Related Applications Research Articles

Numerical investigation into the thermo-hydro-mechanical behavior in unsaturated soils considering hypergravity effect

Unsaturated soils are commonly found in coastal regions and marine sediments, where understanding the thermo-hydro-mechanical (THM) behavior, especially regarding phase transition, is crucial for related practical engineering applications. Centrifugal model offers an effective means to reflect the prototype with smaller scale and shorter duration under hypergravity conditions. Present studies on similarity based on governing equation analysis of the THM coupled responses are mainly focused on saturated rather than unsaturated soils, nor involved with different boundary conditions. This paper theoretically derives the similarity of THM behavior of unsaturated soils between the centrifugal model and prototype, and numerically assesses the similarity considering Dirichlet / Neumann condition based on the open-source FEM code OpenGeoSys. In addition, numerical results shows that phase transition between liquid water and water vapor will occur under temperature variation, and pore pressure difference rather than initial pore pressure causes the difference of THM coupled responses. Significant pore pressure variation will be amplified with increase of gas diffusion coefficient, and large air entry pressure corresponds to high liquid saturation and obvious phase transition.

Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces

Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning and knowledge completion. In this article, we provide a systematic review of existing KGE techniques based on representation spaces. Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) algebraic perspective, (2) geometric perspective and (3) analytical perspective. We introduce the rigorous definitions of fundamental mathematical spaces before diving into KGE models and their mathematical properties. We further discuss different KGE methods over the three categories, as well as summarise how spatial advantages work over different embedding needs. By collating the experimental results from downstream tasks, we also explore the advantages of mathematical space in different scenarios and the reasons behind them. We further state some promising research directions from a representation space perspective, with which we hope to inspire researchers to design their KGE models as well as their related applications with more consideration of their mathematical space properties.

STRUCTURAL, ELECTRONIC AND MAGNETIC PROPERTIES OF VANADIUM DOPED DELAFOSSITE CUGAO2: AN AB INITIO STUDY

Delafossite copper gallium oxide (CuGaO2) is one of the most important copper-based delafossite materials reported. It has variety of applications that include but are not limited to; photo catalysis, dye-sensitized solar cells. However, due to the wide band gap of this material, it appears very attractive as transparent conductive oxide (TCO). Thus, it is very important and applicable in optoelectronic device technologies. In this paper, the structural, electronic and magnetic properties of vanadium (V) doped delafossite CuGaO2 are investigated using first principle study based on density functional theory (DFT) as implemented in the QUANTUM ESPRESSO simulation package. We used Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) exchange-correlation scheme for the undoped and vanadium (V) doped structures. There is no structural transition after the doping. The results indicated that the V doping reduced the band gap of the undoped delafossite CuGaO2 by 0.8 eV. It also contributed more to the conduction band states. However, our results also revealed that the 50 % V doping induced significant changes to the magnetic properties of the undoped CuGaO2. It was found that the undoped CuGaO2 is slightly paramagnetic similar to the same group member CuAlO2, whereas the V doped CuGaO2 system is slightly ferromagnetic. This result is in agreement with previous literature concerning the effect of doping semiconductor material with magnetic metals. Thus, based on our results, V doped CuGaO2 material may be considered as an important candidate for spintronics and other related applications.

Quantifying isotope parameters associated with carbonyl-water oxygen exchange during sucrose translocation in tree phloem.

Stable oxygen isotope ratio of tree-ring α-cellulose (δ18Ocel) yields valuable information on many aspects of tree-climate interactions. However, our current understanding of the mechanistic controls on δ18Ocel is incomplete, with a knowledge gap existent regarding the fractionation effect characterizing carbonyl-water oxygen exchange during sucrose translocation from leaf to phloem. To address this insufficiency, we set up an experimental system integrating a vapor 18O-labeling feature to manipulate leaf-level isotopic signatures in tree saplings enclosed within whole-canopy gas-exchange cuvettes. We applied this experimental system to three different tree species to determine their respective relationships between 18O enrichment of sucrose in leaf lamina (Δ18Ol_suc) and petiole phloem (Δ18Ophl_suc) under environmentally/physiologically stable conditions. Based on the determined Δ18Ophl_suc-Δ18Ol_suc relationships, we estimated that on average, at least 25% of the oxygen atoms in sucrose undergo isotopic exchange with water along the leaf-to-phloem translocation path and that the biochemical fractionation factor accounting for such exchange is c. 34‰, markedly higher than the conventionally assumed value of 27‰. Our study represents a significant step toward quantitative elucidation of the oxygen isotope dynamics during sucrose translocation in trees. This has important implications with respect to improving the δ18Ocel model and its related applications in paleoclimatic and ecophysiological contexts.

Prediction of syngas yield from biomass by gasification and related application

This research focuses on the prediction of synthesis gas generation from biomass through gasification and specifically estimates the syngas yield from rice straw from 2018 to 2020. The data of 2020 is visualized in the form of a colored world map A comprehensive literature review is conducted to explore previous studies on syngas yield models and gasification methods and the utilization of machine learning models. A machine learning model is built to calculate the prediction of the syngas total yield generated from biomass gasification. The inputs of the model include temperature, carbon content, hydrogen content, and oxygen content, with the latter three representing different types of biomasses. The output of the model is the total synthesis gas yield per kilogram of biomass. Subsequently, this model is utilized to predict the amount of syngas obtained from rice straw, which has a carbon, hydrogen, and oxygen content of 43.9%, 5.6%, and 32.1% respectively. From the model, an optimal gasification temperature of 667 degrees Celsius and a maximum syngas yield of 4.71 Nm3/kg for rice straw is obtained. Based on available data on rice straw production worldwide from 2018 to 2020, the amount of rice straw utilized for biomass gasification is estimated. The syngas yield in different regions of the world is calculated based on the maximum syngas yield and the mass of available rice straw. Outcomes of the calculation are visualized into a global map displaying the distribution of syngas yields which provides valuable insights into the potential for syngas production from rice straw in different regions.

Recognition of Ethylene Plasma Spectra 1D Data Based on Deep Convolutional Neural Networks

As a commonly used plasma diagnostic method, the spectral analysis methodology generates a large amount of data and has a complex quantitative relationship with discharge parameters, which result in low accuracy and time-consuming operation of traditional manual spectral recognition methods. To quickly and efficiently recognize the discharge parameters based on the collected spectral data, a one-dimensional (1D) deep convolutional neural network was constructed, which can learn the data features of different classes of ethylene plasma spectra to obtain the corresponding discharge parameters. The results show that this method has a higher recognition accuracy of higher than 98%. This model provides a new idea for plasma spectral diagnosis and its related application.

Computational predictions of optoelectronic energy materials Cs2SiBr6 Cs2GeBr6 & Cs2SnBr6 for phenomenal photovoltaic applications; a first principles study

Exploring prospective materials for energy storage and production is one of the largest confrontation of the century. Solar optoelectronic energy is one of the utmost exceptional renewable resources now a days, due to their small Environmental impact and wide availability. The perovskite type double halides are promising materials for optoelectronic and solar applications. In this article, the optoelectronic characteristics of newly perovskite double halides Cs2SiBr6 Cs2GeBr6 and Cs2SnBr6 are addressed. The calculated band gaps 2.70 eV (Cs2SiBr6), 1.80 eV (Cs2GeBr6), 2.90 eV (Cs2SnBr6) with raising performance for optoelectronic applications. The optical properties are explored by large dielectric constants, high optical absorption coefficients, small optical loss etc. Moreover, the mechanical properties for the studied materials have been performed to confirm the mechanical stability, comactability and reliability of the materials for photovoltaic applications. The thermodynamic and vibrational stability and compatibility has been verified due to negative values of enthalpy, cohesive energies and no soft phonon mode of vibrations. These optoelectronic energy related calculations confirmed that the perovskites type double halides may Cs2SiBr6, Cs2GeBr6 and Cs2SnBr6 may be novel, stimulating potential materials and can be synthesized experimentally for next generation photovoltaic devices and related applications.

Spintronic Phenomena and Applications in Hybrid Organic–Inorganic Perovskites

AbstractThe spin degree of freedom in hybrid organic–inorganic perovskites (HOIPs) has become a rapidly growing research topic in both HOIPs and spintronics fields due to its fundamental scientific significance and tight relevance with optoelectronics. The flourishing achievements of HOIP spintronics call for a timely review to summarize the key progress and guide future developments. In this review, classical spintronic phenomena based on spin‐orbit coupling (SOC) are discussed, especially Rashba splitting and related applications such as spin‐charge conversion. Owing to the unique chirality‐spin coupling, spintronics in chiral HOIPs are particularly focused on, including chirality‐induced spin selectivity (CISS). Based on the complex band structure and carrier/exciton, spin dynamics is also widely investigated and constitutes an indispensable part of HOIP spintronics. Aside from the three main threads, other spintronic phenomena such as magneto‐optical coupling and device exploration are involved as promising opportunities. Despite the continuous breakthroughs in HOIP spintronics, more efforts are required to expand material systems, explore new physics, and optimize device configurations for enhanced performance and high integration. A deep understanding of spin behaviors in HOIPs will create a new platform beyond conventional optoelectronic and photovoltaic applications.

Construction of Anderson−type polyoxometalate−based complexes involving in situ ligand transformation with capacitive and electrochemical sensing performances

For developing polyoxometalate−based hybrids as potential energy storage and electrochemical materials, 3, 5−di(1H−imidazol−1−yl)benzonitrile (DICN) and 5−(1H−imidazol−1−yl)isophthalonitrile (IMIPN) as precursors were employed, and three Anderson−type polyoxometalate−based complexes, [{Cu3(H2O)8(HDIBA)2}{TeMo6O24}]·6H2O (1), [{Cu4(H2O)10(HIMIPA)2}{TeMo6O24}]·2H2O (2) and Ni(H2O)6[{Ni2(H2O)4(H2IMIPA)2}{TeMo6O24}]·4H2O (3), were prepared under hydrothermal condition. The initial DICN and IMIPN were transformed in situ into 3,5−di(1H−imidazol−1−yl)benzoic acid (HDIBA) and 5−(1H−imidazol−1−yl)isophthalic acid (H2IMIPA). In 1, the polyoxometalate−based inorganic chains containing single−bridged copper linkages were extended by the imidazole groups of HDIBA ligand, but the chains in 2 and 3 embodied the double−bridged copper and nickel linkages, further linked by the imidazole and carboxyl groups from HIMIPA ligand. Complexes 1−3 demonstrated outstanding capacitive performances (i.e., specific capacitances of 944 F·g–1 for 1, 1548.6 F·g–1 for 2 and 718.6 F·g–1 for 3 at 1 A·g–1), as well as electrochemical sensing activities for bromate. The results provided a feasible way for the design and synthesis of Anderson−type POM−based complexes used as high−performing electrode materials in energy store and sensor−related applications.

Enhanced electrochemical oxygen generation from sillenite phase of bismuth iron oxide (Bi24Fe2O39) ultrafine particles stabilised at room temperature

Recently, lot of research is going on in development of clean energy-based technology to address the issue of exponentially increasing energy demands. Electrochemical water splitting is a clean and efficient process for generation of H2 and O2. Water electrolysis produces hydrogen and oxygen on cathode and anode respectively as separate half-cell reactions is one of the most important clean alternative processes. This work reports synthesis of Bi24Fe2O39 ultrafine crystalline nanoparticles via facile room temperature chemical method for the first time. Bi24Fe2O39 shows a spherical morphology with an average size of 8 nm. As obtained Bi24Fe2O39 acts as an electrocatalyst for electrochemical oxygen evolution reaction (OER). Electrochemical oxygen generation studies confirm the overpotential of 420 mV in 1 M KOH solution at a current density of 10 mA/cm2. Tafel slope and EIS studies give further insight of oxygen generation process. The results show that Bi24Fe2O39 exhibits low Tafel slope of 63 mV dec−1 and a very low charge transfer resistance of ∼10 Ω. This study paves a new way for future research into bismuth based sillenite structure for various energy related applications.

CTAB-Modulated Electroplating of Copper Micropillar Arrays for Non-Enzymatic Glucose Sensing with Improved Sensitivity.

Micropillar array electrodes represent a promising avenue for enhancing detection sensitivity and response current. However, existing methods for depositing electrode materials on micropillar arrays often result in uneven distribution, with the thin sidewall layer being less conductive and prone to corrosion. In addressing this issue, this study introduces electroplating to enhance the copper layer on the sidewall of micropillar array electrodes. These electrodes, fabricated through standard microelectronics processes and electroplating, are proposed for non-enzymatic glucose detection, with the copper layer deposited via electroplating significantly enhancing sensitivity. Initially, the impact of cetyltrimethylammonium bromide (CTAB) concentration as an inhibitor on the surface morphology and sensitivity of the plated layer was investigated. It was discovered that CTAB could decrease surface roughness, hinder the development of large and coarse grains, generate small particles, and boost sensitivity. Compared to the uncoated electrode and plating without CTAB, sensitivity was elevated by a factor of 1.66 and 1.62, respectively. Subsequently, the alterations in plating morphology and detection performance within a range of 0.3 ASD to 3 ASD were examined. Sensitivity demonstrated a tendency to increase initially and then decrease. The electrode plated at 0.75 ASD achieved a maximum sensitivity of 3314 μA·mM-1·cm-2 and a detection limit of 15.9 μM. Furthermore, a potential mechanism explaining the impact of different morphology on detection performance due to CTAB and current density was discussed. It was believed that the presented effective strategy to enhance the sensitivity of micropillar array electrodes for glucose detection would promote the related biomedical detection applications.

Designing for identity: how and when brand visual aesthetics enable consumer diasporic identity

Purpose This paper aims to investigate how and when visual referents in brand visual aesthetics (i.e. colours, shapes, patterns and materials) serve as design applications that enable consumer diasporic identity. Design/methodology/approach This paper uses an innovative methodology that triangulates 58 in-depth interviews with diasporic consumers, 9 interviews with brand managers and designers and a visual analysis of brands (food retailer, spices and nuts, skincare, hair and cosmetics, ice cream and wine) to provide a view of the phenomenon from multiple perspectives. Findings This study illustrates how and when particular applications and compositions of product and design referents support diasporic identity for Middle Eastern consumers living outside the Middle East. Specifically, it illustrates how the design applications of harmonising (applying separate ancestral homeland and culture of living product and design referents simultaneously), homaging (departing from the culture of living product and design referents with a subtle tribute to ancestral homeland culture) and heritaging (departing from the ancestral homeland culture product and design referents with slight updates to a culture of living style) can enable diasporic identity in particular social situations. Research limitations/implications Although applied to the Middle Eastern diaspora, this research opens up interesting avenues for future research that assesses diasporic consumers’ responses to brands seeking to use visual design to engage with this market. Moreover, future research should explore these design applications in relation to issues of cultural appreciation and appropriation. Practical implications The hybrid design compositions identified in this study can provide brand managers with practical tools for navigating the design process when targeting a diasporic segment. The design applications and their consequences are discussed while visually demonstrating how they can be crafted. Originality/value While previous research mainly focused on how consumption from the ancestral homeland occurred, to the best of the authors’ knowledge, this study is the first to examine how hybrid design compositions that combine a diaspora’s ancestral homeland culture and their culture of living simultaneously and to varying degrees resonate with diasporic consumers.

An Analytical Model of Active Layer Depth Under Changing Ground Heat Flux

AbstractImproved modeling of permafrost active layer freeze‐thaw plays a crucial role in understanding the response of the Arctic ecosystem to the accelerating warming trend in the region over the past decades. However, modeling the dynamics of the active layer at diurnal time scale remains challenging using the traditional models of freeze‐thaw processes. In this study, a physically based analytical model is formulated to simulate the thaw depth of the active layer under changing boundary conditions of soil heat flux. Conservation of energy for the active layer leads to a nonlinear integral equation of the thaw depth using a temperature profile approximated from the analytical solution of the heat transfer equation forced by ground heat flux. Temporally variable ground heat flux is estimated using non‐gradient models when field observations are not available. Validation of the proposed model conducted against field data obtained from three Arctic forest and tundra sites demonstrates that the model is able to simulate both thaw depth and soil temperature profiles accurately. The model has the potential to estimate regional variability of the thaw depth for permafrost related applications.

Atomistic details of grain, crack, and notch effect on the mechanical behavior and fracture mechanisms of monolayer silicon carbide

Recently, sp2-hybridized single-layer SiC, analogous to graphene, has received much attention in nanoelectronics and nanoelectromechanical systems because of its remarkable thermal, electrical, and mechanical properties. However, the unique properties and related applications strongly depend on the growth of a single-crystal and large-area two-dimensional SiC. The majority of synthesized SiC monolayers exhibit a natural polycrystalline structure, comprising single-crystalline grains with diverse sizes and orientations. Moreover, the emergence of structural defects, including cracks and notches, poses challenges that are difficult to overcome during both the fabrication and subsequent processing stages. In this paper, we explore the atomistic details of grain, crack, and notch effect on the mechanical behaviors and fracture mechanisms of single-layer SiC using molecular dynamics simulations. Increasing grain size from 2 nm to 35 nm gradually increases the elastic modulus and fracture strength, which fits well with the inverse Pseudo Hall-Petch relationship. However, an anomalous trend of decreasing as well as increasing fracture strain is observed with the increasing grain size. For polycrystalline SiC with a 2 nm grain size, the elastic modulus and fracture strength are roughly 87% and 41% of those observed in single-crystalline SiC. Notably, when comparing the impact of rectangular crack and circular notch defects, the circular notch exhibited a more pronounced effect in diminishing mechanical behavior under both armchair and zigzag orientational loading. The circular notch produces considerable amorphization around the notch, but the rectangular crack does not show any significant dislocation or amorphization around the crack. The critical stress intensity factor, energy release rate, radial distribution function, and potential energy per atom are calculated to explain all the fracture properties. The effect of temperature and strain rate is also investigated for all the defect-induced samples. Overall, our results provide new insight and understanding of the mechanical behavior and fracture mechanisms of polycrystalline, crack- and notch-induced SiC, and set a standard for making and designing new nanodevices and nanostructures made of SiC.

Virtual testbed for development and evaluation of power system digital twins and their applications

Digital twins (DTs) show considerable promise for development of applications and solutions across a diverse range of power system services. Ensuring these applications function as intended without causing disruptions or unexpected issues requires comprehensive testing prior to practical deployment. However, current approaches to power system digital twin (PSDT) application development and testing experience significant obstacles, including limitations and risks associated with accessing real-world power systems, models and data. This article introduces a virtual testbed based on the concept of a virtual physical twin (VPT), that replaces the need for access to a physical twin (PT), as a platform designed to remove the heavy reliance on online data flows and provide a close to real-world environment. A testbed enables simultaneous development and testing of DTs and their related applications in a realistic, interactive, and safe setting without impact on the operation of the actual power system. A complete workflow for PSDT testbed implementations is proposed, with all necessary steps from concept to the eventual connection to the physical system. To validate the practical implementation steps, utility, and value of the testbed, a real-time transient stability analysis with two artificial intelligence algorithms is provided. The proposed testbed is expandable and adaptable, promoting the development of advanced and intelligent PSDTs. The key benefit of the testbed is that it enables the development of DT and its application closely in a realistic environment without affecting the operation of PT.

Recent Advances in Multi‐Scale Piezoresistive Interfaces for MXene‐Based Flexible Sensors

AbstractFlexible piezoresistive sensors hold great promise in biomedicine and personal motion detection, attracting significant attention from researchers. Particularly, two‐dimensional (2D) MXene has made remarkable strides in biomedicine, electromagnetic shielding, and other practical applications, due to its exceptional hydrophilicity, biocompatibility, mechanical properties, surface functional groups, and large specific surface area. The ease of composite material formation further makes it ideal for high‐sensitivity flexible sensors. This paper summarizes the principal structural configurations for the multi‐scale interfaces, performance assessment metrics, and the corresponding sensing mechanisms of flexible piezoresistive sensors. Subsequently, the main characteristics and synthesis methods of MXene, especially the related applications and parameters are introduced in detail. The research trends and typical applications of MXene‐based flexible piezoresistive sensors focused on film structures, fiber structures, 3D structures, and hydrogel structures, are highlighted systematically. In addition, the challenges and prospects that promoting the advancement of MXene‐based flexible piezoresistive sensors in future are also discussed, which will revolutionize the sensing field and offer a wealth of opportunities in biomedicine and beyond.

Application of Amorphous-Crystalline Coupling Materials in Electrocatalysis.

Interface engineering has proven to be a highly efficient strategy for modulating the physicochemical properties of electrocatalysts and further enhancing their electrochemical performance in related energy applications. In this context, the newly proposed crystalline-amorphous (c-a) heterostructures with unusual atomic arrangements at interfaces show strong competitiveness. Nonetheless, few efforts have been made to reveal and summarize the structure-activity relationship at the two-phase interface and the corresponding electrocatalytic mechanism. This concept is devoted to comprehensively discussing the fundamental characteristics of crystalline-amorphous electrocatalysts and their application in the field of energy conversion with typical examples. In addition, the development prospects and opportunities of crystalline-amorphous heterostructure are summarized to provide potential development directions for other types of clean energy development.

Overview of the application of artificial intelligence in computer animation

With the flourishing development of artificial intelligence and computer animation technologies, there has been an increasing intersection between these two. In the field of computer animation, the use of artificial intelligence significantly reduces the difficulties in design, production, and post-production processes, which has a massive impact on the entire field. The paper attempts to discuss the relationship between artificial intelligence and computer animation. Not only does the paper elaborate on the related applications of artificial intelligence in various subfields of computer animation, but it also analyzes existing problems and future development trends. The research indicates that AI has achieved significant breakthroughs in computer animation, such as auto-generation of animations, real-time character driving, and emotionally responsive animation creation. However, it also faces challenges like handling interactions in complex scenarios, maintaining realism, and animating high-level abstract concepts. Despite these challenges, it is believed that in the future, AI will further propel the development of computer animation, aiding creators in producing animations that are more vibrant, intricate, and personalized.

Structural-topic aware deep neural networks for information cascade prediction.

It is critical to accurately predict the future popularity of information cascades for many related applications, such as online opinion warning or academic influence evaluation. Despite many efforts devoted to developing effective prediction approaches, especially the recent presence of deep learning-based model, the structural information of the cascade network is ignored. Thus, to make use of the structural information in cascade prediction task, we propose a structural-topic aware deep neural networks (STDNN), which firstly learns the structure topic distribution of each node in the cascade, feeds it to a sequential neural network, and finally predicts the future popularity of the cascades. It can inherit the high interpretability of Hawkes process and possesses the high predictive power of deep learning methods, bridging the gap between prediction and understanding of information cascades by capturing indicative graph structures. We evaluate our model through quantitative experiments, where our model exhibits promising performance, efficiency higher than the baselines.

Topological electronic states in holey graphyne

We unveil that the holey graphyne (HGY), a two-dimensional carbon allotrope where benzene rings are connected by two −C≡C− bonds fabricated recently in a bottom-up way, exhibits topological electronic states. Using first-principles calculations and Wannier tight-binding modeling, we discover a higher-order topological invariant associated with C 2 symmetry of the material, and show that the resultant corner modes appear in nanoflakes matching to the structure of precursor reported previously, which are ready for direct experimental observations. In addition, we find that a band inversion between emergent g-like and h-like orbitals gives rise to a nontrivial topology characterized by Z2 invariant protected by an energy gap as large as 0.52 eV, manifesting helical edge states mimicking those in the prominent quantum spin Hall effect, which can be accessed experimentally after hydrogenation in HGY. We hope these findings trigger interests towards exploring the topological electronic states in HGY and related future electronics applications.