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This study aims to elucidate the dyeing kinetics and thermodynamic relationships of CI Reactive Red 24 (RR24) on cotton fabrics, achieve the recycling of inorganic salts and water resources and obtain comprehensive data on color parameters, fastness and other characteristics of fabrics dyed with the recycled dyeing residual wastewater.

The dyeing wastewater obtained through advanced oxidation technology was used as a medium for dyeing cotton fabrics with RR24. The absorbance value of the dyeing residue served as an evaluation index, and the relevant kinetic and thermodynamic parameters were calculated based on this absorbance. The color parameters and fastness of the fabric samples were measured to compare the performance of different dyeing media.

Dyeing cotton with RR24 in both media follows pseudo-second-order kinetics. When dyeing with wastewater media, the dye adsorption in the first 45 min increased by 0.082–1.29 g/kg compared with conventional dyeing. Furthermore, the half-dyeing time was shortened by 4.19–11.99 min and the equilibrium adsorption amount was reduced by 0.277–0.302 g/kg. The adsorption of RR24 on cotton fabrics conformed to the Freundlich model. Fabrics dyed using recycled wastewater exhibit a deeper color, with reduced red light and enhanced blue light, resulting in an overall deeper apparent color.

These dyeing kinetics and thermodynamic properties are beneficial for comprehending and interpreting the dyeing performance and behavior of reactive dyes in dyeing wastewater. They lay a theoretical foundation for the treatment and recycling of dyeing wastewater.

This study explores the immobilisation of enzymes within porous catalysts of various geometries, including spheres, cylinders and flat pellets. The objective is to understand the irreversible Michaelis-Menten kinetic process within immobilised enzymes through advanced mathematical modelling.

Mathematical models were developed based on reaction-diffusion equations incorporating nonlinear variables associated with Michaelis-Menten kinetics. This research introduces fractional derivatives to investigate enzyme reaction kinetics, addressing a significant gap in the existing literature. A novel approximation method, based on the independent polynomials of the complete bipartite graph, is employed to explore solutions for substrate concentration and effectiveness factor across a spectrum of parameter values. The analytical solutions generated through the bipartite polynomial approximation method (BPAM) are rigorously tested against established methods, including the Bernoulli wavelet method (BWM), Taylor series method (TSM), Adomian decomposition method (ADM) and fourth-order Runge-Kutta method (RKM).

The study identifies two main findings. Firstly, the behaviour of dimensionless substrate concentration with distance is analysed for planar, cylindrical and spherical catalysts using both integer and fractional order Michaelis-Menten modelling. Secondly, the research investigates the variability of the dimensionless effectiveness factor with the Thiele modulus.

The study primarily focuses on mathematical modelling and theoretical analysis, with limited experimental validation. Future research should involve more extensive experimental verification to corroborate the findings. Additionally, the study assumes ideal conditions and uniform catalyst properties, which may not fully reflect real-world complexities. Incorporating factors such as mass transfer limitations, non-uniform catalyst structures and enzyme deactivation kinetics could enhance the model’s accuracy and broaden its applicability. Furthermore, extending the analysis to include multi-enzyme systems and complex reaction networks would provide a more comprehensive understanding of biocatalytic processes.

The validated bipartite polynomial approximation method presents a practical tool for optimizing enzyme reactor design and operation in industrial settings. By accurately predicting substrate concentration and effectiveness factor, this approach enables efficient utilization of immobilised enzymes within porous catalysts. Implementation of these findings can lead to enhanced process efficiency, reduced operating costs and improved product yields in various biocatalytic applications such as pharmaceuticals, food processing and biofuel production. Additionally, this research fosters innovation in enzyme immobilisation techniques, offering practical insights for engineers and researchers striving to develop sustainable and economically viable bioprocesses.

The advancement of enzyme immobilisation techniques holds promise for addressing societal challenges such as sustainable production, environmental protection and healthcare. By enabling more efficient biocatalytic processes, this research contributes to reducing industrial waste, minimizing energy consumption and enhancing access to pharmaceuticals and bio-based products. Moreover, the development of eco-friendly manufacturing practices through biocatalysis aligns with global efforts towards sustainability and mitigating climate change. The widespread adoption of these technologies can foster a more environmentally conscious society while stimulating economic growth and innovation in biotechnology and related industries.

This study offers a pioneering approximation method using the independent polynomials of the complete bipartite graph to investigate enzyme reaction kinetics. The comprehensive validation of this method through comparison with established solution techniques ensures its reliability and accuracy. The findings hold promise for advancing the field of biocatalysts and provide valuable insights for designing efficient enzyme reactors.

This study aims to investigate the individual electrochemical transients arising from local anodic events on stainless steel, to uncover the potential mechanisms producing different types of transients and to derive appropriate parameters indicative of the corrosion severity of such transient events.

An equivalent circuit model was used for the transient analysis, which was performed using a local current allocation rule based on the relative instant cathodic resistance of the coupled electrodes, as well as the kinetic parameters derived from the electrochemical polarization measurement.

The shape and size of the electrochemical current transients arising from SS 316 L were influenced by the film stability, local anodic dissolution kinetics and the symmetry of the cathodic kinetics between the coupled electrodes, where the ultralong transient might correspond to the propagation of film damage with a slow anodic dissolution rate. The dynamic cathodic resistance during the final stage of transient current growth can serve as a characteristic parameter that reflects the loss of passive film protection.

Estimation of the local anodic current trace opens a new way for individual electrochemical transient analysis associated with the charges involved, local current densities and changes in film resistance throughout localized corrosion processes.

The purpose of this study is to provide a highly efficient method to obtain the kinetics of the hydrogen evolution reaction (HER) on metal electrodes in an alkaline solution and to analyze the effect of thiourea addition on HER under the same cathodic overpotential.

A novel method based on hydrogen permeation tests, potentiodynamic polarization tests and electrochemical impedance spectroscopy was put forward to characterize the HER kinetics on metal electrode.

The study found that adding thiourea accelerated the Volmer, Heyrovsky and Tafel reactions associated with HER. In addition, it reduced the hydrogen surface coverage and increased the hydrogen permeation steady-state current density. As a result, thiourea facilitated HER, promoted the diffusion of hydrogen atoms into iron and reduced the number of hydrogen atoms in the adsorbed state.

This work provides novel insights into the influence of thiourea on HER kinetics, demonstrating that thiourea addition can significantly enhance HER efficiency by altering reaction dynamics and promoting hydrogen atom diffusion into iron. This has implications for hydrogen energy applications, cathodic protection and understanding hydrogen embrittlement mechanisms.

This study aims to develop a methodology that extracts an architectural concept from a biological analogy that integrates forms and kinetic behavior to identify whether complex forms work better or simple forms with proper kinetic behavior for improving visual comfort and daylight performance.

The research employs a transdisciplinary approach using several methods consisting of a biomimetic functional-morphological approach, kinetic design strategy, case study comparison using algorithmic workflow and parametric simulation and inverse design, to develop an interactive kinetic façade with optimized daylight performance.

A key development is the introduction of a periodic interactive region (PIR), which draws inspiration from the butterfly wings' nanostructure. These findings challenge conventional perspectives on façade complexity, highlighting the efficacy of simpler shapes paired with appropriate kinetic behavior for improving visual comfort. The results show the façade with a simpler “Bookshelf” shape integrated with a tapered shape of the periodic interactive region, outperforms its more complex counterpart (Hyperbolic Paraboloid component) in terms of daylight performance and glare control, especially in southern orientations, ensuring occupant visual comfort by keeping cases in the imperceptible range while also delivering sufficient average spatial Daylight Autonomy of 89.07%, Useful Daylight Illuminance of 94.53% and Exceeded Useful Daylight Illuminance of 5.11%.

The investigation of kinetic façade studies reveals that precedent literature mostly focused on engineering and building physics aspects, leaving the architectural aspect underutilized during the development phase. Recent studies applied a biomimetic approach for involving the architectural elements besides the other aspects. While the biomimetic method has proven effective in meeting occupants' visual comfort needs, its emphasis has been primarily on the complex form which is difficult to apply within the kinetic façade development. This study can address two gaps: (1) the lack of an architectural aspect in the kinetic façade design specifically in the development of conceptual form and kinetic behavior dimensions and (2) exchanging the superficial biomimetic considerations with an in-depth investigation.

The performance of a bladeless Tesla turbine is closely tied to momentum diffusion, kinetic energy transfer and wall shear stress generation on its rotating disks. The surface roughness adds complexity of flow analysis in such a domain. This paper aims to assess the effect of roughness on flow structures and the application of roughness models in flow cross sections with submillimeter height, including both stationary and rotating walls.

This research starts with the examination of flow over a rough flat plate, and then proceeds to study flow within minichannels, evaluating the effect of roughness on flow characteristics. An in-house test stand validates the numerical solutions of minichannel. Finally, flow through the minichannel with corotating walls was analyzed. The k-ω SST turbulent model and Aupoix's roughness method are used for numerical simulations.

The findings emphasize the necessity of considering the constricted dimensions of the flow cross section, thereby improving the alignment of derived results with theoretical estimations. Moreover, this study explores the effects of roughness on flow characteristics within the minichannel with stationary and rotating walls, offering valuable insights into this intricate phenomenon, and depicts the appropriate performance of chosen roughness model in studied cases.

The originality of this investigation is the assessment and validation of flow characteristics inside minichannel with stationary and corotating walls when the roughness is implemented. This phenomenon, along with the effect of roughness on the transportation of kinetic energy to the rough surface of a minichannel in an in-house test setup, is assessed.

This study aims to focuse on the flow behavior of a specific nanofluid composed of blood-based iron oxide nanoparticles, combined with motile gyrotactic microorganisms, in a ciliated channel with electroosmosis.

This study applies a powerful mathematical model to examine the combined impacts of bio convection and electrokinetic forces on nanofluid flow. The presence of cilia, which are described as wave-like motions on the channel walls, promotes fluid propulsion, which improves mixing and mass transport. The velocity and dispersion of nanoparticles and microbes are modified by the inclusion of electroosmosis, which is stimulated by an applied electric field. This adds a significant level of complexity.

To ascertain their impact on flow characteristics, important factors such as bio convection Rayleigh number, Grashoff number, Peclet number and Lewis number are varied. The results demonstrate that while the gyrotactic activity of microorganisms contributes to the stability and homogeneity of the nanofluid distribution, electroosmotic forces significantly enhance fluid mixing and nanoparticle dispersion. This thorough study clarifies how to take advantage of electroosmosis and bio convection in ciliated micro channels to optimize nanofluid-based biomedical applications, such as targeted drug administration and improved diagnostic processes.

First paper discussed “Numerical Computation of Cilia Transport of Prandtl Nanofluid (Blood-Fe₃O₄) Enhancing Convective Heat Transfer along Micro Organisms under Electroosmotic effects in Wavy Capillaries”.

This study aims to introduce a novel machine learning feature vector (MLFV) method to bring machine learning to overcome the time-consuming computational fluid dynamics (CFD) simulations for rapidly predicting turbulent flow characteristics with acceptable accuracy.

In this method, CFD snapshots are encoded in a tensor as the input training data. Then, the MLFV learns the relationship between data with a rod filter, which is named feature vector, to learn features by defining functions on it. To demonstrate the accuracy of the MLFV, this method is used to predict the velocity, temperature and turbulent kinetic energy fields of turbulent flow passing over an innovative nature-inspired Dolphin turbulator based on only ten CFD data.

The results indicate that MLFV and CFD contours alongside scatter plots have a good agreement between predicted and solved data with R² ≃ 1. Also, the error percentage contours and histograms reveal the high precisions of predictions with MAPE = 7.90E-02, 1.45E-02, 7.32E-02 and NRMSE = 1.30E-04, 1.61E-03, 4.54E-05 for prediction velocity, temperature, turbulent kinetic energy fields at Re = 20,000, respectively.

The method can have state-of-the-art applications in a wide range of CFD simulations with the ability to train based on small data, which is practical and logical regarding the number of required tests.

The paper introduces a novel, innovative and super-fast method named MLFV to address the time-consuming challenges associated with the traditional CFD approach to predict the physics of turbulent heat and fluid flow in real time with the superiority of training based on small data with acceptable accuracy.

This investigation delves into the rationale behind the preferential applicability of the non-Newtonian nanofluid model over alternative frameworks, particularly those incorporating porous medium considerations. The study focuses on analyzing the mass and heat transfer characteristics inherent in the Williamson nanofluid’s non-Newtonian flow over a stretched sheet, accounting for influences such as chemical reactions, viscous dissipation, magnetic field and slip velocity. Emphasis is placed on scenarios where the properties of the Williamson nanofluid, including thermal conductivity and viscosity, exhibit temperature-dependent variations.

Following the use of the OHAM approach, an analytical resolution to the proposed issue is provided. The findings are elucidated through the construction of graphical representations, illustrating the impact of diverse physical parameters on temperature, velocity and concentration profiles.

Remarkably, it is discerned that the magnetic field, viscous dissipation phenomena and slip velocity assumption significantly influence the heat and mass transmission processes. Numerical and theoretical outcomes exhibit a noteworthy level of qualitative concurrence, underscoring the robustness and reliability of the non-Newtonian nanofluid model in capturing the intricacies of the studied phenomena.

Available studies show that no work on the Williamson model is conducted by considering viscous dissipation and the MHD effect past over an exponentially stretched porous sheet. This contribution fills this gap.

In this study, we investigate the effects of an extended ternary hybrid Tiwari and Das nanofluid model on ethylene glycol flow, with a focus on heat transfer. Using the Cross non-Newtonian fluid model, we explore the heat transfer characteristics of this unique fluid in various applications such as pharmaceutical solvents, vaccine preservatives, and medical imaging techniques.

Our investigation reveals that the flow of this ternary hybrid nanofluid follows a laminar Cross model flow pattern, influenced by heat radiation and occurring around a stretched cylinder in a porous medium. We apply a non-similarity transformation to the nonlinear partial differential equations, converting them into non-dimensional PDEs. These equations are subsequently solved as ordinary differential equations (ODEs) using MATLAB’s bvp4c tools. In addition, the magnetic number in this study spans from 0 to 5, volume fraction of nanoparticles varies from 5% to 10%, and Prandtl number for EG as 204. This approach allows us to examine the impact of temperature on heat transfer and distribution within the fluid.

Graphical depictions illustrate the effects of parameters such as the Weissenberg number, porous parameter, Schmidt number, thermal conductivity parameter, Soret number, magnetic parameter, Eckert number, Lewis number, and Peclet number on velocity, temperature, concentration, and microorganism profiles. Our results highlight the significant influence of thermal radiation and ohmic heating on heat transmission, particularly in relation to magnetic and Darcy parameters. A higher Lewis number corresponds to faster heat diffusion compared to mass diffusion, while increases in the Soret number are associated with higher concentration profiles. Additionally, rapid temperature dissipation inhibits microbial development, reducing the microbial profile.

The numerical analysis of skin friction coefficients and Nusselt numbers in tabular form further validates our approach. Overall, our findings demonstrate the effectiveness of our numerical technique in providing a comprehensive understanding of flow and heat transfer processes in ternary hybrid nanofluids, offering valuable insights for various practical applications.