Gas-solid reactions II: Reduction of cobalt sulfide in the presence of calcium oxide

The results of an experimental program to test the applicability of the grain-cell model to complex gas-solid reactions are presented. The hydrogen reduction of solid cobalt sulfide (CO_9/8S) in the presence of calcium oxide was chosen for study. Powders of both solids were mixed in a molar ratio of 3.0 (CaO:Co_9/8S), compressed into pellet form, then reduced by hydrogen in the temperature range 600-800°C. The experimental results at different operating conditions were compared with the grain-cell model, using the Thiele type modulus (H) as an adjustable parameter. In the presence of excess calcium oxide, the structural-kinetic parameter (γ) was essentially zero, and (H) was found to be the effective parameter, controlling reduction rates. Good agreement between experimental results and model was obtained.     

Aerobic Oxidation of Benzylic Alcohols in Water by 2,2,6,6‐Tetramethylpiperidine‐1‐oxyl (TEMPO)/Copper(II) 2‐N‐Arylpyrrolecarbaldimino Complexes

Novel copper(II) 2‐N‐arylpyrrolecarbaldimine‐based catalysts for the aerobic oxidation of benzylic alcohols mediated by the 2,2,6,6‐tetramethylpiperidine‐1‐oxyl (TEMPO) radical are reported. The catalytic activity for both synthesized and in situ made complexes in alkaline water solutions was studied revealing high efficiency and selectivity (according to GC selectivity always >99%) for both of these catalytic systems. For example, quantitative conversion of benzyl alcohol to benzaldehyde can be achieved with the in situ prepared bis[2‐N‐(4‐fluorophenyl)‐pyrrolylcarbaldimide]copper(II) catalysts in 2 h with atmospheric pressure of O₂ at 80 °C. Interestingly, these catalysts can utilize dioxygen as well as air or hydrogen peroxide as the end oxidants, producing water as the only by‐product.     

Magnetohydrodynamic bio‐nanoconvective Naiver slip flow of micropolar fluid in a stretchable horizontal channel

The purpose of this paper is to formulate and analyze a nano‐bio transport model for magnetohydrodynamic convective flow, heat, and mass diffusion of micropolar fluid containing gyrotactic microorganisms through a horizontal channel. Both the walls are considered to be stretched, and the Navier slip boundary condition is taken into account. The governing bio‐nano transport partial differential equations are rendered to ordinary differential equations using similarity variables. The resulting normalized self‐similar boundary value problem is solved computationally with the Matlab bvp4c function. The effect of the controlling parameters on the nondimensional velocity, temperature, nanoparticle concentration, and motile microorganism density functions, and their gradients at the wall are visualized graphically and in a tabular form and expounded at length. Validation with a previous simpler model is included. All physical quantities, except the local Nusselt number, increases with an increase in the velocity slip and magnetic parameters. The present problem finds applications in industries related to pharmaceutical, nanofluidic devices, microbial enhanced oil recovery, modeling oil, and gas‐bearing sedimentary basins.     

Silicon Mitigates Drought Stress in Wheat (Triticum aestivum L.) Through Improving Photosynthetic Pigments,Biochemical and Yield Characters

Silicon - Feeling prone to stress differs with plant production stage, water scarcity near commencement of grain filling phase has a significant reduced grain yield through fewer endosperm and sink...     

Enhanced Hydrogen Generation from Empty Fruit Bunches by Charcoal Addition into a Downdraft Gasifier

Hydrogen production by co-gasification of empty fruit bunches of palm oil could be enhanced by adding charcoal. Physiochemical characterization of raw feedstocks was performed to determine their exergy potentiality. The raw feedstocks, gasified charcoal, and the end product of produced gas were analyzed by different techniques. Gasification experiments were performed using a pilot-scale downdraft gasifier. The heating value, composition of product gas, yield of hydrogen, and exergy efficiency were used to verify the improvement of hydrogen production during the co-gasification process. Charcoal with empty fruit bunches of palm oil leads to a much higher yield of hydrogen than lower charcoal ratios or solely empty fruit bunches. This enhanced hydrogen fuel can contribute to future energy demand.     

A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus

Applied Intelligence - In recent years, the prevalence of chronic diseases such as type 2 diabetes mellitus (T2DM) has increased, bringing a heavy burden to healthcare systems. While regular...     

Inconel 617 alloy creep life augmentation by graphene transfer coating

Journal of Mechanical Science and Technology - Presently, countries are trying to increase their energy efficiency. Therefore, the operating temperatures of very high temperature reactor (VHTR) and...     

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest (ROIs) and acquire 52 hybrid statistical features for each ROIs. Also, 12 optimized statistical features are selected via the chi-square feature reduction technique. For the classification, five machine learning classifiers named as deep learning J4, multilayer perceptron, support vector machine, random forest, and naive Bayes are deployed to optimize the hybrid statistical features dataset. It is observed that the deep learning J4 has promising results (sensitivity and specificity: 0.987; accuracy: 98.67%) among all the deployed classifiers. As a complementary study, a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.