Large Degree Of Freedom Research Articles

First-principle prediction of one-dimensional silicon allotropes: Promising new candidate for chemical and electrochemical hydrogen storage

Silicon is an excellent candidate for efficient hydrogen-storage medium, but hydrogen is mainly stored on the surface of silicon structure and the large volume expansion and contraction of structure during hydrogen storage/release seriously damages its structural stability. In order to increase hydrogen concentration values in silicon nanostructures and enhance the cyclic stability of structure, we predicted 1D silicon allotropes at different ambient hydrogen concentration conditions, and investigated the structural stability and electronic properties of these allotropes. The results show that the spaces around these structures give them a larger degree of freedom and the H2 filled in the interspaces also continuously provides them with sufficient hydrogen sources for supplement and replacement. Most of the generated 1D silicon allotropes are both dynamically and thermodynamically stable and most of them are semiconductors. The low structural dimension ensures that these 1D silicon allotropes have high chemical hydrogen storage capacity, and the structural stability at ambient hydrogen concentration provides a strong guarantee for the cyclic stability of chemical and electrochemical hydrogen storage for these allotropes. Moreover, hydrogen atoms can diffuse not only on the surface of 1D silicon allotropes but also between two closely spaced 1D silicon allotropes, which provides more convenience for the application of 1D silicon allotropes in chemical and electrochemical hydrogen storage. Our results can enrich the library of 1D silicon nanostructures used for hydrogen storage and provide more understanding of their structural stability and electronic properties, which can provide theoretical reference for their future application.

Improved NMR-data-compliant protein structure modeling captures context-dependent variations and expands the scope of functional inference.

Nuclear magnetic resonance (NMR) spectroscopy can reveal conformational states of a protein in physiological conditions. However, sparsely available NMR data for a protein with large degrees of freedom can introduce structural artifacts in the built models. Currently used state-of-the-art methods deriving protein structure and conformation from NMR deploy molecular dynamics (MD) coupled with simulated annealing for building models. We provide an alternate graph-based modeling approach, where we first build substructures from NMR-derived distance-geometry constraints combined in one shot to form the core structure. The remaining molecule with inadequate data is modeled using a hybrid approach respecting the observed distance-geometry constraints. One-shot structure building is rarely undertaken for large and sparse data systems, but our data-driven bottom-up approach makes this uniquely feasible by suitable partitioning of the problem. A detailed comparison of select models with state-of-art methods reveals differences in the secondary structure regions wherein the correctness of our models is confirmed by NMR data. Benchmarking of 106 protein-folds covering 38-282 length structures shows minimal experimental-constraint violations while conforming to other structure quality parameters such as the proper folding, steric clash, and torsion angle violation based on Ramachandran plot criteria. Comparative MD studies using select protein models from a state-of-art method and ours under identical experimental parameters reveal distinct conformational dynamics that could be attributed to protein structure-function. Our work is thus useful in building enhanced NMR-evidence-based models that encapsulate the contextual secondary and tertiary structure variations present during the experimentation and expand the scope of functional inference.

Generalized Sequential Rotation Arrays With Full Control of Dual-Circularly-Polarized Aperture-Field Distribution Based on Elliptically-Polarized Elements

In this article, the theory, design, and experimental validation of wideband generalized sequential rotation arrays (GSRAs) are reported. In particular, the GSRA has the ability to provide dual-circularly polarized (dual-CP) beams with different handedness using a single feeding network, whose aperture amplitude and phase distributions can both be independently controlled. Such functionality is achieved by jointly utilizing the dynamic phase and Berry phase, as well as exploiting elliptically polarized (EP) array elements with different axial ratio (AR) values. The proposed technique and the design methodology are verified by two proof-of-concept microstrip GSRAs consisting of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula> array of EP stacked-patch elements operating in the K-band. The first prototype exhibits a 3 dB gain difference between the two CP beams, while the other offers a 6 dB gain difference and different sidelobe levels (SLLs) for the generated dual-CP beams. Both arrays are fabricated and measured, experimentally achieving a joint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{11} &lt; -10$ </tex-math></inline-formula> dB, AR < 3 dB, and 3 dB gain bandwidth of more than 28.4% and 20.1% with a beam squinting of less than 3°. In addition, the two GSRAs enable a bandwidth of about 19% and 3% within which the gain difference between the two CP beams of different handedness deviates from the targeted value by less than 1 dB. With the large degree of freedom in aperture field control, the proposed GSRAs could be potential candidates for use in satellite communications, point-to-multipoint communications, and so on.

Magnetic 3D-Printed Composites-Production and Applications.

Three-dimensional printing enables building objects shaped with a large degree of freedom. Additional functionalities can be included by modifying the printing material, e.g., by embedding nanoparticles in the molten polymer feedstock, the resin, or the solution used for printing, respectively. Such composite materials may be stronger or more flexible, conductive, magnetic, etc. Here, we give an overview of magnetic composites, 3D-printed by different techniques, and their potential applications. The production of the feedstock is described as well as the influence of printing parameters on the magnetic and mechanical properties of such polymer/magnetic composites.

Enhancing thermoelectric properties of isotope graphene nanoribbons via machine learning guided manipulation of disordered antidots and interfaces

Structural manipulation at the nanoscale breaks the intrinsic correlations among different energy carrier transport properties, achieving high thermoelectric performance. However, the coupled multifunctional (phonon and electron) transport in the design of nanomaterials makes the optimization of thermoelectric properties challenging. Machine learning brings convenience to the design of nanostructures with large degree of freedom. Herein, we conducted comprehensive thermoelectric optimization of isotopic armchair graphene nanoribbons (AGNRs) with antidots and interfaces by combining Green's function approach with machine learning algorithms. The optimal AGNR with ZT of 0.894 by manipulating antidots was obtained at the interfaces of the aperiodic isotope superlattices, which is 5.69 times larger than that of the pristine structure. The proposed optimal structure via machine learning provides physical insights that the carbon-13 atoms tend to form a continuous interface barrier perpendicular to the carrier transport direction to suppress the propagation of phonons through isotope AGNRs. The antidot effect is more effective than isotope substitution in improving the thermoelectric properties of AGNRs. The proposed approach coupling energy carrier transport property analysis with machine learning algorithms offers highly efficient guidance on enhancing the thermoelectric properties of low-dimensional nanomaterials, as well as to explore and gain non-intuitive physical insights.

An Explicit Finite Difference Method for Dynamic Interaction of Damped Saturated Soil Site-Pile Foundation-Superstructure System and Its Shaking Table Analysis

The saturated soil site-pile foundation-superstructure system, with large degrees of freedom or strong nonlinear problems, often involves a large amount of calculation and low computational efficiency. In this paper, a fully explicit finite difference method is proposed for a saturated soil site-pile foundation-superstructure system. Since the proposed method has the advantages of decoupling in both time and space, it does not need to solve the equations simultaneously, which grants it high computational efficiency. At the same time, the method is implemented on the open-source software OpenSees and is used to compare and analyze the dynamic responses of the shaking table of a liquefiable soil site-pile foundation-superstructure system. After the calculation and analysis, the numerical solutions were found to be in good agreement with the experimental solutions, which verifies the proposed method and illustrates that the proposed method can reasonably simulate the seismic responses of the whole system. In addition, the proposed calculation platform in OpenSees can be used for the analysis of the liquefaction process and the possible large deformation of soil after liquefaction, as well as for analyzing the failure mode of the complex and nonlinear saturated soil sites and structures under the effects of an earthquake.

An Efficient Sparse Bayesian Learning STAP Algorithm with Adaptive Laplace Prior

Space-time adaptive processing (STAP) encounters severe performance degradation with insufficient training samples in inhomogeneous environments. Sparse Bayesian learning (SBL) algorithms have attracted extensive attention because of their robust and self-regularizing nature. In this study, a computationally efficient SBL STAP algorithm with adaptive Laplace prior is developed. Firstly, a hierarchical Bayesian model with adaptive Laplace prior for complex-value space-time snapshots (CALM-SBL) is formulated. Laplace prior enforces the sparsity more heavily than Gaussian, which achieves a better reconstruction of the clutter plus noise covariance matrix (CNCM). However, similar to other SBL-based algorithms, a large degree of freedom will bring a heavy burden to the real-time processing system. To overcome this drawback, an efficient localized reduced-dimension sparse recovery-based space-time adaptive processing (LRDSR-STAP) framework is proposed in this paper. By using a set of deeply weighted Doppler filters and exploiting prior knowledge of the clutter ridge, a novel localized reduced-dimension dictionary is constructed, and the computational load can be considerably reduced. Numerical experiments validate that the proposed method achieves better performance with significantly reduced computational complexity in limited snapshots scenarios. It can be found that the proposed LRDSR-CALM-STAP algorithm has the potential to be implemented in practical real-time processing systems.

Optimal Mixed Placement and Capacity Distribution of Buckling-Restrained Braces and Conventional Braces on a Large Metal Spatial Structure Without Rigid Diaphragm Assumption

This paper presents a design application of the proposed generalized response spectrum analysis (GRSA)-based seismic optimization method to a large metal spatial structure (constructed in Japan) where a rigid diaphragm assumption is not available and displacement responses are disproportionally distributed in a story. It also discusses the optimal mixed placement and capacity distribution of buckling-restrained braces (BRBs) and conventional braces (CBs) to minimize both the story drift response and the number of BRBs (i.e., the introduction cost of expensive energy-dissipation devices used as dampers). GRSA is a quick and efficient analysis method for estimating the reduced seismic responses of structural models with a large degree of freedom, and GRSA-based computational optimization enables a more efficient seismic design process than trial-and-error approaches with time-consuming nonlinear response history analysis. In this study, the efficiency is verified through a comparison with the Japanese standard BRB design method. According to the results, the optimal design solution by the proposed method has approximately 20% less steel tonnage of BRBs than that obtained from the standard method, whereas the seismic performance is equal to or better than the others. Moreover, although engineers should still consider the possibility of damage concentration, the brace configuration of the substructure where BRBs and CBs are arranged in adjacent stories is the most effective for reducing both the number of BRBs and the story drift response.

Laser‐Printed Emissive Metasurface as an Anticounterfeiting Platform

AbstractOptical security is a promising application of metasurfaces because light has large degrees of freedom in metasurfaces. Although many different structures/materials are proposed for this purpose, the fabrication of dynamic metasurfaces in a straightforward and scalable manner, while maintaining a high security level, remains a significant challenge. Herein, a metasurface consisting of a phase‐changing GeSbTe layer and a metal back reflector is presented to space‐selectively and dynamically control the infrared emission of the surface by a spatially modulated pulsed laser beam. Unlike conventional laser processes using a focused beam, the employed laser printing is an expanded beam‐based parallel process that enables the fabrication of wafer‐sized emission patterns. Owing to the multispectral responses of GeSbTe, mutually independent visible and infrared images can be printed in one region. Grayscale emission patterns can also be obtained by gradually modulating the spatial profile of the laser beam, which makes the replication of laser‐printed emission patterns extremely difficult. These encouraging features indicate that the presented emissive metasurface has the potential for use as an effective platform for anticounterfeiting.

Structure–property relations in semi‐crystalline combinatorial poly(urethane‐isocyanurate)‐type hydrogels

AbstractWithin the fields of regenerative medicine and tissue engineering, the development of tough hydrogel biomaterials is a challenging topic that has received much attention over the past few years. Recently, a method was developed to synthesize tough combinatorial poly(urethane‐isocyanurate) (PUI)‐type hydrogels by the trimerization of mixtures of NCO‐functionalized prepolymers. As this synthesis approach allows a large degree of freedom in terms of polymer network design with a high level of control over the polymer network structure, the resulting systems are ideally suited for studying structure–property relations in tough hydrogel systems. In this work, we aim to systematically investigate the influence of introducing a hydrophobic component into a PUI polymer network on the mechanical properties of the resulting PUI hydrogels. Additionally, the effect of (the degree of) crystallinity of the hydrophobic network component is investigated. For this, two series of combinatorial PUI hydrogels are synthesized, based on a hydrophilic poly(ethylene glycol) prepolymer and increasing amounts of either a crystallizable hydrophobic prepolymer (poly(ε‐caprolactone)) or an amorphous hydrophobic prepolymer (poly(propylene glycol)). It is shown that the toughness of amorphous PUI hydrogels is hardly influenced by the hydrophobic content, whereas the toughness of semi‐crystalline PUI hydrogels strongly increases with increasing hydrophobic content. Also, the toughness of the latter hydrogels increases further with increasing degree of crystallinity of the hydrogel. Finally, it is shown that the semi‐crystalline PUI hydrogels are promising materials for biomedical adhesive and coating applications, as well as for load‐bearing biomedical applications within the fields of tissue engineering and regenerative medicine. © 2022 Covestro Deutschland AG. Polymer International published by John Wiley &amp; Sons Ltd on behalf of Society of Industrial Chemistry.

A focus review on 3D printing of wearable energy storage devices

AbstractThree‐dimensional (3D) printing has gained popularity in a variety of applications, particularly in the manufacture of wearable devices. Aided by the large degree of freedom in customizable fabrication, 3D printing can cater towards the practical requirements of wearable devices in terms of light weight and flexibility. In particular, this focus review aims to cover the important aspect of wearable energy storage devices (WESDs), which is an essential component of most wearable devices. Herein, the topics discussed are the fundamentals of 3D printing inks used, the optimizing strategies in improving the mechanical and electrochemical properties of wearable devices and the recent developments and challenges of wearable electrochemical systems such as batteries and supercapacitors. It can be expected that, with the development of 3D printing technology, realization of the full potential of WESDs and seamless integration into smart devices also needs further in‐depth investigations.

Practical implementable controller design with guaranteed asymptotic stability for nonlinear systems

Establishing the stability of model predictive control formulation is challenging. Using a combination of the terminal cost term and the terminal inequality constraint i.e. terminal set results in nominal asymptotic stability. Larger terminal set results in shorter minimum prediction horizon length required for feasibility from identical initial condition. Current work presents novel linear quadratic Gaussian regulator based approach for the terminal set characterization for the discrete time model predictive control scheme. Proposed approach provides large degrees of freedom in the form of additive matrices as tuning parameters for enlarging the terminal sets as against a single scalar for the literature approaches. Efficacy of the novel approach is demonstrated using a benchmark four tank system. It was observed that the average computation time reduces to significantly when compared to the approaches from the literature for identical initial conditions. This makes the controller design suitable for practical implementation.

Input‐to‐state stability of discrete‐time switched delayed systems with delay‐dependent impulses: Admissible edge‐dependent average impulsive interval

AbstractThis article addresses the input‐to‐state stability (ISS) problem for general discrete‐time impulsive and switched delayed systems coupled by delay‐dependent impulses. In our studied systems, the impulsive signal has a large degree of freedom, that is, the impulse effect can occur simultaneously with or different from the switching effect. A new Lyapunov–Krasovskii functional is proposed to deal with the delay effect of switching signal and impulsive jump, which is divided into the delay‐independent part and delay‐dependent part. A new concept named admissible edge‐dependent average impulsive interval (AED‐AII) is set up to characterize the impulse sequence relative to the admissible edge‐dependent average dwell time (AED‐ADT) concept of the switching signal. Based on AED‐ADT approach and AED‐AII technique, some improved sufficient outcomes guaranteeing ISS of discrete‐time impulsive and switched delayed systems are presented, respectively, for stabilizing and destabilizing delay‐dependent impulses. Meanwhile, the relationship among AED‐AII, AED‐ADT, the jump amplitude of switching signal and impulse signal at their respective discrete points, the decay rate of Lyapunov functional for system dynamics is connected to pursue ISS of the whole system. Compared with previous works, our setups enjoy the following characteristics: it thoroughly digs the influence of time delay existing in impulses and system dynamics on the function part of L‐K functional, which are less conservative relative to other works; the proposed AED‐ADT switching signals and AED‐AII jump signals cover a large set of hybrid systems. Two numerical examples are given to illustrate the effectiveness and less conservativeness of the obtained results.

Applying multiverse analysis to experience sampling data: Investigating whether preprocessing choices affect robustness of conclusions.

The experience sampling method (ESM) has revolutionized our ability to conduct psychological research in the natural environment. However, researchers have a large degree of freedom when preprocessing ESM data, which may hinder scientific progress. This study illustrates the use of multiverse analyses regarding preprocessing choices related to data exclusion (i.e., based on various levels of compliance and exclusion of the first assessment day) and the calculation of constructs (i.e., composite scores calculated as the mean, median, or mode) by reanalyzing established group differences in negative affect, stress reactivity, and emotional inertia between individuals with and without psychosis. Data came from five studies and included 233 individuals with psychosis and 223 healthy individuals (in total, 26,892 longitudinal assessments). Preprocessing choices related to data exclusion did not affect conclusions. For both stress reactivity and emotional inertia of negative affect, group differences were affected when negative affect was calculated as the mean compared to the median or mode. Further analyses revealed that this could be attributed to considerable differences in the within- and between-factor structure of negative affect. While these findings show that observed differences in affective processes between individuals with and without psychosis are robust to preprocessing choices related to data exclusion, we found disagreement in conclusions between different central tendency measures. Safeguarding the validity of future experience sampling research, scholars are advised to use multiverse analysis to evaluate the robustness of their conclusions across different preprocessing scenarios.

A structural damage detection algorithm based on discrete wavelet transform and ensemble pattern recognition models

Damage detection is of great importance in reducing maintenance cost and preventing collapse of structures. Despite existing damage detection methods, the current literature lacks a comprehensive method, which: (i) is applicable to complex structures with large degrees of freedom, (ii) captures even low-level damages, and (iii) gives reasonable accuracy in the presence of uncertainty conditions such as noise and temperature. Hence, this study proposes a damage detection algorithm based on discrete wavelet transform and an ensemble of pattern recognition models, in which: (1) vibration data is decomposed through discrete wavelet transforms, (2) the decomposed data is compressed using principal component analysis, (3) individual damage models of the structure are trained through pattern recognition models of deep neural network and couple sparse coding, where the compressed decomposed vibration data as well as damage data are inputted, and (4) ultimately, the individual damage models are merged into one by majority voting to predict damage location and severity of the structure. The proposed algorithm is tested on a numerical model of a one-bay three-story steel frame, and experimental data of a large-scale bridge structure. It is found that the algorithm can precisely detect low-level damages at multiple locations, even in beam–column connections and complex structures, in the presence of uncertainty conditions such as noise and temperature.

Influence of Vehicle Characteristics on Reduced Order Model of Multi Connected Vehicles

This paper deals with the response behavior of the multi connected vehicles subjected to seismic excitation. The multi connected vehicles are modeled by a set of vehicle models that have 21 degree of freedom. The running road is modeled by mass, spring and damper system that have 1 degree of freedom in three directions. One of the problems with multi connected vehicles is that they have a large degree of freedom, and the calculation cost considerably increase. Therefore, we made the reduced order model by using sparse estimation. We investigated the influence of vehicle characteristics on reduced order model. As a result, the number of modes required for the reduced order model to reproduce the original model is less when the motor car is not considered than when it is considered. In addition, when considering a model that includes motor car, the reproducibility car by car of the original model is affected by the number of motor cars and their positions.

Production Dynamic Prediction Method of Waterflooding Reservoir Based on Deep Convolution Generative Adversarial Network (DC-GAN)

The rapid production dynamic prediction of water-flooding reservoirs based on well location deployment has been the basis of production optimization of water-flooding reservoirs. Considering that the construction of geological models with traditional numerical simulation software is complicated, the computational efficiency of the simulation calculation is often low, and the numerical simulation tools need to be repeated iteratively in the process of model optimization, machine learning methods have been used for fast reservoir simulation. However, traditional artificial neural network (ANN) has large degrees of freedom, slow convergence speed, and complex network model. This paper aims to predict the production performance of water flooding reservoirs based on a deep convolutional generative adversarial network (DC-GAN) model, and establish a dynamic mapping relationship between well location deployment and output oil saturation. The network structure is based on an improved UNet framework. Through a deep convolutional network and deconvolution network, the features of input well deployment images are extracted, and the stability of the adversarial model is strengthened. The training speed and accuracy of the proxy model are improved, and the oil saturation of water flooding reservoirs is dynamically predicted. The results show that the trained DC-GAN has significant advantages in predicting oil saturation by the well-location employment map. The cosine similarity between the oil saturation map given by the trained DC-GAN and the oil saturation map generated by the numerical simulator is compared. In above, DC-GAN is an effective method to conduct a proxy model to quickly predict the production performance of water flooding reservoirs.

Laser-printed emissive metasurface implemented with a planar thin-film resonator

Optical security is a promising application of metasurfaces because light has large degrees of freedom in metasurfaces. Although many different structures/materials have been proposed for this purpose, the fabrication of dynamic metasurfaces in a straightforward and scalable manner while maintaining a high security level remains a significant challenge. Herein, a metasurface consisting of a phase-changing Ge2Sb2Te5 (GST) layer and a thin metal back reflector is presented to space-selectively and dynamically control the infrared emission of the surface by a spatially modulated pulsed laser beam. Unlike conventional laser processes using a focused beam, the employed laser printing is an expanded beam-based parallel process that enables the fabrication of wafer-sized emission patterns. Owing to the multispectral responses of GST, mutually independent visible and infrared images can be printed in one region. Grayscale emission patterns can also be obtained by gradually modulating the spatial profile of the laser beam, which makes the replication of laser-printed emission patterns extremely difficult. These encouraging features are experimentally verified using rigid and flexible substrates, indicating that the presented emissive metasurface has the potential for use as an effective platform for anti-counterfeiting.

Generation of coaxial vortex beams with doubled topological charges using a stacked liquid crystal structure

Optical vortices (OVs), which carry orbital angular momenta are attractive for many optical applications. A facile means of generating OVs is through the use of q-plates, which are patterned liquid crystal (LC) waveplates. However, they are limited to generating beams with topological charge ± In this work, we propose a stacked LC structure consisting of patterned nematic LC with 1/4-wave retardation and planar cholesteric LC, and demonstrate its capability to generate coaxial OVs with topological charges and As an extension of the function of conventional q-plate, this structure provides large degree of freedom in the generating OVs.

The Impact of Renewable Energy Sources on Financial Development, and Economic Growth: The Empirical Evidence from an Emerging Economy

Developing energy from renewable sources and modernizing the energy system are critical components of China’s efforts to combat climate change. Policymakers and authorities have made significant attempts to bring them. However, one of the major impediments to China’s energy revolution is financial limitations, which are inextricably linked to the country’s economic growth. The present research paper intends to investigate the relationship between economic growth and sustainable financial development on the use of energy from renewable sources in both the short and long run in the context of China. To achieve this, the researchers have utilized the panel data consisting of 10 years from 2011 to 2020. When compared to cross-sectional and time-series data samples, the panel data model offers many benefits. For starters, the panel data includes information on the passage of time and the cross-sectional area. Another benefit of using panel-data models with a larger degree of freedom is that they provide more stable and reliable estimates across short periods across cross-sections. In the case of the short run, there is a positive relationship between economic and financial development and the use of energy from renewable sources in the context of all of China. While in the case of long-term effects, the results indicate the adverse impact of financial development on the use of energy from renewable sources in the western regions of China. These results were deduced using the causality test Granger proposed to determine the path of the causal relationship and the direction of the relationship between the variables. These results indicated that the relationship between economic and financial development in east China was unidirectional, and the nature of the underlying relationship was causal. Meanwhile, in east and west China, economic development in China as a whole has been unidirectionally increasing energy from renewable sources. Our empirical findings suggest many strategies for promoting the growth of energy from renewable sources.