Influence of metal-support interactions on reaction pathways over Ni/CeZrOx–Al2O3 catalysts for ethanol steam reforming

This work investigates selective Ni locations over Ni/CeZrO_x–Al₂O₃ catalysts at different Ni loading contents and their influences on reaction pathways in ethanol steam reforming (ESR). Depending on the Ni loading contents, the added Ni selectively interacts with CeZrO_x–Al₂O₃, resulting in the stepwise locations of Ni over CeZrO_x–Al₂O₃. This behavior induces a remarkable difference in hydrogen production and coke formation in ESR. The selective interaction between Ni and CeZrO_x for 10-wt.% Ni generates more oxygen vacancies in the CeZrO_x lattice. The Ni sites near the oxygen vacancies enhance reforming via steam activation, resulting in the highest hydrogen production rate of 1863.0 μmol/g_cat·min. In contrast, for 15 and 20-wt.% Ni, excessive Ni is additionally deposited on Al₂O₃ after the saturation of Ni–CeZrO_x interactions. These Ni sites on Al₂O₃ accelerate coking from the ethylene produced on the acidic sites, resulting in a high coke amount of 19.1 mg_c/g_cat·h (20Ni/CZ-Al).     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Preparation of high-activity coal char-based catalysts from high metals containing coal gangue and lignite for catalytic decomposition of biomass tar

The potential of using high metals containing coal gangue and lignite to prepare high-activity coal char-based catalysts is investigated for effective biomass tar decomposition. Loose structure and rough surface are formed for these char-based catalysts with heterogeneous distribution of a large number of inorganic particles. In the biomass tar decomposition, the performance of the coal char-based catalysts is significantly influenced by the content of the metals in the raw materials and coal gangue char (GC) with the ash content as high as 50.80% exhibits the highest activity in this work. A high biomass tar conversion efficiency of 93.5% is achieved at 800 °C along with a significant increase in the fuel gas product. During the five-time consecutive tests, the catalytic performance of GC increases a little at the second or third times reuse and remains relatively stable, showing the remarkable stability of the catalyst in biomass tar decomposition applications.     

Gyrotactic microorganisms bioconvection in combined convective magnetohydrodynamic Casson nanofluid flow over a vertically surfaced saturated porous media with variable viscosity

The current article focuses on mass and thermal transfer analysis of a two-dimensional immovable combined convective nanofluid flow including motile microorganisms with temperature-dependent viscosity on top of a vertical plate through a porous medium, and a model has been developed to visualize the velocity slip impacts on a nonlinear partial symbiotic flow. The governed equations include all of the above physical conditions, and suitable nondimensional transfigurations are utilized to transfer the governed conservative equations to a nonlinear system of differential equations and obtain numerical solutions by using the Shooting method. Numerical studies have been focusing on the effects of intricate dimensionless parameters, namely, the Casson fluid parameter, Brownian motion parameter, thermophoresis parameter, Peclet number, bioconvection parameter, and Rayleigh number, which have all been studied on various profiles such as momentum, thermal, concentration, and density of microorganisms. The concentration boundary layer thickness and density of microorganisms increased as the Casson fluid parameter, Brownian and thermophoresis parameters increased, whereas the bioconvection parameter, Peclet number, and Rayleigh number increased. The thermal boundary layer thickness, concentration boundary layer thickness, and density of microorganisms all decreased. The velocity distribution decreases as the Peclet number, bioconvection, and thermophoresis parameters rise but rises as the Rayleigh number, Brownian motion parameter, and Casson fluid parameter rise. These are graphed via plots along with divergent fluid parameters.     

Phase composition,microstructure, and properties of Al4O4C–(Al2OC)1-x(AlN)x –Zr2Al3C4–Al2O3 refractories prepared at high temperatures in nitrogen

The novel Al₄O₄C–(Al₂OC)_1-x(AlN)_x–Zr₂Al₃C₄–Al₂O₃ refractories with ultra-low carbon content have been successfully prepared by constructing the core-shell structure of aluminum at 1300–1700°C in nitrogen. The phase composition, microstructure, and properties of the novel refractories are deeply investigated. The cracking temperature on the core-shell structure of aluminum is further explored and the reaction mechanism of Zr₂Al₃C₄ has also added explanation. The results show that the novel refractories have excellent physical properties and cannot be corroded by molten iron. There exist two different Al₂OC solid solutions in the novel refractories, Al₂OC-rich (Al₂OC)_1-x(AlN)_x and AlN-rich (Al₂OC)_1-x(AlN)_x. The temperatures affect their relative content. When temperatures are less than 1600°C, the relative content of Al₂OC-rich (Al₂OC)_1-x(AlN)_x is more than that of AlN-rich (Al₂OC)_1-x(AlN)_x. When temperatures are above 1700°C, the relative content of AlN-rich (Al₂OC)_1-x(AlN)_x is more than that of Al₂OC-rich (Al₂OC)_1-x(AlN)_x. The core-shell structure of aluminum fully ruptures at about 1200°C. Zr₂Al₃C₄ begins to form at about 1000°C and generates in large at 1200°C.     

Investigation on thermal conductivity and mechanical properties of Si3N4 ceramics via one-step sintering

High thermal conductivity Si₃N₄ ceramics were fabricated using a one-step method consisting of reaction-bonded Si₃N₄ (RBSN) and post-sintering. The influence of Si content on nitridation rate, β/(α+β) phase rate, thermal conductivity and mechanical properties was investigated in this work. It is of special interest to note that the thermal conductivity showed a tendency to increase first and then decrease with increasing Si content. This experimental result shows that the optimal thermal conductivity and fracture toughness were obtained to be 66 W (m K)^-1 and 12.0 MPa m^1/2, respectively. As a comparison, the nitridation rate and β/(α+β) phase rate in a static pressure nitriding system, i.e., 97% (MS10), 97% (MS15), 97% (MS20) and 8.3% (MS10), 8.3% (MS15), 8.9% (MS20), respectively, have obvious advantages over those in a flowing nitriding system, i.e., 91% (MS10), 91% (MS15), 93% (MS20) and 3.1% (MS10), 3.3% (MS15), 3.3% (MS20), respectively. Moreover, high lattice integrity of the β-Si₃N₄ phase was observed, which can effectively confine O atoms into the β-Si₃N₄ lattice using MgO as a sintering additive. This result indicates that one-step sintering can provide a new route to prepare Si₃N₄ ceramics with a good combination of thermal conductivity and mechanical properties.     

An automated patient monitoring using discrete‐time wireless sensor networks

In recent years, Internet of Things (IoT) devices are used for remote health monitoring. For remotely monitoring a patient, only the health information at different time points are not sufficient; predicted values of biomarkers (for some future time points) are also important. In this article, we propose a powerful statistical model for an efficient dynamic patient monitoring using wireless sensor nodes through Bayesian Learning (BL). We consider the setting where a set of correlated biomarkers are measured from a patient through wireless sensors, but the sensors only report the ordinal outcomes (say, good, fair, high, or very high) to the sink based on some prefixed thresholds. The challenge is to use the ordinal outcomes for monitoring and predicting the health status of the patient under consideration. We propose a linear mixed model where interbiomarker correlations and intrabiomarker dependence are modeled simultaneously. The estimated and the predicted values of the biomarkers are transferred over the internet so that health care providers and the family members of the patient can remotely monitor the patient. Extensive simulation studies are performed to assess practical usefulness of our proposed joint model, and the performance of the proposed joint model is compared to that of some other traditional models used in the literature.     

Simultaneous synthesis of a heat exchanger network with multiple utilities using utility substages

Heat exchanger network synthesis (HENS) has progressed by using mathematical programming-based simultaneous methodology. Although various considerations such as non-isothermal mixing and bypass streams are applied to consider real world alternatives in modeling phase, many challenges are faced because of its properties within non-convex mixed-integer nonlinear programming (MINLP). We propose a modified superstructure, which contains a utility substage for use in considering multiple utilities in a simultaneous MINLP model. To improve model size and convergence, fixed utility locations according to temperature and series connections between utilities are suggested. The numbers of constraints, discrete, and continuous variables show that overall model size decreases compared with previous research. Thus, it is possible to expand the feasible search area for reaching the nearest global solution. The model's effectiveness and applications are exemplified by several literature problems, where it is used to deduce a network superior to that of any other reported methodology.