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Institution

Harbin Institute of Technology

EducationHarbin, China
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.


Papers
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Journal ArticleDOI
29 Oct 2018
TL;DR: In this article, Wu et al. reported an efficient oxygen reduction reaction (ORR) catalyst that consists of atomically dispersed nitrogen-coordinated single Mn sites on partially graphitic carbon (Mn-N-C).
Abstract: Platinum group metal (PGM)-free catalysts that are also iron free are highly desirable for the oxygen reduction reaction (ORR) in proton-exchange membrane fuel cells, as they avoid possible Fenton reactions. Here we report an efficient ORR catalyst that consists of atomically dispersed nitrogen-coordinated single Mn sites on partially graphitic carbon (Mn-N-C). Evidence for the embedding of the atomically dispersed MnN4 moieties within the carbon surface-exposed basal planes was established by X-ray absorption spectroscopy and their dispersion was confirmed by aberration-corrected electron microscopy with atomic resolution. The Mn-N-C catalyst exhibited a half-wave potential of 0.80 V versus the reversible hydrogen electrode, approaching that of Fe-N-C catalysts, along with significantly enhanced stability in acidic media. The encouraging performance of the Mn-N-C catalyst as a PGM-free cathode was demonstrated in fuel cell tests. First-principles calculations further support the MnN4 sites as the origin of the ORR activity via a 4e− pathway in acidic media. Platinum group metal- and iron-free catalysts are highly desirable for the oxygen reduction reaction in proton-exchange membrane fuel cells. Now, Wu and co-workers show a carbon catalyst with atomically dispersed single Mn sites as an efficient catalyst with enhanced stability in acidic media.

920 citations

Proceedings Article
Long Jiang1, Mo Yu2, Ming Zhou1, Xiaohua Liu1, Tiejun Zhao2 
19 Jun 2011
TL;DR: This paper proposes to improve target-dependent Twitter sentiment classification by incorporating target- dependent features; and taking related tweets into consideration; and according to the experimental results, this approach greatly improves the performance of target- dependence sentiment classification.
Abstract: Sentiment analysis on Twitter data has attracted much attention recently. In this paper, we focus on target-dependent Twitter sentiment classification; namely, given a query, we classify the sentiments of the tweets as positive, negative or neutral according to whether they contain positive, negative or neutral sentiments about that query. Here the query serves as the target of the sentiments. The state-of-the-art approaches for solving this problem always adopt the target-independent strategy, which may assign irrelevant sentiments to the given target. Moreover, the state-of-the-art approaches only take the tweet to be classified into consideration when classifying the sentiment; they ignore its context (i.e., related tweets). However, because tweets are usually short and more ambiguous, sometimes it is not enough to consider only the current tweet for sentiment classification. In this paper, we propose to improve target-dependent Twitter sentiment classification by 1) incorporating target-dependent features; and 2) taking related tweets into consideration. According to the experimental results, our approach greatly improves the performance of target-dependent sentiment classification.

911 citations

Journal ArticleDOI
TL;DR: This tutorial review aims to give a brief overview of the functionalization of C-H bonds and the visible light photoredox catalysis and state the main results obtained in the reactions.
Abstract: The functionalization of C–H bonds and the visible light photoredox catalysis represent two prominent challenges in organic chemistry. In this regard, the combination of visible-light catalysis and C–H bond functionalization adjacent to a tertiary amine has been successfully developed in the past three years. In this tutorial review, we aim to give a brief overview of this issue and state the main results obtained in the reactions.

890 citations

Journal ArticleDOI
TL;DR: Findings have important implications for the development of novel nonradical oxidation processes based on PMS, because 1O2 as a moderately reactive electrophile may suffer less interference from background organic matters compared with nonselective •OH and SO4•-.
Abstract: The reactions between peroxymonosulfate (PMS) and quinones were investigated for the first time in this work, where benzoquinone (BQ) was selected as a model quinone. It was demonstrated that BQ could efficiently activate PMS for the degradation of sulfamethoxazole (SMX; a frequently detected antibiotic in the environments), and the degradation rate increased with solution pH from 7 to 10. Interestingly, quenching studies suggested that neither hydroxyl radical (•OH) nor sulfate radical (SO4•–) was produced therein. Instead, the generation of singlet oxygen (1O2) was proved by using two chemical probes (i.e., 2,2,6,6-tetramethyl-4-piperidinol and 9,10-diphenylanthracene) with the appearance of 1O2 indicative products detected by electron paramagnetic resonance spectrometry and liquid chromatography mass spectrometry, respectively. A catalytic mechanism was proposed involving the formation of a dioxirane intermediate between PMS and BQ and the subsequent decomposition of this intermediate into 1O2. Accordi...

889 citations

Journal ArticleDOI
TL;DR: In this paper, stable aqueous TiO2 nanofluids with different particle sizes and concentrations were formulated and measured for their static thermal conductivity and rheological behaviour.

889 citations


Authors

Showing all 89023 results

NameH-indexPapersCitations
Jiaguo Yu178730113300
Lei Jiang1702244135205
Gang Chen1673372149819
Xiang Zhang1541733117576
Hui-Ming Cheng147880111921
Yi Yang143245692268
Bruce E. Logan14059177351
Bin Liu138218187085
Peng Shi137137165195
Hui Li1352982105903
Lei Zhang135224099365
Jie Liu131153168891
Lei Zhang130231286950
Zhen Li127171271351
Kurunthachalam Kannan12682059886
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023383
20221,896
202110,085
20209,817
20199,659
20188,215