Harbin Institute of Technology

About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.

Nanoconfined Ionic Liquids

Abstract: Ionic liquids (ILs) have been widely investigated as novel solvents, electrolytes, and soft functional materials. Nevertheless, the widespread applications of ILs in most cases have been hampered by their liquid state. The confinement of ILs into nanoporous hosts is a simple but versatile strategy to overcome this problem. Nanoconfined ILs constitute a new class of composites with the intrinsic chemistries of ILs and the original functions of solid matrices. The interplay between these two components, particularly the confinement effect and the interactions between ILs and pore walls, further endows ILs with significantly distinct physicochemical properties in the restricted space compared to the corresponding bulk systems. The aim of this article is to provide a comprehensive review of nanoconfined ILs. After a brief introduction of bulk ILs, the synthetic strategies and investigation methods for nanoconfined ILs are documented. The local structure and physicochemical properties of ILs in diverse porous ...

Light upconverting core–shell nanostructures: nanophotonic control for emerging applications

Abstract: Light upconverting nanostructures employing lanthanide ions constitute an emerging research field recognized with wide ramifications and impact in many areas ranging from healthcare, to energy and, to security. The core–shell design of these nanostructures allows us to deliberately introduce a hierarchy of electronic energy states, thus providing unprecedented opportunities to manipulate the electronic excitation, energy transfer and upconverted emissions. The core–shell morphology also causes the suppression of quenching mechanisms to produce efficient upconversion emission for biophotonic and photonic applications. Using hierarchical architect, whereby each shell layer can be defined to have a specific feature, the electronic structure as well as the physiochemical structure of the upconverting nanomaterials can be tuned to couple other electronic states on the surface such as excitations of organic dye molecules or localized surface plasmons from metallic nanostructures, or to introduce a broad range of imaging or therapeutic modalities into a single conduct. In this review, we summarize the key aspects of nanophotonic control of the light upconverting nanoparticles through governed design and preparation of hierarchical shells in the core–shell nanostructures, and review their emerging applications in the biomedical field, solar energy conversion, as well as security encoding.

Production of Sulfate Radical and Hydroxyl Radical by Reaction of Ozone with Peroxymonosulfate: A Novel Advanced Oxidation Process

Abstract: In this work, simultaneous generation of hydroxyl radical (•OH) and sulfate radical (SO4•–) by the reaction of ozone (O3) with peroxymonosulfate (PMS; HSO5–) has been proposed and experimentally verified. We demonstrate that the reaction between the anion of PMS (i.e., SO52–) and O3 is primarily responsible for driving O3 consumption with a measured second order rate constant of (2.12 ± 0.03) × 104 M–1 s–1. The formation of both •OH and SO4•– from the reaction between SO52– and O3 is confirmed by chemical probes (i.e., nitrobenzene for •OH and atrazine for both •OH and SO4•–). The yields of •OH and SO4•– are determined to be 0.43 ± 0.1 and 0.45 ± 0.1 per mol of O3 consumption, respectively. An adduct, –O3SOO– + O3 → –O3SO5–, is assumed as the first step, which further decomposes into SO5•– and O3•–. The subsequent reaction of SO5•– with O3 is proposed to generate SO4•–, while O3•– converts to •OH. A definition of Rct,•OH and Rct,SO4•– (i.e., respective ratios of •OH and SO4•– exposures to O3 exposure) is ...

Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification

Abstract: Extracting informative image features and learning effective approximate hashing functions are two crucial steps in image retrieval. Conventional methods often study these two steps separately, e.g., learning hash functions from a predefined hand-crafted feature space. Meanwhile, the bit lengths of output hashing codes are preset in the most previous methods, neglecting the significance level of different bits and restricting their practical flexibility. To address these issues, we propose a supervised learning framework to generate compact and bit-scalable hashing codes directly from raw images. We pose hashing learning as a problem of regularized similarity learning. In particular, we organize the training images into a batch of triplet samples, each sample containing two images with the same label and one with a different label. With these triplet samples, we maximize the margin between the matched pairs and the mismatched pairs in the Hamming space. In addition, a regularization term is introduced to enforce the adjacency consistency, i.e., images of similar appearances should have similar codes. The deep convolutional neural network is utilized to train the model in an end-to-end fashion, where discriminative image features and hash functions are simultaneously optimized. Furthermore, each bit of our hashing codes is unequally weighted, so that we can manipulate the code lengths by truncating the insignificant bits. Our framework outperforms state-of-the-arts on public benchmarks of similar image search and also achieves promising results in the application of person re-identification in surveillance. It is also shown that the generated bit-scalable hashing codes well preserve the discriminative powers with shorter code lengths.