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Frost heave of soil extensively exists in northern regions and poses a significant threat to infrastructure in cold regions. Despite over a century of research, challenges persist in numerically simulating frost heave. This study addresses two key issues: (1) What is the primary driving force for liquid water transfer during the freezing process? (2) How can we correctly represent unfrozen water content? Critical insights are derived from the theoretical analysis of coupled hydrothermal migration during soil freezing processes, followed by a case simulation using COMSOL Multiphysics. It concludes that of the water content gradient, suction gradient, and hydraulic gradient, only the hydraulic gradient is the fundamental driving force for liquid water flow. Moreover, unfrozen water content is an intrinsic property of frozen soil and should not be indirectly determined by assessing ice content. This study enhances our understanding of the frost heave mechanism and contributes to developing a unified model for frost heave. 

Abstract The prevailing von Neumann bottleneck has demanded alternatives capable of more efficiently executing massive data in state‐of‐the‐art digital technologies. Mimicking the human brain's operational principles, various artificial synapse devices have emerged, whose fabrications generally require high‐temperature complementary metal‐oxide‐semiconductor (CMOS) processes. Herein, centimeter‐scale tellurium (Te) films‐based optoelectronic synaptic devices are explored by a back‐end‐of‐line (BEOL) compatible low‐temperature (200 °C) chemical vapor deposition (CVD). The CVD‐grown Te films exhibit prominent semiconducting properties such as broadband photo‐responsiveness accompanying a large degree of mechanical deformability. These characteristics coupled with their scalable manufacturability realize a comprehensive set of optically‐stimulated synaptic plasticity; i.e., excitatory postsynaptic current (EPSC), paired‐pulse facilitation (PPF), and short‐to‐long‐term memory conversion, all of which are well preserved even under severe mechanical deformations. A variety of proof‐of‐concept applications for artificial neural networks (ANNs) are demonstrated employing these deformation‐invariant synaptic features; i.e., high‐accuracy (≈90%) pattern recognition, associative learning, and machine learning‐implemented visual perception. The fundamental mechanism for the synaptic operations is discussed in the context of their persistent photoconductivity (PPC) and its associated memory effect. This study highlights high promise of low‐temperature processable semiconductors for emergent neuromorphic architectures with various form factors beyond the conventional CMOS strategy. 

Abstract High‐quality‐factor microring resonators are highly desirable in many applications. Fabricating a microring resonator typically requires delicate instruments to ensure a smooth side wall of waveguides and 100‐nm critical feature size in the coupling region. In this work, a new method “damascene soft nanoimprinting lithography” is demonstrated that can create high‐fidelity waveguide by simply backfilling an imprinted cladding template with a high refractive index polymer core. This method can easily realize high Q‐factor polymer microring resonators (e.g., ≈5 × 10⁵around 770 nm wavelength) without the use of any expensive instruments and can be conducted in a normal lab environment. The high Q‐factors can be attributed to the residual layer‐free feature and controllable meniscus cross‐section profile of the filled polymer core. Furthermore, the new method is compatible with different polymers, yields low fabrication defects, enables new functionalities, and allows flexible substrate. These benefits can broaden the applicability of the fabricated microring resonator.