A Critical Review on Recent Progress of Solution-Processed Monolayer Assembly of Nanomaterials and Applications.

The rapid development in nanotechnology has necessitated accurate and efficient assembly strategies for nanomaterials. Monolayer assembly of nanomaterials (MAN) represents a challenging and important architecture to manufacture and is critical in understanding interactions among nanomaterials, solvents, and substrates. MAN enables highly tunable performance in electronic and photonic devices. This review summarizes the recent progress on the methods to achieve MAN and discusses important control factors. Moreover, the importance of MAN is elaborated by a broad range of applications in electronics and photonics. In the end, the opportunities as well as challenges in manufacturing and new applications are outlooked.

Engineering chirality at wafer scale with ordered carbon nanotube architectures.

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 a g factor 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.

Wafer-Scale Full-Coverage Self-Limiting Assembly of Particles on Flexible Substrates.

Self-limiting assembly of particles represents the state-of-the-art controllability in nanomanufacturing processes where the assembly stops at a designated stage, providing a desirable platform for applications requiring delicate thickness control such as optics, electronics, and catalytic systems. Most successes in self-limiting assembly are limited to self-assembled monolayers (SAMs) of small molecules on inorganic, chemically homogeneous rigid substrates (e.g., Au and SiO2) through surface-interaction mechanisms. Similar mechanisms, however, cannot achieve a uniform assembly of particles on flexible polymer substrates. The complex configurations and conformations of polymer chains create a surface with nonuniform distributions of chemical groups and phases. In addition, most assembly mechanisms require good solvent wettability, where many desirable but hard-to-wet particles and polymer substrates are excluded. Here, we demonstrate a collision-based self-limiting assembly (CSA) to achieve wafer-scale, full-coverage, close-packed monolayers of hydrophobic particles on hydrophobic polymer substrates in aqueous solutions. The kinetic assembly and self-limiting processes are facilitated and controlled by the combined acoustic and shear fields. We envision many applications in functional coatings and showcase their feasibility in structural coloration. Importantly, such functional coatings can be repaired using CSA, and both particles and polymer substrate can be recycled.

Graphene plasmonic spatial light modulator for reconfigurable diffractive optical neural networks.

Terahertz (THz) diffractive optical neural networks (DONNs) highlight a new route toward intelligent THz imaging, where the image capture and classification happen simultaneously. However, the state-of-the-art implementation mostly relies on passive components and thus the functionalities are limited. The reconfigurability can be achieved through spatial light modulators (SLMs), while it is not clear what device specifications are required and how challenging the associated device implementation is. Here, we show that a complex-valued modulation with a π/2 phase modulation in an active reflective graphene-plasmonics-based SLM can be employed for realizing the reconfigurability in THz DONNs. By coupling the plasmonic resonance in graphene nanoribbons with the reflected Fabry-Pérot (F-P) mode from a back reflector, we achieve a minor amplitude modulation of large reflection and a substantial π/2 phase modulation. Furthermore, the constructed reconfigurable reflective THz DONNs consisting of designed SLMs demonstrate >94.0% validation accuracy of the MNIST dataset. The results suggest that the relaxation of requirements on the specifications of SLMs should significantly simplify and enable varieties of SLM designs for versatile DONN functionalities.

Carbon Nanotube Devices for Quantum Technology.

Carbon nanotubes, quintessentially one-dimensional quantum objects, possess a variety of electrical, optical, and mechanical properties that are suited for developing devices that operate on quantum mechanical principles. The states of one-dimensional electrons, excitons, and phonons in carbon nanotubes with exceptionally large quantization energies are promising for high-operating-temperature quantum devices. Here, we discuss recent progress in the development of carbon-nanotube-based devices for quantum technology, i.e., quantum mechanical strategies for revolutionizing computation, sensing, and communication. We cover fundamental properties of carbon nanotubes, their growth and purification methods, and methodologies for assembling them into architectures of ordered nanotubes that manifest macroscopic quantum properties. Most importantly, recent developments and proposals for quantum information processing devices based on individual and assembled nanotubes are reviewed.

Physics-informed recurrent neural network for time dynamics in optical resonances.

Resonance structures and features are ubiquitous in optical science. However, capturing their time dynamics in real-world scenarios suffers from long data acquisition time and low analysis accuracy due to slow convergence and limited time windows. Here we report a physics-informed recurrent neural network to forecast the time-domain response of optical resonances and infer corresponding resonance frequencies by acquiring a fraction of the sequence as input. The model is trained in a two-step multi-fidelity framework for high-accuracy forecast, using first a large amount of low-fidelity physical-model-generated synthetic data and then a small set of high-fidelity application-specific data. Through simulations and experiments, we demonstrate that the model is applicable to a wide range of resonances, including dielectric metasurfaces, graphene plasmonics and ultra-strongly coupled Landau polaritons, where our model captures small signal features and learns physical quantities. The demonstrated machine-learning algorithm can help to accelerate the exploration of physical phenomena and device design under resonance-enhanced light-matter interaction.