Effect of location on virgin olive oils of the two main Tunisian olive cultivars

The olive oil content in phenolic compounds depends on the variety of the fruit used for its extraction as well as on the predominant climate conditions in the tree cultivation area. Here, we report on the characterization of virgin olive oil samples obtained from fruits of the main Tunisian olive cultivars Chemlali and Chétoui, grown in three different Tunisian locations, Zaghouan (North), Sousse (Center) and Sfax (South). Chétoui olive oil samples obtained from fruits of olive trees cultivated in Zaghouan and Chemlali olive oil samples obtained from fruits of olive trees cultivated in Sousse were found to have a higher mean total phenol content (1004 and 330 mg/kg, respectively). Olive oil samples obtained from fruits of both cultivars had different phenolic profiles and a higher content in 3,4‐DHPEA‐EDA when the olive trees were cultivated in Zaghouan. Both olive cultivars were found to have different responses to environmental conditions. Chétoui olive oil showed decreased oxidative stability when the fruits were obtained from olive trees cultivated in the center of Tunisia (34.8 h) and in Sfax (16.17 h). Furthermore, statistical data showed that the phenolic composition and oxidative stability of Chétoui olive oil varied more by location than those of Chemlali olive oils.     

Engineering Aptamer Switches for Multifunctional Stimulus-Responsive Nanosystems

Although RNA and DNA are best known for their capacity to encode biological information, it has become increasingly clear over the past few decades that these biomolecules are also capable of performing other complex functions, such as molecular recognition (e.g., aptamers) and catalysis (e.g., ribozymes). Building on these foundations, researchers have begun to exploit the predictable base-pairing properties of RNA and DNA in order to utilize nucleic acids as functional materials that can undergo a molecular “switching” process, performing complex functions such as signaling or controlled payload release in response to external stimuli including light, pH, ligand-binding and other microenvironmental cues. Although this field is still in its infancy, these efforts offer exciting potential for the development of biologically based “smart materials”. Herein, ongoing progress in the use of nucleic acids as an externally controllable switching material is reviewed. The diverse range of mechanisms that can trigger a stimulus response, and strategies for engineering those functionalities into nucleic acid materials are explored. Finally, recent progress is discussed in incorporating aptamer switches into more complex synthetic nucleic acid-based nanostructures and functionalized smart materials.     

Selective nanofiber deposition via electrodynamic focusing

In this paper, we demonstrate the effect of electrodynamic focusing through a gold-coated PDMS shadow mask on the selective deposition of electrospun nanofibers. Under a suitable applied voltage, the PDMS mask repels the fibers from its surface while simultaneously forcing them into micron-sized holes and onto a collecting substrate. The presented technique is simple and can be used to produce lithographic-scale nanofiber deposition using a wide range of materials.     

Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the identification of the affected plant region from test image. Furthermore, Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network (SNN)-based classification are employed to detect and classify the apple plant leaf images. A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EALD technique on benchmark dataset. The results reported the supremacy of the proposed ESFO-EALD approach than the existing approaches.     

Decolorization of C.I. Acid Blue 9 solution by UV/Nano-TiO(2), Fenton, Fenton-like, electro-Fenton and electrocoagulation processes: a comparative study

This study makes a comparison between UV/Nano-TiO(2), Fenton, Fenton-like, electro-Fenton (EF) and electrocoagulation (EC) treatment methods to investigate the removal of C.I. Acid Blue 9 (AB9), which was chosen as the model organic contaminant. Results indicated that the decolorization efficiency was in order of Fenton>EC>UV/Nano-TiO(2)>Fenton-like>EF. Desired concentrations of Fe(2+) and H(2)O(2) for the abatement of AB9 in the Fenton-based processes were found to be 10(-4)M and 2 x 10(-3) M, respectively. In the case of UV/Nano-TiO(2) process, we have studied the influence of the basic photocatalytic parameters such as the irradiation time, pH of the solution and amount of TiO(2) nanoparticles on the photocatalytic decolorization efficiency of AB9. Accordingly, it could be stated that the complete removal of color, after selecting desired operational parameters could be achieved in a relatively short time, about 25 min. Our results also revealed that the most effective decomposition of AB9 was observed with 150 mg/l of TiO(2) nanoparticles in acidic condition. The effect of operational parameters including current density, initial pH and time of electrolysis were studied in electrocoagulation process. The results indicated that for a solution of 20 mg/l AB9, almost 98% color were removed, when the pH was about 6, the time of electrolysis was 8 min and the current density was approximately 25 A/m(2) in electrocoagulation process.     

Centimeter‐Scale and Visible Wavelength Monolayer Light‐Emitting Devices

Monolayer 2D transition metal dichalcogenides (TMDCs) have shown great promise for optoelectronic applications due to their direct bandgaps and unique physical properties. In particular, they can possess photoluminescence quantum yields (PL QY) approaching unity at the ultimate thickness limit, making their application in light‐emitting devices highly promising. Here, large‐area WS₂ grown via chemical vapor deposition is synthesized and characterized for visible (red) light‐emitting devices. Detail optical characterization of the synthesized films is performed, which show peak PL QY as high as 12%. Electrically pumped emission from the synthetic WS₂ is achieved utilizing a transient‐mode electroluminescence device structure, which consists of a single metal–semiconductor contact and alternating gate fields to achieve bipolar emission. Utilizing this aforementioned structure, a centimeter‐scale ( ≈ 0.5 cm²) visible (640 nm) display is demonstrated, fabricated using TMDCs to showcase the potential of this material system for display applications.     

Effective Thermal Conductivity Modeling of Sandstones: SVM Framework Analysis

Among the most significant physical characteristics of porous media, the effective thermal conductivity (ETC) is used for estimating the thermal enhanced oil recovery process efficiency, hydrocarbon reservoir thermal design, and numerical simulation. This paper reports the implementation of an innovative least square support vector machine (LS-SVM) algorithm for the development of enhanced model capable of predicting the ETCs of dry sandstones. By means of several statistical parameters, the validity of the presented model was evaluated. The prediction of the developed model for determining the ETCs of dry sandstones was in excellent agreement with the reported data with a coefficient of determination value (\({R}^{2})\) of 0.983 and an average absolute relative deviation of 0.35 %. Results from present research show that the proposed LS-SVM model is robust, reliable, and efficient in calculating the ETCs of sandstones.     

Hydrothermal Characteristics of Spinel Manganese Ferrite Nanofluid in a Metal Foam Tube: Modeling of Experimental Results using Artificial Neural Network

In this research, an experimental evaluation is conducted on the hydrothermal behavior of water-based manganese ferrite nanofluid flowing in a metal foam tube. For this purpose, manganese ferrite nanoparticles are synthesized, and X-ray diffraction and scanning electron microscopy are implemented to specify the samples for determination of phase and size of nanoparticles. The effects of Reynolds number, Prandtl number, and presence of MnFe₂O₄ nanoparticles inside the water on the Nusselt number and friction factor have been studied. The experimental analysis shows that the increment of Reynolds number, Prandtl number, and nanoparticles concentration improve the heat transfer performance. The maximum of 19.1% and 10.5% increase in Nusselt number and friction factor have been achieved respectively by dispersion of 2 wt% manganese ferrite nanoparticles inside the deionized water at Reynolds number of 1,000. A hydrothermal index is proposed to consider the thermal and hydrodynamic characteristics of the nanofluid, and it is attained that the convection heat transfer improvement dominates the pressure drop in this work. According to the experimental results, the Nusselt number and friction factor of the nanofluid is modeled as a function of Reynolds number, Prandtl number, and nanoparticles concentration using artificial neural network with an acceptable precision.