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Abstract Two-dimensional (2D) ferroelectric and magnetic van der Waals materials are emerging platforms for the discovery of novel cooperative quantum phenomena and development of energy-efficient logic and memory applications as well as neuromorphic and topological computing. This review presents a comprehensive survey of the rapidly growing 2D ferroic family from the synthesis perspective, including brief introductions to the top-down and bottom-up approaches for fabricating 2D ferroic flakes, thin films, and heterostructures as well as the important characterization techniques for assessing the sample properties. We also discuss the key challenges and future directions in the field, including scalable growth, property control, sample stability, and integration with other functional materials. 

Unique temperature dependences of the out-of-plane anomalous Hall effect and longitudinal magnetoresistance were observed, which can be attributed to the changing dominance between ferromagnetic and antiferromagnetic phases in the Fe₃GeTe₂sample. 

Understanding the thermal stability and degradation mechanism of β-Ga2O3 metal-oxide-semiconductor field-effect transistors (MOSFETs) is crucial for their high-power electronics applications. This work examines the high temperature performance of the junctionless lateral β-Ga2O3 FinFET grown on a native β-Ga2O3 substrate, fabricated by metal-assisted chemical etching with Al2O3 gate oxide and Ti/Au gate metal. The thermal exposure effect on threshold voltage (Vth), subthreshold swing (SS), hysteresis, and specific on-resistance (Ron,sp), as a function of temperature up to 298 °C, is measured and analyzed. SS and Ron,sp increased with increasing temperatures, similar to the planar MOSFETs, while a more severe negative shift of Vth was observed for the high aspect-ratio FinFETs here. Despite employing a much thicker epilayer (∼2 μm) for the channel, the high temperature performance of Ion/Ioff ratios and SS of the FinFET in this work remains comparable to that of the planar β-Ga2O3 MOSFETs reported using epilayers ∼10–30× thinner. This work paves the way for further investigation into the stability and promise of β-Ga2O3 FinFETs compared to their planar counterparts. 

Abstract Biosignals from wearable sensors have shown great potential for capturing environmental distress that pedestrians experience from negative stimuli (e.g., abandoned houses, poorly maintained sidewalks, graffiti, and so forth). This physiological monitoring approach in an ambulatory setting can mitigate the subjectivity and reliability concerns of traditional self-reported surveys and field audits. However, to date, most prior work has been conducted in a controlled setting and there has been little investigation into utilizing biosignals captured in real-life settings. This research examines the usability of biosignals (electrodermal activity, gait patterns, and heart rate) acquired from real-life settings to capture the environmental distress experienced by pedestrians. We collected and analyzed geocoded biosignals and self-reported stimuli information in real-life settings. Data was analyzed using spatial methods with statistical and machine learning models. Results show that the machine learning algorithm predicted location-based collective distress of pedestrians with 80% accuracy, showing statistical associations between biosignals and the self-reported stimuli. This method is expected to advance our ability to sense and react to not only built environmental issues but also urban dynamics and emergent events, which together will open valuable new opportunities to integrate human biological and physiological data streams into future built environments and/or walkability assessment applications.