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| 1 | Nano-polycrystalline diamond formation under ultra-high pressure显示文摘 | Chao Xu Duanwei He Haikuo Wang Junwei Guan Chunmei Liu Fang Peng Wendan Wang Zili Kou Kai He Xiaozhi Yan Yan Bi Lei Liu Fengjiao Li Bo Hui | 2013 | International Journal of Refractory Metals and Hard Materials2013，，： | 2 |
| 2 | Synthesis of novel superhard materials under ultrahigh pressure显示文摘Superhard materials are solids whose Vickers hardness is beyond 40 GPa. They have wide applications in industry such as cutting and polishing tools, wear-resistant coatings. Most preparations of superhard materials are conducted under extreme pressure and temperature conditions, not only for scientific investigations, but also for the practical applications. In this paper, we would introduce the recent progress on the design and preparations of novel superhard materials, mainly on nanopolycrystalline diamond, B–C–N superhard solid solutions, and cubic-Si3N4/diamond nanocomposites prepared under ultrahigh pressure and high temperature(HPHT), using multi-anvil apparatus based on the hinged-type cubic press. Bulk materials of all these superhard phases have been successfully synthesized and are systematically tested. We emphasize that ultra-HPHT method plays an important role in the scientific research and industrial production of superhard materials. It provides the driving forces for the light elements forming novel superhard phases as well as the way for sintering high-density nanosuperhard materials. | Chao Xu Duanwei He Haikuo Wang Wendan Wang Mingjun Tang Pei Wang | 2014 | Chinese Science Bulletin2014，59，36： | 2 |
| 3 | Deep Learning Accelerates the Discovery of Two- Dimensional Catalysts for Hydrogen Evolution Reaction显示文摘Two-dimensional materials with active sites are expected to replace platinum as large-scale hydrogen production catalysts.However,the rapid discovery of excellent two-dimensional hydrogen evolution reaction catalysts is seriously hindered due to the long experiment cycle and the huge cost of high-throughput calculations of adsorption energies.Considering that the traditional regression models cannot consider all the potential sites on the surface of catalysts,we use a deep learning method with crystal graph convolutional neural networks to accelerate the discovery of high-performance two-dimensional hydrogen evolution reaction catalysts from two-dimensional materials database,with the prediction accuracy as high as 95.2%.The proposed method considers all active sites,screens out 38 high performance catalysts from 6,531 two-dimensional materials,predicts their adsorption energies at different active sites,and determines the potential strongest adsorption sites.The prediction accuracy of the two-dimensional hydrogen evolution reaction catalysts screening strategy proposed in this work is at the density-functional-theory level,but the prediction speed is 10.19 years ahead of the high-throughput screening,demonstrating the capability of crystal graph convolutional neural networks-deep learning method for efficiently discovering high-performance new structures over a wide catalytic materials space. | Sicheng Wu Zhilong Wang Haikuo Zhang Junfei Cai Jinjin Li | 2023 | Energy & Environmental Materials2023，6，1： | 1 |
| 4 | Nano-polycrystalline diamond formation under ultra-high pres- sure显示文摘 | Chao Xu Duanwei He Haikuo Wang Junwei Guan | 2013 | Journal of Refractory Metals and Hard Materials2013，36，2013： | 1 |
| 5 | Well-balanced ambipolar diketopyrrolopyrrole-based copolymers for OFETs,inverters and frequency doublers显示文摘Conjugated polymers with well-balanced ambipolar charge transport is essential for organic circuits at low cost and large area with simplified fabrication techniques.Aiming at this point,herein,a novel asymmetric thiophene/pyridine-flanked diketopyrrolopyrrole-based copolymer(PPyTDPP-2FBT)is designed and synthesized.Due to the effect of incorporating F atoms on molecular energy alignment and conjugation conformation,the PPyTDPP-2FBT copolymer exhibits typical V-shaped ambipolar field-effect transfer characteristics with well-balanced hole and electron mobilities of 0.64 and 0.46 cm^(2)V^(−1)s^(−1),respectively.Furthermore,organic digital and analog circuits such as inverters and frequency doublers are successfully constructed based on solution-processed films of the PPyTDPP-2FBT copolymers which show a typical circuit operating mode with a high gain of 133 due to the well-balanced electrical properties.In addition,PPyTDPP-2FBT-based devices also demonstrate good stability and batch repeatability,suggesting their great potential applications in organic integrated electronic circuits. | Jiaxin Yang Qingqing Liu Mengxiao Hu Shang Ding Jinyu Liu Yongshuai Wang Dan Liu Haikuo Gao Wenping Hu Huanli Dong | 2021 | Science China Chemistry2021，64，8： | 1 |
| 6 | Quantitative measurements of pressure gradients for the pyrophyllite and magnesium oxide pressure-transmitting mediums to 8?GPa in a large-volume cubic cell显示文摘 | Haikuo Wang Duanwei He Xiaozhi Yan Chao Xu Junwei Guan Ning Tan Wendan Wang | 2011 | High Pressure Research2011，，4： | 1 |
| 7 | Diluent decomposition-assisted formation of Li F-rich solid-electrolyte interfaces enables high-energy Li-metal batteries显示文摘Passivation by the inorganic-rich solid electrolyte interphase(SEI),especially the LiF-rich SEI,is highly desirable to guarantee the durable lifespan of Li metal batteries(LMBs).Here,we report a diluent with the capability to facilitate the formation of LiF-rich SEI while avoiding the excess consumption of Li salts.Dissimilar to most of reported inert diluents,heptafluoro-l-methoxypropane(HM) is firstly demonstrated to cooperate with the decomposition of anions to generate LiF-rich SEI via releasing Fcontaining species near Li surface.The designed electrolyte consisting of 1.8 M LiFSI in the mixture of1,2-dimethoxyethane(DME)/HM(2:1 by vol.) achieves excellent compatibility with both Li metal anodes(Coulombic efficiency~99.8%) and high-voltage cathodes(4.4 V LiNi_(0.8)Mn_(0.1)Co_(0.1)O_(2)(NMC811) and 4.5 V LiCoO_(2)(LCO) vs Li^(+)/Li).The 4.4 V Li(20μm)‖NMC811(2.5 mA h cm^(-2)) and 4.5 V Li(20μm)‖LCO(2.5 mA h cm^(-2)) cells achieve capacity retentions of 80% over 560 cycles and 80% over 505 cycles,respectively.Meanwhile,the anode-free pouch cell delivers an energy density of~293 W h kg^(-1)initially and retains 70% of capacity after 100 deep cycles.This work highlights the critical impact of diluent on the SEI formation,and opens up a new direction for designing desirable interfacial chemistries to enable high-performance LMBs. | Junbo Zhang Haikuo Zhang Ruhong Li Ling Lv Di Lu Shuoqing Zhang Xuezhang Xiao Shujiang Geng Fuhui Wang Tao Deng Lixin Chen Xiulin Fan | 2023 | Journal of Energy Chemistry2023，，3： | 0 |
| 8 | Aortic Dissection Diagnosis Based on Sequence Information and Deep Learning显示文摘Aortic dissection(AD)is one of the most serious diseases with high mortality,and its diagnosis mainly depends on computed tomography(CT)results.Most existing automatic diagnosis methods of AD are only suitable for AD recognition,which usually require preselection of CT images and cannot be further classified to different types.In this work,we constructed a dataset of 105 cases with a total of 49021 slices,including 31043 slices expertlevel annotation and proposed a two-stage AD diagnosis structure based on sequence information and deep learning.The proposed region of interest(RoI)extraction algorithm based on sequence information(RESI)can realize high-precision for RoI identification in the first stage.Then DenseNet-121 is applied for further diagnosis.Specially,the proposed method can judge the type of AD without preselection of CT images.The experimental results show that the accuracy of Stanford typing classification of AD is 89.19%,and the accuracy at the slice-level reaches 97.41%,which outperform the state-ofart methods.It can provide important decision-making information for the determination of further surgical treatment plan for patients. | Haikuo Peng Yun Tan Hao Tang Ling Tan Xuyu Xiang Yongjun Wang Neal N.Xiong | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 9 | Incorporating multifunctional LiAlSiO_(4) into polyethylene oxide for high-performance solid-state lithium batteries显示文摘High ionic conductivity,good electrochemical stability,and satisfactory mechanical property are the crucial factors for polymer solid state electrolytes.Herein,fast ion conductor LiAlSiO_4(LASO) is incorporated into polyethylene oxide(PEO)-based solid-state electrolytes(SSEs).The SSEs containing LASO exhibit enhanced mechanical properties performance compared to pristine PEO-LiTFSI electrolyte.A reduced melting transition temperature of 40.57℃ is enabled by introducing LASO in to PEO-based SSE,which is beneficial to the motion of PEO chain and makes it possible for working at a moderate environment.Coupling with the enhanced motion of PEO,dissociation of the lithium salt,and conducting channel of LASO,the optimized composite polymer SSE exhibits a high ionic conductivity of 4.68×10^(-4),3.16×10^(-4) and 1.62×10^(-4) S cm^(-1) at 60,50 and 40℃,respectively.The corresponding LiFePO_4//Li solid-state battery exhibits high specific capacities of 166,160 and 139 mAh g^(-1) at 0.2 C under 60,40 and 25℃.In addition,it remains 130 mAh g^(-1) at 4.0 C,and maintains 91.74% after 500 cycles at 1.0 C under 60℃.This study provides a simple approach for developing ionic conductor-filled polymer electrolytes in solid-state lithium battery application. | Yuqi Wu Xinhai Li Guochun Yan Zhixing Wang Huajun Guo Yong Ke Lijue Wu Haikuo Fu Jiexi Wang | 2021 | Journal of Energy Chemistry2021，30，2： | 0 |
| 10 | Proteomic Portrait of Human Lymphoma Reveals Protein Molecular Fingerprint of Disease Specific Subtypes and Progression显示文摘An altered proteome in lymph nodes often suggests abnormal signaling pathways that may be associated with diverse lymphatic disorders.Current clinical biomarkers for histological classification of lymphomas have encountered many discrepancies,particularly for borderline cases.Therefore,we launched a comprehensive proteomic study aimed to establish a proteomic landscape of patients with various lymphatic disorders and identify proteomic variations associated with different disease subgroups.In this study,109 fresh-frozen lymph node tissues from patients with various lymphatic disorders(with a focus on Non-Hodgkin’s Lymphoma)were analyzed by data-independent acquisition mass spectrometry.A quantitative proteomic landscape was comprehensively characterized,leading to the identification of featured protein profiles for each subgroup.Potential correlations between clinical outcomes and expression profiles of signature proteins were also probed.Two representative signature proteins,phospholipid-binding proteins Annexin A6(ANXA6)and Phospholipase C Gamma 2(PLCG2),were successfully validated via immunohistochemistry.We also evaluated the capability of acquired proteomic signatures to segregate multiple lymphatic abnormalities and identified several core signature proteins,such as Sialic Acid Binding Ig Like Lectin 1(SIGLEC1)and GTPase of immunity-associated protein 5(GIMAP5).In summary,the established lympho-specific data resource provides a comprehensive map of protein expression in lymph nodes during multiple disease states,thus extending the existing human tissue proteome atlas.Our findings will be of great value in exploring protein expression and regulation underlying lymphatic malignancies,while also providing novel protein candidates to classify various lymphomas for more precise medical practice. | Xin Ku Jinghan Wang Haikuo Li Chen Meng Fang Yu Wenjuan Yu Zhongqi Li Ziqi Zhou Can Zhang Ying Hua Wei Yan Jie Jin | 2023 | Phenomics2023，3，2： | 0 |
| 11 | Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning显示文摘Accurately evaluating the adsorption ability of adsorbents for heavy metal ions(HMIs)and organic pollutants in water is critical for the design and preparation of emerging highly efficient adsorbents.However,predicting adsorption capabilities of adsorbents at arbitrary sites is challenging,with currently unavailable measuring technology for active sites and the corresponding activities.Here,we present an efficient artificial intelligence(AI)approach to predict the adsorption ability of adsorbents at arbitrary sites,as a case study of three HMIs(Pb(Ⅱ),Hg(Ⅱ),and Cd(Ⅱ))adsorbed on the surface of a representative two-dimensional graphitic-C_(3)N_(4).We apply the deep neural network and transfer learning to predict the adsorption capabilities of three HMIs at arbitrary sites,with the predicted results of Cd(Ⅱ)>Hg(Ⅱ)>Pb(Ⅱ)and the root-mean-squared errors less than 0.1 eV.The proposed AI method has the same prediction accuracy as the ab initio DFT calculation,but is millions of times faster than the DFT to predict adsorption abilities at arbitrary sites and only requires one-tenth of datasets compared to training from scratch.We further verify the adsorption capacity of g-C_(3)N_(4) towards HMIs experimentally and obtain results consistent with the AI prediction.It indicates that the presented approach is capable of evaluating the adsorption ability of adsorbents efficiently,and can be further extended to other interdisciplines and industries for the adsorption of harmful elements in aqueous solution. | Zhilong Wang Haikuo Zhang Jiahao Ren Xirong Lin Tianli Han Jinyun Liu Jinjin Li | 2021 | npj Computational Materials2021，，1： | 0 |
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