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| 1 | Penta-P2X (X=C, Si) monolayers as wide-bandgap semiconductors: A first principles prediction显示文摘 | Mosayeb Naseri Shiru Lin Jaafar Jalilian Jinxing Gu Zhongfang Chen | 2018 | Frontiers of physics2018，13，3： | 3 |
| 2 | Biosynthesis of spherical Fe 3 O 4 /bacterial cellulose nanocomposites as adsorbents for heavy metal ions显示文摘 | Huixia Zhu Shiru Jia Tong Wan Yuanyuan Jia Hongjiang Yang Jing Li Lin Yan Cheng Zhong | 2011 | Carbohydrate Polymers2011，，4： | 1 |
| 3 | Sentence Similarity Measurement with Convolutional Neural Networks Using Semantic and Syntactic Features显示文摘Calculating the semantic similarity of two sentences is an extremely challenging problem.We propose a solution based on convolutional neural networks(CNN)using semantic and syntactic features of sentences.The similarity score between two sentences is computed as follows.First,given a sentence,two matrices are constructed accordingly,which are called the syntax model input matrix and the semantic model input matrix;one records some syntax features,and the other records some semantic features.By experimenting with different arrangements of representing the syntactic and semantic features of the sentences in the matrices,we adopt the most effective way of constructing the matrices.Second,these two matrices are given to two neural networks,which are called the sentence model and the semantic model,respectively.The convolution process of the neural networks of the two models is carried out in multiple perspectives.The outputs of the two models are combined as a vector,which is the representation of the sentence.Third,given the representation vectors of two sentences,the similarity score of these representations is computed by a layer in the CNN.Experiment results show that our algorithm(SSCNN)surpasses the performance MPCPP,which noticeably the best recent work of using CNN for sentence similarity computation.Comparing with MPCNN,the convolution computation in SSCNN is considerably simpler.Based on the results of this work,we suggest that by further utilization of semantic and syntactic features,the performance of sentence similarity measurements has considerable potentials to be improved in the future. | Shiru Zhang Zhiyao Liang Jian Lin | 2020 | Computers, Materials & Continua2020，，5： | 1 |
| 4 | Dry reforming of methane on doped Ni nanoparticles:Featureassisted optimizations and ranking of doping metals for direct activations of CH_(4) and CO_(2)显示文摘As a vital energy resource and raw material for many industrial products,syngas(CO and H_(2))is of great significance.Dry reforming of methane(DRM)is an important approach to producing syngas(with a hydrogen-to-carbon-monoxide ratio of 1:1 in principle)from methane and carbon dioxide,with a lower operational cost as compared to other reforming techniques.However,many pure metallic catalysts used in DRM face deactivation issues due to coke formation or sintering of the metal particles.A systematic search for highly efficient metallic catalysts,which reduce the reaction barriers for the rate-determining steps and resist carbon deposition,is urgently needed.Nickel is a typical low-cost transition metal for activating the C–H bond in methane.In this work,we applied a two-step workflow to search for nickel-based bimetallic catalysts with doping metals M(M-Ni)by combining density functional theory(DFT)calculations and machine learning(ML).We focus on the two critical steps in DRM—CH_(4) and CO_(2) direct activations.We used DFT and slab models for the Ni(111)facet to explore the relevant reaction pathways and constructed a data set containing structural and energetic information for representative M-Ni systems.We used this dataset to train ML models with chemical-knowledge-based features and predicted CH_(4) and CO_(2) dissociation energies and barriers,which revealed the composition–activity relationships of the bimetallic catalysts.We also used these models to rank the predicted catalytic performance of candidate systems to demonstrate the applicability of ML for catalyst screening.We emphasized that ML ranking models would be more valuable than regression models in high-throughput screenings.Finally,we used our trained model to screen 12 unexplored M-Ni systems and showed that the DFT-computed energies and barriers are very close to the ML-predicted values for top candidates,validating the robustness of the trained model. | Shiru Lin Jean-Baptiste Tristan Yang Wang Junwei Lucas Bao | 2022 | Nano Research2022，15，10： | 1 |
| 5 | Intrinsically patterned corrals in monolayer Ag_(5)Se_(2) and selective molecular co-adsorption显示文摘Functionalized two-dimensional(2D)materials play an important role in both fundamental sciences and practical applications.The construction and precise control of patterns at the atomic-scale are necessary for selective and multiple functionalization.Here we report the fabrication of monolayer pentasilver diselenide(Ag_(5)Se_(2)),a new type of intrinsically patterned 2D material,by direct selenization of a Ag(111)surface.The atomic arrangement is determined by a combination of scanning tunneling microscopy(STM),low-energy electron diffraction(LEED),and density-functional-theory(DFT)calculations.Large-scale STM images exhibit a quasi-periodic pattern of stoichiometric triangular domains with a side length of~15 nm and apical offsets.The boundaries between triangular domains are sub-stoichiometric.Deposition of different molecules on the patterned Ag_(5)Se_(2) exhibits selective adsorption behavior.Pentacene molecules preferentially adsorb on the boundaries,while tetracyanoquinodimethane(TCNQ)molecules adsorb both on the boundaries and the triangular domains.By co-depositing pentacene and TCNQ molecules,we successfully construct molecular corrals with pentacene on the boundaries encircling TCNQ molecules on the triangular domains.The realization of epitaxial large-scale and high-quality,monolayer Ag_(5)Se_(2) extends the family of intrinsically patterned 2D materials and provides a paradigm for dual functionalization of 2D materials. | Jianchen Lu Shiru Song Shuai Zhang Yang Song Yun Cao Zhenyu Wang Li Huang Hongliang Lu Yu-Yang Zhang Sokrates T.Pantelides Shixuan Du Xiao Lin Hong-Jun Gao | 2022 | Nano Research2022，15，7： | 0 |
| 6 | Zeolite-templated carbons as effective sorbents to remove methylsiloxanes and derivatives:A computational screening显示文摘Though widely used in our daily lives,volatile methylsiloxanes and derivatives are emerging contaminants and becoming a high-priority environment and public health concern.Developing effective sorbent materials can remove siloxanes in a cost-effective manner.Herein,by means of Grand Canonical Monte Carlo(GCMC)simulations,we evaluated the potentials of the recently proposed 68 stable zeolite-templated carbons(ZTCs)(PNAS 2018,115,E8116-E8124)for the removal of four linear methylsiloxanes and derivatives as well as two cyclic methylsiloxanes by the calculated average loading and average adsorption energy values.Four ZTCs,namely ISV,FAU1,FAU3,and H8326836,were identified with the top 50%adsorption performance toward all the six targeted contaminants,which outperform activated carbons.Further first principles computations revealed that steric hindrance,electrostatic interactions(further enhanced by charge transfer),and CH-p interactions account for the outstanding adsorption performance of these ZTCs.This work provides a quick procedure to computationally screen promising ZTCs for siloxane removal,and help guide future experimental and theoretical investigations. | Shiru Lin Kaitlyn A.Jacoby Jinxing Gu Dariana R.Vega-Santander Arturo J.Hernaandez-Maldonado Zhongfang Chen | 2021 | Green Energy & Environment2021，6，6： | 0 |
| 7 | Single-atom catalysts with anionic metal centers: Promising electrocatalysts for the oxygen reduction reaction and beyond显示文摘Ongoing efforts to develop single-atom catalysts(SACs) for the oxygen reduction reaction(ORR) typically focus on SACs with cationic metal centers,while SACs with anionic metal centers(anionic SACs) have been generally neglected.However,anionic SACs may offer excellent active sites for ORR,since anionic metal centers could facilitate the activation of O_(2) by back donating electrons to the antibonding orbitals of O_(2).In this work,we propose a simple guideline for designing anionic SACs:the metal centers should have larger electronegativity than the surrounding atoms in the substrate on which the metal atoms are supported.By means of density functional theory(DFT) simulations,we identified 13 anionic metal centers(Co,Ni,Cu,Ru,Rh,Pd,Ag,Re,Os,Ir,Pt,Au,and Hg) dispersed on pristine or defective antimonene substrates as new anionic SACs,among which anionic Au and Co metal centers exhibit limiting potentials comparable to,or even better than,conventional Pt-based catalysts towards ORR.We also found that anionic Os and Re metal centers on the defective antimonene can electrochemically catalyze the nitrogen reduction reaction(NRR) with a limiting potential close to that of stepped Ru(0001).Overall,our work shows promise towards the rational design of anionic SACs and their utility for applications as electrocatalysts for ORR and other important electrochemical reactions. | Jinxing Gu Yinghe Zhao Shiru Lin Jingsong Huang Carlos R.Cabrera Bobby G.Sumpter Zhongfang Chen | 2021 | Journal of Energy Chemistry2021，30，12： | 0 |
| 8 | Two-dimensional aluminum monoxide nanosheets: A computational study显示文摘 | Shiru Lin Yanchao Wang Zhongfang Chen | 2018 | Frontiers of physics2018，13，3： | 0 |
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