The discovery of two-dimensional (2D) magnetism within atomically thin structures obtained from layered magnetic crystals has opened up a new realm for exploring magnetic heterostructures. This emerging field provides a foundational platform for investigating unique physical properties and exquisite phenomena at the nanometer and molecular/atomic scales. By engineering 2D interfaces using physical methods and selecting interlayer interactions, we can unlock the potential for extraordinary exchange dynamics, which extends to high-performance and high-density magnetic memory applications, as well as future advancements in neuromorphic and quantum computing. This review delves into recent advances in magnetic 2D materials, elucidates the mechanisms behind 2D interfaces, and highlights the development of 2D devices for spintronics and quantum information processing. Particular focus is placed on 2D magnetic heterostructures with topological properties, promising a resilient and low-error information system. Finally, we discuss the trends of 2D heterostructures for future electronics, considering the challenges and opportunities from physics, material synthesis, and technological perspectives.

Multiple magnetic skyrmion phases add an additional degree of freedom for skyrmion‐based ultrahigh‐density spin memory devices. Extending the field to 2D van der Waals magnets is a rewarding challenge, where the realizable degree of freedoms (e.g., thickness, twist angle, and electrical gating) and high skyrmion density result in intriguing new properties and enhanced functionality. In this work, a van der Waals interface, formed by two 2D ferromagnets Cr₂Ge₂Te₆ and Fe₃GeTe₂ with a Curie temperature of ≈65 and ≈205 K, respectively, hosting two groups of magnetic skyrmions, is reported. Two sets of topological Hall effect signals are observed below 6s0 K when Cr₂Ge₂Te₆ is magnetically ordered. These two groups of skyrmions are directly imaged using magnetic force microscopy, and supported by micromagnetic simulations. Interestingly, the magnetic skyrmions persist in the heterostructure with zero applied magnetic field. The results are promising for the realization of skyrmionic devices based on van der Waals heterostructures hosting multiple skyrmion phases.

Two-dimensional materials are a class of atomically thin materials with assorted electronic and quantum properties. Accurate identification of layer thickness, especially for a single monolayer, is crucial for their characterization. This characterization process, however, is often time-consuming, requiring highly skilled researchers and expensive equipment like atomic force microscopy. This project aims to streamline the identification process by using machine learning to analyze optical images and quickly determine layer thickness. In this paper, we evaluate the performance of three machine learning models - SegNet, 1D U-Net, and 2D U-Net- in accurately identifying monolayers in microscopic images. Additionally, we explore labeling and image processing techniques to determine the most effective and accessible method for identifying layer thickness in this class of materials.

The unveiling of two-dimensional (2D) van der Waals magnetism ignited a surge of interest in low-dimensional magnetism. With dimensions reduced, research has delved into facile electric control of 2D magnetism, high-quality heterostructure design, and new device functionality. These atomically thin magnetic materials have spawned a burgeoning field known as 2D spintronics, holding immense promise for future quantum technologies. In this review, we comprehensively survey the current advancements in 2D magnet-based quantum devices, accentuating their role in manifesting exotic properties and enabling novel functionalities. Topological states, spin torques, voltage control of magnetic anisotropy, strain engineering, twistronics, and designer interface will be discussed. Furthermore, we offer an outlook for their development in future CMOS and quantum hardware paradigms.

A novel multilayer film based on gold nanoparticles (AuNPs), thionine (Thi), and TiO₂–graphene (TiO₂–Gr) was exploited to develop a highly sensitive amperometric immunosensor for detecting carcinoembryonic antigen (CEA). Firstly, Nafion–TiO₂–Gr homogeneous composite was dropped on the surface of a glassy carbon electrode (GCE). Then Thi was chemisorbed by the TiO₂–Gr–Nafion composite. Furthermore, the negative ly charged AuNPs were chemisorbed onto Thi film through the electrostatic force with the amino groups of Thi. Cyclic voltammetry (CV) was employed to characterize the assembly process and the performance of the immunosensor. Because of the synergistic effect of the AuNPs, Thi, and the unique properties of TiO₂–Gr, the obtained immunosensor exhibited a wide linear response to CEA in two ranges from 0.1 to 10.0 ng mL⁻¹ and from 10.0 to 120.0 ng mL⁻¹ with a relatively low detection limit of 0.01 ng mL⁻¹ (S/N = 3), as well as good stability and repeatability.

The demand for computing power has been growing exponentially with the rise of artificial intelligence (AI), machine learning, and the Internet of Things (IoT). This growth requires unconventional computing primitives that prioritize energy efficiency, while also addressing the critical need for scalability. Neuromorphic computing, inspired by the biological brain, offers a transformative paradigm for addressing these challenges. This review paper provides an overview of advancements in 2D spintronics and device architectures designed for neuromorphic applications, with a focus on techniques such as spin-orbit torque, magnetic tunnel junctions, and skyrmions. Emerging van der Waals materials like CrI₃, Fe₃GaTe₂, and graphene-based heterostructures have demonstrated unparalleled potential for integrating memory and logic at the atomic scale. This work highlights technologies with ultra-low energy consumption (0.14 fJ/operation), high switching speeds (sub-nanosecond), and scalability to sub-20 nm footprints. It covers key material innovations and the role of spintronic effects in enabling compact, energy-efficient neuromorphic systems, providing a foundation for advancing scalable, next-generation computing architectures.