Smart Engineering Research Articles

STEM Tools for Semiconductor Characterization: Beyond High-Resolution Imaging.

The smart engineering of novel semiconductor devices relies on the development of optimized functional materials suitable for the design of improved systems with advanced capabilities aside from better efficiencies. Thereby, the characterization of these materials at the highest level attainable is crucial for leading a proper understanding of their working principle. Due to the striking effect of atomic features on the behavior of semiconductor quantum- and nanostructures, scanning transmission electron microscopy (STEM) tools have been broadly employed for their characterization. Indeed, STEM provides a manifold characterization tool achieving insights on, not only the atomic structure and chemical composition of the analyzed materials, but also probing internal electric fields, plasmonic oscillations, light emission, band gap determination, electric field measurements, and many other properties. The emergence of new detectors and novel instrumental designs allowing the simultaneous collection of several signals render the perfect playground for the development of highly customized experiments specifically designed for the required analyses. This paper presents some of the most useful STEM techniques and several strategies and methodologies applied to address the specific analysis on semiconductors. STEM imaging, spectroscopies, 4D-STEM (in particular DPC), and in situ STEM are summarized, showing their potential use for the characterization of semiconductor nanostructured materials through recent reported studies.

Research on Efficacy Evaluation of Large-Scale Networked Intelligent Perception System

Under the smart engineering system (SES), there is a huge demand for evaluating the efficacy of a large-scale networked intelligent perception system (IPS). Considering the large-scale, distributed, and networked system characteristics and perception task demands, this paper proposes a conceptual system for IPS efficacy evaluation and, on this basis, designs the architecture of the efficacy evaluation system. A networked IPS model is constructed based on domain ontology, an index system is quickly established for efficacy evaluation, the evaluation methods are assembled automatically, and adaptive real-time organization strategies are generated for networked perception based on efficacy estimate. After exploring these key technologies, a prototype system is created for the service-oriented integrated efficacy evaluation platform and used to verify and integrate research results. The research provides support for the efficacy evaluation theories and methods of large-scale networked IPS.

Machine learning algorithms based advanced optimization of EDM parameters: An experimental investigation into shape memory alloys

In the era of advancement and progressive fourth industrial revolution culture, the demand of advanced and smart engineering materials has been increased. In this way, shape memory alloys are an excellent choice for industrial applications such as orthopedic implacers, actuators, micro-tools, fitting and screening elements, aircraft component components, military instruments, fabricating elements, and bio-medical devices, among others. This paper has been aimed to attempt the machine learning (ML) algorithms-based optimization of the different process inputs in electrical discharge machining of Cu-based shape memory alloy. The current study focused on study the behavior of response parameters along with the variation in machining input parameters The considered process input factors are namely as; pulse on time (Ton), pulse off time (Toff), peak current (Ip), and gap voltage (GV) and their effects were studied on dimensional deviation (DD) and tool wear rate (TWR). The central composite design matrix has been employed for planning the main runs. The 2-D and 3-D graphs represents the behavior of the response parameters along with variations in the machining inputs. The novelty of the work is machining of Cu-based Shape Memory Alloy (SMA) in EDM operations and optimization of parameters using Machine Learning techniques. Furthermore, machine learning based, single and multi-objective optimization of investigated responses were conducted using the desirability approach, Genetic Algorithm (GA) and Teacher Learning based Optimization (TLBO) techniques. The parametric combination attained for optimization of multiple responses (TWR and DD) is: Ton ​= ​90.10 ​μs, Toff ​= ​149.69 ​μs, Ip ​= ​24.59 A & GV ​= ​60 ​V; Ton ​= ​255 ​μs, Toff ​= ​15 ​μs, Ip ​= ​50 A & GV ​= ​15 ​V; Ton ​= ​255 ​μs, Toff ​= ​15 ​μs, Ip ​= ​50 A & GV ​= ​15 ​V, using desirability approach, GA method and TLBO method, respectively.

Rationalizing the Thermal Response of Dual‐Center Molecular Thermometers: The Example of an Eu/Tb Coordination Complex

AbstractLuminescence thermometry allows the remote monitoring of the temperature and holds the promise to drive the next generation of future nano or micrometric devices. Materials able to sense the temperature are usually based on one or several lanthanide ions allowing a ratiometric measurement. Optimizing the thermometric features is usually achieved through a serendipity approach, but it still appears difficult to accurately predict the sensing performance. Through a combination of experiment and theoretical calculations, the first example of an energy‐driven luminescent molecular thermometer [Tb0.94Eu0.06(bpy)2(NO3)3] (bpy = 2,2′‐bipydine) displaying an exceptional thermal cyclability around room temperature is reported, for which the thermal properties could be theoretically forecasted. This work provides comprehensive guidelines that can be easily extended for any dual‐center thermometer in which energy transfer drives the thermometric performance opening the avenue for the smart engineering of sensing devices.

Construction of three-dimensional electronic interconnected Na3V2(PO4)3/C as cathode for sodium ion batteries

The NASICON (Sodium super ion conductor)-type of Na3V2(PO4) (NVP) has a great potential for energy storage application due to its high voltage plateau, three-dimensional (3D) ionic diffusion channel, perfect thermal stability, and high structural robustness. Here, Na3V2(PO4)-based composites with 3D porous carbon skeleton structure were synthesized via hydrothermal methods. After a series of hydrolysis and polymerization processes, transparent colloid mainly containing (NH4)4Na2V10O28 phase was formed. The 3D porous hydrogel with the (NH4)4Na2[V10O28]·10H2O phase was produced by adjusting the reaction time. The product has a lamellar structure, and its ion is suitable to grow the nanosheets, and the present PO43- groups may be distributed in the (NH4)4Na2[V10O28]·10H2O phase of the interlayer structure, leading to the enhanced electronic conductivity. The 3D porous Na3V2(PO4)/C (NVP/C) with the size of 100–200 nm particles can be used as cathode for sodium ion batteries. The NVP/C delivers a specific capacity of 62.1 mAh g−1 at 10 C. The excellent stability of NVP/C can be obtained with 98% capacity retention after 140 cycles at 1 C. After 2000 cycles (5 C), the capacity is still 80.4 mAh g−1, with the capacity retention of about 86%. Our encouraging results may boost further studies of SIBs device by smart engineering of the electrode materials.

A three-dimensional intelligent engineering management and control system for the construction of a long-span valve hall project based on a microservice architecture.

Three-dimensional intelligent engineering management and control systems (EMCS) based on the browser/server (B/S) model are an important part of intelligent engineering development. These systems are used for solving the difficulties encountered in engineering management with frequent cross-specialties and are vital tools for data exchange and service sharing among multiple departments. Currently, most engineering management and control systems are based on service-oriented architectures (SOAs). The integration mechanism and high coupling of SOAs leads to the reduction in system expansibility, service quality and service safety of the engineering system, making it difficult for these architectures to serve the construction of long-span valve hall engineering. To address these concerns, the management and application technology of the multidisciplinary data of valve hall engineering based on a microservice architecture (MSA) is proposed to improve the management efficiency of engineering data. A 3D integration modeling method for valve hall engineering structures and geological environments is proposed to establish the topological association between engineering structures and geological environments, without increasing the amount of model data required. A 3D intelligent engineering management and control technology for the entire process of the construction of long-span valve hall engineering is proposed, which realizes the entire process simulation and control of engineering construction based on WebGL technology. Accordingly, a three-dimensional intelligent engineering management and control system for the entire construction process of a long-span valve hall project in Southeast China is established, which can effectively manage and apply the data, display and analyze the three-dimensional model, and control and make decisions regarding the construction schedule. This study provides support for the construction of "smart engineering", promotes information communication and transmission between different project units, and speeds up the transformation from traditional construction management relying on drawings to three-dimensional intelligent construction management based on cloud services.

Architecture of Distributed Control System for Gearbox-Free More Electric Turbofan Engine

This article presents the development of the electric turbofan engine in distributed architecture with a design thrust in the range of 3 to 7.5 and from 7.5 to 30 kN for small and medium-sized unmanned aerial vehicles. The engine subsystems are considered as separate smart modules with a built-in control system, exchanging data via a digital channel with the central engine control and diagnostics unit. The key smart engine units are combined in the following subsystems: starter and turbine generators, oil pumps, actuator of guide vanes, fuel pumps, fuel metering unit, control and diagnostic unit. All pumps and guide vane actuator are electrically driven. Control and monitoring signals are transmitted via a digital bus. Functional and reliability analysis and the technical configuration design of each subsystem are presented. Based on analysis of the architecture of distributed control systems for a gearbox-free electric engine, different configurations of described subsystems are proposed.

Quasi-BIC-governed light absorption of monolayer transition-metal dichalcogenide-based absorber and its sensing performance

The efficiency and bandwidth of an optical absorbing material, especially for the promising two-dimensional transition metal dichalcogenides (TMDCs) are critical to underpin advances in photonic and optoelectronic devices. In this work, we present a general method to manipulate the bandwidth of an absorber with high efficiency through coupling with the quasi-bound states in the continuum (quasi-BIC). We demonstrate this strategy by taking absorbing material monolayer TMDC coupled with the lossless symmetry-broken photonic crystal (PhC) slab. An optimal-efficiency TMDC-based absorber with over three orders of magnitude of bandwidth adjustment is realized, by simultaneously adjusting the structure asymmetry parameter of PhC slab and the locations of monolayer TMDC in the structure. Interestingly, the absorption bandwidth is tailored quadratically with the asymmetry parameter, which derived from the powerful physics of bound state in the continuum (BIC) in radiation engineering. Moreover, a superior-performance optical refractive index sensor is further designed. We demonstrate that our proposed method based on quasi-BIC structure can also effectively govern the sensing performance. Present work not only provides further insight into BIC physics, but also offers a promising strategy of smart engineering in active optical devices with the properties on demand.

Telechelic Triblock Poly(α‐Amino Acid)‐Poly(Tetrahydrofuran)‐Poly(α‐Amino Acid) Copolymers: Chain‐End Transformation, Polymerization and pH‐Responsive Hydrolysis†

Main observation and conclusionBlock copolymers not only combine properties of different segments but also generate new application prospects. Poly(α‐amino acid)‐b‐poly(tetrahydrofuran)‐b‐poly(α‐amino acid) (PAA‐PTHF‐PAA) is one of the copolymers. In this contribution, di‐hydroxyl‐ended HO‐PTHF‐OH is transferred into di‐oxyamino‐ended H2NO‐PTHF‐ONH2 quantitatively, which is used as a macroinitiator to polymerize sarcosine N‐thiocarboxyanhydride (Sar‐NTA) and N‐phenyoxycarbonyl N‐ε‐carbobenzyloxy‐D‐lysine (ZDL‐NPC). Well‐defined triblock PAA‐NHO‐PTHF‐ONH‐PAA is produced with high molecular weight (up to 25.3 kg/mol) and narrow dispersity. The amphiphilic PSar‐NHO‐PTHF‐ONH‐PSar (STS) self‐assembles into micelles with uniform diameters of 30—40 nm according to DLS. Owing to oxygen amide groups inside the backbone of these copolymers, the polyether‐poly(amino acid)s block copolymers are cleavable under an acidic environment and therefore have potential applications in smart biomedicine and engineering.

An Intermetallic NiTi-Based Shape Memory Coil Spring for Actuator Technologies

Amongst various intermetallic shape memory alloys (SMAs), nickel–titanium-based SMAs (NiTi) are known for their unique elastocaloric property. This widely used shape remembering material demonstrates excellent mechanical and electrical properties with superior corrosion resistance and super-long fatigue life. The straight-drawn wire form of NiTi has a maximum restorable strain limit of ~4%. However, a maximum linear strain of ~20% can be attained in its coil spring structure. Various material/mechanical engineers have widely exploited this superior mechanic characteristic and stress-triggered heating/cooling efficiency of NiTi to design smart engineering structures, especially in actuator technologies. This short technical note reflects the characteristics of the NiTi coil spring structure with its phase transformations and thermal transformation properties. The micro-actuators based on NiTi have been found to be possible, suggesting uses from biomedical to advanced high-tech applications. In recent years, the technical advancements in modular robotic systems involving NiTi-based SMAs have gained speculative commercial interest.

Smart engineering of gold nanoparticles to improve intestinal barrier penetration

Overcoming the harsh environment and epithelial barrier of the gastrointestinal (GI) tract is an important challenge for orally administered nanodrugs. Among the various nano-sized drug delivery systems, gold nanoparticles (AuNPs) possess unique characteristics that make them promising candidates for improving drug bioavailability. A profound understanding of the GI tract microenvironment and the mechanisms of nanoparticle transportation through the intestinal epithelium is required to develop an effective oral drug delivery system. Moreover, the physicochemical properties of AuNPs, including their size, surface charge, rigidity, and shape, are relevant factors that affect the penetration processes. In this review, we describe the nanotechnologies that can be incorporated into AuNPs to efficiently target intestinal epithelial cells, penetrate mucus barriers, and respond to specific stimuli. Thus, we provide a comprehensive description of AuNPs to formulate a rational and effective engineering strategy to overcome the GI tract barriers.

Fabrication of Topographically Controlled Electrospun Scaffolds to Mimic the Stem Cell Microenvironment in the Dermal-Epidermal Junction.

The use of microfabrication techniques for the development of innovative constructs for tissue regeneration is a growing area of research. This area comprises both manufacturing and biological approaches for the development of smart materials aiming to control and direct cell behavior to enhance tissue healing. Many groups have focused their efforts on introducing complexity within these innovative constructs via the inclusion of nano- and microtopographical cues mimicking physical and biological aspects of the native stem cell niche. Specifically, in the area of skin tissue engineering, seminal work has reported replicating the microenvironments located in the dermal-epithelial junction, which are known as rete ridges. The rete ridges are key for both stem cell control and the physiological performance of the skin. In this work, we have introduced complexity within electrospun membranes to mimic the morphology of the rete ridges in the skin. We designed and tested three different patterns, characterized them, and explored their performance in vitro, using 3D skin models. One of the studied patterns (pattern B) was shown to aid in the development of an in vitro rite-ridgelike skin model that resulted in the expression of relevant epithelial markers such as collagen IV and integrin β1. In summary, we have developed a new skin model including synthetic rete-ridgelike structures that replicate both morphology and function of the native dermal-epidermal junction and that offer new insights for the development of smart skin tissue engineering constructs.

Revolutionizing enzyme engineering through artificial intelligence and machine learning.

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.

Supported Ni@Ni2P Core–Shell Nanotube Arrays on Ni Foam for Hydrazine Electrooxidation

Development of high-performance electrocatalysts requires a judicious selection of intrinsically highly active materials and smart structure engineering of electrodes to maximize active site densit...

Model-Based Big Data Analytics-as-a-Service: Take Big Data to the Next Level

The Big Data revolution promises to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, major hurdles still need to be overcome on the road that leads to commoditization and wide adoption of Big Data Analytics (BDA). Big Data complexity is the first factor hampering the full potential of BDA. The opacity and variety of Big Data technologies and computations, in fact, make BDA a failure prone and resource-intensive process, which requires a trial-and-error approach. This problem is even exacerbated by the fact that current solutions to Big Data application development take a bottom-up approach, where the last technology release drives application development. Selection of the best Big Data platform, as well as of the best pipeline to execute analytics, represents then a deal breaker. In this paper, we propose a return to roots by defining a Model-Driven Engineering (MDE) methodology that supports automation of BDA based on model specification. Our approach lets customers declare requirements to be achieved by an abstract Big Data platform and smart engines deploy the Big Data pipeline carrying out the analytics on a specific instance of such platform. Driven by customers' requirements, our methodology is based on an OWL-S ontology of Big Data services and on a compiler transforming OWL-S service compositions in workflows that can be directly executed on the selected platform. The proposal is experimentally evaluated in a real-world scenario focusing on the threat detection system of SAP.

Acceleration of radiative recombination in quasi-2D perovskite films on hyperbolic metamaterials

Hyperbolic metamaterials are a family of nanophotonic architectures allowing for the unique control of photonic local density of states. Such a property makes metamaterials prospective to use them with light-emitting objects or to apply as meta-electrodes for optoelectronic devices, where the control of recombination properties plays a decisive role. On the other hand, layered quasi-2D halide perovskites (Ruddlesden–Popper phase) attract high attention due to their low cost, broadband spectral tunability, and outstanding optoelectronic properties. Here, we show how to accelerate photoluminescence with smart engineering of photonic density of states (i.e., via the Purcell effect) by depositing a perovskite film on a hyperbolic metamaterial. We experimentally confirm acceleration of radiative recombination by almost 3 times. This effect can be useful in light-emitting devices, where interplay between radiative and non-radiative channels of charge carrier recombination is crucial.

Metaheuristic and Machine Learning-Based Smart Engine for Renting and Sharing of Agriculture Equipment

Recently, many companies have substituted human labor with robotics. Some farmers are sharing different perspectives on the incorporation of technology into farming techniques. Some are willing to accept the technology, some are hesitant and bemused to adapt modern technology, and others are uncertain and are worried about the potential of technology to cause havoc and decrease yields. The third group prevails the most in the developed world, for lack of know-how, including translation of utility and, most significantly, the expense involved. A special Smart Tillage platform is established to solve the above issues. A smart-engine-based decision has been developed, which further uses classification and regression trees to shift towards decision-making. The decision is focused entirely on different input factors, such as type of crop, time/month of harvest, type of plant required for the crop, type of harvest, and authorised rental budget. Sitting on top of this would be a recommendation engine that is powered by deep learning network to suggest the escalation of a farmer from lower to higher category, namely, small to medium to large. A metaheuristic is one of the best computing techniques that help for solving a problem without the exhaustive application of a procedure. Recommendations will be cost-effective and suitable for an escalating update depending on the use of sufficient amends, practices, and services. We carried out a study of 562 agriculturists. Owing to the failure to buy modern equipment, growers are flooded by debt. We question if customers will be able to rent and exchange appliances. The farmers would be able to use e-marketplace to develop their activities.

Bacterial cellulose reinforced double-network hydrogels for shape memory strand

Tough hydrogels with shape memory property are highly desirable for actuators and smart engineering materials. Herein, super-tough polyacrylamide/iota-carrageenan double-network hydrogels were synthesized via a one-pot radical polymerization and strengthened by incorporating bacterial cellulose microclusters, through the intermolecular hydrogen bonds and topological interlock between microclusters and polymer network. Such hydrogels were able to withstand over 200 kPa of tensile stress, or be stretched over 27 times of initial length, and reached a high toughness of ∼2000 kJ/m3. By tension-drying and post-annealing treatments on the strongest hydrogel, dry strands were fabricated to withstand over 100 MPa of tensile stress. Moreover, these strands presented water-stimulated shape memory by a recovery ratio of 84.3 % in 4 min. Based on these characteristics, this super-tough hydrogel may serve as smart textile or actuator for a variety of applications.

A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design

To proactively fulfill multiple stakeholders’ needs in the engineering solution design process, the knowledge recommendation approach is adopted as a key element in the knowledge management system. Nevertheless, most existing knowledge recommendation approaches cannot simultaneously meet the higher standard of in-context accuracy and diversity. To address the issue, this paper proposes a context-aware diversity-oriented knowledge recommendation approach, thereby assisting stakeholders to accomplish engineering solution design in a smarter manner. Three diversity concerns, namely item-diversity, context-diversity, and user-diversity are addressed by semantic-based content analysis, context definition and awareness, and user profile modeling, respectively. Hence, the proposed approach not only maximizes the diversity of the recommended knowledge but also guarantees its accuracy under multiple problem-solving contexts. Moreover, a practical engineering solution design case on a Smart 3D printer platform is conducted, to validate the efficacy of the proposed approach in providing usable and diverse knowledge items. It is anticipated this work can provide useful insights to practitioners in their knowledge-based engineering solution design process.