most of the MAPE values obtained from the mechanical tests were below 10%, the model was considered a reliable model.

The creep behavior of crushed sugarcane end-leaf (SEL) was studied, with consideration of different loading force, moisture content, and feeding amount. Statistical analysis software was used to develop and fit the regression data to a strain change law as a function of time. The four-element Burgers model was used. A further goal was to analyze the effect of different test conditions on the fitted creep characteristic parameters. The instantaneous elasticity coefficient E₀ was found to increase when the loading force increased; the value of delayed elasticity coefficient E₁ increased and the value of cohesion coefficient η₀ decreased when the feeding amount increased; the value of delayed elasticity coefficient E₁ and cohesion coefficient η₀ decreased when the moisture content was increased. Therefore, the loading force, moisture content, and feeding amount of crushed SEL all affected the creep capacity of crushed SEL in compression. The results can provide substantial theoretical reference for the silage production of crushed SEL.

The 3D reconstruction and virtual display of wooden furniture cultural relics were investigated using laser scanning and CT scanning techniques. The suitability of different 3D reconstruction techniques and virtual display approaches were considered. The experiments demonstrated that digital models obtained from both laser scanning and CT scanning can be integrated seamlessly into virtual environments created with 3DMAX for exhibition purposes. Additionally, post-processing software, such as PR or AE, can be utilized to synthesize virtual display video. The resulting images exhibit self-adaptation capabilities, with clear and undistorted 3D model and texture image. Moreover, other types of scanned models are suitable for 3D micro-scale model printing, although CT-based models tend to achieve higher printing accuracy compared to those generated by laser scanning technology. However, the precision of 3D printing model is contingent upon factors such as the precision of digital model, the type of printer, and the printing material.

Mixed integer linear programming (MILP) is known as a type of programming that can combine continuous variables, integer variables, and (0-1) variables in the same algorithm and generate fitting results for the data. Using this technique, it is possible to model and solve complex problems in many different fields such as economics, biology, engineering, etc. In the present study, a regional planning model was developed using MILP technique for the conversion of manure from dairy and beef cattle into biogas and electrical energy. For this regional planning study, considering the locations of future facilities, data on dairy and beef cattle in the Isparta province of Türkiye were used. According to the model written and solution outputs, to utilize all manure obtained from dairy and beef cattles in Isparta, 5 biogas plants with a total manure processing capacity of approximately 522,000 tons should be built in different districts. It is possible to produce a total of approximately 21,000,000 m³ of biogas and 38,500 MW of electricity per year in these biogas plants. This electrical energy obtained can meet 3.83% of the annual electricity consumption of Isparta province.

The quality detection of alfalfa hay is crucial for the development of animal husbandry. In this study, a method for quality detection of alfalfa hay based on the fusion of multisource information including near-infrared spectroscopy, image processing techniques, and electronic nose is proposed. After SG convolution smoothing, feature wavelengths were extracted using Competitive Adaptive Re-weighting Scheme and Successive Projections Algorithm from the spectral data. The image data were denoised using adaptive wavelet thresholding, and color and texture features were extracted using color histograms and random forest algorithms, respectively. Electronic nose data using principal component analysis was used for data dimensionality reduction. Support Vector Machine, Extreme Learning Machine, and Multi-Layer Perceptron were employed to establish quality detection models of alfalfa hay based on spectroscopy, image, gas information, and their combination, respectively. Experimental results demonstrate that the fusion of near-infrared spectroscopy, image data, and gas information effectively enhances the classification accuracy of the model. The accuracy of the test set reaches 100%, with root mean square error and determination coefficient values of 0.1728 and 0.9239, respectively, surpassing prediction models established solely on individual information. This study provides new insights into alfalfa hay quality detection.

Seven groups of uniaxial tensile experiments on wood plastic composites with a High Density Polyethylene (HDPE) matrix at different temperatures were completed in this paper. The test temperatures ranged from -60 °C to 60 °C, with a temperature difference of 20 °C for each group. All samples exhibited tensile brittle fracture. The test results showed that the tensile strength of the specimens decreased continuously with increasing temperature. Taking 0 °C as the reference temperature, the ultimate strength of the sample at -60 °C was 1.63 times that at 0 °C. When the temperature was 60 °C, this value was 0.41. Then, it can be calculated that the ratio of the strength of the sample at -60 °C to that at 60 °C was approximately 3.93, and the corresponding ratio of the elastic modulus was approximately 4.52. This shows that the mechanical properties of WPC are sensitive to changes in temperature. The variation coefficient of the average strength and elastic modulus of WPC for different specimens at different temperatures was less than 0.17, showing good stability due to the small dispersion of mechanical properties among different samples at any specific temperature.