Observation of complete inversion of the hysteresis loop in a bimodal magnetic thin film

The existence of inverted hysteresis loops (IHLs) in magnetic materials is still in debate due to the lack of direct evidence and convincing theoretical explanations. Here we report the direct observation and physical interpretation of complete IHL in Ni45Fe55 films with 1 to 2 nm thin Ni3Fe secondary phases at the grain boundaries. The origin of the inverted loop, however, is shown to be due to the exchange bias coupling between Ni45Fe55 and Ni3Fe, which can be broken by the application of a high magnetic field. A large positive exchange bias (HEB=14×HC) is observed in the NiFe composite material giving novel insight into the formation of a noninverted hysteresis loop (non-IHL) and IHL, which depend on the loop tracing field range (HR). The crossover from non-IHL to IHL is found to be at 688 Oe

Comparative of machine learning classification strategies for electron energy loss spectroscopy: Support vector machines and artificial neural networks

Machine Learning (ML) strategies applied to Scanning and conventional Transmission Electron Microscopy have become a valuable tool for analyzing the large volumes of data generated by various S/TEM techniques. In this work, we focus on Electron Energy Loss Spectroscopy (EELS) and study two ML techniques for classifying spectra in detail: Support Vector Machines (SVM) and Artificial Neural Networks (ANN). Firstly, we systematically analyze the optimal configurations and architectures for ANN classifiers using random search and the tree-structured Parzen estimator methods. Secondly, a new kernel strategy is introduced for the soft-margin SVMs, the cosine kernel, which offers a significant advantage over the previously studied kernels and other ML classification strategies. This kernel allows us to bypass the normalization of EEL spectra, achieving accurate classification. This result is highly relevant for the EELS community since we also assess the impact of common normalization techniques on our spectra using Uniform Manifold Approximation and Projection (UMAP), revealing a strong bias introduced in the spectra once normalized. In order to evaluate and study both classification strategies, we focus on determining the oxidation state of transition metals through their EEL spectra, examining which feature is more suitable for oxidation state classification: the oxygen K peak or the transition metal white lines. Subsequently, we compare the resistance to energy loss shifts for both classifiers and present a strategy to improve their resistance. The results of this study suggest the use of soft-margin SVMs for simpler EELS classification tasks with a limited number of spectra, as they provide performance comparable to ANNs while requiring lower computational resources and reduced training times. Conversely, ANNs are better suited for handling complex classification problems with extensive training data

Prospects for Engineering Thermoelectric Properties in La _1/3NbO ₃ Ceramics Revealed via Atomic-Level Characterization and Modeling

A combination of experimental and computational techniques has been employed to explore the crystal structure and thermoelectric properties of A-site-deficient perovskite La 1/3NbO 3 ceramics. Crystallographic data from X-ray and electron diffraction confirmed that the room temperature structure is orthorhombic with Cmmm as a space group. Atomically resolved imaging and analysis showed that there are two distinct A sites: one is occupied with La and vacancies, and the second site is fully unoccupied. The diffuse superstructure reflections observed through diffraction techniques are shown to originate from La vacancy ordering. La 1/3NbO 3 ceramics sintered in air showed promising high-temperature thermoelectric properties with a high Seebeck coefficient of S 1 = -650 to -700 μV/K and a low and temperature-stable thermal conductivity of k = 2-2.2 W/m·K in the temperature range of 300-1000 K. First-principles electronic structure calculations are used to link the temperature dependence of the Seebeck coefficient measured experimentally to the evolution of the density of states with temperature and indicate possible avenues for further optimization through electron doping and control of the A-site occupancies. Moreover, lattice thermal conductivity calculations give insights into the dependence of the thermal conductivity on specific crystallographic directions of the material, which could be exploited via nanostructuring to create high-efficiency compound thermoelectrics. </p

Isotopic compositions, nitrogen functional chemistry, and low‐loss electron spectroscopy of complex organic aggregates at the nanometer scale in the carbonaceous chondrite Renazzo

Organic matter (OM) was widespread in the early solar nebula and might have played an important role for the delivery of prebiotic molecules to the early Earth. We investigated the textures, isotopic compositions, and functional chemistries of organic grains in the Renazzo carbonaceous chondrite by combined high spatial resolution techniques (electron microscopy–secondary ion mass spectrometry). Morphologies are complex on a submicrometer scale, and some organics exhibit a distinct texture with alternating layers of OM and minerals. These layered organics are also characterized by heterogeneous 15N isotopic abundances. Functional chemistry investigations of five focused ion beam‐extracted lamellae by electron energy loss spectroscopy reveal a chemical complexity on a nanometer scale. Grains show absorption at the C‐K edge at 285, 286.6, 287, and 288.6 eV due to polyaromatic hydrocarbons, different carbon‐oxygen, and aliphatic bonding environments with varying intensity. The nitrogen K‐edge functional chemistry of three grains is shown to be highly complex, and we see indications of amine (C‐NHx) or amide (CO‐NR2) chemistry as well as possible N‐heterocycles and nitro groups. We also performed low‐loss vibrational spectroscopy with high energy resolution and identified possible D‐ and G‐bands known from Raman spectroscopy and/or absorption from C=C and C‐O stretch modes known from infrared spectroscopy at around 0.17 and 0.2 eV energy loss. The observation of multiglobular layered organic aggregates, heterogeneous 15N‐anomalous compositions, and indication of NHx‐(amine) functional chemistry lends support to recent ideas that 15N‐enriched ammonia (NH3) was a powerful agent to synthesize more complex organics in aqueous asteroidal environments

Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy : Generative Adversarial Networks

Recent advances in machine learning (ML) have highlighted a novel challenge concerning the quality and quantity of data required to effectively train algorithms in supervised ML procedures. This article introduces a data augmentation (DA) strategy for electron energy loss spectroscopy (EELS) data, employing generative adversarial networks (GANs). We present an innovative approach, called the data augmentation generative adversarial network (DAG), which facilitates data generation from a very limited number of spectra, around 100. Throughout this study, we explore the optimal configuration for GANs to produce realistic spectra. Notably, our DAG generates realistic spectra, and the spectra produced by the generator are successfully used in real-world applications to train classifiers based on artificial neural networks (ANNs) and support vector machines (SVMs) that have been successful in classifying experimental EEL spectra

Accurate EELS background subtraction – an adaptable method in MATLAB

Electron energy-loss spectroscopy (EELS) is a technique that can give useful information on elemental composition and bonding environments. However in practice, the complexity of the background contributions, which can arise from multiple sources, can hamper the interpretation of the spectra. As a result, background removal is both an essential and difficult part of EELS analysis, especially during quantification of elemental composition. Typically, a power law is used to fit the background but this is often not suitable for many spectra such as in the low-loss region (< 50 eV) and when there are overlapping EELS edges. In this article, we present a series of scripts written in MATLAB v. R2019b that aims to provide statistical information on the model used to fit the background, allowing the user to determine the accuracy of background subtraction. The scripts were written for background subtraction of vibrational EELS in the ultralow-loss region near the zero-loss peak but can also be applied to other kinds of EEL spectra. The scripts can use a range of models for fitting, provided by the Curve Fitting Toolbox of MATLAB, and the user is able to precisely define the window for fitting as well as for edge integration. We demonstrate the advantages of using these scripts by comparing their background subtraction of example spectra to the most commonly used software, Gatan Microscopy Suite 3. The example spectra include those containing multiple scattering, multiple overlapping peaks, as well as vibrational EELS. Additionally, a comprehensive guide to using the scripts has been included in the Supplementary Information

Confined magnon dispersion in ferromagnetic and antiferromagnetic thin films in a second quantization approach: the case of Fe and NiO

We present a methodology based on the calculation of the inelastic scattering from magnons via the spin scattering function in confined geometries such as thin films using a second quantization formalism, for both ferromagnetic and antiferromagnetic materials. The case studies are chosen with an aim to demonstrate the effects of film thickness and crystal orientation on magnon modes, using bcc Fe(100) and NiO with (100) and (111) crystallographic orientations as prototypical systems. Due to the quantization of the quasi-momentum we observe a granularity in the inelastic spectra in the reciprocal space path reflecting the orientation of the thin film. This approach also allows to capture softer modes that appear due to the partial interaction of magnetic moments close to the surface in a thin film geometry, in addition to bulk modes. The softer modes are also affected by crystallographic orientations as illustrated by the different surface-related peaks of NiO magnon density of states at approximately ~ 65 meV for (100) and ~ 42 meV for (111). Additionally, we explore the role of anisotropy on magnon modes, revealing that introducing anisotropy to both Fe and NiO films increases the overall hardness of the magnon modes. The introduction of a surface anisotropy produces a shift of the surface-related magnon DOS peak to higher energies with increased surface anisotropy, and in some cases leading to surface confined mode

Atomic-Scale Spectroscopic Imaging of the Extreme-UV Optical Response of B- and N-Doped Graphene

Abstract Substitutional doping of graphene by impurity atoms such as boron and nitrogen, followed by atom-by-atom manipulation via scanning transmission electron microscopy, can allow for accurate tailoring of its electronic structure, plasmonic response, and even the creation of single atom devices. Beyond the identification of individual dopant atoms by means of ?Z contrast? imaging, spectroscopic characterization is needed to understand the modifications induced in the electronic structure and plasmonic response. Here, atomic scale spectroscopic imaging in the extreme UV-frequency band is demonstrated. Characteristic and energy-loss-dependent contrast changes centered on individual dopant atoms are highlighted. These effects are attributed to local dopant-induced modifications of the electronic structure and are shown to be in excellent agreement with calculations of the associated densities of states