The outstanding optoelectronic and valleytronic properties of transition metal dichalcogenides (TMDs) have triggered intense research efforts by the scientific community. An alternative to induce long‐range ferromagnetism (FM) in TMDs is by introducing magnetic dopants to form a dilute magnetic semiconductor. Enhancing ferromagnetism in these semiconductors not only represents a key step toward modern TMD‐based spintronics, but also enables exploration of new and exciting dimensionality‐driven magnetic phenomena. To this end, tunable ferromagnetism at room temperature and a thermally induced spin flip (TISF) in monolayers of V‐doped WSe2 are shown. As vanadium concentration increases, the saturation magnetization increases, which is optimal at ≈4 at% vanadium; the highest doping level ever achieved for V‐doped WSe2 monolayers. The TISF occurs at ≈175 K and becomes more pronounced upon increasing the temperature toward room temperature. The TISF can be manipulated by changing the vanadium concentration. The TISF is attributed to the magnetic‐field‐ and temperature‐dependent flipping of the nearest W‐site magnetic moments that are antiferromagnetically coupled to the V magnetic moments in the ground state. This is fully supported by a recent spin‐polarized density functional theory study. The findings pave the way for the development of novel spintronic and valleytronic nanodevices and stimulate further research.

Atomically thin transition metal dichalcogenide (TMD) semiconductors hold enormous potential for modern optoelectronic devices and quantum computing applications. By inducing long-range ferromagnetism (FM) in these semiconductors through the introduction of small amounts of a magnetic dopant, it is possible to extend their potential in emerging spintronic applications. Here, we demonstrate light-mediated, room temperature (RT) FM, in V-doped WS 2 (V-WS 2 ) monolayers. We probe this effect using the principle of magnetic LC resonance, which employs a soft ferromagnetic Co-based microwire coil driven near its resonance in the radio frequency (RF) regime. The combination of LC resonance with an extraordinary giant magneto-impedance effect, renders the coil highly sensitive to changes in the magnetic flux through its core. We then place the V-WS 2 monolayer at the core of the coil where it is excited with a laser while its change in magnetic permeability is measured. Notably, the magnetic permeability of the monolayer is found to depend on the laser intensity, thus confirming light control of RT magnetism in this two-dimensional (2D) material. Guided by density functional calculations, we attribute this phenomenon to the presence of excess holes in the conduction and valence bands, as well as carriers trapped in the magnetic doping states, which in turn mediates the magnetization of the V-WS 2 monolayer. These findings provide a unique route to exploit light-controlled ferromagnetism in low powered 2D spintronic devices capable of operating at RT.

Significance A large Raman dataset collected on a variety of viruses enables the training of machine learning (ML) models capable of highly accurate and sensitive virus identification. The trained ML models can then be integrated with a portable device to provide real-time virus detection and identification capability. We validate this conceptual framework by presenting highly accurate virus type and subtype identification results using a convolutional neural network to classify Raman spectra of viruses. The accurate and interpretable ML model developed for Raman virus identification presents promising potential in a real-time, label-free virus detection system that could be used in future outbreaks and pandemics.

Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this technically difficult and time consuming problem. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for spectral identification. Acquired data enable defect segmentation, and a workflow is provided for machine-driven decision making during experimentation with capability for user customization. We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Hyperspectral investigations on WS2 sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS2, Au face-centered cubic, and Au hexagonal close-packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping.

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/smll.202205800.