Engineering Applications Research Articles

Synergistic effects of Monel 400 filler wire in gas metal arc welding of CoCrFeMnNi high entropy alloy

Weldability plays a crucial role in the journey of high entropy alloys towards their engineering applications. In this study, gas metal arc welding was performed to join an as-rolled CoCrFeMnNi high entropy alloy using Monel 400 as the filler wire. The present research findings demonstrate a favorable metallurgical chemical reaction between the Monel 400 filler and the CoCrFeMnNi high entropy alloy, resulting in compositional mixing within the fusion zone that promotes a solid-solution strengthening effect, counteracting the typical low hardness associated to the fusion zone of these alloys. The weld thermal cycle induced multiple microstructure changes across the joint, including variations in the grain size, existing phases and local texture. The grain size was seen to increase from the base material toward the fusion zone. An FCC matrix and finely sparse Cr-Mn-based oxides existed in both base material and heat affected zone, while in the fusion zone new FCC phases and carbides were formed upon the mixing of the Monel 400 filler. The role of the filler material on the fusion zone microstructure evolution was rationalized using thermodynamic calculations. Texture shifted from a γ-fiber (in the base material) to a strong cubic texture in the fusion zone. Digital image correlation during tensile testing to fracture coupled with microhardness mapping revealed that, stemming from the process-induced microstructure changes, the micro and macromechanical response differed significantly from the original base material. This study successfully established a correlation between the impact of the process on the developed microstructural features and the resultant mechanical behavior, effectively assessing the processing-microstructure-properties relationships towards an improved understanding of the physical metallurgy associated to these advanced engineering alloys. In conclusion, this work provides an important theoretical framework and practical guidance for optimizing the engineering applications of high entropy alloys.

Fault diagnosis method of PEMFC system based on ensemble learning

Due to the coupling of multiple physical fields inside proton exchange membrane fuel cell (PEMFC) and the complex and changeable external application scenarios, the faults of PEMFC system greatly affects its engineering application. The paper proposes an ensemble learning-based fault diagnosis method for PEMFC systems, utilizing five distinct algorithms to develop an integrated ensemble classifier for fault detection. The original sample dataset collected from fuel cells undergoes a process of deep feature extraction to obtain a composite feature sample dataset, which is then inputted into an ensemble classifier to establish a fault diagnosis model. The experimental data of four kinds of faults of 80 W fuel cell test bench, EC fuel cell system and 50 W water-cooled fuel cell test bench are analyzed by using this classification model. The results show that the accuracy and generalization of the ensemble learning algorithm for different fault diagnosis of PEMFC system in different application environments are verified.

Knowledge graph and development hotspots of biochar as an emerging aquatic antibiotic remediator: A scientometric exploration based on VOSviewer and CiteSpace

As an emerging material in the field of environmental remediation, biochar produced by carbonisation of organic solid waste has been widely used in the remediation of antibiotic wastewater due to its environmental friendliness and excellent adsorption properties. This study analyses the current literature in the field in a comprehensive and scientific manner using CiteSpace and VOSviewer technologies. Between 2011 and 2023, a total of 1162 papers were published in this domain, spanning three distinct stages: applied methods, mechanism investigation, and enhanced improvement. The results of keyword clustering indicate that the remediation of antibiotics complexed with multiple pollutants by biochar is the main research topic, followed by the remediation of antibiotics by biochar in combination with other technologies. Furthermore, drawing from current research hotspots in antibiotic remediation using biochar, this study identified the pivotal mechanisms involved: (1) The primary mechanisms by which raw biochar remediates antibiotics include π-π electron donor-acceptor interactions, hydrophobic interactions, electrostatic interactions, hydrogen-bonding, and pore filling. (2) Steam activation, acid/base, metal salt/metal oxide, and clay mineral modification can improve the physical/chemical properties of biochar, enhancing its adsorptive removal of antibiotics. (3) Biochar activated persulfate and degraded antibiotics via free radical pathways (SO4−•, •OH and O2−•) as well as non-free radical pathways (1O2 and electron transfer). In addition, the challenge and prospect of biochar engineering applications for antibiotic remediation lies in improving the main mechanism of antibiotic remediation by biochar. The prospective utilization of biochar in enhancing the remediation of antibiotic-related pollutants holds tremendous value for the future.

Stimuli-Responsive Biomaterials for Tissue Engineering Applications.

The use of 'smart materials,' or 'stimulus-responsive' materials, has proven useful in a variety of fields, including tissue engineering and medication delivery. Many factors, including temperature, pH, redox-state, light, and magnetic fields, are being studied for their potential to affect a material's properties, interactions, structure, and/or dimensions. New tissue engineering and drug delivery methods are made possible by the ability of living systems to respond to both external stimuli and their own internal signals (for example, materials composed of stimuli-responsive polymers that self-assemble or undergo phase transitions or morphology transformation. The researcher examines the potential of smart materials as controlled drug release vehicles in tissue engineering, aiming to enable the localized regeneration of injured tissue by delivering precisely dosed drugs at precisely timed intervals.

Anti-seepage performance and underlying mechanism of carboxymethyl cellulose sodium-modified bentonite in hydraulic engineering applications

Bentonite is widely utilized in hydraulic engineering projects. However, its performance may deteriorate under extreme environmental conditions. Hence, we aim to develop a sodium carboxymethyl cellulose (CMC)-modified bentonite and investigate its anti-seepage performance. The results showed that the 10% CMC-modified bentonite exhibited the best performance. Compared with unmodified bentonite, the expansion index of 10% CMC-modified bentonite significantly increased to 40 mL/2 g, indicating a high water absorption capacity and expansion property. Moreover, it showed a significant decrease in permeability coefficient (3.72×10−11 m/s) and an increase in porosity (1.90), indicating its excellent anti-seepage effect. Furthermore, under unconventional environmental conditions and long-term performance tests, the 10% CMC-modified bentonite shows improved stability compared to the unmodified bentonite. Therefore, it can be concluded that this study provides support for the utilization of CMC-modified bentonite in anti-seepage engineering and hydraulic engineering applications.

Multiaxial yield behavior of 2D re-entrant auxetic cellular materials

A series of re-entrant honeycombs (RH) with different off-axis angles and perturbation parameters are constructed and utilized to numerically explore the multiaxial yield behavior for 2D auxetic cellular materials. The off-axis angle significantly affects the anisotropic properties and Poisson's ratio of RH. The effects of microstructural irregularity on the multiaxial yield surfaces are also evaluated for all of the topologies. Simulation results under principal stress space, mean stress-effective stress space and normal stress-shear stress space reveal that the unique microstructure of auxetic cellular materials makes its mechanical properties significantly different from conventional cellular materials with positive Poisson's ratio. Furthermore, a pressure-dependent anisotropic phenomenological yield criterion is proposed for 2D auxetic cellular materials, which can effectively capture the multiaxial yield behavior of RH with different topologies in principal stress space. The present conclusions provide fresh insights into the microstructural design of auxetic cellular materials and pave the way for their future engineering applications.

A novel design method based onmulti–objective optimization for graded lattice structure by additive manufacturing

Purpose While performance demands in the natural world are varied, graded lattice structures reveal distinctive mechanical properties with tremendous engineering application potential. For biomechanical functions where mechanical qualities are required from supporting under external loading and permeability is crucial which affects bone tissue engineering, the geometric design in lattice structure for bone scaffolds in loading-bearing applications is necessary. However, when tweaking structural traits, these two factors frequently clash. For graded lattice structures, this study aims to develop a design-optimization strategy to attain improved attributes across different domains. Design/methodology/approach To handle diverse stress states, parametric modeling is used to produce strut-based lattice structures with spatially varied densities. The tailored initial gradients in lattice structure are subject to automatic property evaluation procedure that hinges on finite element method and computational fluid dynamics simulations. The geometric parameters of lattice structures with numerous objectives are then optimized using an iterative optimization process based on a non-dominated genetic algorithm. Findings The initial stress-based design of graded lattice structure with spatially variable densities is generated based on the stress conditions. The results from subsequent dual-objective optimization show a series of topologies with gradually improved trade-offs between mechanical properties and permeability. Originality/value In this study, a novel structural design-optimization methodology is proposed for mathematically optimizing strut-based graded lattice structures to achieve enhanced performance in multiple domains.

Analytical solution for forced vibration response of rectangular thin plates attached by straight and curved acoustic black hole beams based on symplectic and impedance method

In recent years, due to the excellent vibration reduction performance of the acoustic black hole (ABH), its application in engineering has become increasingly widespread. This paper proposes an analytical method which combines the symplectic method with the impedance method, for solving the forced response of the rectangular thin plates attached by the straight and curved ABH beams. The ABH beams and plate are connected by a short bar. Firstly, the impedance method is used to obtain the dynamic stiffness of the coupling end of the ABH beam. Secondly, the symplectic method is used to obtain analytical waves of the plates. By utilizing the relationship between force and displacement at plate-beam junction, the dynamic stiffness of the plate at the coupling point can be obtained. Finally, based on wave propagation analysis, the forced vibration response of the hybrid system are obtained. The numerical examples compared the calculation results of this method with the finite element method. The results show that the proposed method can effectively predict the forced response of the hybrid system considered in this paper. Then, the influence of the relative position between the coupling point and the excitation point on the vibration reduction effect was analyzed. This method is analytical, with accurate results and high computational efficiency.

Decellularization and characterization of camel pericardium as a new scaffold for tissue engineering and regenerative medicine.

Valvular heart diseases (VHDs) have become prevalent in populations due to aging. Application of different biomaterials for cardiac valve regeneration and repair holds a great promise for treatment of VHD. Aortic valve replacement using tissue-engineered xenografts is a considered approach, and the pericardium of different species such as porcine and bovine has been studied over the last few years. It has been suggested that the animal origin can affect the outcomes of replacement. So, herein, we at first decellularized and characterized the camel pericardium (dCP), then characterized dCP with H&E staining, in vitro and in vivo biocompatibility and mechanical tests and compared it with decellularized bovine pericardium (dBP), to describe the potency of dCP as a new xenograft and bio scaffold. The histological assays indicated less decluttering and extracellular matrix damage in dCP after decellularization compared to the dBP also dCP had higher Young Modulus (105.11), and yield stress (1.57 ± 0.45). We observed more blood vessels and also less inflammatory cells in the dCP sections after implantation. In conclusion, the results of this study showed that the dCP has good capabilities not only for use in VHD treatment but also for other applications in tissue engineering and regenerative medicine.

Endolysins: a new antimicrobial agent against antimicrobial resistance. Strategies and opportunities in overcoming the challenges of endolysins against Gram-negative bacteria

Antibiotic-resistant bacteria are rapidly emerging, and the increasing prevalence of multidrug-resistant (MDR) Acinetobacter baumannii poses a severe threat to humans and healthcare organizations, due to the lack of innovative antibacterial drugs. Endolysins, which are peptidoglycan hydrolases encoded by a bacteriophage, are a promising new family of antimicrobials. Endolysins have been demonstrated as an effective therapeutic agent against bacterial infections of A. baumannii and many other Gram-positive and Gram-negative bacteria. Endolysin research has progressed from basic in vitro characterization to sophisticated protein engineering methodologies, including advanced preclinical and clinical testing. Endolysin are therapeutic agent that shows antimicrobial properties against bacterial infections caused by drug-resistant Gram-negative bacteria, there are still barriers to their implementation in clinical settings, such as safety concerns with outer membrane permeabilizers (OMP) use, low efficiency against stationary phase bacteria, and stability issues. The application of protein engineering and formulation techniques to improve enzyme stability, as well as combination therapy with other types of antibacterial drugs to optimize their medicinal value, have been reviewed as well. In this review, we summarize the clinical development of endolysin and its challenges and approaches for bringing endolysin therapies to the clinic. This review also discusses the different applications of endolysins.

Hydrogels with Ultrasound-Treated Hyaluronic Acid Regulate CD44-Mediated Angiogenic Potential of Human Vascular Endothelial Cells In Vitro

The development of hydrogels that allow vascular endothelial cells to form capillary-like networks is critical for advancing tissue engineering and drug discovery. In this study, we developed hydrogels composed of phenolated hyaluronic acid (HA-Ph) with an average molecular weight of 490–159 kDa via sonication in an aqueous solution. These hydrogels were synthesized by the horseradish peroxidase-catalyzed crosslinking of phenol moieties in the presence of hydrogen peroxide and phenolated gelatin. The sonication-degraded HA-Ph (198 kDa) significantly enhanced the migration ability of human umbilical vein endothelial cells (HUVECs) on cell culture plates when added to the medium compared to the original HA-Ph (490 kDa) and less-degraded HA-Ph (312–399 kDa). In addition, HUVECs cultured on these hydrogels formed networks that did not occur on hydrogels made from the original HA-Ph. CD44 expression and PI3K gene expression, both markers related to angiogenesis, were 3.5- and 1.8-fold higher, respectively, in cells cultured on sonication-degraded HA-Ph hydrogels than in those cultured on hydrogels comprising the original HA-Ph. These results highlight the potential of hydrogels containing sonication-degraded HA-Ph for tissue engineering and drug-screening applications involving human vascular endothelial cells.

Advances in Materials with Self-Healing Properties: A Brief Review

The development of materials with self-healing capabilities has garnered considerable attention due to their potential to enhance the durability and longevity of various engineering and structural applications. In this review, we provide an overview of recent advances in materials with self-healing properties, encompassing polymers, ceramics, metals, and composites. We outline future research directions and potential applications of self-healing materials (SHMs) in diverse fields. This review aims to provide insights into the current state-of-the-art in SHM research and guide future efforts towards the development of innovative and sustainable materials with enhanced self-repair capabilities. Each material type showcases unique self-repair mechanisms tailored to address specific challenges. Furthermore, this review investigates crack healing processes, shedding light on the latest developments in this critical aspect of self-healing materials. Through an extensive exploration of these topics, this review aims to provide a comprehensive understanding of the current landscape and future directions in self-healing materials research.

A Lightweight Face Detector via Bi-Stream Convolutional Neural Network and Vision Transformer

Lightweight convolutional neural networks are widely used for face detection due to their ability to learn local representations through spatial induction bias and translational invariance. However, convolutional face detectors have limitations in detecting faces under challenging conditions like occlusion, blurring, or changes in facial poses, primarily attributed to fixed-size receptive fields and a lack of global modeling. Transformer-based models have advantages on learning global representations but are insensitive to capture local patterns. To address these limitations, we propose an efficient face detector that combines convolutional neural network and transformer architectures. We introduce a bi-stream structure that integrates convolutional neural network and transformer blocks within the backbone network, enabling the preservation of local pattern features and the extraction of global context. To further preserve the local details captured by convolutional neural networks, we propose a feature enhancement convolution block in a hierarchical backbone structure. Additionally, we devise a multiscale feature aggregation module to enhance obscured and blurred facial features. Experimental results demonstrate that our method has achieved improved lightweight face detection accuracy with an average precision of 95.30%, 94.20%, and 87.56% across the easy, medium, and hard subdatasets of WIDER FACE, respectively. Therefore, we believe our method will be a useful supplement to the collection of current artificial intelligence models and benefit the engineering applications of face detection.

A facile strategy for tuning the density of surface-grafted biomolecules for melt extrusion-based additive manufacturing applications

AbstractMelt extrusion-based additive manufacturing (ME-AM) is a promising technique to fabricate porous scaffolds for tissue engineering applications. However, most synthetic semicrystalline polymers do not possess the intrinsic biological activity required to control cell fate. Grafting of biomolecules on polymeric surfaces of AM scaffolds enhances the bioactivity of a construct; however, there are limited strategies available to control the surface density. Here, we report a strategy to tune the surface density of bioactive groups by blending a low molecular weight poly(ε-caprolactone)5k (PCL5k) containing orthogonally reactive azide groups with an unfunctionalized high molecular weight PCL75k at different ratios. Stable porous three-dimensional (3D) scaffolds were then fabricated using a high weight percentage (75 wt.%) of the low molecular weight PCL5k. As a proof-of-concept test, we prepared films of three different mass ratios of low and high molecular weight polymers with a thermopress and reacted with an alkynated fluorescent model compound on the surface, yielding a density of 201–561 pmol/cm2. Subsequently, a bone morphogenetic protein 2 (BMP-2)-derived peptide was grafted onto the films comprising different blend compositions, and the effect of peptide surface density on the osteogenic differentiation of human mesenchymal stromal cells (hMSCs) was assessed. After two weeks of culturing in a basic medium, cells expressed higher levels of BMP receptor II (BMPRII) on films with the conjugated peptide. In addition, we found that alkaline phosphatase activity was only significantly enhanced on films containing the highest peptide density (i.e., 561 pmol/cm2), indicating the importance of the surface density. Taken together, these results emphasize that the density of surface peptides on cell differentiation must be considered at the cell-material interface. Moreover, we have presented a viable strategy for ME-AM community that desires to tune the bulk and surface functionality via blending of (modified) polymers. Furthermore, the use of alkyne–azide “click” chemistry enables spatial control over bioconjugation of many tissue-specific moieties, making this approach a versatile strategy for tissue engineering applications. Graphic abstract

First‐Principles Investigations of Structural, Electronic, and Elastic Properties of ZrSiO3 Perovskite: Layer Dependence, Surface Termination, and Pressure Effects

Zirconium silicate (ZrSiO3) perovskite is a promising material for various technological applications. The structural, electronic, and thermodynamic properties of ZrSiO3 perovskite are studied under different conditions, including pressure and layer configuration variations using density functional theory. The present investigation includes a thorough analysis of 2D perovskite derivatives derived from its basic 3D structure. The bulk and surface‐terminated silicon‐dominant SiO2 and zirconium‐dominant ZrO compounds are found to be mechanically stable with an anisotropy factor above 1. The calculated indirect‐bandgap values for the ZrO termination and SiO2 termination are found to be 2.585 and 1.639 eV, respectively. Moreover, the pore size of the SiO2‐terminated slab model of ZrSiO3 is calculated to be 105.39 μm and that for ZrO‐termination to be 129.30 μm. Thus, the material considered for the study can have potential applications in bone regeneration and tissue engineering. Further, the possibilities for modifying ZrSiO3 for uses in electrical devices, sensors, sustainable energy materials, and even biomedical applications like tissue engineering are intriguingly expanded by the present findings.

Engineering Gut Symbionts: A Way to Promote Bee Growth?

Bees play a crucial role as pollinators, contributing significantly to ecosystems. However, the honeybee population faces challenges such as global warming, pesticide use, and pathogenic microorganisms. Promoting bee growth using several approaches is therefore crucial for maintaining their roles. To this end, the bacterial microbiota is well-known for its native role in supporting bee growth in several respects. Maximizing the capabilities of these microorganisms holds the theoretical potential to promote the growth of bees. Recent advancements have made it feasible to achieve this enhancement through the application of genetic engineering. In this review, we present the roles of gut symbionts in promoting bee growth and collectively summarize the engineering approaches that would be needed for future applications. Particularly, as the engineering of bee gut symbionts has not been advanced, the dominant gut symbiotic bacteria Snodgrassella alvi and Gilliamella apicola are the main focus of the paper, along with other dominant species. Moreover, we propose engineering strategies that will allow for the improvement in bee growth with listed gene targets for modification to further encourage the use of engineered gut symbionts to promote bee growth.

A novel method for air assisted nanosecond pulsed fiber laser beam transmission micro-channelling on thick PMMA material

The demand for scaling up polymeric materials for mass production or scaling down for microscale applications is at its peak in the modern era. Laser beam technology enables the formation of micro-channels on polymers, specifically polymethyl methacrylate (PMMA). This study examines the viability and cost-effectiveness of employing the diode pump fiber laser transmission technique to create micro-channels on thick, transparent PMMA under air-assisted circumstances for biomedical engineering and micro-fluidics applications. The analysis has included process variables such as pulse frequency, power, and cutting speed, as well as machining parameters including depth of cut, kerf width, and heat-affected zone (HAZ) width. The results of multi-objective optimization indicate that the following parameters—a pulse frequency of 51.30 kHz, a working power of 13 % of 50 W, and a cutting speed of 0.60 mm/s—contribute to the desired microfluidics response values: 12.54 µm depth of cut, 24.05 µm kerf width, and 5.98 µm HAZ width.

Study on size effect and model optimization of methane desorption-diffusion in high rank coal

ABSTRACT Methane diffusion models are numerous, but their application, pros, and cons need to be summarized urgently, the link between particle size and methane desorption-diffusion is uncertain. Therefore, this paper carries out the methane desorption-diffusion experiments under multi-scale pressure and particle size conditions to study the effect of coal sample particle size on methane desorption-diffusion. Based on MATLAB, different diffusion models were fitted and comparatively analyzed, and the models were improved in view of their deficiencies. The results showed that the cumulative methane desorption amount and rate are inversely related to the particle size of coal. Comparative analysis of the models revealed that diffusion model under the third type of boundary conditions has a low fitting accuracy for full process of methane desorption, whereas it has a high fitting accuracy of 0.9831 for the first 1000 s of desorption data. Unipore diffusion model, bidisperse diffusion model and new model of dynamic diffusion coefficient have high fitting accuracy for the desorption data of high homogeneous coal, reaching above 0.97, but unipore diffusion model has only 0.5434 fitting accuracy for non-homogeneous coal samples. The fitting accuracy of improved classical unipore diffusion model for both homogeneous and non-homogeneous coals reaches 0.99, which is more extensive than that of unipore diffusion model, only take the first term can satisfy the accuracy requirement, so it is more convenient in the practical engineering application, and has certain reference significance for engineering calculations of gas content and prediction of gas protrusion.

Accelerating Conjugate Heat Transfer Simulations in Squared Heated Cavities through Graphics Processing Unit (GPU) Computing

This research develops an innovative framework for accelerating Conjugate Heat Transfer (CHT) simulations within squared heated cavities through the application of Graphics Processing Units (GPUs). Although leveraging GPUs for computational speed improvements is well recognized, this study distinguishes itself by formulating a tailored optimization strategy utilizing the CUDA-C programming language. This approach is specifically designed to tackle the inherent challenges of modeling squared cavity configurations in thermal simulations. Comparative performance evaluations reveal that our GPU-accelerated framework reduces computation times by up to 99.7% relative to traditional mono-core CPU processing. More importantly, it demonstrates an increase in accuracy in heat transfer predictions compared to existing CPU-based models. These results highlight not only the technical feasibility but also the substantial enhancements in simulation efficiency and accuracy, which are crucial for critical engineering applications such as aerospace component design, electronic device cooling, and energy system optimization. By advancing GPU computational techniques, this work contributes significantly to the field of thermal management, offering a potential for broader application and paving the way for more efficient, sustainable engineering solutions.

Intelligent Diagnosis of Compound Faults of Gearboxes Based on Periodical Group Sparse Model

A gearbox compound fault intelligent diagnosis method based on the period group sparse model is proposed for the problem that the fault features are coupled with each other and the fault components are superimposed on each other and difficult to be separated in the gearbox compound fault signal. Firstly, a binary sequence is constructed to embed the fault pulse period as a priori knowledge into the group sparse model to decouple and separate the composite fault signal while maintaining the amplitude and sparsity of the extracted features. Secondly, the wavelet packet energy features of the decoupled signals are extracted to improve the data quality while enhancing the characterization ability of the dictionary in the classification model. Finally, the wavelet packet energy features are imported into the sparse dictionary classification model, and the fault diagnosis is completed by outputting the fault categories using the self-driven characteristics of the data. The results show that the fault identification accuracy using the proposed method is 97%. In addition, the experimental validation under different states and working conditions with different rotational speeds allows the superiority and effectiveness of the algorithm in this paper to be tested and has the feasibility of a practical application in engineering.