Instant Cyclohexene Epoxidation Over Ni-TUD-1 Under Ambient Conditions

Catalysis Letters - To avoid the aggregation problem and activity loss of nickel oxide (NiO) nanoparticles (NPs) in organic reactions, NiO NPs were incorporated into TUD-1 mesoporous material....     

Studies on influence of hydrogen and carbon monoxide concentration on reduction progression behavior of molybdenum oxide catalyst

Temperature programmed reduction (TPR) analysis was applied to investigate the chemical reduction progression behavior of molybdenum oxide (MoO₃) catalyst. The composition and morphology of the reduced phases were characterized by X-ray diffraction spectroscopy (XRD), X-ray photoelectron spectroscopy (XPS), and field emission scanning electron microscopy (FE-SEM). The reduction progression of MoO₃ catalyst was attained with different reductant types and concentration (10% H₂/N₂, 10% and 20% CO/N₂ (%, v/v)). Two different modes of reduction process were applied. The first approach of reduction involved non-isothermal mode reduction up to 700 °C, while the second approach of reduction involved the isothermal mode reduction for 60 min at 700 °C. Hydrogen temperature programmed reduction (H₂-TPR) results showed the reduction progression of three-stage reduction of MoO₃ (Mo⁶⁺ → Mo⁵⁺ → Mo⁴⁺ → Mo⁰) with Mo⁵⁺ and Mo⁴⁺. XRD analysis confirmed the formation of Mo₄O₁₁ phase as an intermediate phase followed by MoO₂ phase. After 60 min of isothermal reduction, peaks of metallic molybdenum (Mo) appeared. Whereas, FESEM analysis showed porous crater-like structure on the surface cracks of MoO₂ layer which led to the growth of Mo phase. Meanwhile, the reduction of MoO₃ catalyst in 10% carbon monoxide (CO) showed the formation of unstable intermediate phase of Mo₉O₂₆ at the early stage of reduction. Furthermore, by increasing 20% CO led to the carburization of MoO₂ phase, resulted in the formation of Mo₂C rather than the formation of metallic Mo, as confirmed by XPS analysis. Therefore, the presented study shows that hydrogen gave better reducibility due to smaller molecular size, which contributed to high diffusion rate and achieved deeper penetration into the MoO₃ catalyst compared to carbon monoxide reductant. Hence, the reduction of MoO₃ in carbon monoxide atmosphere promoted the formation of Mo₂C which was in agreement with the thermodynamic assessment.     

Synthesis of polymer/MWCNT nanocomposite catalyst supporting materials for high-temperature PEM fuel cells

In this study, the polymer/MWCNT nanocomposites were synthesized from the pristine MWCNT and polymer binders using functionalization with solution processing methods. The synthesized polymer/MWCNT nanocomposites exhibited high specific surface areas than the pristine MWCNT. The MWCNT/Nafion nanocomposite attributed to the maximum peak current at 5.795 × 10^?5 (A) while the peak current of MWCNT/PBI was obtained at 3.662 × 10^?5 (A). Moreover, polymer/MWCNT based electrocatalysts performed better electrochemical activity because of polymers binders can assist electrochemical interaction using the high surface areas of the catalyst supporting material. Also, the MEAs fabricated using the hot pressing method, while the acid doped PBI membrane sandwiched between the electrodes. The fabricated MEAs were successfully demonstrated in a single cell and found capable of measuring a maximum power density of 112.10 mW/cm² under 150 °C temperature. In conclusion, the synthesized catalyst-supporting materials enhanced the electrochemical activity and catalyst stability which fulfilling the main objective of this study.     

Selective Recovery of Uranium from El-Sebaiya Phosphate Rocks Using Urea

Radiochemistry - Urea CO(NH2)2 is an effective selective lixiviant for uranium from phosphate raw material. Experiments were performed to determine the factors of uranium dissolution from...     

A hidden Markov model to detect regime changes in cryptoasset markets

The objective of this work is to understand the dynamics of cryptocurrency prices. Specifically, how prices switch between different regimes, going from “bull” to “stable” and “bear” times. For this purpose, we propose a hidden Markov model that aims at explaining the evolution of Bitcoin prices through different, unobserved states. The implementation of the proposed model includes a likelihood ratio test that allows to compare models with different states and with different covariance structures. Our empirical findings show that the time movements of Bitcoin prices across different exchange markets are well-described by the proposed model. In particular, a parsimonious model with a diagonal covariance matrix leads to better predictions, compared with a model with a full covariance matrix.     

Auto-recognition and part model complexity quantification of regular-freeform revolved surfaces through delta volume generations

Engineering with Computers - Vast research works implementing feature-based technology have successfully been devoted. However, work on recognition of revolved regular-freeform surfaces is still...     

Physical properties of Bi2 (Te,Se)3 and Bi2Se1.2Te1.8 prepared using solid-state microwave synthesis

Bi₂(Te, Se)₃ and Bi₂Se_1.2Te_1.8 bulk products were synthesised using standard solid-state microwave synthesis. The Bi₂(Te, Se)₃ and Bi₂Se_1.2Te_1.8 were then deposited thermally onto glass substrates at a pressure of 10^? 6 Torr. The structure of the samples was analysed using X-ray diffraction (XRD), and the powders and thin films were observed to be polycrystalline and rhombohedral in structure. The surface morphology of the samples was determined using scanning electron microscopy (SEM). From the measurements of optical properties, the energy gap values for the Bi₂Te₃, Bi₂Se₃, and Bi₂Se_1.2Te_1.8 thin films were 0.43, 0.73, and 0.65 eV, respectively.     

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.     

The optimization of conditions for the production of acid-hydrolysed winged bean and soybean proteins with reduction of 3-monochloropropane-1,2-diol (3-MCPD)

Response Surface Methodology (RSM) was used to establish the optimum time and temperature for production of acid‐hydrolysed winged bean protein (aHWBP) and acid‐hydrolysed soybean protein (aHSBP). Seven hours of hydrolysis at 125 °C was the optimum condition for producing aHWBP, whereas it was 5 h of hydrolysis at 125 °C for production of aHSBP. Although aHWBP and aHSBP produced using these conditions had favourable sensory qualities, they were found to have up to 25 mg kg^?1 of 3‐monochloropropane‐1,2‐diol (3‐MCPD). This exceeds the maximum level permissible in Commission Regulation (EC) No 466/2001. However, additional alkaline thermal treatment at pH 8.5 for 2 h at 100 °C effectively reduced the 3‐MCPD contents of aHWBP and aHSBP to undetectable levels. aHWBP has a distinctive flavour, which is different from that of aHSBP. The former has higher mean scores for meaty and vegetable attributes but lower mean scores for soy, umami and beany attributes than the latter.