Field Spectra Research Articles

Experimental verification of liquid multilayer spectrometer in neutron irradiation field for boron neutron capture therapy

Our group is currently developing a multilayer neutron spectrometer based on moderator combination selection to evaluate neutron energy spectra in irradiation fields for boron neutron capture therapy (BNCT). In a previous study, we optimally selected (based on simulations) liquid moderator combinations of the spectrometer to achieve high accuracy in the evaluated neutron energy spectra. According to the optimization result, we conducted a spectrometer verification test in the BNCT irradiation field at the Heavy Water Neutron Irradiation Facility of Kyoto University Reactor (KUR-HWNIF). The neutron energy spectrum result was evaluated, and the uncertainty was found to be less than 15%. The experimental results prove that the multilayer neutron spectrometer based on moderator combination selection effectively evaluates the neutron energy spectrum in a BNCT irradiation field.

Unveiling the exceptional evolution of solute aggregates: From micro to trace, solution to interface

AbstractExistential state of solutes substantially affects the efficiency and direction of various chemical and biological processes, about which current consensus is still limited at macro and micro levels. At the trace level, solutes assume a pivotal role across a spectrum of critical fields. However, their existential states, especially at interfaces, remain largely elusive. Herein, an exceptional evolution of solute molecules is unveiled from micro to trace, solution to interface, with the aid of surface‐enhanced Raman spectroscopy, extinction, DLS and theoretical simulations. Given predominant existence of monomers within the solution, these aggregates dominate the interfacial behavior of solute molecules. Moreover, a universal, aggregate‐controlled mechanism is demonstrated that aggregates triggered by cosolvent, which can dramatically promote efficiency of catalytic reactions. The results provide novel insights into the interaction mechanisms between reactants and catalysts, potentially offering fresh perspectives for the manipulation of multiphase catalysis and related biological processes.

The analytical model of time-harmonic electric field and impedance spectrum in the multilayered interdigital electrode structure

Accurately determining the time-harmonic electric field and impedance spectrum in the multilayered interdigital electrode (IDE) transducers with lossy dielectrics is essential for the design of the structure, the property estimation of the dielectric, and performance optimization. This paper presents an analytical model of the electric field and impedance within a multilayered interdigitated electrode (IDE) when subjected to alternating excitation through the method of separation of variables (MSV). The equivalent circuit of IDE is established, where the impedance is the parallel of capacitive coupling and resistive coupling between electrodes. The general solutions for the electric field and impedance involving arbitrary numbers and complex permittivities of the dielectric layers are analytically derived. However, due to the approximation of boundary conditions, these physical quantities cannot be accurately obtained by the conventional analytical method using conformal mapping technique (CMT) and partial capacitance technique (PCT). The proposed model exhibits proficiency in generating precise electric field images and impedance values that closely correspond with simulated data, even when accounting for the frequency-dependent characteristics of permittivity and conductivity in lossy dielectrics. Moreover, the approaches to improve the sensitivity of a traditional three-layered IDE-based sensor are demonstrated. For a three-layered IDE-based capacitive sensor, improving the sensitivity can be accomplished through the incorporation of a ground plane at the substrate's bottom and increasing the metallization rate. Conversely, it is recommended to reduce the metallization rate, increase substrate thickness, and remove the ground plane in order to enhance the sensitivity of the IDE-based impedance sensor. The experimental results demonstrate that the proposed model generates outstanding impedance spectra (involving capacitance spectra and resistance spectra) results for various metallization ratios and top layers. This exemplifies the model's potential for predicting, designing, and enhancing a complex multilayered IDE-based transducer to achieve precise and sensitive detection.

Reprogrammable, Recyclable Origami Robots Controlled by Magnetic Fields

Origami, the art of paper folding, has emerged as a versatile technique for crafting intricate 3D structures from 2D sheets. Combined with the magnetic actuation, origami paper becomes the building blocks for cost‐effective, wirelessly controllable magnetic robots. Herein, a biodegradable magnetic paper with excellent formability and recyclability is developed, facilitating its convenient utilization and disposal. The programable magnetic paper, fabricated with specific magnetization and crease patterns, enables the transformation of 2D sheets into predetermined 3D structures. Leveraging the lightweight and pliable nature of paper‐based materials, exceptional control of origami robots with fast response is demonstrated, enabling a wide range of locomotion. Furthermore, the paper‐based approach enables the incorporation of electronic functionality into magnetic actuators. By introducing conductive nanoparticles into magnetic paper, an electrically conductive substance is created. Constructing electronic circuits and integrating electronic components onto the paper‐based printed circuit board platform enables the repairing of broken circuits inside complicated equipment and optical sensing of surrounding environments in conjunction with locomotive robots. The origami robots have a huge potential to be facilitated in diverse fields with various functions, demonstrating complex locomotion, and integrating chemical, optical, thermal, and mechanical sensors for monitoring environmental conditions in hard‐to‐reach locations. The array of possibilities holds significant promise for the widespread application of these origami magnetic robots across a diverse spectrum of research fields in soft robotics.

Electroluminescent vertical tunneling junctions based on WSe2 monolayer quantum emitter arrays: Exploring tunability with electric and magnetic fields

We experimentally demonstrate the creation of defects in monolayer WSe2 via nanopillar imprinting and helium ion irradiation. Based on the first method, we realize atomically thin vertical tunneling light-emitting diodes based on WSe2 monolayers hosting quantum emitters at deterministically specified locations. We characterize these emitters by investigating the evolution of their emission spectra in external electric and magnetic fields, as well as by inducing electroluminescence at low temperatures. We identify qualitatively different types of quantum emitters and classify them according to the dominant electron-hole recombination paths, determined by the mechanisms of intervalley mixing occurring in fundamental conduction and/or valence subbands.

Enhancing Hydrogels with Quantum Dots

This manuscript explores the interdisciplinary integration of quantum dot–hydrogel composites and smart materials and their applications across a spectrum of fields, including biomedical engineering, environmental sensing, and energy harvesting. It covers the synthesis of novel materials like fluorescent hydrogel nanocomposites that display enhanced chemical stability, mechanical strength, and thermal resistance, highlighting their utility in environmental monitoring and catalysis. In the biomedical sector, innovations include hydrogel composites for targeted drug delivery and advanced therapies such as photothermal DNA hydrogels for tumor treatment. This review also discusses the application of these materials in imaging, diagnostics, and the development of smart sensors capable of detecting various biological and environmental changes. Its scope further extends to optoelectronics and the design of energy-efficient systems, underscoring the versatile functionalities of hydrogels in modern technological applications. Challenges remain in scaling up these technologies for commercial use and ensuring their long-term stability and safety, necessitating future research focused on sustainable, scalable solutions that can be integrated into existing systems.

Real-imaginary spectrum decomposition of the transparency spectra in microwave dressed Rydberg systems

To distinguish the contributions of electromagnetically induced transparency (EIT) and Autler-Townes splitting (ATS) in their applications in precision laser spectroscopy, we propose a real-imaginary spectrum decomposition method to investigate the transparency spectra in a four-level microwave (MW) dressed Rydberg system. We show that the opening transparency windows in the absorption spectra of probe field is a prominent character by EIT, EIT-ATS crossover, and ATS when the MW field is turned off and the intensity of the control field is adjusted. When the MW field is turned on and gradually increased, the EIT is destroyed and disappears. In addition, the most prominent characters that open a transparency window are the EIT-ATS crossover and the ATS. Then, if we further increase the intensity of the MW field, we find that the transparency windows open mainly due to the ATS. Compared to the previous considerations of this issue, which were limited to three-level systems, our four-level scheme reported here is useful for understanding the features of quantum interference in multilevel atomic systems, and has potential applications to study enhanced sensitivity, measurement spectroscopic, quantum processing, quantum communication, and transmission.

Resonance of Low-frequency Electromagnetic and Ion-sound Modes in the Solar Wind

Parker Solar Probe measurements have recently shown that coherent fast magnetosonic and Alfvén ion-cyclotron waves are abundant in the solar wind and can be accompanied by higher-frequency electrostatic fluctuations. In this Letter we reveal the nonlinear process capable of channelling the energy of low-frequency electromagnetic to higher-frequency electrostatic fluctuations observed on board Parker Solar Probe. We present Hall-MHD simulations demonstrating that low-frequency electromagnetic fluctuations can resonate with the ion-sound mode, which results in steepening of plasma density fluctuations, electrostatic spikes, and harmonics in the electric field spectrum. The resonance can occur around the wavenumber determined by the ratio between local sound and Alfvén speeds, but only in the case of oblique propagation to the background magnetic field. The resonance wavenumber, its width, and steepening timescale are estimated, and all indicate that the revealed two-wave resonance can frequently occur in the solar wind. This process can be a potential channel of energy transfer from cyclotron resonant ions producing the electromagnetic fluctuations to Landau resonant ions and electrons absorbing the energy of the higher-frequency electrostatic fluctuations.

Regional evaluation of global geopotential models and three types of digital elevation models with ground-based gravity and GNSS/levelling data using several techniques over Sudan

Abstract To evaluate the performance of the global geopotential models (GGMs) in a more unbiased way, ground-based gravity and GNSS/levelling datasets are highly required. In this study, the eight latest releases of the satellite-only and combined GGMs are evaluated on the regional scale using the available terrestrial gravity and GNSS/Levelling data over Sudan, considering the spectral consistency issue by applying the spectral enhancement method (SEM). The evaluation process consists of three stages: firstly, the eight GGMs are evaluated globally with each other by using different degree variances in terms of geoid heights, gravity anomalies, and signal-to-noise ratio (SNR); secondly, the GGMs are compared against the Earth Gravitational Model 2008 (EGM2008) on a regional scale over Sudan; thirdly, apply the SEM strategy by incorporating high (SEM_WITHOUT_RTM technique) and very-high (SEM technique) frequencies of the gravity field spectrum from the EGM2008 and high-resolution residual terrain model (RTM), respectively. For reliable robustness of the latter evaluation process, three different DEMs are used, namely, SRTM30, ASTER30, and GTOPO30. Our findings on the evaluation process using SEM_WITHOUT_RTM technique show improved gravity anomalies solutions regarding differences of standard deviations (STD) from 19–20.7 mGal to about 14 mGal. When applying the SEM technique, more improvements are achieved, providing STD differences in gravity anomalies and geoid heights of about 12 mGal and 45 cm, respectively. Among the three applied DEMs, it has been found that despite the slight refinements, the ASTER30 and GTOPO30 models show better performance than the SRTM30 model.

Dispersion-assisted high-dimensional photodetector.

Intensity, polarization and wavelength are intrinsic characteristics of light. Characterizing light with arbitrarily mixed information on polarization and spectrum is in high demand1-4. Despite the extensive efforts in the design of polarimeters5-18 and spectrometers19-27, concurrently yielding high-dimensional signatures of intensity, polarization and spectrum of the light fields is challenging and typically requires complicated integration of polarization- and/orwavelength-sensitive elements in the space or time domains. Here we demonstrate that simple thin-film interfaces with spatial and frequency dispersion can project and tailor polarization and spectrum responses in the wavevector domain. By this means, high-dimensional light information can be encoded into single-shot imaging and deciphered with the assistance of a deep residual network. To the best of our knowledge, our work not only enables full characterization of light with arbitrarily mixed full-Stokes polarization states across a broadband spectrum with a single device and a single measurement but also presents comparable, if not better, performance than state-of-the-art single-purpose miniaturized polarimeters or spectrometers. Our approach can be readily used as an alignment-free retrofit for the existing imaging platforms, opening up new paths to ultra-compact and high-dimensional photodetection and imaging.

Tomographic cross correlations between galaxy surveys and the CMB gravitational lensing potential. Effect of the redshift bin mismatch

Upcoming surveys of the large-scale structure of our Universe will employ a large coverage area of about half of the sky and will significantly increase the observational depth. With these surveys, we will be able to cross-correlate cosmic microwave background (CMB) gravitational lensing and galaxy surveys divided into narrow redshift bins to map the evolution of the cosmological parameters with redshift. We study the effect of the redshift bin mismatch of objects that is due to photometric redshift errors in tomographic cross-correlation measurements. We used the code FLASK to create Monte Carlo simulations of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) and Planck CMB lensing convergence. We simulated log-normal fields and divided galaxies into nine redshift bins with the Gaussian and modified Lorentzian photometric redshift errors. To estimate the parameters, we used angular power spectra of CMB lensing and galaxy density contrast fields and the maximum likelihood estimation method. We show that even with simple Gaussian errors with a standard deviation of $ the galaxy auto-power spectra in tomographic bins are offset by $2-15<!PCT!>$. The estimated cross-power spectra between galaxy clustering and CMB lensing are also biased, with smaller deviations $<5<!PCT!>$. As a result, the $ $ parameter deviates between $0.2-1.2\ due to the redshift bin mismatch of the objects. We propose a computationally fast and robust method based on the scattering matrix approach to correct for the redshift bin mismatch of the objects. The estimates of the parameters in tomographic studies such as the linear galaxy bias, the cross-correlation amplitude, and $ $ are biased due to the redshift bin mismatch of the objects. The biases in these parameters are alleviated with our scattering matrix approach.

Cosmological first-order vacuum phase transitions in an expanding anisotropic universe

We examine the anisotropy originated from a first-order vacuum phase transitions through three-dimensional numerical simulations. We apply Bianchi Type-I metric to our model that has one scalar field minimally coupled to the gravity. We calculate the time evolution of the energy density for the shear scalar and the directional Hubble parameters as well as the power spectra for the scalar field and the gravitational radiation although there are a number of caveats for the tensor perturbations in Bianchi Type-I universe. We run simulations with different mass scales of the scalar field, therefore, in addition to investigation of anisotropy via the shear scalar, we also determine at which mass scale the phase transition completes successfully, hence, neglecting the expansion of the Universe does not significantly affect the results. Finally, we showed that such an event may contribute to the total anisotropy depending on the mass scale of the scalar field and the initial population of nucleated bubbles.

Highest fusion performance without harmful edge energy bursts in tokamak

The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.

Acoustic heterostructures designed by topology optimization for interface states

Interface states can be induced through the design of heterostructures comprised of structures or materials with different physical properties. Compared with the traditional method of the manual experience adjustment, the topology optimization method is more systematic and scientific in designing heterostructures to achieve the desired performance of interface states. We combine the multiple population genetic algorithm (MPGA) with the finite element method, and consider the transmission spectrum characteristic of interface states as a fitness function forming a topology optimization method to find optimal designs for acoustic heterostructures. The frequency of the interface state induced by acoustic heterostructures after topology optimization is highly consistent with the objective frequency. The characteristic analyses of the sound field and transmission spectrum show that interface states possess high transmission and localization. Such a topology optimization method opens up design solutions in various engineering fields, which include wavefront manipulation, sound detection, and acoustic topological materials.

AI-Driven Sensing Technology: Review.

Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements.

Novel competitive fractional chaotic circuits interaction networks with hierarchical structure and encryption application

In the realm of complex networks, the challenge of ensuring secure communication amidst the vulnerabilities of conventional encryption methods has become increasingly critical. This study delves into the complex realm of synchronized behaviors in networks, employing fractional-order chaotic circuits within hierarchically structured competitive interaction networks to enhance encryption security, particularly for medical image transmission. We propose a novel paradigm that transcends traditional synchronization methods used across various disciplines, from engineering to social sciences, by unveiling the intricate dynamics of how units within networks share interactions. Our approach leverages the unique properties of fractional chaos and network hierarchy, demonstrating that the proposed model, characterized by multi-directed links and competitive strategies, significantly improves synchronization. Through detailed analysis, including bifurcation diagrams and Lyapunov exponent plots, we uncover the optimal configurations of coupling strength and fractional order that lead to enhanced network synchronization. This synchronization is pivotal for our encryption application, showcasing a high level of security and privacy in the transmission of medical images. The encryption technique benefits from the network’s complex and synchronized dynamics, rendering it a formidable challenge for potential attackers to decipher the encrypted data. While our findings offer a promising mechanism for creating robust communication networks capable of securing sensitive medical data, the implications of our work extend beyond this application. The successful application of fractional-order chaotic circuits sets a groundwork for securing diverse types of data transmissions against the evolving landscape of cyber threats. This research not only marks a significant advancement in network security but also opens new avenues for applying these principles across a spectrum of fields where data security and privacy are paramount.

Exploring unique quasinormal modes of a massive scalar field in brane-world scenarios

We compute precise values of quasinormal modes of a massive scalar field in the background of the Schwarzschild-like brane-localised black holes. It is shown that the quasinormal spectrum of the massive field differs qualitatively from that previously known for other black hole models, due to the presence of two kinds of modes: those whose damping rate vanishes as the mass of the field μ increases up to some critical value, and those whose real oscillation frequency vanishes at a certain value of μ. While the first type of modes, which are arbitrarily long-lived, are recognized in various four-dimensional backgrounds as quasi-resonances, the second type is a novel feature for asymptotically flat black holes. When Re(ω) reaches zero, the fundamental mode disappears from the spectrum and the first overtone becomes the fundamental mode. We also demonstrate that quasi-resonances may not exist for brane-localised black holes immersed in D≥6 - dimensional bulk.

Review on Competency Assessment Instrumentation in Computer-based Simulation

AbstractComputer-based simulation is utilised across various educational fields, employing diverse technologies to facilitate practical understanding of content and the acquisition of skills that can help close the gap between theory and practice. The possibility of providing scenarios that resemble on-the-job tasks, enables instructors to both train and assess the trainee’s comprehension of the tasks at hand. The practices as well as the technologies for the assessment of simulation-based training vary across disciplines. Our motivation is to address quality procedures from a cross-discipline perspective. There seems to be a lack of scientific investigation that takes one step back from the specific application and investigates how assessment instruments can be developed to fit training outcomes regardless of the professional discipline. This scoping literature review on empirical studies aims to do so by exploring how competency is assessed with computer-based simulation. Objectives to achieve this are: (1) apply established training research theory to structure a decomposition of assessment instruments; to (2) review approaches to assessments factored over this structure; and (3) discuss quality procedures taken in the creation of the reported instruments and then propose an approach to assessment instrumentation that can be applied independent of discipline, with the range of current technology, and for any focal outcome competency. By reviewing a spectrum of fields, we capture reported assessment practices across a range of currently employed technologies. This literature review combines the methods of a scoping review with the qualities of a systematic literature review while keeping to conventional reporting guidelines. This allowed us to provide insight into current approaches and research designs that applied measurements in the range from automated assessment to observer rating of simulation-based training in professional work settings. This study found that all reviewed studies measured skill-based outcomes with some variation and that there is more theoretical and empirical work to be done to close the gap on quality instrumentation and its validity evidence. Our contribution to the field of training research is the operationalized component structure and the synthesised approach to instrumentation that could offer researchers and practitioners guidance and inspiration to develop and conduct quality assessments in competency development.

Parameters Optimization and Physicochemical Properties Characterization of Nanobiochar Prepared from Apple Branches by Ball Milling

Nanobiochar exhibits promising applications across a spectrum of fields due to its exceptional physicochemical properties. Ball milling, a prominent nanobiochar synthesis method, is influenced by process factors and biochar pretreatments, yet dominant controls still remain uncertain. In this study, the apple branch bulk biochar (pyrolyzed under [Formula: see text][Formula: see text]C) was ball milled under different milling speeds, ball-to-powder mass ratios, and milling durations to investigate the optimal process parameter combinations for ball milling method. Then, we evaluated the impact of pyrolysis temperature and pretreatment temperature of bulk biochar on ball milling effect using optimized parameter combinations. Subsequently, the physicochemical properties of the bulk biochar and nanobiochar were measured. The results showed that high speed with low ball-to-powder mass ratio, low speed with high ball-to-powder mass ratio, and high pyrolysis temperature could significantly decrease the particle sizes of nanobiochar. Ball milling duration and pretreatment temperature had no significant effect on the particle sizes of biochar. The optimal combination of the rotational speed, ball-to-powder mass ratio and milling duration was 400 RPM, 100:1, 2[Formula: see text]h and 700 RPM, 20:1, 2[Formula: see text]h. The bulk biochar pyrolyzed at [Formula: see text][Formula: see text]C could be milled into biochar nanoparticles with an average particle size smaller than 100[Formula: see text]nm via above parameters. Nanobiochar shows improved properties such as specific surface area, cation exchange capacity, and surface functional groups over bulk biochar. This study provides a scientific basis for the widespread application of ball milling in the field of preparing nanobiochar.

Sample entropy-based quantitative assessment of the arc magnetic field spectrum for improved arc welding quality

Arc magnetic field analysis is a valuable approach for assessing the stability of the arc welding process, yet existing methods lack the ability to effectively quantify the disorder within the process. Through an investigation into the characteristics of the arc magnetic field signal, it was observed that the occurrence of low-frequency random fluctuations in arc magnetic field power, induced by unstable factors such as bubbles or short circuits, contributed to increased complexity and randomness in the arc magnetic field signals. To visualise the arc magnetic field signals in a time-frequency domain, a spectrogram was employed, revealing a strong correlation between the distribution of maximum power spectral density (PSD) in the spectrogram and the stability of the arc welding process. Furthermore, a novel method based on sample entropy was introduced to provide a quantitative measure of this relationship. A comprehensive quantitative assessment indicator called arc magnetic field sample entropy (AMFSE) was proposed. This indicator effectively mitigates the influence of varying parameters on the quantitative results, enabling a more accurate and consistent representation of the stability of the arc welding process. The proposed method was validated through testing, yielding an accuracy rate exceeding 90%.