Surface functionalization and self-assembly of ligand-stabilized silicon nanocrystals

textSilicon nanocrystals or quantum dots combine the abundance and nontoxicity of silicon with size-tunable energy band structure of quantum dots to form a new type of functional material that has applications in biomedical fluorescence imaging, photodynamic therapy, light-emitting devices, and solar cells. The surface is the major concern for using silicon nanocrystals in bio-related applications. Room temperature hydrosilylation is introduced to functionalize silicon nanocrystals in the dark to minimize temperature/photon-induced side reactions that can potentially damage the nanocrystal surface and capping ligands. As a proof of concept, silicon nanocrystals are passivated with styrene at room temperature, without showing styrene polymerization. Silicon nanocrystals are also conjugated to iron oxide nanocrystals through room temperature hydrosilylation to generate fluorescent/magnetic cell labeling probes. Thermally-induced thiolation is used to generate silicon nanocrystals passivated with silicon-sulfur bond that is metastable and can turn to silicon-carbon bond through a ligand exchange. The band gap and emission color of silicon nanocrystals depend on size. Monodisperse silicon nanocrystals and their self-assembly are of great importance for the applications in light-emitting devices and solar cells. Silicon nanocrystals are size-selected through a modified size-selective precipitation. Face-centered cubic superlattices are formed with monodisperse silicon nanocrystals, and characterized by using grazing incidence small angle X-ray scattering. The structure of silicon nanocrystal superlattice is stable at temperatures up to 375oC, due to the covalent Si-C bond on the nanocrystal surface. Silicon and gold nanocrystals are assembled to a simple hexagonal AlB2 binary superlattice that shows interesting thermal behavior. Finally, superlattices made with alkane thiol-capped sub-2 nm gold nanocrystals are used as model systems to study the superlattice phase transitions. Halide ions are found to be critical for order-to-order structural rearrangements in dodecanethiol-capped 1.9 nm gold nanocrystals superlattices at 190oC. Reversible amorphous-to-crystalline transition upon heating is discovered for octadecanethiol capped 1.66 nm gold nanocrystal superlattices, which is attributed to the ligand melting transition.Chemical Engineerin

Training deep neural networks for the inverse design of nanophotonic structures

Data inconsistency leads to a slow training process when deep neural networks are used for the inverse design of photonic devices, an issue that arises from the fundamental property of non-uniqueness in all inverse scattering problems. Here we show that by combining forward modeling and inverse design in a tandem architecture, one can overcome this fundamental issue, allowing deep neural networks to be effectively trained by data sets that contain non-unique electromagnetic scattering instances. This paves the way for using deep neural networks to design complex photonic structures that requires large training sets

TDLE: 2-D LiDAR Exploration With Hierarchical Planning Using Regional Division

Exploration systems are critical for enhancing the autonomy of robots. Due to the unpredictability of the future planning space, existing methods either adopt an inefficient greedy strategy or require a lot of resources to obtain a global solution. In this work, we address the challenge of obtaining global exploration routes with minimal computing resources. A hierarchical planning framework dynamically divides the planning space into subregions and arranges their orders to provide global guidance for exploration. Indicators that are compatible with the subregion order are used to choose specific exploration targets, thereby considering estimates of spatial structure and extending the planning space to unknown regions. Extensive simulations and field tests demonstrate the efficacy of our method in comparison to existing 2D LiDAR-based approaches. Our code has been made public for further investigation.Comment: Accepted in IEEE International Conference on Automation Science and Engineering (CASE) 202

Numerical Air Quality Forecast over Eastern China: Development, Uncertainty and Future

Air pollution is severely focused due to its distinct effect on climate change and adverse effect on human health, ecological system, etc. Eastern China is one of the most polluted areas in the world and many actions were taken to reduce air pollution. Numerical forecast of air quality was proved to be one of the effective ways to help to deal with air pollution. This chapter will present the development, uncertainty and thinking about the future of the numerical air quality forecast emphasized in eastern China region. Brief history of numerical air quality modeling including that of Shanghai Meteorological Service (SMS) was reviewed. The operational regional atmospheric environmental modeling system for eastern China (RAEMS) and its performance on forecasting the major air pollutants over eastern China region was introduced. Uncertainty was analyzed meanwhile challenges and actions to be done in the future were suggested to provide better service of numerical air quality forecast

TTDM: A Travel Time Difference Model for Next Location Prediction

Next location prediction is of great importance for many location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Unfortunately, due to the time and space complexity, these methods (e.g., Markov models) only use the just passed locations to predict next locations, without considering all the passed locations in the trajectory. In this paper, we seek to enhance the prediction performance by considering the travel time from all the passed locations in the query trajectory to a candidate next location. In particular, we propose a novel method, called Travel Time Difference Model (TTDM), which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Further, we integrate the TTDM with a Markov model via a linear interpolation to yield a joint model, which computes the probability of reaching each possible next location and returns the top-rankings as results. We have conducted extensive experiments on two real datasets: the vehicle passage record (VPR) data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over existing solutions. For example, compared with the Markov model, the top-1 accuracy improves by 40% on the VPR data and by 15.6% on the Taxi data

Self-Reference Deep Adaptive Curve Estimation for Low-Light Image Enhancement

In this paper, we propose a 2-stage low-light image enhancement method called Self-Reference Deep Adaptive Curve Estimation (Self-DACE). In the first stage, we present an intuitive, lightweight, fast, and unsupervised luminance enhancement algorithm. The algorithm is based on a novel low-light enhancement curve that can be used to locally boost image brightness. We also propose a new loss function with a simplified physical model designed to preserve natural images' color, structure, and fidelity. We use a vanilla CNN to map each pixel through deep Adaptive Adjustment Curves (AAC) while preserving the local image structure. Secondly, we introduce the corresponding denoising scheme to remove the latent noise in the darkness. We approximately model the noise in the dark and deploy a Denoising-Net to estimate and remove the noise after the first stage. Exhaustive qualitative and quantitative analysis shows that our method outperforms existing state-of-the-art algorithms on multiple real-world datasets