High-resolution Measurements Research Articles

Local chemical heterogeneity enabled superior zero thermal expansion in nonstoichiometric pyrochlore magnets

Abstract Design of zero thermal expansion (ZTE) materials is urgently required as dimension stable component in widespread modern high-precision technologies. Local chemical order has been of great importance in engineering advanced inorganic materials, but its role in optimizing the ZTE is often overlooked. Herein, we propose local composition heterogeneity for developing superior ZTE via a nonstoichiometric strategy. A remarkably low coefficient of thermal expansion of αa = +1.07 × 10−6 K−1 is achieved from 3 to 440 K in a quaternary Zr-Nb-Fe-Co pyrochlore magnet, which is the widest temperature range among known cubic ZTE metals. High-resolution synchrotron X-ray diffraction and magnetisation measurements reveal that all the Bragg peaks split as resulting from two cubic phases with different magnetic orders. Scanning transmission electron microscopy, Mössbauer spectroscopy and theoretical calculations indicate that such phase separation intimately derive from excess Co dopant preferentially clustering on the Fe pyrochlore-lattice (16d) and simultaneously yielding an antisite Fe on Zr/Nb sublattice (8a). The Co content in pyrochlore-lattice has weaker exchange interactions than that of Fe, but the antisite Fe introduces extra positive exchange interactions between 8a-16d site. Local composition fluctuation of Co and Fe thus affects interplanar ferromagnetic order of pyrochlore-lattice and balances the normal phonon effect successively on heating. Superior corrosion resistance to both acid and alkaline conditions merits potential applications of the present ZTE metal.

Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers

Interferometric radiometers operating at L-band, such as ESA’s SMOS mission, enable crucial Earth observations providing high-resolution measurements of soil moisture, ocean salinity, and other geophysical parameters. However, the increasing electromagnetic spectrum utilization has led to significant Radio Frequency Interference (RFI) challenges, particularly critical given the sensors’ fine temperature resolution requirements of less than 1 K. This work presents the hardware implementation of an advanced RFI detection and mitigation algorithm specifically designed for interferometric radiometers, targeting future L-band missions. The implementation processes 1-bit quantized signals at 57.69375 MHz from multiple receivers, employing time-frequency analysis and polarimetric detection techniques while optimizing Field Programmable Gate Array (FPGA) resource utilization. Novel optimization strategies include overclocked processing cores operating at 230.775 MHz, efficient resource sharing through operation serialization, and strategic memory management. The system achieves real-time processing capabilities while maintaining detection probabilities above 63% with false alarm rates below 1% for typical interference scenarios. Performance validation using synthetic datasets demonstrates robust operation across various RFI conditions, making this implementation suitable as part of the RFI detection and mitigation efforts for future interferometric radiometer missions beyond SMOS.

Assessing Operational Efficiency of Bubble Plumes for Water Circulation Enhancement

Bubble plumes are essential for promoting mass transfer, flow, and mixing in water bodies by generating vertical circulation via buoyancy forces. They are widely used in various applications, such as restoring water environments and improving the conditions at the bottom of lakes and reservoirs. For example, thermal stratification in lakes can lead to environmental issues such as the depletion of dissolved oxygen. To address this problem, bubble plume systems have been used to destratify lakes and reservoirs. However, few studies have been performed on the effectiveness of bubble plumes. In this study, the impact of a bubble plume in a dam reservoir was assessed using a numerical model based on high-resolution field measurements. Vertical profiles were obtained before and after the operation of the density-current generator to capture seasonal changes in the water characteristics. These measurements indicated the alteration of the vertical structure and mixing within the water column due to the bubble plume while stable temperatures were maintained at specific depths across seasons. Numerical simulations using large eddy simulations were conducted to analyze the dynamics and mixing efficiency of the bubble plume. The findings of this study provide valuable data for optimizing the design and operational strategies of bubble plume systems in lakes and reservoirs, which can increase the water mixing efficiency and support environmental management.

Examining the use of multiple cognitive load measures in evaluating online shopping convenience: an EEG study

PurposeIn the past decade, the use of neurophysiological measures as a complementary source of information has contributed to our understanding of human–computer interaction. However, less attention has been given to their capability in providing measures with high temporal resolution. Two studies are designed to address the challenge of measuring users’ cognitive load in an online shopping environment and investigate how it is related to task difficulty, task uncertainty and shopping convenience.Design/methodology/approachTwo experiments using behavioral and neurophysiological measures are conducted to investigate how various types of the cognitive load construct can be measured and used in an online shopping context.FindingsResults of the first study suggest that although all cognitive load measures are influenced by task difficulty, only accumulated load (i.e. total cognitive load experienced during a task) is sensitive to task uncertainty. Results of the second study show that convenience negatively influences accumulated load, and the latter negatively influences user satisfaction.Practical implicationsOur research offers practical value by providing designers with a validated method to measure users’ cognitive load, enabling the identification of usability issues and design improvement.Originality/valueThis study contributes to the literature by developing a rich and temporally high-resolution measurement of the cognitive load construct and examining how it can inform us about users’ cognitive state in an online shopping environment.

First Observation of the Complete Rotation Period of the Ultraslowly Rotating Magnetic O Star HD 54879

HD 54879 is the most recently discovered magnetic O-type star. Previous studies ruled out a rotation period shorter than 7 yr, implying that HD 54879 is the second most slowly rotating known magnetic O-type star. We report new high-resolution spectropolarimetric measurements of HD 54879, which confirm that a full stellar rotation cycle has been observed. We derive a stellar rotation period from the longitudinal magnetic field measurements of P=2562−58+63 days (about 7.02 yr). The radial velocity of HD 54879 has been stable over the last decade of observations. We explore equivalent widths and longitudinal magnetic fields calculated from lines of different elements, and conclude the atmosphere of HD 54879 is likely chemically homogeneous, with no strong evidence for chemical stratification or lateral abundance nonuniformities. We present the first detailed magnetic map of the star, with an average surface-magnetic-field strength of 2954 G, and a strength for the dipole component of 3939 G. There is a significant amount of magnetic energy in the quadrupole components of the field (23%). Thus, we find HD 54879 has a strong magnetic field with a significantly complex topology.

Fast measurement of group index variation with optimum precision using Hong–Ou–Mandel interferometry

Hong–Ou–Mandel (HOM) interferometry has emerged as a valuable means for quantum sensing applications, particularly in measuring physical parameters that influence the relative optical delay between photon pairs. Unlike classical techniques, HOM-based quantum sensors offer higher resolution due to the intrinsic dispersion cancellation property of correlated photon pairs. Due to the use of single photons, HOM-based quantum sensors typically involve a large integration time to acquire the signal and subsequent post-processing for high-resolution measurements, restricting their use for real-time operations. Based on our understanding of the relationship between measurement resolution and the gain medium length that produces photon pairs, we report here on the development of an HOM-based quantum sensor for high-precision group index measurements. Using a 1 mm long periodically poled KTP (PPKTP) crystal for photon-pair generation, we have measured the group index with a precision of ∼6.75×10−6 per centimeter of sample length at an integration time of 100 ms, surpassing the previous reports by 400%. Typically, the measurement range reduces with the increase in the resolution. However, using a novel scheme compensating photon delay due to group index changes stepwise with an optical delay stage, we have measured the group index variation of PPKTP crystal over a range of 3.5 × 10−3 for a temperature change from 25 to 200 °C, corresponding to an optical delay adjustment of ∼200 μm while maintaining the same precision (∼6.75×10−6 per centimeter of sample length). The current results establish the usefulness of HOM-interferometer-based quantum sensors for fast, precise, and long-range measurements in various applications, including quantum optical coherence tomography.

Analyzing AZ-non-Maxwellian distributions in Earth’s magnetosphere: MMS observations

A study of velocity distribution function in Earth’s magnetosphere is conducted using high-resolution measurements from the Magnetospheric Multiscale Spacecraft (MMS). The analysis focuses on the AZ-non-Maxwellian distribution that is a complex velocity-space structure, exhibiting a power-law distribution of moments. By imposing constraints on the spectral indices of the AZ-velocity distribution, the most suitable distribution for modeling Earth’s magnetosphere is determined. The combined use of MMS data and analysis of velocity distribution provides new insights into the behavior of plasma particles and the velocity space structures responsible for energy dissipation in plasmas. Understanding particle distribution, behavior, and the influence of cold ions in Earth’s magnetosphere is crucial for advancing our knowledge of plasma physics and has practical implications for space weather and magnetospheric dynamics. Addressing the remaining uncertainties and challenges in measurements, analysis of non-Maxwellian distribution of ions is essential for a comprehensive understanding of space plasma environments.

Enhancing Regional Topsoil Total Nitrogen Mapping Through Differentiated Fusion of Ground Hyperspectral Data and Satellite Images Under Low Vegetation Cover

Total nitrogen in soil (STN) serves as a crucial indicator of soil nutrient content and provides an essential nitrogen source necessary for crop growth. Precisely inversion of STN content is crucial for the sustainable management of soil resources and the advancement of agricultural development, particularly to achieve efficient fertilization—reduction in fertilizer usage without compromising yield or increase in yield while maintaining the total fertilization amount. Spectroscopy technology is regarded as an ideal non-destructive method for nutrient detection. However, due to the weak spectral signals of STN and its spatial heterogeneity, hyperspectral imaging technology presents significant potential for high-resolution measurements and precise characterization of STN heterogeneity. In this paper, the STN content was selected as the study subject, and three aspects of soil spectral feature enhancement, multi-source remote sensing data differentiated fusion, and STN content inversion model construction were studied. Therefore, a differentiated fusion of enhanced multispectral image bands (DFE_MSIBs) method combined with Random Forest (RF) algorithms was developed for spectral inversion of STN content. The findings demonstrate the following: 1. The enhanced spectral characteristics and differentiated fusion method not only strengthen the relationship between STN and Sentinel-2A MSI data but also enhance the precision of regional STN inversion models. 2. For the differentiated fusion of enhanced multispectral image bands (DFE_MSIBs) method combined with Random Forest (RF) algorithms, the R2 was 0.95, RMSE was 0.10 g/kg, and LCCC was 0.89. 3. Compared to the unfused model, the average R2 value was increased by 0.02, the average RMSE was decreased by 0.01 g/kg, and the average LCCC was increased by 0.03. These findings hold practical significance for utilizing multi-source remote sensing data in STN mapping and precision fertilization in agricultural fields.

High spatiotemporal resolution measurement of water flow boiling heat transfer in a horizontal square minichannel using infrared thermography

In this study, the heat transfer fluctuations of water flow boiling in a horizontal square minichannel with a side length of 2 mm was investigated using infrared thermography with a high spatiotemporal resolution (4000 fps, 0.025 mm/pixel). Simultaneously, two high-speed cameras were used to visualize the behavior of the gas-liquid interface. The mass flux was 100 or 200 kg/(m2·s), and the vapor quality ranged from slug to annular flow. The wall heat flux was varied in the range of 10–220 kW/m2, focusing on the case of 220 kW/m2, where fast and complex fluctuations in the flow-boiling heat transfer were clearly visualized. In addition, image analysis was performed to partition the instantaneous heat transfer coefficient distribution into the fundamental processes of flow boiling (liquid convection, microlayer evaporation, dryout, three-phase contact line, and rewetting), and the contribution of each process to the heat transfer was investigated. The results showed that liquid convection was dominant under the present experimental conditions, accounting for approximately 85–95 % of the total heat transfer. Here, liquid convection includes the effects of turbulence in the liquid phase caused by boiling nucleation and acceleration associated with the two-phase gas-liquid flow. The contribution of microlayer evaporation due to boiling nucleation was approximately 5–8 % of the total heat transfer when the heat flux was 220 kW/m2, which decreased significantly as the heat flux decreased. It was also found that dryout occurred even under low vapor quality, and that when the dryout was partial, the decrease in heat transfer was limited by the contribution of the three-phase contact line formed at the outer edge of the dryout.

Technical note: High-resolution analyses of concentrations and sizes of refractory black carbon particles deposited in northwestern Greenland over the past 350 years – Part 1: Continuous flow analysis of the SIGMA-D ice core using the wide-range Single-Particle Soot Photometer and a high-efficiency nebulizer

Abstract. Ice cores can provide long-term records of refractory black carbon (rBC), an important aerosol species closely linked to the climate and environment. However, previous studies of ice cores only analyzed rBC particles with a diameter of < 500 nm, which could have led to an underestimation of rBC mass concentrations. Information on the size distribution of rBC particles is very limited, and there are no Arctic ice core records of the temporal variation in rBC size distribution. In this study, we applied a recently developed improved technique to analyze the rBC concentration in an ice core drilled at the SIGMA-D site in northwestern Greenland. The improved technique, which uses the modified Single-Particle Soot Photometer (SP2) and a high-efficiency nebulizer, widens the measurable range of rBC particle size. For high-resolution continuous analyses of ice cores, we developed a continuous flow analysis (CFA) system. Coupling of the improved rBC measurement technique with the CFA system allows accurate high-resolution measurements of the size distribution and concentration of rBC particles with a diameter between 70 nm and 4 µm, with minimal particle losses. Using this technique, we reconstructed the size distributions and the number and mass concentrations of rBC particles during the past 350 years. On the basis of the size distributions, we assessed the underestimation of rBC mass concentrations measured using the conventional SP2s. For the period 2003–2013, the underestimation of the average mass concentration would have been 12 %–31 % for the SIGMA-D core.

A compact and mobile stray-field NMR sensor

In this paper, we introduce a compact, single-sided stray field sensor for NMR relaxometry applications. The sensor consists of four main components: the magnet, the RF coil, the spectrometer, and the translation stage. Our proposed magnet, an improved design of the Profile NMR−MOUSE, is designed for low weight, compactness, and magnetic field homogeneity, achieved through various shim strategies using a mixed genetic algorithm. The magnet comprises eight NdFeB blocks, generating a magnetic field of 0.424T within the sensitive region, positioned 12 mm above the magnet surface. For high spatial resolution measurements, we optimized the sensor performance by using a custom-designed rf coil, providing maximum sensitivity, lateral selectivity, and a dead time of less than 20µs. Moreover, we utilized 3D-printed structures to precisely align the sensitive slice within the object, using an experimental approach based on CPMG measurements. The presented setup achieved a spatial resolution of 50µm, with resolution changes proportional to acquisition time. We demonstrate the sensor’s versatility and high resolution with measurements on materials such as cosmetics, elastomers, glue, and wood, verifying the good performance of our design, our alignment strategy, and the measuring scheme.

A conceptual model explaining spatial variation in soil nitrous oxide emissions in agricultural fields

Soil emissions of nitrous oxide contribute substantially to global warming from agriculture. Spatiotemporal variation in nitrous oxide emissions within agricultural fields leads to uncertainty in the benefits of climate-smart agricultural practices. Here, we present a conceptual model explaining spatial variation in temporal patterns of soil nitrous oxide emissions developed from high spatial resolution measurements of soil nitrous oxide emissions, gross nitrous oxide fluxes, and soil physicochemical properties in two maize fields in Illinois, USA. In sub-field locations with consistently low nitrous oxide emissions, soil nitrate and dissolved organic carbon constrained nitrous oxide production irrespective of changes in soil moisture. In sub-field locations where high emissions occurred episodically, soil nitrate and dissolved organic carbon availability were higher, and increases in soil moisture stimulated nitrous oxide production. These findings form the ‘cannon model’ which conceptualizes how sub-field scale variation in soil nitrate and dissolved organic carbon determines where increases in soil moisture can trigger high soil nitrous oxide emissions within agricultural fields.

3D characterisation of metal powders for additive manufacturing using Nano-CT and deep learning

ABSTRACT The geometrical properties of metal powders, the main raw material in additive manufacturing processes, can have a significant impact on the quality of the parts produced. Therefore, it is essential to characterise these powders effectively. Nano-computed tomography (Nano-CT) technology, with its high-resolution 3D measurement capability, is ideally suited to measure the geometrical characteristics of tiny powders. However, the current CT testing methods for metal powders has two main drawbacks. Firstly, it does not take into account the effect of particle adhesion. Second, it cannot achieve high-precision measurements of the internal pores of powders. To address these limitations, we propose a novel testing method for metal powders that utilises Nano-CT technology. The method first obtains highly dispersed powder samples through a specially sample preparation technique. Then, an improved U-shape networks with multi-level supervision are proposed to achieve high-precision segmentation of the internal pores of the powder. Ultimately, this paper achieves high-precision 3D characterisation of metal powders.

Identifying spatially variable genes by projecting to morphologically relevant curves.

Spatial transcriptomics enables high-resolution gene expression measurements while preserving the two-dimensional spatial organization of the sample. A common objective in spatial transcriptomics data analysis is to identify spatially variable genes within predefined cell types or regions within the tissue. However, these regions are often implicitly one-dimensional, making standard two-dimensional coordinate-based methods less effective as they overlook the underlying tissue organization. Here we introduce a methodology grounded in spectral graph theory to elucidate a one-dimensional curve that effectively approximates the spatial coordinates of the examined sample. This curve is then used to establish a new coordinate system that better reflects tissue morphology. We then develop a generalized additive model (GAM) to detect genes with variable expression in the new morphologically relevant coordinate system. Our approach directly models gene counts, thereby eliminating the need for normalization or transformations to satisfy normality assumptions. We demonstrate improved performance relative to current methods based on hypothesis testing, while also accurately estimating gene expression patterns and precisely identifying spatial loci where deviations from constant expression are observed. We validate our approach through extensive simulations and by analyzing experimental data from multiple platforms such as Slide-seq and MERFISH.

In-plane angular dependence of superconducting gaps in FeSe probed by high-resolution specific heat measurements

In-plane angular dependence of superconducting gaps in FeSe probed by high-resolution specific heat measurements

Reconstruction of unstable atmospheric surface layer streamwise turbulence based on multi-layer perceptron neural network architecture

The accurate simulation of sand-laden turbulence under different stratification stabilities remains a critical challenge in turbulence research. This study presents an innovative approach to reconstructing streamwise turbulence in an unstable atmospheric surface layer (ASL) using a multi-layer perceptron (MLP) neural network architecture. Leveraging high-resolution measurements of three-dimensional wind velocity and temperature from multiple observational sites, the study develops prediction models for streamwise wind velocity at varying heights in the unstable ASL. The predictive model integrates large-scale motions (LSMs) generated by the MLP, small-scale motions (SSMs) derived from the Kaimal spectrum, and mean wind velocity, providing a comprehensive representation of turbulence. The impact of sand content and stratification stability on model performance is analyzed, with discussion highlighting the model's strengths and limitations under weak instability conditions. Validation is conducted through cross-site comparison, statistical analysis, and power spectrum assessment, demonstrating the model's ability to capture the temporal and spectral characteristics of wind velocity in sand-laden, unstable ASL conditions. The study also reveals that, under weak instability, shear forces dominate the formation of coherent structures, while buoyancy effects enhance vertical mixing as instability increases. Compared to existing models, the proposed prediction model is applicable over a broader range of conditions, offering a valuable data source for the study of atmospheric sand-laden turbulence and serving as a reference for practical sand control projects.

Battle of the CH motions: aliphatic versus aromatic contributions to astronomical PAH emission and exploration of the aliphatic, aromatic, and ethynyl CH stretches.

Strong anharmonic coupling between vibrational states in polycyclic aromatic hydrocarbons (PAH) produces highly mixed vibrational transitions that challenge the current understanding of the nature of the astronomical mid-infrared PAH emission bands. Traditionally, PAH emission bands have been characterized as either aromatic or aliphatic, and this assignment is used to determine the fraction of aliphatic carbon in astronomical sources. In reality, each of the transitions previously utilized for such an attribution is highly mixed with contributions from both aliphatic and aromatic CH motions as well as non-CH motions such as CC stretches. High-resolution gas-phase IR absorption measurements of the spectra of the aromatic molecules indene and 2-ethynyltoluene at the Canadian Light Source combined with high-level anharmonic quantum chemical computations reveal the complex nature of these transitions, implying that the use of these features as a marker for the aliphatic fraction in astronomical sources is not uniquely true or actually predictive. Further, the presence of aliphatic, aromatic, and ethynyl CH groups in 2-ethynyltoluene provides an internally consistent opportunity to simultaneously study the spectroscopy of all three astronomically important groups. Finally, this study makes an explicit connection between fundamental quantum mechanical principles and macroscopic astronomical chemical physics, an important link necessary to untangle the lifecycle of stellar and planetary systems.

Unique High-Resolution Temperature Mapping of Stage 1 Turbine Vane in a Long-Term Engine Test

Abstract In this study, surface temperature maps resulting from a long-term engine test were evaluated on a stage 1 turbine vane using thermal history coatings (THCs). THCs are ceramic-type sensor coatings doped with a lanthanide ion, giving the coating photoluminescent properties. The THC's structure permanently changes when exposed to high temperatures, which in turn alters its photoluminescent properties. Therefore, historic maximum temperature maps are evaluated by optically probing the THC point-by-point across the vane's surface. The vane was exposed to a non-dedicated (multi-cycle, multi temperature-level) engine test lasting over 8 months. Two passes of THC measurements were taken, termed low resolution (LR) and high resolution (HR), each, respectively, with point pitches of 5 mm and 1 mm. The latter provided a unique insight into the historic maximum temperature profile of the vane, particularly around high-temperature gradient regions, such as those close to effusion cooling holes. The THC temperatures were compared to the engine manufacturer's (Doosan Enerbility) heat transfer code (Doosan Integrated Thermal Analysis for Cooling System (DiTACS)) for validation. The THC temperature mapping was successful with good coverage across the vane. The leading edge and pressure side trailing edge regions were typically the hottest. The HR measurements clearly show sharp thermal gradients around the effusion cooling holes on the vane's airfoil and platforms. Critically, the THC measurements were compared very well to the DiTACS measurements, validating THCs as a credible temperature measurement technique for long-term, non-dedicated engine tests for components operating in some of the most extreme environments of an engine.

Seamless Optimization of Wavelet Parameters for Denoising LFM Radar Signals: An AI-Based Approach

Linear frequency modulation (LFM) signals are pivotal in radar systems, enabling high-resolution measurements and target detection. However, these signals are often degraded by noise, significantly impacting their processing and interpretation. Traditional denoising methods, including wavelet-based techniques, have been extensively used to address this issue, yet they often fall short in terms of optimizing performance due to fixed parameter settings. This paper introduces an innovative approach by combining wavelet denoising with long short-term memory (LSTM) networks specifically tailored for LFM signals in radar systems. By generating a dataset of LFM signals at various signal-to-noise Ratios (SNR) to ensure diversity, we systematically identified the optimal wavelet parameters for each noisy instance. These parameters served as training labels for the proposed LSTM-based architecture, which learned to predict the most effective denoising parameters for a given noisy LFM signal. Our findings reveal a significant enhancement in denoising performance, attributed to the optimized wavelet parameters derived from the LSTM predictions. This advancement not only demonstrates a superior denoising capability but also suggests a substantial improvement in radar signal processing, potentially leading to more accurate and reliable radar detections and measurements. The implications of this paper extend beyond modern radar applications, offering a framework for integrating deep learning techniques with traditional signal processing methods to optimize performance across various noise-dominated domains.

Anomalous Diffusion by Ocean Waves and Eddies

Understanding the dispersion of floating objects and ocean properties at the ocean surface is crucial for various applications, including oil spill management, debris tracking and search and rescue operations. While mesoscale turbulence has been recognized as a primary driver of dispersion, the role of submesoscale processes is poorly understood. This study investigates the largely unexplored mechanism of dispersion by refracted wave fields. In situ observations demonstrate significantly faster and distinct dispersion patterns for objects influenced by wind, waves and currents compared to those solely driven by ocean currents. Numerical simulations of wave fields refracted by ocean eddies corroborate these findings, revealing diffusivities that exceed those of turbulent diffusion at scales up to 10 km during energetic sea states. Our results highlight the importance of ocean waves in dispersing surface material, suggesting that refracted wave fields may play a significant role in submesoscale spreading. As atmospheric forcing at the ocean surface will only strengthen due to anthropogenic contributions, additional research into wave refraction is necessary. This requires concurrent high-resolution measurements of wind, waves and currents to inform the revisions of large-scale coupled models to better include the submesoscale physics.