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Abstract The ability to design and dynamically control chiroptical responses in solid-state matter at a wafer scale enables new opportunities in various areas. Here, we present a full stack of computer-aided designs and experimental implementations of a dynamically programmable, unified, scalable chiroptical heterostructure containing wafer-scale twisted aligned one-dimensional carbon nanotubes and non-volatile phase change materials. We develop a software infrastructure based on high-performance machine learning frameworks, including differentiable programming and derivative-free optimization, to efficiently optimize the tunability of both reciprocal and nonreciprocal circular dichroism responses, which are experimentally validated. Further, we demonstrate the heterostructure scalability regarding stacking layers and the dual roles of aligned carbon nanotubes - the layer to produce chiroptical responses and the Joule heating electrode to electrically program phase change materials. This heterostructure platform is versatile and expandable to a library of one-dimensional nanomaterials, phase change materials, and electro-optic materials for exploring novel chiral phenomena and photonic and optoelectronic devices. 

Abstract Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throughput and energy-efficient ML hardware, their demonstration for solving PDEs is limited. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time-dependent and time-independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson’s equation in demagnetization, the Navier-Stokes equation in incompressible fluid, Maxwell’s equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high-performance dual-space processing for outperforming traditional PDE solvers and being comparable with state-of-the-art ML models but also can be implemented using optical computing hardware with unique features of low-energy and highly parallel constant-time processing irrespective of model scales and real-time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large-scale scientific and engineering computations. 

Abstract Most commercial systems for ultraviolet-visible (UV–VIS), Fourier-transform infrared, circular dichroism (CD), and fluorescence spectroscopies are designed for measurement of liquid samples. Moreover, adapters enabling the measurement of solid samples are expensive or unavailable for most commercial instruments. Consequently, there is a significant need for solid sample adapters that enable measurement of both liquid and solid samples with a single system. Here, we report two versions of a solid sample adapter cuvette that can be used in most commercial spectroscopy instruments designed for transmission measurement of liquid samples. One version is designed for techniques that do not require changing the sample orientation, and the other allows easy sample rotation. We successfully fabricated these cuvettes by 3D printing with both fused deposition modeling and stereolithography and demonstrated how they enable us to study the optical properties of macroscopic films of aligned carbon nanotubes by performing UV–VIS and CD spectroscopy measurements with the cuvettes. These 3D printed cuvettes and their implementation will help enable a wide range of experiments at a low cost. 

Creating artificial matter with controllable chirality in a simple and scalable manner brings new opportunities to diverse areas. Here we show two such methods based on controlled vacuum filtration - twist stacking and mechanical rotation - for fabricating wafer-scale chiral architectures of ordered carbon nanotubes (CNTs) with tunable and large circular dichroism (CD). By controlling the stacking angle and handedness in the twist-stacking approach, we maximize the CD response and achieve a high deep-ultraviolet ellipticity of 40 ± 1 mdeg nm⁻¹. Our theoretical simulations using the transfer matrix method reproduce the experimentally observed CD spectra and further predict that an optimized film of twist-stacked CNTs can exhibit an ellipticity as high as 150 mdeg nm⁻¹, corresponding to agfactor of 0.22. Furthermore, the mechanical rotation method not only accelerates the fabrication of twisted structures but also produces both chiralities simultaneously in a single sample, in a single run, and in a controllable manner. The created wafer-scale objects represent an alternative type of synthetic chiral matter consisting of ordered quantum wires whose macroscopic properties are governed by nanoscopic electronic signatures and can be used to explore chiral phenomena and develop chiral photonic and optoelectronic devices.