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At least 379 records · Page 21

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Crystallization induced ordering of hard magnetic L1{sub 0} phase in melt-spun FeNi-based ribbons

The microstructure of newly developed hard magnetic Fe{sub 42}Ni{sub 41.3}Si{sub x}B{sub 12-x}P{sub 4}Cu{sub 0.7} (x = 2 to 8 at%) nanocrystalline alloy ribbons has been studied by transmission electron microscopy (TEM) and electron diffraction. A high-density polycrystalline grains, ∼30 nm in size, were formed in a ribbon after annealing at 673 K for 288 hours. Elemental mapping of the annealed specimen revealed the coexistence of three regions, Fe-rich, Ni-rich, and nearly equiatomic Fe-Ni, with areal fractions of 37%, 40%, and 23 %, respectively. The equiatomic L1{sub 0}-type ordered phase of FeNi was detected in between the Fe and Ni-rich phases. The presence of superlattice reflections in nanobeam electron diffraction patterns confirmed the formation of the hard magnetic L1{sub 0} phase beyond any doubt. The L1{sub 0} phase of FeNi was detected in alloys annealed in the temperature range of 673 to 813 K. The present results suggest that the order-disorder transition temperature of L1{sub 0} FeNi is higher than the previously reported value (593 K). The high diffusion rates of the constituent elements induced by the crystallization of an amorphous phase at relatively low temperature (∼673 K) are responsible for the development of atomic ordering in FeNi.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Patterned probes for high precision 4D-STEM bragg measurements

Nanoscale strain mapping by four-dimensional scanning transmission electron microscopy (4D-STEM) relies on determining the precise locations of Bragg-scattered electrons in a sequence of diffraction patterns, a task which is complicated by dynamical scattering, inelastic scattering, and shot noise. These features hinder accurate automated computational detection and position measurement of the diffracted disks, limiting the precision of measurements of local deformation. Here, we investigate the use of patterned probes to improve the precision of strain mapping. We imprint a "bullseye" pattern onto the probe, by using a binary mask in the probe-forming aperture, to improve the robustness of the peak finding algorithm to intensity modulations inside the diffracted disks. We show that this imprinting leads to substantially improved strain-mapping precision at the expense of a slight decrease in spatial resolution. In experiments on an unstrained silicon reference sample, we observe an improvement in strain measurement precision from 2.7% of the reciprocal lattice vectors with standard probes to 0.3% using bullseye probes for a thin sample, and an improvement from 4.7% to 0.8% for a thick sample. Additionally, we use multislice simulations to explore how sample thickness and electron dose limit the attainable accuracy and precision for 4D-STEM strain measurements.

47 OTHER INSTRUMENTATION↗

High Energy Micron Scale Pixel Hybrid Detector

The objective of this technology development project has been to fabricate and characterize a new X-ray area image sensor innovation designed for high efficiency detection and imaging of hard X-rays (>20 KeV) with micron scale pixel resolution. A key technology component consists of a monolithic hybrid X-ray detector built by layering an amorphous Selenium X-ray photoconductor film directly deposited on a small pitch (7.8μm) CMOS active pixel readout array. A specific goal of this work is to provide a new detector tool to researchers working with synchrotron light sources, to extend the depth of exploration into nanoscale structures, and do so cost effectively. Early generation X-ray imaging detector tools fail to provide adequate resolving power and efficiency at high energies and our innovation provides a solution. A majority of proposed phase I tasks were completed. Those tasks included, in collaboration with KA Imaging, successful fabrication of specified a-Se layers on a 1Mpixel CMOS readout array to create the direct hybrid X-ray imaging array, packaging of the imaging array, assembly of electronics into a shielded detector box, loading user interface software for camera control and image capture, and performing initial (no X-ray) detector evaluation at Farrier Microengineering facilities. After initial tests, extensive testing using the X-ray beam line 1-BM source at Argonne National Laboratory Advanced Photon Source(ANL-APS) was performed. Imaging performance studies were conducted at multiple X-ray beam energies. Studies included X-ray response vs. X-ray energy, vs. applied detector field strength (HV), response linearity vs. integration time, MTF vs. beam energy, image lag measurements, and micron scale resolution pattern target imaging. This high energy micron scale X-ray detection technology is a first of a kind to be evaluated at the ANL-APS and is supported by Dr. Antonino Miceli, head of the detector physics group. Accomplishments include the first successful demonstration of a 1Mpix amorphous Selenium on CMOS readout direct hybrid X-ray imaging detector at a DOE sponsored National Laboratory. Modeled performance predictions were confirmed, proving micron scale resolution as well as an order of magnitude increase in conversion efficiency over conventional scintillator detector performance. A discovery of evidence of propagation-based phase contrast edge enhancement was unplanned and will be exploited in future experiments. This phase 1 technology demonstration verifies the efficacy of proposed high energy micron scale pixel X-ray hybrid a-Se imaging array products. The new detector products will cost effectively replace early generation detector technologies that currently fail to address increasing demand for tools to study nano structures and physical processes at high X-ray energies above 20keV. For example, current systems for transmission X-ray microscopy have low efficiency and available large pixel (~55µm) direct detectors do not offer sufficient resolution for Bragg fringe imaging at high energies. This detector innovation will solve these inadequacies. The new detector technology will compete in a $50M scientific X-ray detector market, growing at 6%, and will benefit Microtomography(µCT), X-ray diffractometry (XRD), Coherent Diffraction Imaging (CDI) applications. One application specified by Dr. Miceli at ANL is the 3D reconstruction of compact crystals at high energies requires small pixel high efficiency X-ray detectors. Farrier Microengineering, working with colleagues at KA Imaging and the University of Waterloo, successfully achieved the critical objectives of the Phase I proposal. We have proven the technical feasibility of this innovation by building and successfully testing a 1Mpixel a-Se/CMOS hybrid direct X-ray imaging array. Our commercialization roadmap of detector products will be implemented during a phase II project. We will demonstrate a migration of the technology to larger detector array sizes, improved temporal performance with detector layer engineering, and improved environmental and operational stability. Manufacturability and cost effectiveness are key objectives and product designs will leverage current state of the art CMOS wafer fabrication quality and volume manufacturing economics.

36 MATERIALS SCIENCE↗

Legacy surface change analyses of the 1993 Rock Valley earthquake sequence for direct comparison to planned NA-22 underground conventional high-explosive experiments (Source Physics Experiment 3 (SPE3) - RV/DC Task 1.3 FY22 Final Report)

Recent work under two previous phases of the NNSA NA-22 Source Physics Experiment (SPE) have shown that underground chemical high-explosive experiments can produce detectable surface changes that differ in spatial extent and vertical magnitude depending on the geologic media at the site (Schultz-Fellenz et al., 2018; 2020; Crawford et al., 2021). Neither of these two prior phases of SPE identified natural earthquake-related surface effects in the same region that occurred at a similar depth as the explosive experiments for direct comparison. While earthquakes can also produce surface changes that are detectable using remote sensing data analyses, it is expected that the pattern and spatial extent of surface changes would vary between earthquakes and explosions. However, no direct-observed surface-change signature comparison between earthquakes and explosions has ever been performed. The SPE Phase 3 Rock Valley/Direct Comparison (RV/DC) program presents a unique opportunity to investigate and characterize co-occurrence of both earthquakes and explosions. In this project, we worked to address five tasks in a workflow, as follows: 1. Identify and obtain existing high-resolution legacy satellite and aerial imagery as close in time before and after the 1993 Rock Valley earthquake sequence to temporally constrain the analyses. 2. Transform these pre-earthquake and post-earthquake datasets into digital elevation models (DEMs) using geospatial analysis software packages (e.g., ArcGIS, Agisoft Metashape, and Google Earth Engine). 3. Perform DEM differencing analyses to assess and quantify earthquake-related changes from the 1993 sequence, and develop map products that visualize these analyses. 4. Use the analyses from (3) to: (a) assess spatial distribution and magnitude of surface changes due to the 1993 Rock Valley earthquake sequence, and (b) determine parameters of forthcoming, planned explosion-related surface change data collection from sensors mounted on unmanned aerial vehicles (UAVs) (e.g., spatial extent of collection, design and density of survey control, sensors to deploy, forward speed and line spacing of UAV flight lines, flight altitude). 5. Develop a summary report on the analyses, including how the analyses define parameters and identify focus areas for any future surface change analytical field campaigns related to the explosive experiment. Analyzing these legacy data and identifying whether they can detect any surface changes related to the earthquake sequence facilitates opportunities for direct signature comparison of surface change from explosions at one location, which has never previously been performed. Comparing the surface change signatures from a co-located and depth-equivalent earthquake and an explosion could help to advance remote sensing event discrimination techniques. This report summarizes the work completed toward this ambitious goal.

42 ENGINEERING↗

THE HYDROGEN EPOCH OF REIONIZATION ARRAY DISH. I. BEAM PATTERN MEASUREMENTS AND SCIENCE IMPLICATIONS

The Hydrogen Epoch of Reionization Array (HERA) is a radio interferometer aiming to detect the power spectrum of 21 cm fluctuations from neutral hydrogen from the epoch of reionization (EOR). Drawing on lessons from the Murchison Widefield Array and the Precision Array for Probing the EOR, HERA is a hexagonal array of large (14 m diameter) dishes with suspended dipole feeds. The dish not only determines overall sensitivity, but also affects the observed frequency structure of foregrounds in the interferometer. This is the first of a series of four papers characterizing the frequency and angular response of the dish with simulations and measurements. In this paper, we focus on the angular response (i.e., power pattern), which sets the relative weighting between sky regions of high and low delay and thus apparent source frequency structure. We measure the angular response at 137 MHz using the ORBCOMM beam mapping system of Neben et al. We measure a collecting area of 93 m{sup 2} in the optimal dish/feed configuration, implying that HERA-320 should detect the EOR power spectrum at z ∼ 9 with a signal-to-noise ratio of 12.7 using a foreground avoidance approach with a single season of observations and 74.3 using a foreground subtraction approach. Finally, we study the impact of these beam measurements on the distribution of foregrounds in Fourier space.

79 ASTRONOMY AND ASTROPHYSICS↗

Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks

Here this paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%.

42 ENGINEERING↗

Assessing the Impact of Lightning NOx Emissions in CMAQ Using Lightning Flash Data from WWLLN over the Contiguous United States

Comparison of lightning flash data from the National Lightning Detection Network (NLDN) and from the World Wide Lightning Location Network (WWLLN) over the contiguous United States (CONUS) for the 2016–2018 period reveals temporally and spatially varying flash rates that would influence lightning NO x (LNO x ) production due to known detection efficiency differences especially during summer months over land (versus over ocean). However, the lightning flash density differences between the two networks show persistent seasonal patterns over geographical regions (e.g., land versus ocean). Since the NLDN data are considered to have higher accuracy (lightning detection with >95% efficiency), we developed scaling factors for the WWLLN flash data based on the ratios of WWLLN to NLDN flash data over time (months of year) and space. In this study, sensitivity simulations using the Community Multiscale Air Quality (CMAQ) model are performed utilizing the original data sets (both NLDN and WWLLN) and the scaled WWLLN flash data for LNO x production over the CONUS. The model performance of using the different lightning flash datasets for ambient O 3 and NO x mixing ratios that are directly impacted by LNO x emissions and the wet and dry deposition of oxidized nitrogen species that are indirectly impacted by LNO x emissions is assessed based on comparisons with ground-based observations, vertical profile measurements, and satellite products. During summer months, the original WWLLN data produced less LNO x emissions (due to its lower lightning detection efficiency) compared to the NLDN data, which resulted in less improvement in model performance than the simulation using NLDN data as compared to the simulation without any LNO x emissions. However, the scaled WWLLN data produced LNO x estimates and model performance comparable with the NLDN data, suggesting that scaled WWLLN may be used as a substitute for the NLDN data to provide LNO x estimates in air quality models when the NLDN data are not available (e.g., due to prohibitive cost or lack of spatial coverage).

54 ENVIRONMENTAL SCIENCES↗

A unifying Bayesian framework for merging X-ray diffraction data

Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering.

59 BASIC BIOLOGICAL SCIENCES↗

Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning

Coulomb explosion imaging (CEI) is a powerful technique for capturing the real-time motion of individual atoms during ultrafast photochemical reactions. CEI generates high-dimensional data with naturally embedded correlations that allow mapping the coordinated motion of nuclei in molecules. This enables reliable separation of competing reaction pathways and makes this approach uniquely suited for characterizing weak reaction channels. However, rich information contained in experimental CEI patterns remains largely underexploited due to challenges in visualizing correlations between multiple observables in multi-dimensional parameter space. Here we present a new approach to CEI of intermediate-sized polyatomic molecules, detecting up to eight ionic fragments in coincidence and leveraging machine-learning-based analysis to identify patterns and correlations in the resulting high-dimensional momentum-space data, enabling robust molecular structure identification and differentiation. Our approach provides high-dimensional background-free data encoding exceptionally rich structural information and establishes an automated, scalable framework for extracting insightful information from the data. As a demonstration, we apply this method to image and distinguish dichloroethylene isomers, showcasing its potential for broader applications in molecular imaging. Our results pave the way for channel-specific analysis of ultrafast structural dynamics in chemically relevant systems, particularly for disentangling mixed reaction pathways and detecting contributions from weak channels and minority species.

Chemical Physics (physics.chem-ph)↗

Rapid assessment of beech leaf disease in Fagus sylvatica buds

Abstract The European beech (Fagus sylvatica) is threatened by the foliar nematodeLitylenchus crenataesubsp.mccannii(Lcm), the causal agent of beech leaf disease (BLD). Thus far, the majority of studies regarding BLD have focused on American beech (F. grandifolia). To better determine the impact of Lcm in buds of European beech, a total of 54 buds were collected from naturally symptomatic trees. Here, we characterized for the first time the bud scale morphology of two different cultivars ofF. sylvaticainfected with Lcm. Detailed observations of asymptomatic and symptomatic bud scales provided insight into the physical changes and arrangements of cells in the bud scale, shedding light on the dynamic processes occurring during Lcm infection. In addition, we evaluated the suitability of using the bud scale morphology for the early detection of BLD and Lcm in naturally infected buds. The distinct cellular arrangement of symptomatic bud scales cells (i.e., asymmetric pattern of enlarged cells) provides a rapid and visual, user‐friendly methodology to prematurely diagnose BLD symptoms within the buds, as well as the detection of associated nematodes.

Forestry↗

Automated Image Segmentation and Processing Pipeline Applied to X–Ray Computed Tomography Studies of Pitting Corrosion in Aluminum Wires

Understanding pitting corrosion is critical, yet its kinetics and morphology remain challenging to study from X-ray computed tomography (XCT) due to manual segmentation barriers. To address this, an automated pipeline leveraging deep learning for efficient large-scale XCT analysis is developed, revealing new corrosion insights. The pipeline enables pit segmentation, 3D reconstruction, statistical characterization, and a topological transformation for visualization. Here, the pipeline is applied to 87 648 XCT images capturing commercial purity aluminum (1100 Al) wire exposed to sodium chloride (NaCl) salt particles over a period of 122 h. The pipeline achieves complete feature extraction and statistical quantification across the entire XCT dataset, leveraging distributed computing environment for high efficiency. Global growth kinetics such as high-level stepwise sigmoidal volume loss patterns and granular individual pit developments are both captured for 36 detected pits. By combining automation, computer vision, and extensive XCT datasets, this research accelerates precise corrosion assessment to enable materials science discoveries at scale.

36 MATERIALS SCIENCE↗

Hardenability and microstructural evolution of a precipitation strengthened Ni 50 Ti 21 Hf 25 Al 4 alloy

NiTi-based quaternary alloys are used in a variety of mechanical components, such as bearings, actuators, and dampers, owing to their good hardenability, wear resistance, and corrosion resistance. Additionally, one of the most notable characteristics of NiTi-based alloys is their shape memory effect and pseudoelastic properties. Connecting the macroscopic processing parameters employed in the design of new intermetallic alloys to the nanoscale structural characteristics dictating their behavior is crucial for improving their mechanical properties and expanding the spectrum of potential applications. Here, in this work, an arc melted Ni 50 Ti 21 Hf 25 Al 4 (at%) alloy was solution treated at 1050 °C followed by quenching and aging at 600 °C to investigate the effect of aging time on the microstructure and mechanical properties. Two types of nano-sized precipitates were observed and determined as face-centered orthorhombic H-phase (TiHf)Ni and L2 1 Heusler precipitates Ni 2 TiAl. The morphology and orientation of the H-phase were investigated using scanning and transmission electron microscopy (SEM and TEM), elucidating the coarsening kinetics and strengthening contribution of that phase to the intermetallic mechanical behavior. Following coarsening, the presence of Heusler nanoprecipitates was detected under overaged conditions through TEM imaging and nanobeam electron diffraction patterns. A peak hardness condition of 756 HV was achieved after 70 h of aging, indicating that the co-precipitation of H-phase and Heusler precipitates through a well-designed aging treatment can lead to optimal mechanical performance, thus elevating the alloy’s potential as a viable material for industrial applications.

36 MATERIALS SCIENCE↗

Keratinophilic fungi: Specialized fungal communities in a desert ecosystem identified using cultured-based and Illumina sequencing approaches

Soil fungi in desert ecosystems are adapted to harsh environmental conditions such as high soil surface temperatures and limited organic matter and water. Given limited carbon inputs from plant material, heterotrophic fungi likely use unconventional sources of carbon in these systems. A baiting method was used to culture keratinophilic fungi from biocrust and rhizosphere soils in an arid grassland in Utah, USA. Fungi were baited using llama and sheep wool, horsehair, and snakeskin on two media, and pure cultures were identified using ITS and LSU rRNA sequences. One hundred-eighteen fungal colonies were grown, representing a total of 32 Operational Taxonomic Units (OTUs) at 97 % similarity. Cultures were dominated by the phylum Ascomycota (88 %) followed by Mucoromycota (8.6 %) and Basidiomycota (3.4 %). The orders Pleosporales, Eurotiales, Hypocreales, and Sordariales were commonly isolated, with the dominant taxa Alternaria (27 %), Aspergillus (22 %), Fusarium (11 %), and Chaetomium (8%). Thirty percent of the fungi isolated have the capacity to degrade keratin in vitro using a keratin azure assay, with Penicillium showing the highest degradation followed by Geomyces, Alternaria, and Fusarium. Although keratin degraders can be infectious, dermatophytes associated with skin infections were not isolated in culture or detected in Illumina sequencing. Illumina sequencing was used to determine general patterns in seasonal variation and habitat preference of keratinophiles. Alternaria was the most abundant genus with >70 % of the sequences. The combination of Illumina data with culture-dependent approaches facilitated the characterization of a specialized community and confirmed the low abundance of dermatophytes in this arid site.

59 BASIC BIOLOGICAL SCIENCES↗

V-ATPase as an effective therapeutic target for sarcomas

Malignant tumors show intense glycolysis and, as a consequence, high lactate production and proton efflux activity. We investigated proton dynamics in osteosarcoma, rhabdomyosarcoma, and chondrosarcoma, and evaluated the effects of esomeprazole as a therapeutic agent interfering with tumor acidic microenvironment. All sarcomas were able to survive in an acidic microenvironment (up to 5.9–6.0 pH) and abundant acidic lysosomes were found in all sarcoma subtypes. V-ATPase, a proton pump that acidifies intracellular compartments and transports protons across the plasma membrane, was detected in all cell types with a histotype-specific expression pattern. Esomeprazole administration interfered with proton compartmentalization in acidic organelles and induced a significant dose-dependent toxicity. Among the different histotypes, rhabdomyosarcoma, expressing the highest levels of V-ATPase and whose lysosomes are most acidic, was mostly susceptible to ESOM treatment. - Highlights: • Osteosarcoma, rhabdomyosarcoma, and chondrosarcoma survive in acidic microenvironment. • At acidic extracellular pH, sarcoma survival is dependent on V-ATPase expression. • Esomeprazole administration induce a significant dose-dependent toxicity.

60 APPLIED LIFE SCIENCES↗

Heavy metal imprints in Antarctic snow from research and tourism

Antarctica, long regarded as one of the last pristine environments on Earth, is increasingly affected by human activity. As tourism surges and scientific operations expand, air pollution from local emissions is raising new environmental concerns. Here, in this study, we analyze surface snow samples collected along a ~2,000 km transect, from the South Shetland Islands (62°S) to the Ellsworth Mountains (79°S), to map the geochemical fingerprints of aerosol deposition. We identify distinct spatial patterns shaped by crustal, marine, biogenic, and anthropogenic sources. Notably, we detect heavy metal imprints in the snow chemistry of the northern Antarctic Peninsula, where major research stations are concentrated and marine tourism traffic is most intense. Our findings shed light on the extent of the impacts from energy-intensive local activities in Antarctica, underscoring the need for enhanced environmental monitoring and sustainable management strategies in this fragile region.

Cordero, Raúl R. [Univ. of Groningen, Leeuwarden (↗

Electromechanical response of amorphous LaAlO{sub 3} thin film probed by scanning probe microscopies

The electromechanical response of a 3 nm thick amorphous LaAlO{sub 3} layer obtained by molecular beam epitaxy has been studied using scanning probe microscopies. Although this kind of sample is not ferroelectric due to its amorphous nature, the resulting images are identical to what is generally obtained on truly ferroelectric samples probed by piezoresponse force microscopy: domains of apparently opposite polarisation are detected, and perfect, square shaped hysteresis loops are recorded. Moreover, written patterns are stable within 72 h. We discuss in the general case the possible origins of this behaviour in terms of charge injection, ionic conduction and motion of oxygen vacancies. In the case presented in this paper, since the writing process has been conducted with applied voltages lower than the injection threshold measured by conductive atomic force Microscopy, allowing to withdraw the hypothesis of charge injection in the sample, we propose that a bistable distribution of oxygen vacancies is responsible for this contrast.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Nanometer scale structural and compositional inhomogeneities of half-Heusler CoTi 1-x Fe x Sb thin films

The correlative use of X-ray diffraction (XRD), scanning transmission electron microscopy (STEM), and atom probe tomography (APT) allows the structure of substitutionally alloyed half-Heusler compound CoTi 1-x Fe x Sb to be characterized at the nanometer scale. For x = 0.2, 0.3, and 0.5, XRD patterns indicate an epitaxial cube-on-cube growth with no detectable secondary phases and are suggestive of high crystalline quality for all three of the films. This is supported using atomic resolution STEM images, which revealed the absence of secondary phases or polycrystalline regions, thus confirming the half-Heusler structure of the CoTi 1-x Fe x Sb films. For all three samples, the APT reconstructions were optimized to resolve the atomic planes in the [001] growth direction allowing the exploitation of reliable datasets. We note the presence of Fe-rich phases in the samples with Fe atoms sitting on Ti crystallographic sites is revealed. A strong phase separation is observed for x = 0.2 and x = 0.3 and is almost no longer observed for x = 0.5 in good agreement with the magnetic properties of the films.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗