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

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

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

Plasmonic properties of aluminium nanowires in amorphous silicon

Plasmonic structures can help enhance optical activity in the ultraviolet (UV) region and therefore enhancing photocatalytic reactions and the detection of organic and biological species. Most plasmonic structures are composed of Ag or Au. However, producing structures small enough for optical activity in the UV region has proved difficult. In this study, we demonstrate that aluminium nanowires are an excellent alternative. We investigated the plasmonic properties of the Al nanowires as well as the optoelectronic properties of the surrounding a − Si matrix by combining scanning transmission electron microscopy imaging, electron energy loss spectroscopy and electrodynamic modelling. We have found that the Al nanowires have distinct plasmonic modes in the UV and far UV region, from 0.75 eV to 13 eV. In addition, simulated results found that the size and spacing of the Al nanowires, as well as the embedding material were shown to have a large impact on the type of surface plasmon energies that can be generated in the material. Using electromagnetic modelling, we have identified the modes and illustrated how they could be tuned further.publishedVersio

Mapping grain boundary heterogeneity at the nanoscale in a positive temperature coefficient of resistivity ceramic

Despite being of wide commercial use in devices, the orders of magnitude increase in resistance that can be seen in some semiconducting BaTiO3-based ceramics, on heating through the Curie temperature (TC), is far from well understood. Current understanding of the behavior hinges on the role of grain boundary resistance that can be modified by polarization discontinuities which develop in the ferroelectric state. However, direct nanoscale resistance mapping to verify this model has rarely been attempted, and the potential approach to engineer polarization states at the grain boundaries, that could lead to optimized positive temperature coefficient (PTC) behavior, is strongly underdeveloped. Here we present direct visualization and nanoscale mapping in a commercially optimized BaTiO3-PbTiO3-CaTiO3 PTC ceramic using Kelvin probe force microscopy, which shows that, even in the low resistance ferroelectric state, the potential drop at grain boundaries is significantly greater than in grain interiors. Aberration-corrected scanning transmission electron microscopy and electron energy loss spectroscopy reveal new evidence of Pb-rich grain boundaries symptomatic of a higher net polarization normal to the grain boundaries compared to the purer grain interiors. These results validate the critical link between optimized PTC performance and higher local polarization at grain boundaries in this specific ceramic system and suggest a novel route towards engineering devices where an interface layer of higher spontaneous polarization could lead to enhanced PTC functionality