Expansion Of Domain Research Articles

Direct visualization of local magnetic domain dynamics in a 2D Van der Walls material/ferromagnet interface

Controlling the magnetic domain propagation is the key to realize ultrafast, high-density domain wall-based memory and logic devices for next generation computing. Two-Dimensional (2D) Van der Waals materials introduce localized modifications to the interfacial magnetic order, which could enable efficient control over the propagation of magnetic domains. However, there is limited direct experimental evidence and understanding of the underlying mechanism, for 2D material mediated control of domain wall propagation. Here, using Lorentz-Transmission Electron Microscopy (L-TEM) along with the Modified Transport of Intensity equations (MTIE), we demonstrate controlled domain expansion with in-situ magnetic field in a ferromagnet (Permalloy, NiFe) interfacing with a 2D VdW material Graphene (Gr). The Gr/NiFe interface exhibits distinctive domain expansion rate with magnetic field selectively near the interface which is further analysed using micromagnetic simulations. Our findings are crucial for comprehending direct visualization of interface controlled magnetic domain expansion, offering insights for developing future domain wall-based technology.

Automation and Extraction. Shifting (In)Visibilities at New Technological Frontiers: An Introduction

Abstract Coined as Industry 4.0, innovations in automation are portrayed as heralding a new era of change and possibility amid the climate crisis and resource depletion. As research fields, however, the domain of automation and that of extraction are often studied in isolation and within different research traditions. With this special issue, we bring these research fields into dialogue by exploring the mutual reinforcements of automation and extraction in altering work life, the environment, and ways of relating to the material world. By emphasizing how current technologies enable relocations of work functions and decision making, design and representation, our introduction provides a basis for understanding automation and extraction as interconnected and discusses the empirical rationale and conceptual possibilities of understanding these interconnections. We explore how automation and digitalization are facilitating an expansion of domains for value extraction, in a broad sense, by making visible otherwise inaccessible domains and areas of life, while simultaneously placing other aspects out of sight.

A formulation of the fast multipole boundary element method applied to the analysis of anisotropic materials under body forces

This work presents a formulation of the boundary element method with fast multipole expansion (MECMP) applied to the analysis of anisotropic elastic materials subject to body forces. Integral equations are obtained using the Somigliana identity. Integrals are divided into near field and far field. Near field, when the source points and integration elements are close, are treated as in the standard boundary element method, that is, integrating along the element and considering the interaction between source points (nodes) and the elements. On the other hand, in the far field, when the source points and integration elements are far away, the fast multipole method is applied. In this case, the fundamental solution is expanded in a Laurent series and the node-to-node interaction is replaced by a cell-to-cell interaction. Cells are generated by a hierarchical decomposition of the domain using the quad-tree algorithm. Different fast multipole operations are used to take advantage of the hierarchical domain decomposition and expansions of the fundamental solutions. Influence matrices are never explicitly obtained and the matrix-vector product are carried out with linear complexity. The linear system is solved by an iterative method. A preconditioning matrix is used to reduce the number of iterations to obtain a result with an specified accuracy. Effectiveness and efficiency in solving large-scale problems are discussed. The treatment of problems involving body forces are taken into account by the utilization of the modified boundary condition method. This approach entails augmenting the boundary condition with a specific solution tailored to the problem. Following the solution of the linear system, the particular solution is subsequently subtracted from both displacements and tractions. Importantly, this procedural step obviates the need for generating additional vectors or matrices within the matrix equation. The formulation presented in this article is based on a representation of complex variables of the integrands, similar to the formulation previously developed for potential (scalar) problems. Validation is carried out by comparing the results obtained by the two formulations: the standard boundary element method and the boundary element method with fast multipole expansion. The influence of the number of terms of the series expansion in the calculation of fundamental solutions and influence matrices is analyzed. Numerical examples are presented to demonstrate the efficiency, accuracy, and potential of the boundary element method with fast multipole expansion to solve large-scale problems, i.e., with tens of thousands of degrees of freedom.

Exploring stress-dependent impedance behavior via magnetization dynamics in amorphous magnetic fibers in high frequency: Experimental and modeling

The magnetoelastic effect plays a crucial role in influencing the magnetization dynamics and impedance characteristics of magnetic fibers (MFs). In this work, we investigate the modulation of the domain structure and impedance behaviors under stress within Co-based MFs aided by experimental and theoretical approaches. The remarkable changes of natural ferromagnetic resonance and the transition of domain inclination angles indicate that the stress-impedance effect derives from the evolution of the magnetic domain structure and anisotropy field, which are induced by magnetoelastic coupling. The ferromagnetic resonance linewidths over a range of applied tensile strains (0–0.54%) serve to elucidate the contribution of magnetoelastic coupling to magnetic damping in ferromagnetic fibers. By utilizing the shell domain expansion method, we derive circular dynamic permeability and compute the impedance properties at high frequencies of MFs under multi-field stimulus. The theoretical model accurately predicts key features of magnetization dynamics, the evolution of ferromagnetic resonance, and impedance curves of MFs, in good agreement with experimental results including very fine observation of domain evolution. This comprehensive approach provides profound insights into the stress modulation of impedance characteristics, with implications for sensing applications of MFs.

Advancing Lithium-Ion Battery Manufacturing Sustainability through Microstructural Analysis of Composite Cathodes

To accelerate electric vehicle (EV) adoption and mitigate transportation emissions, lithium-ion battery (LIB) energy density and rate capability must be improved, advancing EV range and charging speed, respectively. The composite cathode is a major driver of these performance metrics, as its microstructure governs the flow of ions and electrons.1 Intentionally engineering cathode microstructure for efficient ionic transport will enable fast charging, thick cathodes for increased cell energy density. Here, while focusing our efforts on cathode design, we have the opportunity to prioritize sustainable manufacturing. We utilize our understanding of the slurry-cast cathode microstructure to facilitate the transition to solvent-free cathodes. Solvent-free cathode manufacturing promises advancements in performance, environmental safety, and economics, including increased cell energy density and reduced process toxicity, energy consumption and capital costs.2 In this presentation, I will first address relationships we have uncovered between slurry-cast cathode microstructure and resulting battery performance. We use a combination of small-angle neutron scattering (SANS), plasma focused ion beam scanning electron microscopy (PFIB-SEM), and electrochemical impedance spectroscopy (EIS) to elucidate the cathode microstructure in both dry and wet states, at the nanoscale, and over representative volumes. Using SANS, we see an expansion of carbon-binder domain (CBD) microstructure in the wet state and after calendaring. Further, we can deconvolute polymer binder and carbon black surface areas, allowing us to relate a decline in cycling performance to increased electrolyte-accessible carbon black in the CBD. We use these findings to inform the design of cathode microstructure for improved cycling stability.Next, I will discuss how we apply our understanding of slurry-cast microstructures to dry-processed cathodes. The performance principles of dry-processed microstructures remain the same: they must efficiently conduct both lithium ions and electrons. However, the dry microstructure is vastly different than that of slurry-cast cathodes, and thus even less understood. We fabricate dry electrodes via the formation of binder fibers under applied shear, creating matrix with carbon fibers to support the active material particles. Utilizing binder fibers allows us to decrease the mass fraction of binder and increase the thickness of the cathode, increasing cell energy density. We apply the tools discussed here to understand the distinct dry microstructure and its impact on performance. Gaining a nanoscale understanding of both slurry-cast and dry-processed systems allows us to compare fundamental transport processes across various microstructures and manufacturing processes, informing future cathode design while advancing sustainability and reducing manufacturing cost.

Systematic analysis identifies a connection between spatial and genomic variations of chromatin states

Chromatin states play important roles in the maintenance of cell identities, yet their spatial patterns remain poorly characterized at the organism scale. We developed a systematic approach to analyzing spatial epigenomic data and then applied it to a recently published spatial-CUT&Tag dataset that was obtained from a mouse embryo. We identified a set of spatial genes whose H3K4me3 patterns delineate tissue boundaries. These genes are enriched with tissue-specific transcription factors, and their corresponding genomic loci are marked by broad H3K4me3 domains. Integrative analysis with H3K27me3 profiles showed coordinated spatial transitions across tissue boundaries, which is marked by the continuous shortening of H3K4me3 domains and expansion of H3K27me3 domains. Motif-based analysis identified transcription factors whose activities change significantly during such transitions. Taken together, our systematic analyses reveal a strong connection between the genomic and spatial variations of chromatin states. A record of this paper's transparent peer review process is included in the supplemental information.

Facile synthesis of GexSiO(1-x) alloys as a superior anode enables high-energy lithium-ion batteries

The large-scale development of low-cost silicon monoxide (SiO) anode is limited due to its low initial Coulombic efficiency (ICE) and poor cycle stability. Herein, the GexSiO(1-x) (0<x<1) composite anode has been facile synthesized by introducing germanium (Ge) atoms disrupted the structure of SiO matrix, which exhibits superior lithium storage performance. In particular, the optimized Ge0.5SiO0.5 electrode with nanoscale dispersion shows a high ICE of 80.2 % and a reversible capacity of 673.9 mAh g−1 after 100 cycles. To evaluate the effect of the Ge addition on the electrochemical properties, the lithium storage behaviors and structural evolution of the GexSiO(1-x) electrodes are investigated systematically. The in-situ electrochemical dilatometry reveals that the Ge0.5SiO0.5 electrode displays a better reversible thickness variation during cycling process. Density functional theory (DFT) calculation demonstrates that the SiGe domain can accommodate the volume expansion of Si and Ge domains. Moreover, the relationships between the electrochemical performance and morphology evolution demonstrate that the disordered structure and nanoscale distribution of Ge0.5SiO0.5 electrodes are the main reasons for improving ICE and cycle stability. This work provides a commercially available synthetic route for the design of advanced anode materials for large-scale energy storage applications.

Strengthening regulation for medical products in Tanzania: An assessment of regulatory capacity development, 1978-2020.

Improving medicines regulation can lead to better population health, but how this process works in low- and middle-income countries remains underexplored. Tanzania's pharmaceutical sector is often cited as a successful example of a well-functioning regulatory system in a developing country, attributed to the work of the Tanzania Food and Drugs Authority (TFDA), now the Tanzania Medicines and Medical Devices Authority (TMDA). This raises the question: how was this regulatory capacity developed, and what lessons can other countries learn from Tanzania's experience? This paper analyzes changes in Tanzania's pharmaceutical regulation over three periods of significant sectoral reform. A desk review was conducted of Tanzania's policies, laws, regulations, guidelines, procedures, and institutional reports. The study reveals that Tanzania's regulatory capacity improved significantly through targeted reforms that addressed challenges in key regulatory areas. The three key periods examined are: 1) The separation of medicines regulation from food safety (1978-2003), 2) The expansion of regulatory domains and the establishment of a semi-autonomous regulatory agency (2003-2011), and 3) The expanded role of the Pharmacy Council to include premises regulation (2011-2020). The development of a well-functioning regulatory system in Tanzania resulted from advancements in four key areas: 1) The evolution of a legal regulatory framework, 2) Strong stakeholder engagement, 3) Continuous capacity building, and 4) Effective organizational leadership. Tanzania's regulatory system has evolved from being relatively ineffective to leading regional harmonization efforts in East Africa. This progress was not linear, requiring sustained effort, collaboration, and support from key development partners such as the Global Fund, WHO, and UNDP. Future efforts to enhance regulatory effectiveness should focus on creating adaptive systems that respond to changing needs, rather than solely prescriptive functions.

Graphene-enhanced ferroelectric domain wall high-output memristor

Recent studies on memristive materials and technologies have expanded beyond conventional memory elements, driven by their potential application in novel information processing concepts. Among these materials, conductive domain walls in ferroics are especially promising, offering conductive tunability suitable for reconfigurable multi-state devices. However, challenges such as domain stability, time-dependent conductivity, and low current output have impeded progress in the field. Here, we study the graphene/Pb(Zr,Ti)O3/SrRuO3 system, which demonstrates robust domain wall conduction up to 100 nA/μm2 for 2 V bias, while addressing the critical issue of stability of switched domains. The introduction of graphene electrodes enhances low-voltage stochastic domain formation with limited domain expansion that promotes the emergence of multi-domain states. The developed micrometer sized capacitor devices enable electrically programmable multiple distinct conduction states with robust retention combined with high current output and low operation voltage. These features are highly desirable for memristors and mark the significant potential of domain wall electronics for neuromorphic computing.

Revealing Fast Negative Capacitance in PbZr0.2Ti0.8O3 Thin Film with Acicular Ferroelastic Domains.

Negative capacitance effects with fast response times hold great potential for reducing the power consumption in high-frequency nanoelectronics. Nevertheless, the negative capacitance effect faces considerable complexity arising from the dynamic interplay among electrostatic, nucleation energies, and domain evolution. This intricate balance poses a formidable challenge to achieving fast negative capacitance. Herein, we have achieved a fast negative capacitance time of ∼16.23 ns in PbZr0.2Ti0.8O3 (PZT) thin film, and our investigation confirms the presence of acicular ferroelastic domains within the PZT thin film. Under reversal electric fields, these acicular ferroelastic domains undergo a unique flipping process, transitioning through domain expansion and contraction. This distinct domain flipping manner accelerates the nucleation and growth of ferroelectric domains, thereby facilitating the observed fast negative capacitance. The realization of fast negative capacitance holds substantial promise for reducing operational time and power consumption, offering prospects for the design of nanoelectronics with significantly lower power requirements.

Domain expansion fusion single-domain generalization framework for mechanical fault diagnosis under unknown working conditions

In real industrial scenarios, mechanical systems often adjust working conditions based on specific tasks, leading to challenges in collecting data for all possible machine states in advance. Consequently, applying deep learning models trained on data from a single working condition directly to other unknown working condition poses a significant challenge. To tackle this issue, a novel domain expansion fusion single-domain generalization framework is proposed for machinery fault diagnosis under unknown working conditions. Firstly, a domain expansion module that can be controlled via a constraint function is developed to create expanded domains that generate samples with controlled differences from the source domain. Subsequently, a dual-branch feature fusion network is proposed in the feature extraction module. It combines two distinct feature extractors and employs a weighted average fusion strategy to extract discriminative features. Additionally, a state recognition module is implemented through a multi-classifier ensemble strategy to enhance the robustness and accuracy of health state identification. Lastly, an adversarial contrastive training strategy is employed to optimize the network and enhance its generalization capabilities and fault diagnosis performance. Through case studies conducted on two mechanical fault datasets, the proposed method demonstrates good diagnosis performance on single-domain generalized diagnosis tasks with an average accuracy of 87.53%. Its generalization effect is validated. Furthermore, the comparison and ablation experiment results confirm the effectiveness and superior performance of the proposed intelligent fault diagnosis method in scenarios with unknown working conditions with an average accuracy improvement of at least 3.94%.

Properties of the Octonion Linear Canonical Transform

Abstract The linear canonical transform is a widely utilized integral transform in the field of signal analysis, characterized by three independent parameters. It not only facilitates rotation of the time-frequency plane but also enables expansion and contraction of the frequency domain, thereby playing a crucial role in handling non-stationary signals. In recent years, the linear canonical transform has been extended to octonion domains, which possess a more generalized form and offer greater research potential. This extension effectively harnesses the processing capabilities of the linear canonical transform for non-stationary signals in high-dimensional spaces. In this paper, we explore the definition and the conjugacy of the left side octonion linear canonical transform. Moreover, we thoroughly examine the differential properties of octonion linear canonical transform. The study of these properties is helpful for understanding the characteristics and applications of geometric transformations as well as expanding the scope of mathematical theory and practical applications.

Intrusion detection systems for the internet of things: a probabilistic anomaly detection approach

The expansion of IoT domains presents new cybersecurity challenges. In this paper, we propose an enhanced Intrusion Detection System (IDS) designed to tackle these emerging security issues. Our approach harnesses the power of unsupervised learning techniques, particularly the Variational Autoencoder (VAE), to reduce data dimensionality while preserving essential features. By integrating advanced clustering algorithms–including K-means, Gaussian Mixture Model (GMM), and Variational Bayesian Gaussian Mixture Model (VBGMM)–we facilitate precise data categorization within IoT networks. Our conducted experiments using the IoT Network Intrusion Dataset demonstrate that VAE-based methods consistently outperform traditional Autoencoders (AE), achieving superior performance across critical metrics such as False Alarm Rate (FAR), Detection Rate (DR), Accuracy, Precision, and Area Under the ROC Curve (AUC).

Evaluation of foaming performance for polymer modified and virgin asphalt binders

The pavement suffers rapid deterioration as a result of the weak bonding strength exhibited by the foamed virgin asphalt in the mixture. Consequently, it is crucial to develop a modified asphalt suitable for foaming. The expansion height and bubble size of foamed asphalt can be continuously and simultaneously monitored by binocular ranging equipment. By analyzing these dynamic responses, the evolution of the spatial domain expansion characteristics and the geometric features of the surface bubbles of foamed thermoplastic resin modified asphalt (TRA) was analyzed. Meanwhile, multi-stress creep recovery (MSCR) and bending beam rheometer (BBR) tests were conducted to analyze its rheological properties. Subsequently, the dispersibility of foamed TRA in the mixture was explored. The results showed that thermoplastic resin significantly improved the physical performance stability of foamed asphalt, as evidenced by an increase in its half-life by 2–3 times compared to the virgin asphalt when the dosage of thermoplastic resin reached 8 %. Foamed modified asphalt has fewer bubbles, but it has larger bubble sizes and more uniform distribution of bubbles. Meanwhile, the TRA exhibited excellent high-temperature deformation resistance and elastic recovery ability. The dispersion uniformity of foamed TRA in the mixture was superior to that of virgin asphalt. This study serves as a valuable reference for the exploration of foaming behavior in modified asphalt and the utilization of foamed modified asphalt in the mixture.

Strain evolution from the ferroelectric to the relaxor state in (0.67 - x)BiFeO3-0.33BaTiO3-xBi(Mg0.5Zr0.5)O3 lead-free ceramics.

The BiFeO3-BaTiO3 solid solution exhibits enhanced electric properties due to its modified phase structure with relaxor characteristics and reduced leakage current. Despite these advancements, the underlying mechanism behind the phase transition from a ferroelectric to a relaxor state in BF-BT ceramics remains largely unexplored. Here, the evolution of strain in (0.67 - x)BiFeO3-0.33BaTiO3-xBi(Mg0.5Zr0.5)O3 ceramics is investigated, with a focus on the strain transition from a ferroelectric to a relaxor phase. A strengthening of relaxor behavior is observed in the modified rhombohedral (R) and pseudocubic (PC) phase structure, resulting in optimal strain (Suni = 0.25%, Spos = 0.24%) at x = 0.04. The enhanced strain is attributed to the promotion of domain switching and the presence of strong random fields, with polar nanoregions integrating into a long-range ordered matrix. Furthermore, a gradual increase in strain with rising temperature is noted, driven by increased polarization and the expansion of ferroelectric domains. This study underscores the critical role of structural modifications in augmenting the electric response of BF-BT ceramics, thereby advancing the development of lead-free piezoelectric materials.

Dual client binding sites in the ATP-independent chaperone SurA

The ATP-independent chaperone SurA protects unfolded outer membrane proteins (OMPs) from aggregation in the periplasm of Gram-negative bacteria, and delivers them to the β-barrel assembly machinery (BAM) for folding into the outer membrane (OM). Precisely how SurA recognises and binds its different OMP clients remains unclear. Escherichia coli SurA comprises three domains: a core and two PPIase domains (P1 and P2). Here, by combining methyl-TROSY NMR, single-molecule Förster resonance energy transfer (smFRET), and bioinformatics analyses we show that SurA client binding is mediated by two binding hotspots in the core and P1 domains. These interactions are driven by aromatic-rich motifs in the client proteins, leading to SurA core/P1 domain rearrangements and expansion of clients from collapsed, non-native states. We demonstrate that the core domain is key to OMP expansion by SurA, and uncover a role for SurA PPIase domains in limiting the extent of expansion. The results reveal insights into SurA-OMP recognition and the mechanism of activation for an ATP-independent chaperone, and suggest a route to targeting the functions of a chaperone key to bacterial virulence and OM integrity.

Weight-induced radial growth in plant stems depends on PIN3

How multiple growth programs coordinate during development is a fundamental question in biology. During plant stem development, radial growth is continuously adjusted in response to longitudinal-growth-derived weight increase to guarantee stability.1,2,3 Here, we demonstrate that weight-stimulated stem radial growth depends on the auxin efflux carrier PIN3, which, upon weight increase, expands its cellular localization from the lower to the lateral sides of xylem parenchyma, phloem, procambium, and starch sheath cells, imposing a radial auxin flux that results in radial growth. Using the protein synthesis inhibitor cycloheximide (CHX) or the fluorescent endocytic tracer FM4-64, we reveal that this expansion of the PIN3 cellular localization domain occurs because weight increase breaks the balance between PIN3 biosynthesis and removal, favoring PIN3 biosynthesis. Experimentation using brefeldin A (BFA) treatments or arg1 and arl2 mutants further supports this conclusion. Analyses of CRISPR-Cas9 lines for Populus PIN3 orthologs reveals that PIN3 dependence of weight-induced radial growth is conserved at least in these woody species. Altogether, our work sheds new light on how longitudinal and radial growth coordinate during stem development.

Comprehensive design of all-optical logic devices utilizing polarization holography.

Polarization holography has emerged as a promising method for manipulating the amplitude, phase, and polarization states of light waves. This study proposes what we believe to be a novel design method for various all-optical logic devices, including a complete set of all-optical Boolean logic gates and a polarization-controlled 1 × 4 optical switch, utilizing polarization holography. Through the angle multiplexing technique, specially designed polarization holograms are recorded in polarization-sensitive material, transforming it into all-optical Boolean logic gates and a polarization-controlled 1 × 4 optical switch. The all-optical logic devices developed in this work function as passive diffractive optical elements, enabled by a single piece of polarization-sensitive material, eliminating the need for additional circuit control. This approach offers the advantages of a simple structure, low cost, and instantaneous response. We contend that this advancement will facilitate the expansion of the application domains of polarization holography, particularly enhancing the capabilities of all-optical information processing.

Intelligent fault diagnosis for unbalanced battery data using adversarial domain expansion and enhanced stochastic configuration networks

An accurate and efficient fault diagnosis method for battery systems is crucial to ensuring the safety of battery packs. Addressing the issue of insufficient actual fault data in battery operations, this paper proposes an intelligent fault diagnosis method based on feature-enhanced stochastic configuration networks and adversarial domain expansion of imbalanced battery fault data (AFDEM-FESCN). Firstly, we designed an adversarial fault domain data expansion method (AFDEM). By learning the distribution of fault data through adversarial training, the distribution of sample domains is balanced, thereby reducing model bias. Subsequently, we adjusted the distribution of SCN iterative parameters and added a linear feature layer. This enhances the feature extraction capability of the network through distribution overlay, enabling fault diagnosis. Finally, the effectiveness and feasibility of the proposed method were validated through a practical battery system fault diagnosis case, achieving a diagnostic accuracy of 92.1%. Experimental results demonstrate that the AFDEM-FESCN method exhibits good accuracy in battery system fault diagnosis, providing an effective solution to the challenge of imbalanced data in intelligent fault diagnosis.

Causality-inspired Domain Expansion network for single domain generalization

Most existing single domain generalization (SDG) methods focus on extending the distribution of the training data to achieve good generalizability in the target domain by synthesizing out-of-domain data. However, in practice, the generated data are insufficient to cover all possible shifts. When the target distributions are not covered, the spurious associations between irrelevant features and labels captured by a model will deteriorate the generalization performance. To alleviate this problem, in this paper, we first analyze that augmenting the source domain with samples of new styles (e.g., different backgrounds) can mitigate spurious associations between irrelevant features and labels from a causal perspective, which can reduce the interference of non-causal features on the classification labels then propose a novel Causality-inspired Domain Expansion Network (CDEN) for SDG. Specifically, CDEN contains a domain augmentation subnetwork and a representation learning subnetwork. The former uses a special style generator module AdaIN∗ to generate out-of-domain data with the same semantic information but different styles from the source domain data to help the model capture true associations between discriminative features and labels. The latter introduces a novel class-level contrastive learning, instance-level contrastive learning, and invariant risk minimization to help the model further eliminate spurious associations. CDEN progressively generates diverse style data and learns domain invariant representations containing few spurious associations by optimizing the two subnetworks in an adversarial learning manner. We conduct extensive experiments on three benchmark datasets to evaluate the superiority of CDEN, comparing it to fifteen state-of-the-art methods.