Design and development of a simple and highly sensitive anthocyanin-based sensing device for colorimetric urea determination

We introduce a rapid anthocyanin-based paper sensor with very high sensitivity and optical visibility for colorimetric detection of urea. The working principle is based on a colour change from purple to blue upon sensor exposure to ammonia generated from urea hydrolysis in the presence of urease as a catalyst. To improve sensor sensitivity and optical visibility, anthocyanin storage, urease solvent, urea hydrolysis time, and temperature were investigated. The results indicated that the anthocyanin extracted from red cabbage and stored in dark and low-temperature conditions, urease extracted into water₅₅ + glycerol₄₅ (Aq₅₅ + G₄₅), and urea hydrolysis time of 40 min and temperature of 45°C offer the best detection condition. The fabricated sensor showed exceptional sensitivity of 0.018 pixel/mg urea-N/L with a very low limit of detection (2.01 mg urea-N/L) and a limit of quantification (6.71 mg urea-N/L). Moreover, the sensor reaction zone is optically visible for urea concentration as low as 5 mg urea-N/L, making it a promising tool for urea screening in diverse applications. The unique analytical features and accuracy of the sensor compared to the spectrophotometry method also suggest that it can be used as a replacement for environmentally unfriendly spectrophotometry methods for on-site urea determination.     

A turbulent air-flow corrugating photovoltaic model to enhance power output

This paper investigated the influence of the cooling of turbulent blowing air on the photovoltaic (PV) modules using the corrugation technique. The well-known five-parameter diode equation side by side with conservation principles were used to study the effect of corrugating lower PV sides to cool PV's and enhance efficiency. As the main restriction of PV systems is low efficiency that is powerfully counting on its operational temperature, therefore, reducing the operating temperature of PV cells is critical for the PV panel to work. To achieve this aim, a mathematical model describing PV temperatures and efficiency using continuity, momentum, and energy side by side with the Shockley diode equation was built. It was found that the corrugating of lower sides of PV's up to 1 mm in a distance of 20 mm considering turbulent flow conditions considerably reduced temperature and consequently enhanced thermal efficiency from 13% to 15%; the eddy viscosity and Reynolds shear stress increased boundary layer thickness and velocities, so increased the coefficient of heat transfer and consequently electrical power output and thermal efficiency.     

Nano-Ceramic Coating: A Novel Idea to Reduce Laparoscope Lens Fogging – Experimental Study

Silicon - One of the basic principles of a successful laparoscopic procedure is a clear operating area. The endoscopic lens should be able to visualize the internal organs as clearly as possible....     

Impacts of alginate–basil seed mucilage–prebiotic microencapsulation on the survival rate of the potential probiotic Leuconostoc mesenteroides ABRIINW.N18 in yogurt

We aimed at improving probiotic survival, stability and release in digestive settings during yogurt storage. Alginate–basil seed mucilage was formulated with various concentrations of prebiotics (inulin, fructooligosaccharides and fenugreek) and selected for microencapsulation. When compared with uncapsulated probiotic cells, all examined formulations had high encapsulation effectiveness of over 98.2% and a viable potential probiotic cell survival (62%) when studied under simulated settings. These findings indicate the use of home-grown-based gums such as basil seed adhesive and fenugreek in combination with alginate are good media for probiotic encapsulation.     

Thermoelectric performance of Ni,Co, and Fe nanoparticles incorporated into their metal borates glassy matrices

Here, we present our current attempt to intrinsically dope Ni⁰, Co⁰, and Fe⁰ nanoparticles within Ni^II-, Co^II-, and Fe^II-borate glassy matrices, respectively. The system was prepared by one-pot reaction of the desired M_T^II salt with excess NaBH₄ through an in-situ reduction and hydrolysis processes to afford metallic M_T⁰ nanoparticles dispersed into the M_T-BO₃ matrix. The composition and structural characteristics of these M_T⁰:M_T-BO₃ materials were identified by thermal oxidation, ATR-IR, X-ray powder diffraction, and magnetic techniques as glassy/amorphous borate matrices containing magnetic nanoparticles. The electrical conductivity (σ) of cold-pressed discs of these metal-doped composites shows that they behave as nonohmic semiconductors within the temperature range of 303 ≤ T ≤ 373 K suggesting a mixed electronic-ionic conduction. However, their thermal conductivity (κ) occurs through phonon lattice vibration dynamics rather than electronic. The σ/κ ratio shows a steep non-linear increase from 9.4 to 270 KV⁻² in Ni⁰:Ni-BO₃. In contrast, a moderate-weak increase is observed for Co⁰:Co-BO₃ and Fe⁰:Fe-BO₃ analogs. The obtained materials are examined for thermoelectric (TE) applications by determining their Seebeck coefficient (S) power factor (PF), figure of merit (ZT), and conversion efficiency (η%). All the TE data shows that Ni⁰:Ni-BO₃ (S, 80 μVK⁻¹; PF, 97.7 mWm⁻¹ K⁻¹; ZT 0.54; η, 2.15%) is a better TE semiconductor than the other two M_T⁰:M_T-BO₃. This finding shows that Ni⁰:Ni-BO₃ is a promising candidate to exploit low-temperature waste heat from body heat, sunshine, and small domestic devices for small-scale TE applications.     

An Improved Deep Structure for Accurately Brain Tumor Recognition

Brain neoplasms are recognized with a biopsy, which is not commonly done before decisive brain surgery. By using Convolutional Neural Networks (CNNs) and textural features, the process of diagnosing brain tumors by radiologists would be a noninvasive procedure. This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure. The proposed model extracts Gray Level Co-occurrence Matrix (GLCM) textural features from MRI brain tumor images. Moreover, a deep neural network (DNN) model has been proposed to select the most salient features from the GLCM. Moreover, it manipulates the extraction of the additional high levels of salient features from a proposed CNN model. Finally, a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process. Two common datasets have been applied and tested, Br35H and FigShare datasets. The first dataset contains binary labels, while the second one splits the brain tumor into four classes; glioma, meningioma, pituitary, and no cancer. Moreover, several performance metrics have been evaluated from both datasets, including, accuracy, sensitivity, specificity, F-score, and training time. Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies. The proposed system has achieved about 98.22% accuracy value in the case of the Br35H dataset however, an accuracy of 98.01% has been achieved in the case of the FigShare dataset.     

Supramolecular Self-Assembly Built by Hydrogen,Stacking and Br···Br Interactions in 4-((4-Bromobenzyl)Selanyl)Aniline: Structure,Hirshfeld Surface Analysis, 3D Energy Framework Approach and Global Reactivity Descriptors

Journal of Inorganic and Organometallic Polymers and Materials - A novel organoselenium compound named 4-((4-bromobenzyl)selanyl)aniline, C13H12BrNSe, (A), was synthesized via reduction of...     

A data-driven controller for position tracking of a long-stroke piezoelectric actuator

Microsystem Technologies - Hysteresis and other nonlinearities exhibited by piezoelectric materials significantly limit the tracking capability of piezoelectric actuators (PAs). To eliminate these...