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The study of atomically thin ferromagnetic crystals has led to the discovery of unusual magnetic behaviour and provided insight into the magnetic properties of bulk materials. However, the experimental techniques that have been used to explore ferromagnetism in such materials cannot probe the magnetic field directly. Here, we show that ballistic Hall micromagnetometry can be used to measure the magnetization of individual two-dimensional ferromagnets. Our devices are made by van der Waals assembly in such a way that the investigated ferromagnetic crystal is placed on top of a multi-terminal Hall bar made from encapsulated graphene. We use the micromagnetometry technique to study atomically thin chromium tribromide (CrBr3). We find that the material remains ferromagnetic down to monolayer thickness and exhibits strong out-of-plane anisotropy. We also find that the magnetic response of CrBr3 varies little with the number of layers and its temperature dependence cannot be described by the simple Ising model of two-dimensional ferromagnetism.
Motivated by recent progress on synthesizing two-dimensional magnetic van der Waals systems, we propose a setup for detecting the topological Berezinskii-Kosterlitz-Thouless (BKT) phase transition in spin-transport experiments on such structures. We
The manipulation of topologically protected field configurations, already predicted and experimentally observed in non-centrosymmetric magnets, as skyrmions, merons and antimerons could definitely have potential applications in logic gate operations
We have analyzed magnetic-field-dependent small-angle neutron scattering (SANS) data of soft magnetic two-phase nanocomposite ferromagnets in terms of a recent micromagnetic theory for the magnetic SANS cross section [D. Honecker and A. Michels, Phys
An important problem in contemporary physics concerns quantum-critical fluctuations in metals. A scaling function for the momentum, frequency, temperature and magnetic field dependence of the correlation function near a 2D-ferromagnetic quantum-criti
Principles of machine learning are applied to models that support skyrmion phases in two dimensions. Successful feature predictions on various phases of the skyrmion model were possible with several layers of convolutional neural network inserted tog