Scale Engineering Research Articles

Fano Resonance and Incoherent Interlayer Excitons in Molecular van der Waals Heterostructures.

Complex van der Waals heterostructures from layered molecular stacks are promising optoelectronic materials offering the means to efficient, modular charge separation and collection layers. The effect of stacking in the electrodynamics of such hybrid organic-inorganic two-dimensional materials remains largely unexplored, whereby molecular scale engineering could lead to advanced optical phenomena. For instance, tunable Fano engineering could make possible on-demand transparent conducting layers or photoactive elements, and passive cooling. We employ an adapted Gersten-Nitzan model and real time time-dependent density functional tight-binding to study the optoelectronics of self-assembled monolayers on graphene nanoribbons. We find Fano resonances that cause electromagnetic induced opacity and transparency and reveal an additional incoherent process leading to interlayer exciton formation with a characteristic charge transfer rate. These results showcase hybrid van der Waals heterostructures as paradigmatic 2D optoelectronic stacks, featuring tunable Fano optics and unconventional charge transfer channels.

Modular, synthetic chromosomes as new tools for large scale engineering of metabolism

The construction of powerful cell factories requires intensive genetic engineering for the addition of new functionalities and the remodeling of native pathways and processes. The present study demonstrates the feasibility of extensive genome reprogramming using modular, specialized de novo-assembled neochromosomes in yeast. The in vivo assembly of linear and circular neochromosomes, carrying 20 native and 21 heterologous genes, enabled the first de novo production in a microbial cell factory of anthocyanins, plant compounds with a broad range of pharmacological properties. Turned into exclusive expression platforms for heterologous and essential metabolic routes, the neochromosomes mimic native chromosomes regarding mitotic and genetic stability, copy number, harmlessness for the host and editability by CRISPR/Cas9. This study paves the way for future microbial cell factories with modular genomes in which core metabolic networks, localized on satellite, specialized neochromosomes can be swapped for alternative configurations and serve as landing pads for the addition of functionalities.

Effect of wall modifications in a vortex shedding-based mechanical micromixer for heat/mass exchange

Despite the advances achieved to date in micromechanics, the resources needed to develop&test new designs may be a drawback for reduced scale engineering applications. In this sense, the use of computational tools allows to test multiple designs without the need to build a prototype physically and predict the performance of passive elements. The objective of the present paper is to test numerically the performance of different micro heat/mass exchanger designs composed by passive mechanical elements: a single pillar object leading to vortex shedding in the centre of a microchannel and microchannel wall modifications that can modify the detached oscillatory flow. To that aim, several side wall modifications (squared, triangular and circular cavities&protrusions) are tested. The final aim is to promote and enhance the thermal/molecular mixing promoted by the vortex shedding, thus increasing the homogeneity and the mixing efficiency. It is concluded that wall modifications with triangular cavities are able to increase the mixing efficiency more than a 2.3%. Also, this static fluid–structure interaction may boost/suppress vortex shedding, so it is important to predict the performance beforehand for any related application.

Traffic flow modeling and feedback control for future Low-Altitude Air city Transport: An MFD-based approach

The imminent penetration of low-altitude passenger and delivery aircraft into the urban airspace will give rise to new urban air transport systems, which we call low-altitude air city transport (LAAT) systems. As the urban mobility revolution approaches, we must investigate (i) the collective behavior of LAAT aircraft in cities, and (ii) ways of controlling LAAT systems. Future LAAT systems exemplify a new class of modern large scale engineering systems — networked control systems. They are spatially distributed, consist of many interconnected elements with control loops through digital communication networks such that the system signals can be exchanged among all components through a common network. Therefore, a decentralized controller design in framework of the unilateral event-driven paradigm is considered. Inspired by controlled urban road networks, in this paper we first establish the concept of Macroscopic Fundamental Diagram (MFD) for LAAT systems and develop a collective and aggregate aircraft traffic flow model. Then, based on that, we design an adaptive boundary feedback flow control which is robust to various anomalies in technical devices and network communication links for LAAT systems.

Industrial Waste Heat District-Heating Design Based on Geographic Information System: Case Study in Vitoria-Gasteiz (Spain)

The use of georeferenced information system and Light Detection and Ranging (LiDAR) data in combination with traditional data analytics tools is very promising in urban scale engineering and especially in energy urban planning. This paper explores the use of new DH networks for industrial waste heat exploitation and for that purpose, a case-study in Vitoria-Gasteiz (Spain) is proposed. The methodology explained in this paper explores the incorporation of data from industrial emplacements, buildings and road network in order to identify optimal areas in the city for the construction of a new district-heating network. An area of influence of a buffer of radius 1.5km from the industry location is defined and the proposed algorithm divides this area into grids of different sizes. The path for the network is calculated by optimizing the economic performance of the network. The results show that the district-heating may be built in the south-west direction from the industry and among the 40 configurations studied, payback periods from 6 to 8.5 years are obtained.

Surface Engineering Strategies to Enhance the In Situ Performance of Medical Devices Including Atomic Scale Engineering.

Decades of intense scientific research investigations clearly suggest that only a subset of a large number of metals, ceramics, polymers, composites, and nanomaterials are suitable as biomaterials for a growing number of biomedical devices and biomedical uses. However, biomaterials are prone to microbial infection due to Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), Staphylococcus epidermidis (S. epidermidis), hepatitis, tuberculosis, human immunodeficiency virus (HIV), and many more. Hence, a range of surface engineering strategies are devised in order to achieve desired biocompatibility and antimicrobial performance in situ. Surface engineering strategies are a group of techniques that alter or modify the surface properties of the material in order to obtain a product with desired functionalities. There are two categories of surface engineering methods: conventional surface engineering methods (such as coating, bioactive coating, plasma spray coating, hydrothermal, lithography, shot peening, and electrophoretic deposition) and emerging surface engineering methods (laser treatment, robot laser treatment, electrospinning, electrospray, additive manufacturing, and radio frequency magnetron sputtering technique). Atomic-scale engineering, such as chemical vapor deposition, atomic layer etching, plasma immersion ion deposition, and atomic layer deposition, is a subsection of emerging technology that has demonstrated improved control and flexibility at finer length scales than compared to the conventional methods. With the advancements in technologies and the demand for even better control of biomaterial surfaces, research efforts in recent years are aimed at the atomic scale and molecular scale while incorporating functional agents in order to elicit optimal in situ performance. The functional agents include synthetic materials (monolithic ZnO, quaternary ammonium salts, silver nano-clusters, titanium dioxide, and graphene) and natural materials (chitosan, totarol, botanical extracts, and nisin). This review highlights the various strategies of surface engineering of biomaterial including their functional mechanism, applications, and shortcomings. Additionally, this review article emphasizes atomic scale engineering of biomaterials for fabricating antimicrobial biomaterials and explores their challenges.

Selecting the best stochastic systems for large scale engineering problems

Selecting a subset of the best solutions among large-scale problems is an important area of research. When the alternative solutions are stochastic in nature, then it puts more burden on the problem. The objective of this paper is to select a set that is likely to contain the actual best solutions with high probability. If the selected set contains all the best solutions, then the selection is denoted as correct selection. We are interested in maximizing the probability of this selection; P(CS). In many cases, the available computation budget for simulating the solution set in order to maximize P(CS) is limited. Therefore, instead of distributing these computational efforts equally likely among the alternatives, the optimal computing budget allocation (OCBA) procedure came to put more effort on the solutions that have more impact on the selected set. In this paper, we derive formulas of how to distribute the available budget asymptotically to find the approximation of P(CS). We then present a procedure that uses OCBA with the ordinal optimization (OO) in order to select the set of best solutions. The properties and performance of the proposed procedure are illustrated through a numerical example. Overall results indicate that the procedure is able to select a subset of the best systems with high probability of correct selection using small number of simulation samples under different parameter settings.

Plasma processing for advanced microelectronics beyond CMOS

The scientific study of plasma discharges and their material interactions has been crucial to the development of semiconductor process engineering and, by extension, the entire microelectronics industry. In recent years, the proliferation of the big data business model has led to heightened interest in technology candidates with the potential to supplant CMOS architectures in critical metrics such as computational capacity or power consumption. These novel technologies share many common material elements with existing logic and memory devices, but the impact of mass fabrication techniques on their performance is largely unknown due to differences in the underlying physics of their operation. Two components are thus vital to this endeavor: fundamental evaluation of any emerging plasma process interactions and the ability to tailor any aspect of the plasma process necessary to produce the desired specifications. In this article, we review relevant advances in the study of plasma-induced damage mechanisms as well as characterization methods such as diagnostic probes and simulation tools. We also provide an outlook for the application of techniques such as plasma doping, area-selective etch/deposition, and heterogeneous integration. The frontiers of any new computing paradigms can only be explored through a focus on atomic scale engineering, and progress in the field of plasma science supplies the necessary toolset.

Statistical modelling of fracture using cellular atomata finite element

Fracture is an inherently statistical phenomenon as it is a function of micro-structural heterogeneities such as distributed defects and inclusions. This is evidenced by scatter in the toughness of seemingly identical specimens. Therefore, deterministic approaches do not give full picture of scatter in fracture behaviour. More suitable probabilistic methods have been devised to describe the scatter associated with fracture. While the probabilistic approaches provide a sound scientific basis for capturing the scatter in the fracture data through assuming a probability for the presence of fracture initiators, their microstructurally agnostic assumptions can limit their predictive capability. This is because there is no information on the microstructure such as grain size and morphology, texture, and other important features considered in them. An alternative class of models which take into account the distribution of toughness is cellular automata finite element models (CAFE). CAFE models are stronger in simulating the scatter in the fracture data through their ability to represent the microstructure although so far, they have been limited to fully brittle or quasi-brittle materials. In addition, the CAFE models they are computationally expensive, and their running time can be prohibitive for their application to large scale engineering components thus reducing their appeal. In this study, a CAFE model was developed to take advantage of the microstructural fidelity of CAFE but presented within the context of a probabilistic fracture approach. The CAFE based model calculates the macroscopic strain from the continuum FE model. The strain is then used to load a model which is defined in the cellular automata space. The CAFE model then simulates the initiation and propagation of fracture in the microstructure to fully capture the heterogeneity of the material at the lower length scale. The critical stress acting normal to the cleavage plane of each grain is calculated in the CAFE model and used to decide the onset of cracking in a probabilistic manner; the stress depends on the orientation of the grain in which microcrack initiates as well as depending on the orientation of the surrounding grains. To evaluate its performance, the model was calibrated using a set of experimental fracture toughness data and the results of its prediction were compared with an intendent and separate set of warm prestress experiments of the same material. Good agreement between the prediction and the experiment of the second set was observed giving confidence in the model.

Engineering atomic-scale magnetic fields by dysprosium single atom magnets

Atomic scale engineering of magnetic fields is a key ingredient for miniaturizing quantum devices and precision control of quantum systems. This requires a unique combination of magnetic stability and spin-manipulation capabilities. Surface-supported single atom magnets offer such possibilities, where long temporal and thermal stability of the magnetic states can be achieved by maximizing the magnet/ic anisotropy energy (MAE) and by minimizing quantum tunnelling of the magnetization. Here, we show that dysprosium (Dy) atoms on magnesium oxide (MgO) have a giant MAE of 250 meV, currently the highest among all surface spins. Using a variety of scanning tunnelling microscopy (STM) techniques including single atom electron spin resonance (ESR), we confirm no spontaneous spin-switching in Dy over days at ≈ 1 K under low and even vanishing magnetic field. We utilize these robust Dy single atom magnets to engineer magnetic nanostructures, demonstrating unique control of magnetic fields with atomic scale tunability.

Development of common rail lube oil injector for large two-stroke marine diesel engines

The two-stroke crosshead diesel engines, nowadays moving the majority of merchant vessels, have lubrication systems which significantly contribute to their overall emissions, since they work on total loss basis: a relevant fraction of lubricant enters the exhaust duct, increasing total exhaust emissions. This paper demonstrates that a viable solution to reduce lubrication system related emissions can be found in the application of a new common rail type ( CR) lubrication system. Particularly, in the first part of this study, a common rail injector was simulated and numerically optimized by means of AMESim. The main parameters influencing lube oil injected mass were identified, with the purpose to design a highly time responsive injector. Therefore, a CR injector was realized and experimentally characterized by means of a dedicated test cell, defining the lube oil injection map over the entire engine load range. Finally, full scale engine tests allowed to evaluate oil loss at exhaust, proposing, and applying the Sulfur tracing methodology. A comparison with a pulse jet lubrication system demonstrated a relevant reduction in oil loss: 100% engine load testes demonstrated decreases ranging from 56.2% to 63.3%, while once fixed the lube oil feed rate to 0.8 g/kWh a maximum reduction in oil loss equal to 66.7% was reached. These results allow nominating the common rail lubrication system as a feasible solution to significantly reduce oil loss at exhaust of large two-stroke marine diesel engines.

An advanced hybrid meta-heuristic algorithm for solving small- and large-scale engineering design optimization problems

An advanced hybrid algorithm (haDEPSO) is proposed in this paper for small- and large-scale engineering design optimization problems. Suggested advanced, differential evolution (aDE) and particle swarm optimization (aPSO) integrated with proposed haDEPSO. In aDE a novel, mutation, crossover and selection strategy is introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. The performance of proposed haDEPSO and its component aDE and aPSO are validated on 23 unconstrained benchmark functions, then solved five small (structural engineering) and one large (economic load dispatch)-scale engineering design optimization problems. Outcome analyses confirm superiority of proposed algorithms over many state-of-the-art algorithms.

Mechanical properties and reaction mechanism of one-part geopolymer mortars

The use of traditional two-part geopolymers is limited to small scale engineering applications, due to difficulties with the transportation, storage and prefabrication of alkaline activator solutions. In this study, “just adding water” or one-part geopolymers were developed using solid activators. Flow tests, flexural and compressive strength tests were carried out on a number of one-part and two-part geopolymer mortar specimens to evaluate the effect of concentration, modulus and type of activators on the workability and mechanical properties of geopolymer mortar. The reaction heat during the first 7 curing days and the degree of reaction of one-part and its two-part geopolymer counterparts were measured to compare their reaction mechanism. The test results showed that the flexural and compressive strengths of one-part geopolymer mortar specimens were 88%-94% and 65%-95% respectively, of the corresponding strengths of the two-part counterparts. The results also revealed that anhydrous sodium silicate (Na2SiO3) is a more effective solid activator than the combination of hydrous Na2SiO3 and sodium hydroxide (NaOH) for metakaolin and fly ash blending precursor. Due to a higher degree of geopolymerization reaction, the highest compressive strength of one-part geopolymer mortar using anhydrous Na2SiO3 with modulus of 1.4, reached 49.2 MPa after 7-day ambient temperature curing. When using a combination of solid hydrous Na2SiO3 and NaOH as an activator, the strength difference between one-part and its corresponding two-part geopolymer mortar specimens decreased with an increase in the modulus of the activator.

Synthetic preparations and atomic scale engineering of silver nanoparticles for biomedical applications.

Owing to their peculiar oxidative effect, silver cations (Ag+) are well known for their antimicrobial properties and explored as therapeutic agents for biomedical applications. Size control with improved dispersion and stability are the key factors of Ag NPs (silver nanoparticles) to be used in biomedical applications. Silver based nano-materials are highly efficient due to their biological, chemical and physical properties in comparison with bulk silver. Atomic scale fabrication is achieved by rearranging the internal components of a material, in turn, influencing the mechanical, electrical, magnetic, thermal and chemical properties. For instance, size and shape have a strong impact on the optical, thermal and catalytic properties of Ag NPs. Such properties can be tuned by controlling the surface/volume ratio of Ag nanostructures with a small size (ideally <100 nm), in turn showing peculiar biological activity different from that of bulk silver. Silver nanomaterials such as nanoparticles, thin films and nanorods can be synthesized by various physical, chemical and biological methods whose most recent implementations will be described in this review. By controlling the structure-functionality relationship, silver based nano-materials have high potential for commercialization in biomedical applications. Antimicrobial, antifungal, antiviral, and anti-inflammatory Ag NPs can be applied in several fields such as pharmaceutics, sensors, coatings, cosmetics, wound healing, bio-labelling agents, antiviral drugs, and packaging.

Predicting the Behaviour of a Vortex Shedding-Based Passive Mechanical Micro Heat Exchanger

In the recent years, microscale applications are gaining increasing importance. Despite their advances, the technology and resources needed to develop new designs may be a drawback for reduced scale engineering testing. To overcome this, computational methods are an efficient tool to predict how a real-life system may behave prior physical construction. The present work aims to investigate numerically effective models to predict the conditions at which a micro heat exchanger (MHE) is able to promote mixing by vortex shedding mechanics. In spite of vortex-shedding is a well-known mechanism in flow physics, it is not possible to know a priori whether a configuration (for a given geometry and flow velocity) may or may not lead to this desired vortex detachment to enhace mixing. Thus, Machine Learning methods are used for prediction, trained with finite-volume numerical simulations of different MHE devices selected based on their performance. A classification model is used to predict which configurations lead to vortex shedding. Also, a correlation regression model is developed to predict the critical Reynolds number. When the critical Reynolds number is surpassed for a given geometry, vortex shedding appears and its intensity controls the thermal mixing efficiency of the microdevice. These predictors could be useful in the search of optimal configurations by optimisation algorithms, since in the sampling process could be used to define constrains.

General synthetic iterative scheme for nonlinear gas kinetic simulation of multi-scale rarefied gas flows

The general synthetic iterative scheme (GSIS) is extended to find the steady-state solution of the nonlinear gas kinetic equation, resolving the long-standing problems of slow convergence and requirement of ultra-fine grids in near-continuum flows. The key ingredient of GSIS is the tight coupling of gas kinetic and macroscopic synthetic equations, where the constitutive relations explicitly contain Newton's law of shear stress and Fourier's law of heat conduction. The higher-order constitutive relations describing rarefaction effects are calculated from the velocity distribution function; however, their constructions are simpler than our previous work (Su et al., 2020 [28]) for linearized gas kinetic equations. On the other hand, solutions of macroscopic synthetic equations are used to accelerate the evolution of gas kinetic equation at the next iteration step. A rigorous linear Fourier stability analysis of the present schemes in periodic system shows that the error decay rate of GSIS can be smaller than 0.5, which means that the deviation to steady-state solution can be reduced by 3 orders of magnitude in 10 iterations. Other important advantages of the GSIS are: (i) it does not rely on the specific form of Boltzmann collision operator, and (ii) it can be solved by sophisticated techniques in computational fluid dynamics, making it amenable to large scale engineering applications. In this paper, the efficiency and accuracy of GSIS are demonstrated by a number of canonical test cases in rarefied gas dynamics, covering different flow regimes.

Damage and Failure in a Statistical Crack Model

In this paper I will take a close look at a statistical crack model (SCM) as is used in engineering computer codes to simulate fracture at high strain rates. My general goal is to understand the macroscopic behavior effected by the microphysical processes incorporated into an SCM. More specifically, I will assess the importance of including local interactions between cracks into the growth laws of an SCM. My strategy will be to construct a numerical laboratory that represents a single computational cell containing a realization of a statistical distribution of cracks. The cracks will evolve by the microphysical models of the SCM, leading to quantifiable damage and failure of the computational cell. I will use the numerical data generated by randomly generated ensembles of the fracture process to establish scaling laws that will modify and simplify the implementation of the SCM into large scale engineering codes.

Research Status Summary and Development Trend Analysis of Porous Elastic Road Surface

Low-noise pavement can efficiently reduce the tire-road noise and improve the driving experience of passengers and life quality of residents along the line. Porous elastic road surface (PERS) is a type of low-noise pavement with excellent performance. This study summarizes the research status and analyzes the development trend of PERS. The research results show that PERS has a large elastic deformation capacity and a large porosity, thereby achieving elastic vibration damping and porous sound absorption. The effect is an effective way to fundamentally solve the current traffic noise problem. However, the lack of relevant research in the design theory of composite material composition, noise reduction function evaluation test method, and index system is a major factor that restricts the breakthrough of PERS in technical difficulties and the achievement of large scale engineering applications.

Three-dimensional phase-field modeling of mode I + II/III failure in solids

One major merit of phase-field models for fracture is that cracks nucleation, propagation, branching, merging, coalescence and even fragmentation, etc., can be accounted for seamlessly within a standalone regularized variational framework. This fascinating feature overcomes the cumbersomeness in the characterization of non-smooth crack surfaces and the tracking of complex crack paths. However, the numerical algorithms frequently adopted in solving the coupled governing equations are not robust or efficient enough, together with the high computational cost in resolving the fracture process zone, largely hindering application of these models to general 3D problems. In this work, several 3D benchmark problems involving mode I, I+II or I+III failure in brittle and quasi-brittle solids is addressed based on our recent theoretical and numerical progresses on the unified phase-field theory for damage and fracture (Wu, 2017). Complex 3D fracture problems with over 2 million elements and more than 6 million degrees of freedom (dofs) can be tackled using normal computation facilities within acceptable computational time. Moreover, we are able to not only reproduce qualitatively evolution of the complex fracture pattern, but also compare quantitatively the global responses against experimental results. With the need neither to characterize the non-smooth crack surface nor to track the twisting crack path, the 3D computer implementation is almost the same as the 2D counterpart, paving the way to the phase-field modeling of large scale engineering problems.

Computer Aided Assembly and Verification of Synthetic Chromosomes.

Synthetic biology aims at engineering biological systems, ranging from genes to entire genomes. The emerging field of synthetic genomics provides new tools to address questions and tackle challenges in biology and biotechnology impossible to address with current methods. Chromosome scale engineering requires computational tools and workflows to streamline designing, building and verifying long DNA molecules. While a systematic and generic genome design workflow does not exist, here we outline chromosome assembly and verification operations that are at the foundation of every genome scale engineering efforts.