Direct Evidence of a Graded Magnetic Interface in Bimagnetic Core/Shell Nanoparticles Using Electron Magnetic Circular Dichroism (EMCD)

Experimental evidence of magnetism in a 2D electron gas at the CoO/Co3O4 interface by employing EMCD

In the present study we investigate the CoO/Co3O4 interface in order to determine its intriguing magnetic behavior, which can be utilized for tailoring magnetic properties, enabling spin transport, enhancing magnetic coupling, tuning device functionalities, and realizing miniaturized magnetic devices for various technological applications. We decipher the magnetic properties of the CoO/Co3O4 interface from first principles calculations using Wien2k and probe them experimentally by employing electron energy-loss magnetic chiral dichroism (EMCD), which is an electron-energy loss spectrometry (EELS) based technique in the transmission electron microscope (TEM). Both, theory and experiment, are in perfect agreement and result in a ferromagnetic 2D-electron gas of 5Å thickness directly at the interface.

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.

Noise-dependent bias in quantitative STEM-EMCD experiments revealed by bootstrapping

Electron magnetic circular dichroism (EMCD) is a powerful technique for estimating element-specific magnetic moments of materials on nanoscale with the potential to reach atomic resolution in transmission electron microscopes. However, the fundamentally weak EMCD signal strength complicates quantification of magnetic moments, as this requires very high precision, especially in the denominator of the sum rules. Here, we employ a statistical resampling technique known as bootstrapping to an experimental EMCD dataset to produce an empirical estimate of the noise-dependent error distribution resulting from application of EMCD sum rules to bcc iron in a 3-beam orientation. We observe clear experimental evidence that noisy EMCD signals preferentially bias the estimation of magnetic moments, further supporting this with error distributions produced by Monte-Carlo simulations. Finally, we propose guidelines for the recognition and minimization of this bias in the estimation of magnetic moments.

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.

Elucidating Individual Magnetic Contributions in Bi-Magnetic Fe3 O4 /Mn3 O4 Core/Shell Nanoparticles by Polarized Powder Neutron Diffraction

Heterogeneous bi-magnetic nanostructured systems have had a sustained interest during the last decades owing to their unique magnetic properties and the wide range of derived potential applications. However, elucidating the details of their magnetic properties can be rather complex. Here, a comprehensive study of Fe3 O4 /Mn3 O4 core/shell nanoparticles using polarized neutron powder diffraction, which allows disentangling the magnetic contributions of each of the components, is presented. The results show that while at low fields the Fe3 O4 and Mn3 O4 magnetic moments averaged over the unit cell are antiferromagnetically coupled, at high fields, they orient parallel to each other. This magnetic reorientation of the Mn3 O4 shell moments is associated with a gradual evolution with the applied field of the local magnetic susceptibility from anisotropic to isotropic. Additionally, the magnetic coherence length of the Fe3 O4 cores shows some unusual field dependence due to the competition between the antiferromagnetic interface interaction and the Zeeman energies. The results demonstrate the great potential of the quantitative analysis of polarized neutron powder diffraction for the study of complex multiphase magnetic materials.

Redox phase transformations in magnetite nanoparticles: impact on their composition, structure and biomedical applications

Magnetite nanoparticles (NPs) are one of the most investigated nanomaterials so far and modern synthesis methods currently provide an exceptional control of their size, shape, crystallinity and surface functionalization. These advances have enabled their use in different fields ranging from environmental applications to biomedicine. However, several studies have shown that the precise composition and crystal structure of magnetite NPs depend on their redox phase transformations, which have a profound impact on their physicochemical properties and, ultimately, on their technological applications. Although the physical mechanisms behind such chemical transformations in bulk materials have been known for a long time, experiments on NPs with large surface-to-volume ratios have revealed intriguing results. This article is focused on reviewing the current status of the field. Following an introduction on the fundamental properties of magnetite and other related iron oxides (including maghemite and wüstite), some basic concepts on the chemical routes to prepare iron oxide nanomaterials are presented. The key experimental techniques available to study phase transformations in iron oxides, their advantages and drawbacks to the study of nanomaterials are then discussed. The major section of this work is devoted to the topotactic oxidation of magnetite NPs and, in this regard, the cation diffusion model that accounts for the experimental results on the kinetics of the process is critically examined. Since many synthesis routes rely on the formation of monodisperse magnetite NPs via oxidation of wüstite counterparts, the modulation of their physical properties by crystal defects arising from the oxidation process is also described. Finally, the importance of a precise control of the composition and structure of magnetite-based NPs is discussed and its role in their biomedical applications is highlighted.