Article
Chemistry, Physical
Ziwei Xu, Feng Ding
Summary: This study demonstrates that zigzag or near-zigzag SWCNTs are highly sensitive to the size of the docked catalyst particle, while armchair or near-armchair SWCNTs are less sensitive. A small change in catalyst particle size can lead to a variation in SWCNT chirality for zigzag tubes, but not for armchair tubes. This research provides quantitative guidance for SWCNT cloning and deepens understanding of SWCNT growth mechanisms.
Article
Chemistry, Physical
Jianian Hu, Zhengyuan Liu, Yongyuan You, Haotian Zhang, Xiang Chen, Yi Sun, Jian Zhang, Guoqiang Luo
Summary: This study used ex-situ transmission electron microscopy observations and molecular dynamics simulation methods to investigate the process of forming carbon nanotube forests catalyzed by Fe nanoparticles. The results reveal the rooting path of C in Fe and its motion evolution mechanism. It is found that C reacts with Fe to form Fe3C, and C enters from Fe (110) crystal plane, which transforms Fe (110) crystal plane into Fe3C (013) crystal plane. C then precipitates and grows into carbon nanotubes from Fe3C, creating the parallel relationship between the growth direction of carbon nanotubes and Fe3C (013) crystal plane. Molecular dynamics simulation also confirms the correctness of the experimental results by showing consistent motion paths of C.
COLLOID AND INTERFACE SCIENCE COMMUNICATIONS
(2023)
Article
Chemistry, Physical
Lei Luo, Pengfei Liu, Sabine Leischner, Markus Oeser
Summary: The effects of carbon nanotubes (CNTs) on bitumen's load-induced cracking process were investigated using molecular dynamics (MD) simulation. It was found that CNTs can enhance load transfer efficiency and mechanical performance, elongate the crack propagation path, and increase overall fracture resistance. However, debonding and sliding at the bitumen-CNT interface weakened the interfacial bonding and reduced the strengthening effect. Future research will focus on improving load-transfer efficiency at the interface to fully exploit CNTs' superior mechanical properties.
APPLIED SURFACE SCIENCE
(2023)
Article
Biochemistry & Molecular Biology
Aigul Shamsieva, Alexander Evseev, Irina Piyanzina, Oleg Nedopekin, Dmitrii Tayurskii
Summary: The use of carbon nanotubes is a promising direction in materials science for improving the mechanical properties of polymers. The addition of single-walled carbon nanotubes to a polymer can significantly enhance its mechanical, electrical, optical, and structural properties. However, there is a limit to the improvement, and exceeding a certain content of nanotubes can lead to a decrease in mechanical properties.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Chemistry, Physical
Tanyu Li, Ni Liu, Jialei Huang
Summary: The development of gas hydrate technology is beneficial for efficient storage and transportation of methane. This study simulated the formation of methane hydrate in the presence of carbon nanotubes (CNTs) and found that CNTs can promote the formation and growth of hydrates, and the promotion effect depends on the concentration and temperature.
JOURNAL OF MOLECULAR LIQUIDS
(2022)
Article
Chemistry, Multidisciplinary
Lu Qiu, Feng Ding
Summary: Revealing the true picture of the carbon nanotube growth front at the catalyst surface is crucial for understanding the mechanism of controlled CNT growth. Through simulation experiments, it has been found that a clean CNT-catalyst interface dominates the growth kinetics during real CNT experimental growth, indicating the feasibility of controlling CNT growth by tuning the CNT-catalyst interface.
Article
Mechanics
Lei Luo, Ahmed M. Awed, Markus Oeser, Pengfei Liu
Summary: In this study, molecular dynamics and density functional theory methods were used to investigate the nanoscale interaction and load-transfer mechanisms at the bitumen-CNT interface. The results revealed a non-uniform distribution phenomenon of interfacial shear strength (ISS), with interfacial friction prevailing at the embedded section and van der Waals attraction distributed at the entrance of the interface. Moreover, the fundamental interaction behavior between CNTs and bitumen molecules was also studied.
ENGINEERING FRACTURE MECHANICS
(2023)
Article
Engineering, Mechanical
Seunghwa Yang
Summary: In this study, the impact of covalent grafting between CNTs and a PET matrix on the interface, interphase, and elasticity of the nanocomposite was investigated. The results show that covalent grafting can improve the transverse modulus and shear modulus of the nanocomposite. Additionally, the elastic modulus of the interphase is always higher than that of the neat PET matrix.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2022)
Article
Chemistry, Physical
Hanqing Wei, Haifei Zhan, Yanjie Wang, Yizhuo Gu, Shaokai Wang, Zuoguang Zhang, Min Li
Summary: The mechanical properties of carbon nanotube (CNT) bundles are influenced by their stacking morphology, with bundles containing zigzag DWNTs exhibiting higher Young's modulus but lower yielding strength. Initial off-axial angle perturbations in constituent DWNTs can lead to misalignments that degrade the bundle structures' yielding strength and yielding strain. The impacts of stacking morphology on the overall tensile properties of DWNT bundles can be quantitatively predicted using an elastic column approximation for each DWNT.
Article
Chemistry, Multidisciplinary
Fan Wu, Yushun Zhao, Yifan Zhao, Yue Zhao, Chao Sui, Xiaodong He, Chao Wang, Huifeng Tan
Summary: Fiber-based fabrics have great potential in impact protection, and a novel nanostructure using SWCNTs to weave 2D films has been proposed. Through MD simulation, it was found that SWFs exhibit significant anisotropy in mechanical properties, with the best performance when loaded along the CNT axis. The SWF demonstrated high strength and energy absorption, attributed to the intrinsic strength and flexibility of CNTs.
Article
Chemistry, Physical
Daniel A. Holdbrook, Jan K. Marzinek, Slawomir Boncel, Alister Boags, Yaw Sing Tan, Roland G. Huber, Chandra S. Verma, Peter J. Bond
Summary: Hypothesis: CNTs can be a platform for cellular delivery of therapeutic peptides, and chemically-modified CNTs can enhance peptide uptake. The addition of a peptide surface sheath can slow membrane permeation, and CNT conjugates can desheath their peptide layer at the bilayer interface to potentially enhance therapeutic molecule delivery efficiency.
JOURNAL OF COLLOID AND INTERFACE SCIENCE
(2021)
Article
Materials Science, Composites
Zhaobo Song, Yunlong Li, Bin Yang
Summary: This study comprehensively compared the interfacial load transfer properties of aminofunctionalized carbon nanotube (CNT)/epoxy composites using molecular dynamics simulations. The introduction of amino functional groups was found to enhance interfacial shear strength, especially when considering crosslinking interactions. Additionally, significant load transfer interactions between the end of CNTs and the matrix were observed at the nanoscale, challenging the classical shear lag model assumption of zero traction between fiber ends and matrix.
COMPOSITES SCIENCE AND TECHNOLOGY
(2021)
Article
Thermodynamics
Tingting Miao, Zhengyang Liu, Dongsheng Chen, Meng An, Weigang Ma
Summary: This study proposes a capillary wick material based on carbon nanotube arrays, which can regulate its heat transfer performance by controlling the water filling ratio. Molecular dynamics simulation results show that the thermal conductivity of water-filled carbon nanotubes is reduced, which is beneficial for start-up operation. In addition, empty carbon nanotubes have high thermal conductivity and can reduce the temperature of the endothermic surface.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2022)
Article
Computer Science, Interdisciplinary Applications
Azadeh Kordzadeh, Mahdi Zarif, Sepideh Amjad-Iranagh
Summary: The performance of functionalized carbon nanotubes (f-CNT) as drug carriers was studied. The results showed that CNT-COO-FA has better performance in releasing the anticancer drug doxorubicin compared to CNT-COO, with both pH and ligand sensitive mechanisms being responsible for the higher drug delivery efficiency of CNT-COO-FA.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Chemistry, Physical
Xianhua Nie, Li Zhao, Yu Zhu, Xi Chen, Shuai Deng
Summary: This study investigates the transport and composition separation of a binary organic mixed working fluid in a double-walled, T-shaped carbon nanotube, driven by temperature differences, achieving satisfactory composition separation results.
JOURNAL OF MOLECULAR LIQUIDS
(2021)
Correction
Materials Science, Multidisciplinary
A. D. Boccardo, M. Tong, S. B. Leen, D. Tourret, J. Segurado
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Tao Li, Qing Hou, Jie-chao Cui, Jia-hui Yang, Ben Xu, Min Li, Jun Wang, Bao-qin Fu
Summary: This study investigates the thermal and defect properties of AlN using molecular dynamics simulation, and proposes a new method for selecting interatomic potentials, developing a new model. The developed model demonstrates high computational accuracy, providing an important tool for modeling thermal transport and defect evolution in AlN-based devices.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Shin-Pon Ju, Chao-Chuan Huang, Hsing-Yin Chen
Summary: Amorphous boron nitride (a-BN) is a promising ultralow-dielectric-constant material for interconnect isolation in integrated circuits. This study establishes a deep learning potential (DLP) for different forms of boron nitride and uses molecular dynamics simulations to investigate the mechanical behaviors of a-BN. The results reveal the structure-property relationships of a-BN, providing useful insights for integrating it in device applications.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
M. Salman, S. Schmauder
Summary: Shape memory polymer foams (SMPFs) are lightweight cellular materials that can recover their undeformed shape through external stimulation. Reinforcing the material with nano-clay filler improves its physical properties. Multiscale modeling techniques can be used to study the thermomechanical response of SMPFs and show good agreement with experimental results.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Laura Gueci, Francesco Ferrante, Marco Bertini, Chiara Nania, Dario Duca
Summary: This study investigates the acidity of 30 Bronsted sites in the beta-zeolite framework and compares three computational methods. The results show a wide range of deprotonation energy values, and the proposed best method provides accurate calculations.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
K. A. Lopes Lima, L. A. Ribeiro Junior
Summary: Advancements in nanomaterial synthesis and characterization have led to the discovery of new carbon allotropes, including biphenylene network (BPN). The study finds that BPN lattices with a single-atom vacancy exhibit higher CO2 adsorption energies than pristine BPN. Unlike other 2D carbon allotropes, BPN does not exhibit precise CO2 sensing and selectivity by altering its band structure configuration.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Jay Kumar Sharma, Arpita Dhamija, Anand Pal, Jagdish Kumar
Summary: In this study, the quaternary Heusler alloys LiAEFeSb were investigated for their crystal structure, electronic properties, and magnetic behavior. Density functional theory calculations revealed that LiSrFeSb and LiBaFeSb exhibit half-metallic band structure and 100% spin polarization, making them excellent choices for spintronic applications.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Roman A. Eremin, Innokentiy S. Humonen, Alexey A. Kazakov, Vladimir D. Lazarev, Anatoly P. Pushkarev, Semen A. Budennyy
Summary: Computational modeling of disordered crystal structures is essential for studying composition-structure-property relations. In this work, the effects of Cd and Zn substitutions on the structural stability of CsPbI3 were investigated using DFT calculations and GNN models. The study achieved accurate energy predictions for structures with high substitution contents, and the impact of data subsampling on prediction quality was comprehensively studied. Transfer learning routines were also tested, providing new perspectives for data-driven research of disordered materials.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Zhixin Sun, Hang Dong, Yaohui Yin, Ai Wang, Zhen Fan, Guangyong Jin, Chao Xin
Summary: In this study, the crystal structure, electronic structure, and optical properties of KH2PO4: KDP crystals under different pressures were investigated using the generalized gradient approximate. It was found that high pressure caused a phase transition in KDP and greatly increased the band gap. The results suggest that high pressure enhances the compactness of KDP and improves the laser damage threshold.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Tingting Yu
Summary: This study presents atomistic simulations revealing that an increase in driving force may result in slower grain boundary movement and switches in the mode of grain boundary shear coupling migration. Shear coupling behavior is found to effectively alleviate stress and holds potential for stress relaxation and microstructure manipulation in materials.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Y. Zhang, X. Q. Deng, Q. Jing, Z. S. Zhang
Summary: The electronic properties of C2N/antimonene van der Waals heterostructure are investigated using density functional theory. The results show that by applying horizontal strain, vertical strain, electric field, and interlayer twist, the electronic structure can be adjusted. Additionally, the band alignment and energy states of the heterostructure can be significantly changed by applying vertical strain on the twisted structure. These findings are important for controlling the electronic properties of heterostructures.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Chad E. Junkermeier, Evan Larmand, Jean-Charles Morais, Jedediah Kobebel, Kat Lavarez, R. Martin Adra, Jirui Yang, Valeria Aparicio Diaz, Ricardo Paupitz, George Psofogiannakis
Summary: This study investigates the adsorption properties of carbon dioxide (CO2), methane (CH4), and dihydrogen (H2) in carbophenes functionalized with different groups. The results show that carbophenes can be promising adsorbents for these gases, with high adsorption energies and low desorption temperatures. The design and combination of functional groups can further enhance their adsorption performance.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Y. Borges, L. Huber, H. Zapolsky, R. Patte, G. Demange
Summary: Grain boundary structure is closely related to solute atom segregation, and machine learning can predict the segregation energy density. The study provides a fresh perspective on the relationship between grain boundary structure and segregation properties.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
M. R. Jones, L. T. W. Fey, I. J. Beyerlein
Summary: In this work, a three-dimensional ab-initio informed phase-field-dislocation dynamics model combined with Langevin dynamics is used to investigate glide mechanisms of edge and screw dislocations in Nb at finite temperatures. It is found that the screw dislocation changes its mode of glide at two distinct temperatures, which coincides with the thermal insensitivity and athermal behavior of Nb yield strengths.
COMPUTATIONAL MATERIALS SCIENCE
(2024)
Article
Materials Science, Multidisciplinary
Joshua A. Vita, Dallas R. Trinkle
Summary: This study introduces a new machine learning model framework that combines the simplicity of spline-based potentials with the flexibility of neural network architectures. The simplified version of the neural network potential can efficiently describe complex datasets and explore the boundary between classical and machine learning models. Using spline filters for encoding atomic environments results in interpretable embedding layers that can incorporate expected physical behaviors and improve interpretability through neural network modifications.
COMPUTATIONAL MATERIALS SCIENCE
(2024)