Effect of structural characteristics on the stability of multi-weak rock slopes considering the spatial variability of geotechnical parameters.

The slope of open-pit mines is typically characterized by an interaction structure involving multiple weak layers, with these structural characteristics serving as key factors in determining rock slope stability. Under the influence of random factors such as engineering activities and geological structures, the weak layers of the slope and the intact rock layers undergo relative changes. This interaction leads to a more pronounced spatial variability in the geotechnical parameters that inherently exist.

Ultrathin BIC Metasurfaces Based on Ultra-Low-Loss SbSe Phase-Change Material.

Ultrathin and low-loss phase-change materials (PCMs) are highly valued for their fast and effective phase transitions and applications in reconfigurable photonic chips, metasurfaces, optical modulators, sensors, photonic memories, and neuromorphic computing. However, conventional PCMs mostly suffer from high intrinsic losses in the near-infrared (NIR) region, limiting their potential for high quality factor (-factor) resonant metasurfaces. Here we present the design and fabrication of tunable bound states in the continuum (BIC) metasurfaces using the ultra-low-loss PCM SbSe.

Lanthanide ion-doped upconversion nanoparticles for low-energy super-resolution applications.

Energy-intensive technologies and high-precision research require energy-efficient techniques and materials. Lens-based optical microscopy technology is useful for low-energy applications in the life sciences and other fields of technology, but standard techniques cannot achieve applications at the nanoscale because of light diffraction. Far-field super-resolution techniques have broken beyond the light diffraction limit, enabling 3D applications down to the molecular scale and striving to reduce energy use.

Memory-less scattering imaging with ultrafast convolutional optical neural networks.

The optical memory effect in complex scattering media including turbid tissue and speckle layers has been a critical foundation for macroscopic and microscopic imaging methods. However, image reconstruction from strong scattering media without the optical memory effect has not been achieved. Here, we demonstrate image reconstruction through scattering layers where no optical memory effect exists, by developing a multistage convolutional optical neural network (ONN) integrated with multiple parallel kernels operating at the speed of light.

From design to clinic: Engineered peptide nanomaterials for cancer immunotherapy.

Immunotherapy has revolutionized the field of cancer therapy. Nanomaterials can further improve the efficacy and safety of immunotherapy because of their tunability and multifunctionality. Owing to their natural biocompatibility, diverse designs, and dynamic self-assembly, peptide-based nanomaterials hold great potential as immunotherapeutic agents for many malignant cancers, with good immune response and safety.

Perspective on 3D vertically-integrated photonic neural networks based on VCSEL arrays.

The rapid development of artificial intelligence has stimulated the interest in the novel designs of photonic neural networks. As three-dimensional (3D) neural networks, the diffractive neural networks (DNNs) relying on the diffractive phenomena of light, has demonstrated their superb performance in the direct parallel processing of two-dimensional (2D) optical data at the speed of light. Despite the outstanding achievements, DNNs utilize centimeter-scale devices to generate the input data passively, making the miniaturization and on-chip integration of DNNs a challenging task.

Neuron-Inspired Steiner Tree Networks for 3D Low-Density Metastructures.

Three-dimensional (3D) micro-and nanostructures have played an important role in topological photonics, microfluidics, acoustic, and mechanical engineering. Incorporating biomimetic geometries into the design of metastructures has created low-density metamaterials with extraordinary physical and photonic properties. However, the use of surface-based biomimetic geometries restricts the freedom to tune the relative density, mechanical strength, and topological phase.

Artificial neural networks enabled by nanophotonics.

The growing demands of brain science and artificial intelligence create an urgent need for the development of artificial neural networks (ANNs) that can mimic the structural, functional and biological features of human neural networks. Nanophotonics, which is the study of the behaviour of light and the light-matter interaction at the nanometre scale, has unveiled new phenomena and led to new applications beyond the diffraction limit of light. These emerging nanophotonic devices have enabled scientists to develop paradigm shifts of research into ANNs.

Three-dimensional direct laser writing of biomimetic neuron structures.

Building biomimetic neuron structures that emulate the topological features of biological neural networks at multiple scales has been an active area in neuron cell culturing, neuron-chip interface and computer chip design. However, due to the fact that biological neural networks possess extraordinary connectivity and complexity from millimeter down to nanometer scale, with different dendritic branch angles, branch lengths, and branch diameters, previous methods to reproduce the topological features of biological neural networks are either limited to two dimensions or lack of fabrication resolution in building three-dimensional (3D) structures. Here we report on the generation of 3D biomimetic neuron structures at a micrometer scale, with high mechanical stability and controlled topologies by studying the effect of 3D direct laser writing (DLW) on the capillary force.