Industrial-Scale Production of High-Quality Graphene Sheets by Millstone Grinders

2021 ◽  
Author(s):  
Peng Lv ◽  
Xiaoshi Li ◽  
Zihan Zhang ◽  
Biao Nie ◽  
Yiliang Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Industrial Scale ◽  
Graphene Sheets ◽  
Scale Production ◽  
Large Area ◽  
High Quality ◽  
Art Methods ◽  
Mechanical Cleavage

Abstract Graphene exhibits a variety of unprecedented innate properties and has sparked great interest in both fundamental science and regarding prospective commercial applications. To meet the ever-increasing demand for high-quality graphene sheets, an industrial-scale, reliable, environmental-friendly, low-cost production process is required. However, large-scale production high quality graphene remains elusive. Here we demonstrate a scalable mechanical cleavage method for large-quantity production of high quality large-area and few-layer graphene sheets by introducing a millstone grinding process. The average thickness of the graphene sheets is around 5 nm. This procedure is simpler than the state-of-the-art methods that allows for scalable preparation of graphene dispersion in hundreds of litres by mechanical cleavage of graphite, and the yield is 30-40%. The size of the prepared graphene sheets can be tuneable from few micrometres to tens of micrometres by varying the dimension of raw graphite, which is larger than that produced by the state-of-the-art methods. Moreover, comparing to conductive agents, the conductivity of wafers containing graphene can be increased by one order of magnitude, suggesting a high potential of the prepared graphene sheets for the application as conductive agent in lithium battery cathodes. This allows the requirements of different sizes graphene sheets for industry applications in different fields.

Thermal Laser Separation – A Novel Dicing Technology Fulfilling the Demands of Volume Manufacturing of 4H-SiC Devices

2015 ◽  
Vol 821-823 ◽  
pp. 528-532 ◽  
Author(s):  
Dirk Lewke ◽  
Karl Otto Dohnke ◽  
Hans Ulrich Zühlke ◽  
Mercedes Cerezuela Barret ◽  
Martin Schellenberger ◽  
...  
Keyword(s):  
Experimental Data ◽  
Process Control ◽  
Tool Wear ◽  
Large Scale ◽  
State Of The Art ◽  
Scale Production ◽  
High Quality ◽  
Large Scale Production ◽  
Laser Separation

One challenge for volume manufacturing of 4H-SiC devices is the state-of-the-art wafer dicing technology – the mechanical blade dicing which suffers from high tool wear and low feed rates. In this paper we discuss Thermal Laser Separation (TLS) as a novel dicing technology for large scale production of SiC devices. We compare the latest TLS experimental data resulting from fully processed 4H-SiC wafers with results obtained by mechanical dicing technology. Especially typical product relevant features like process control monitoring (PCM) structures and backside metallization, quality of diced SiC-devices as well as productivity are considered. It could be shown that with feed rates up to two orders of magnitude higher than state-of-the-art, no tool wear and high quality of diced chips, TLS has a very promising potential to fulfill the demands of volume manufacturing of 4H-SiC devices.

scholarly journals Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3603
Author(s):  
Dasol Jeong ◽  
Hasil Park ◽  
Joongchol Shin ◽  
Donggoo Kang ◽  
Joonki Paik
Keyword(s):  
Large Scale ◽  
Body Shape ◽  
State Of The Art ◽  
The State ◽  
Whole Body ◽  
Distinctive Features ◽  
Common Features ◽  
Siamese Network ◽  
Art Methods ◽  
Triplet Loss

Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. In this paper, we present a novel attentive learning-based Siamese network for person Re-ID. Unlike existing methods, we designed an attention module and attention loss using the properties of the Siamese network to concentrate attention on common and distinctive features. The attention module consists of channel attention to select important channels and encoder-decoder attention to observe the whole body shape. We modified the triplet loss into an attention loss, called uniformity loss. The uniformity loss generates a unique attention map, which focuses on both common and discriminative features. Extensive experiments show that the proposed network compares favorably to the state-of-the-art methods on three large-scale benchmarks including Market-1501, CUHK03 and DukeMTMC-ReID datasets.

scholarly journals TET-GAN: Text Effects Transfer via Stylization and Destylization

2019 ◽  
Vol 33 ◽  
pp. 1238-1245 ◽  
Author(s):  
Shuai Yang ◽  
Jiaying Liu ◽  
Wenjing Wang ◽  
Zongming Guo
Keyword(s):  
Network Design ◽  
State Of The Art ◽  
The State ◽  
High Quality ◽  
Style Transfer ◽  
Feature Representations ◽  
Art Methods ◽  
Matching Criteria

Text effects transfer technology automatically makes the text dramatically more impressive. However, previous style transfer methods either study the model for general style, which cannot handle the highly-structured text effects along the glyph, or require manual design of subtle matching criteria for text effects. In this paper, we focus on the use of the powerful representation abilities of deep neural features for text effects transfer. For this purpose, we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a stylization subnetwork and a destylization subnetwork. The key idea is to train our network to accomplish both the objective of style transfer and style removal, so that it can learn to disentangle and recombine the content and style features of text effects images. To support the training of our network, we propose a new text effects dataset with as much as 64 professionally designed styles on 837 characters. We show that the disentangled feature representations enable us to transfer or remove all these styles on arbitrary glyphs using one network. Furthermore, the flexible network design empowers TET-GAN to efficiently extend to a new text style via oneshot learning where only one example is required. We demonstrate the superiority of the proposed method in generating high-quality stylized text over the state-of-the-art methods.

scholarly journals Dialogue State Tracking with Incremental Reasoning

2021 ◽  
Vol 9 ◽  
pp. 557-569
Author(s):  
Lizi Liao ◽  
Le Hong Long ◽  
Yunshan Ma ◽  
Wenqiang Lei ◽  
Tat-Seng Chua
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Policy Learning ◽  
Dialogue Management ◽  
Empirical Results ◽  
Art Methods ◽  
State Tracking ◽  
Incremental Reasoning ◽  
Multiple Domains

Abstract Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method outperforms the state-of-the-art methods in terms of joint belief accuracy for MultiWOZ 2.1, a large-scale human--human dialogue dataset across multiple domains.

Large-scale production of high-quality graphene sheets by a non-electrified electrochemical exfoliation method

Carbon ◽  
2018 ◽  
Vol 126 ◽  
pp. 507-513 ◽  
Author(s):  
P.C. Shi ◽  
J.P. Guo ◽  
X. Liang ◽  
S. Cheng ◽  
H. Zheng ◽  
...  
Keyword(s):  
Large Scale ◽  
Graphene Sheets ◽  
Scale Production ◽  
High Quality ◽  
Electrochemical Exfoliation ◽  
Large Scale Production

scholarly journals Efficient and large-area all vacuum-deposited perovskite light-emitting diodes via spatial confinement

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peipei Du ◽  
Jinghui Li ◽  
Liang Wang ◽  
Liang Sun ◽  
Xi Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
High Efficiency ◽  
Light Emitting Diodes ◽  
Scale Production ◽  
Nonradiative Recombination ◽  
Large Area ◽  
Spatial Confinement ◽  
Light Emitting ◽  
Large Scale Production

AbstractWith rapid advances of perovskite light-emitting diodes (PeLEDs), the large-scale fabrication of patterned PeLEDs towards display panels is of increasing importance. However, most state-of-the-art PeLEDs are fabricated by solution-processed techniques, which are difficult to simultaneously achieve high-resolution pixels and large-scale production. To this end, we construct efficient CsPbBr3 PeLEDs employing a vacuum deposition technique, which has been demonstrated as the most successful route for commercial organic LED displays. By carefully controlling the strength of the spatial confinement in CsPbBr3 film, its radiative recombination is greatly enhanced while the nonradiative recombination is suppressed. As a result, the external quantum efficiency (EQE) of thermally evaporated PeLED reaches 8.0%, a record for vacuum processed PeLEDs. Benefitting from the excellent uniformity and scalability of the thermal evaporation, we demonstrate PeLED with a functional area up to 40.2 cm2 and a peak EQE of 7.1%, representing one of the most efficient large-area PeLEDs. We further achieve high-resolution patterned perovskite film with 100 μm pixels using fine metal masks, laying the foundation for potential display applications. We believe the strategy of confinement strength regulation in thermally evaporated perovskites provides an effective way to process high-efficiency and large-area PeLEDs towards commercial display panels.

scholarly journals Large-scale Semantic Parsing without Question-Answer Pairs

2014 ◽  
Vol 2 ◽  
pp. 377-392 ◽  
Author(s):  
Siva Reddy ◽  
Mirella Lapata ◽  
Mark Steedman
Keyword(s):  
Natural Language ◽  
Large Scale ◽  
Graph Matching ◽  
State Of The Art ◽  
The State ◽  
Semantic Parsing ◽  
Matching Problem ◽  
Weak Supervision ◽  
Benchmark Datasets

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.

Large-scale production of high-quality reduced graphene oxide

2013 ◽  
Vol 233 ◽  
pp. 297-304 ◽  
Author(s):  
Shichoon Lee ◽  
Sung Hun Eom ◽  
Jin Suk Chung ◽  
Seung Hyun Hur
Keyword(s):  
Graphene Oxide ◽  
Reduced Graphene Oxide ◽  
Large Scale ◽  
Scale Production ◽  
High Quality ◽  
Large Scale Production ◽  
Reduced Graphene

scholarly journals Extensive Screening of Green Solvents for Safe and Sustainable UiO-66 Synthesis

2020 ◽  
Author(s):  
Diletta Morelli Venturi ◽  
Filippo Campana ◽  
Fabio Marmottini ◽  
Ferdinando Costantino ◽  
Luigi Vaccaro
Keyword(s):  
Large Scale ◽  
Low Cost ◽  
Metal Organic Framework ◽  
Dimethyl Formamide ◽  
Scale Production ◽  
Green Solvents ◽  
High Quality ◽  
Color Code ◽  
Large Scale Production ◽  
Metal Organic

<p>Zirconium based Metal-Organic Framework UiO-66 is to date considered one of the benchmark compound among stable MOFs and it has attracted a huge attention for its employment in many strategic applications. Large scale production of UiO-66 for industrial purposes requires the use of safe and green solvents, fulfilling the green chemistry principles and able to replace the use of <i>N,N</i>-Dimethyl-Formamide (DMF), which, despite its toxicity, is still considered the most efficient solvent for obtaining UiO-66 of high quality. Herein we report on a survey of about 40 different solvents with different polarity, boiling point and acidity, used for the laboratory scale synthesis of high quality UiO-66 crystals. The solvents were chosen according the European REACH Regulation 1907/2006 among those having low cost, low toxicity and fully biodegradable. Concerning MOF synthesis, the relevant parameters chosen for establishing the quality of the results obtained are the degree are the crystallinity, microporosity and specific surface area, yield and solvent recyclability. Taking into account also the chemical physical properties of all the solvents, a color code was assigned in order to give a final green assessment for the UiO-66 synthesis. Defectivity of the obtained products, the use of acidic modulators and the use of alternative Zr-salts have been also taken into consideration. Preliminary results lead to conclude that GVL (γ-valerolactone) is among the most promising solvents for replacing DMF in UiO-66 MOF synthesis. </p>

Sign in / Sign up

Export Citation Format

Share Document