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.

Dopant-induced electron localization drives CO 2 reduction to C 2 hydrocarbons

Abstract: The electrochemical reduction of CO2 to multi-carbon products has attracted much attention because it provides an avenue to the synthesis of value-added carbon-based fuels and feedstocks using renewable electricity Unfortunately, the efficiency of CO2 conversion to C2 products remains below that necessary for its implementation at scale Modifying the local electronic structure of copper with positive valence sites has been predicted to boost conversion to C2 products Here, we use boron to tune the ratio of Cuδ+ to Cu0 active sites and improve both stability and C2-product generation Simulations show that the ability to tune the average oxidation state of copper enables control over CO adsorption and dimerization, and makes it possible to implement a preference for the electrosynthesis of C2 products We report experimentally a C2 Faradaic efficiency of 79 ± 2% on boron-doped copper catalysts and further show that boron doping leads to catalysts that are stable for in excess of ~40 hours while electrochemically reducing CO2 to multi-carbon hydrocarbons

Pre-Training with Whole Word Masking for Chinese BERT

Abstract: Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks. Recently, an upgraded version of BERT has been released with Whole Word Masking (WWM), which mitigate the drawbacks of masking partial WordPiece tokens in pre-training BERT. In this technical report, we adapt whole word masking in Chinese text, that masking the whole word instead of masking Chinese characters, which could bring another challenge in Masked Language Model (MLM) pre-training task. The proposed models are verified on various NLP tasks, across sentence-level to document-level, including machine reading comprehension (CMRC 2018, DRCD, CJRC), natural language inference (XNLI), sentiment classification (ChnSentiCorp), sentence pair matching (LCQMC, BQ Corpus), and document classification (THUCNews). Experimental results on these datasets show that the whole word masking could bring another significant gain. Moreover, we also examine the effectiveness of the Chinese pre-trained models: BERT, ERNIE, BERT-wwm, BERT-wwm-ext, RoBERTa-wwm-ext, and RoBERTa-wwm-ext-large. We release all the pre-trained models: \url{this https URL

(α-NaYbF4:Tm3+)/CaF2 Core/Shell Nanoparticles with Efficient Near-Infrared to Near-Infrared Upconversion for High-Contrast Deep Tissue Bioimaging

Abstract: We describe the development of novel and biocompatible core/shell (α-NaYbF(4):Tm(3+))/CaF(2) nanoparticles that exhibit highly efficient NIR(in)-NIR(out) upconversion (UC) for high contrast and deep bioimaging. When excited at ~980 nm, these nanoparticles emit photoluminescence (PL) peaked at ~800 nm. The quantum yield of this UC PL under low power density excitation (~0.3 W/cm(2)) is 0.6 ± 0.1%. This high UC PL efficiency is realized by suppressing surface quenching effects via heteroepitaxial growth of a biocompatible CaF(2) shell, which results in a 35-fold increase in the intensity of UC PL from the core. Small-animal whole-body UC PL imaging with exceptional contrast (signal-to-background ratio of 310) is shown using BALB/c mice intravenously injected with aqueously dispersed nanoparticles (700 pmol/kg). High-contrast UC PL imaging of deep tissues is also demonstrated, using a nanoparticle-loaded synthetic fibrous mesh wrapped around rat femoral bone and a cuvette with nanoparticle aqueous dispersion covered with a 3.2 cm thick animal tissue (pork).

Neutrino Physics with JUNO

Abstract: The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy as a primary physics goal. It is also capable of observing neutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, solar neutrinos, as well as exotic searches such as nucleon decays, dark matter, sterile neutrinos, etc. We present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. By detecting reactor antineutrinos from two power plants at 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4 sigma significance with six years of running. The measurement of antineutrino spectrum will also lead to the precise determination of three out of the six oscillation parameters to an accuracy of better than 1\%. Neutrino burst from a typical core-collapse supernova at 10 kpc would lead to ~5000 inverse-beta-decay events and ~2000 all-flavor neutrino-proton elastic scattering events in JUNO. Detection of DSNB would provide valuable information on the cosmic star-formation rate and the average core-collapsed neutrino energy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400 events per year, significantly improving the statistics of existing geoneutrino samples. The JUNO detector is sensitive to several exotic searches, e.g. proton decay via the $p\to K^++\bar u$ decay channel. The JUNO detector will provide a unique facility to address many outstanding crucial questions in particle and astrophysics. It holds the great potential for further advancing our quest to understanding the fundamental properties of neutrinos, one of the building blocks of our Universe.