Готові списки джерел за темами / Computational Nano Science

Добірка наукової літератури з теми "Computational Nano Science"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Computational Nano Science"

1

Ghafooriadl, Naser, Sohrab Asadzadeh Olghi, and Ali Moghani. "Computational Algorithms for Topological Cycle Indices of Tert-Butyl Alcohol by Computational Science." Defect and Diffusion Forum 312-315 (April 2011): 39–44. http://dx.doi.org/10.4028/www.scientific.net/ddf.312-315.39.

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Recently, the dominant classes and integer-valued characters of un-matured full non-rigid group of tert-butyl alcohol has been found by the third author (see, J. Nano Res. 11, 7-11, 2010). In this paper, the unit subdued cycle index table introduced by S. Fujita for the above molecule is successfully derived for the first time.
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2

Kisała, Joanna, Kinga I. Hęclik, Krzysztof Pogocki, and Dariusz Pogocki. "Essentials and Perspectives of Computational Modelling Assistance for CNS-oriented Nanoparticle-based Drug Delivery Systems." Current Medicinal Chemistry 25, no. 42 (February 6, 2019): 5894–913. http://dx.doi.org/10.2174/0929867325666180517095742.

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The blood-brain barrier (BBB) is a complex system controlling two-way substances traffic between circulatory (cardiovascular) system and central nervous system (CNS). It is almost perfectly crafted to regulate brain homeostasis and to permit selective transport of molecules that are essential for brain function. For potential drug candidates, the CNSoriented neuropharmaceuticals as well as for those of primary targets in the periphery, the extent to which a substance in the circulation gains access to the CNS seems crucial. With the advent of nanopharmacology, the problem of the BBB permeability for drug nano-carriers gains new significance. Compared to some other fields of medicinal chemistry, the computational science of nano-delivery is still premature to offer the black-box type solutions, especially for the BBB-case. However, even its enormous complexity can spell out the physical principles, and as such subjected to computation. The basic understanding of various physicochemical parameters describing the brain uptake is required to take advantage of their usage for the BBB-nano delivery. This mini-review provides a sketchy introduction of essential concepts allowing application of computational simulation to the BBB-nano delivery design.
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3

Grujicic, M., JS Snipes, and S. Ramaswami. "Multi-scale computational analysis of the nano-indentation and nano-scratch testing of Kevlar® 49 single fibers." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 232, no. 6 (February 27, 2016): 495–513. http://dx.doi.org/10.1177/1464420716635851.

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To carry out virtual nano-indentation and nano-scratch Kevlar® 49 single-fiber tests, a multi-scale computational framework has been developed and employed. Such tests are generally conducted to determine fiber local properties, as well as to provide some insight into the interaction of hard nano-particles with the fibers. The Kevlar® fabric-based soft armor is infused with these nano-particles for improved ballistic resistance, and tip geometry of the nano-indentation/-scratch probes is selected to match nano-particle size and geometry. Due to the fact that Kevlar® 49 fibers (typical diameter 12 µm) are effectively assemblies of parallel fibrils (typical diameter 100–300 nm), while atomic bond length in Kevlar® fibers is of the order of 0.2 nm, a continuum-level finite-element framework has been developed. However, to more accurately account for some of the key aspects of the fiber-material constitutive behavior, e.g. inter-fibril cohesion, the continuum-level computational analysis has been supplemented with atomic-level molecular-statics/-dynamics calculations. In good agreement with their experimental counterparts, the results obtained revealed that the extent of participation of different fibril-deformation modes (e.g. transverse compression, inter-fibril shear, axial tension, axial tensile fracture, fibrillation, axial compression, buckling and pile-up formation ahead of the nano-scratch probe, etc.) is a function of the indentation/scratch depth. Also, a relatively good agreement was obtained between the computed and experimentally measured nano-indentation forces/energies for both shallow and deep indentations, and for the nano-scratch forces/energies, but only for shorter scratch lengths. At longer scratch lengths, the “short-fiber” effects cause the computation/experiment agreement to worsen.
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4

Alavinasab, A., R. Jha, G. Ahmadi, C. Cetinkaya, and I. Sokolov. "Computational modeling of nano-structured glass fibers." Computational Materials Science 44, no. 2 (December 2008): 622–27. http://dx.doi.org/10.1016/j.commatsci.2008.05.004.

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5

LAMBA, V. K., O. P. GARG, and D. ENGLES. "SCATTERING IN NANO-FILMS." Journal of Multiscale Modelling 04, no. 02 (June 2012): 1250007. http://dx.doi.org/10.1142/s1756973712500072.

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In this communication, a quantum mechanical technique for treatment of effects of scattering transport at nanoscale in thin films is discussed. We implemented a rigorous treatment of scattering within the NEGF simulation platform. Results obtained by applying the rigorous scattering model to simulate the devices were used as a benchmark to validate a simple computationally-efficient, phenomenological treatment of scattering. The NEGF method is used to study the effect of electron confinement on silicon nano-films and wires. Electron confinement results in almost a factor of 3 decreases in the electrical conductivity of the 5 nm silicon film compared to the 10 nm film. Increase in the amount of confinement also leads to a 35% decrease in the conductivity of a 5 nm × 5 nm wire compared to the 5 nm film. Our simple model provides an excellent tradeoff between increased computational cost and the physics of scattering that needs to be captured in devices of the future.
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6

Chong, Ken P. "Nano Science and Engineering in Solid Mechanics." Acta Mechanica Solida Sinica 21, no. 2 (April 2008): 95–103. http://dx.doi.org/10.1007/s10338-008-0812-7.

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7

Dubey, A., G. Sharma, C. Mavroidis, M. S. Tomassone, K. Nikitczuk, and M. L. Yarmush. "Computational Studies of Viral Protein Nano-Actuators." Journal of Computational and Theoretical Nanoscience 1, no. 1 (March 1, 2004): 18–28. http://dx.doi.org/10.1166/jctn.2003.003.

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8

Hajder, Piotr, and Łukasz Rauch. "Moving Multiscale Modelling to the Edge: Benchmarking and Load Optimization for Cellular Automata on Low Power Microcomputers." Processes 9, no. 12 (December 9, 2021): 2225. http://dx.doi.org/10.3390/pr9122225.

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Numerical computations are usually associated with the High Performance Computing. Nevertheless, both industry and science tend to involve devices with lower power in computations. This is especially true when the data collecting devices are able to partially process them at place, thus increasing the system reliability. This paradigm is known as Edge Computing. In this paper, we propose the use of devices at the edge, with lower computing power, for multi-scale modelling calculations. A system was created, consisting of a high-power device—a two-processor workstation, 8 RaspberryPi 4B microcomputers and 8 NVidia Jetson Nano units, equipped with GPU processor. As a part of this research, benchmarking was performed, on the basis of which the computational capabilities of the devices were classified. Two parameters were considered: the number and performance of computing units (CPUs and GPUs) and the energy consumption of the loaded machines. Then, using the calculated weak scalability and energy consumption, a min–max-based load optimization algorithm was proposed. The system was tested in laboratory conditions, giving similar computation time with same power consumption for 24 physical workstation cores vs. 8x RaspberryPi 4B and 8x Jetson Nano. The work ends with a proposal to use this solution in industrial processes on example of hot rolling of flat products.
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9

Khitun, Alexander, and Kang L. Wang. "Nano scale computational architectures with Spin Wave Bus." Superlattices and Microstructures 38, no. 3 (September 2005): 184–200. http://dx.doi.org/10.1016/j.spmi.2005.07.001.

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10

Sankaran, Krishnaswamy. "Recent Trends in Computational Electromagnetics for Defence Applications." Defence Science Journal 69, no. 1 (January 10, 2019): 65–73. http://dx.doi.org/10.14429/dsj.69.13275.

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Innovations in material science, (nano) fabrication techniques, and availability of fast computers are rapidly changing the way we design and develop modern defence applications. When we want to reduce R&D and the related trial-and-error costs, virtual modelling and prototyping tools are valuable assets for design engineers. Some of the recent trends in computational electromagnetics are presented highlight the challenges and opportunities . Why researchers should equip themselves with the state-of-the-art tools with multiphysics and multiscale capabilities to design and develop modern defence applications are discussed.
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Дисертації з теми "Computational Nano Science"

1

Dahl, Anna Caroline E. "Membrane protein mechanotransduction : computational studies and analytics development." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:67798647-8ed5-46e0-bde9-c71235fe70ba.

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Membrane protein mechanotransduction is the altered function of an integral membrane protein in response to mechanical force. Such mechanosensors are found in all kingdoms of life, and increasing numbers of membrane proteins have been found to exhibit mechanosensitivity. How they mechanotransduce is an active research area and the topic of this thesis. The methodology employed is classical molecular dynamics (MD) simulations. MD systems are complex, and two programs were developed to reduce this apparent complexity in terms of both visual abstraction and statistical analysis. Bendix detects and visualises helices as cylinders that follow the helix axis, and quantifies helix distortion. The functionality of Bendix is demonstrated on the symporter Mhp1, where a state is identified that had hitherto only been proposed. InterQuant tracks, categorises and orders proximity between parts of an MD system. Results from multiple systems are statistically interrogated for reproducibility and significant differences at the resolution of protein chains, residues or atoms. Using these tools, the interaction between membrane and the Escherichia coli mechanosensitive channel of small conductance, MscS, is investigated. Results are presented for crystal structures captured in different states, one of which features electron density proposed to be lipid. MD results supports this hypothesis, and identify differential lipid interaction between closed and open states. It is concluded that propensity for lipid to leave for membrane bulk drives MscS state stability. In a subsequent study, MscS is opened by membrane surface tension for the first time in an MD setup. The gating mechanism of MscS is explored in terms of both membrane and protein deformation in response to membrane stretch. Using novel tension methodology and the longest MD simulations of MscS performed to date, a molecular basis for the Dashpot gating mechanism is proposed. Lipid emerges as an active structural element with the capacity to augment protein structure in the protein structure-function paradigm.
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2

Kabiraj, Arnab. "High-Throughput Computational Techniques for Discovery of Application-Specific Two-Dimensional Materials." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5852.

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Two-dimensional (2D) materials have revolutionized the field of materials science since the successful exfoliation of graphene in 2004. Consequently, the advances in computational science have resulted in massive generic databases for 2D materials, where the structure and the basic properties are predicted using density functional theory (DFT). However, discovering material for a given application from these vast databases is a challenging feat. In this thesis, we have developed various automated high-throughput computational pipelines combining DFT and machine learning (ML) to assess the suitability of 2D materials for specific applications. Methods have also been developed to draw valuable insights into what makes these materials suitable for these applications. The assessed properties include suitability for energy storage in the form of Li-ion battery (LIB) and supercapacitor electrodes, along with high-temperature ferromagnetism and the presence of exotic charge density waves (CDW). The ultra-large surface-to-mass ratio of 2D materials has made them an ideal choice for electrodes of compact LIBs and supercapacitors. We combine explicit-ion and implicit-solvent formalisms to develop high-throughput pipelines and define four descriptors to map “computationally soft” single-Li-ion adsorption to “computationally hard” multiple-Li-ion-adsorbed configuration located at global minima for insight finding and rapid screening. Leveraging this large dataset, we also develop crystal-graph-based ML models for the accelerated discovery of potential candidates. A reactivity test with commercial electrolytes is further performed for wet experiments. Our unique approach, which predicts both Li-ion storage and supercapacitive properties and hence identifies various important electrode materials common to both devices, may pave the way for next-generation energy storage systems. Although there are numerous studies computationally exploring 2D materials as Li-ion battery electrodes, these studies are mostly material-specific, i.e., only a few materials are explored in each of these studies. In our work, however, using the novel descriptor-based technique, we explore thousands of 2D materials for LIB electrode applications. Moreover, to the best of our knowledge, no study has explored these thousands of 2D materials for supercapacitor electrodes yet, which we also achieve. The discovery of 2D ferromagnets with high Curie temperature is challenging since its calculation involves a manually intensive complex process. We develop a Metropolis Monte-Carlo-based pipeline and conduct a high-throughput scan of 786 materials from a database to discover 26 materials with a Curie point beyond 400 K. For rapid data mining, we further use these results to develop an end-to-end ML model with generalized chemical features through an exhaustive search of the model space as well as the hyperparameters. We discover a few more high Curie point materials from different sources using this data-driven model. CDW materials are an important subclass of two-dimensional materials exhibiting significant resistivity switching with the application of external energy. We combine a first-principles-based structure-searching technique and unsupervised machine learning to develop a high-throughput pipeline, which identifies CDW phases from a unit cell with an inherited Kohn anomaly. The proposed methodology not only rediscovers the known CDW phases but also predicts a host of easily exfoliable CDW materials (30 materials and 114 phases) along with associated electronic structures. Apart from these, we have also investigated Li-ion storage in distorted rhenium disulfide crystal, polymorphism-driven Li-ion storage of monoelemental 2D materials, and cation intercalation-driven reversible magnetism in ferrous dioxide using global-energy-minima search technique. Our findings could provide useful guidelines for future experimental efforts. All the data, ML models, and computer codes are available freely for community usage. We stress that the automated methodologies/workflows developed in this thesis are as important as the results obtained and generalized enough to be applicable to any 2D materials. The available 2D materials databases are ever-growing, and the workflows introduced by us can aid in the discovery of even better application-specific 2D materials in the future.
Indian Institute of Science and Ministry of Education, Government of India
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Книги з теми "Computational Nano Science"

1

From Nano to Space: Applied Mathematics Inspired by Roland Bulirsch. Springer, 2007.

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2

Nepomnyashchy, Alexander A., and Alexander A. Golovin. Self-Assembly, Pattern Formation and Growth Phenomena in Nano-Systems: Proceedings of the NATO Advanced Study Institute, held in St. Etienne de Tinee, ... 11, 2004. Springer, 2014.

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3

Succi, Sauro. The Lattice Boltzmann Equation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199592357.001.0001.

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Over the past near three decades, the Lattice Boltzmann method has gained a prominent role as an efficient computational method for the numerical simulation of a wide variety of complex states of flowing matter across a broad range of scales, from fully developed turbulence, to multiphase micro-flows, all the way down to nano-biofluidics and lately, even quantum-relativistic subnuclear fluids. After providing a self-contained introduction to the kinetic theory of fluids and a thorough account of its transcription to the lattice framework, this book presents a survey of the major developments which have led to the impressive growth of the Lattice Boltzmann across most walks of fluid dynamics and its interfaces with allied disciplines, such as statistical physics, material science, soft matter and biology. This includes recent developments of Lattice Boltzmann methods for non-ideal fluids, micro- and nanofluidic flows with suspended bodies of assorted nature and extensions to strong non-equilibrium flows beyond the realm of continuum fluid mechanics. In the final part, the book also presents the extension of the Lattice Boltzmann method to quantum and relativistic fluids, in an attempt to match the major surge of interest spurred by recent developments in the area of strongly interacting holographic fluids, such as quark-gluon plasmas and electron flows in graphene. It is hoped that this book may provide a source information and possibly inspiration to a broad audience of scientists dealing with the physics of classical and quantum flowing matter across many scales of motion.
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4

Hoang, Bao Hung, Cong Phap Huynh, Gottfried Vossen, Bogdan Trawiński, and Ngoc Thanh Nguyen. Computational Collective Intelligence: 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30-December 3, 2020, Proceedings. Springer International Publishing AG, 2020.

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Частини книг з теми "Computational Nano Science"

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Mukherjee, Kasturi, Arpan Deyasi, and Deepam Gangopadhyay. "Quantized conductance characteristics of Nano–MESFET under optical illumination." In Computational Science and Engineering, 267–72. CRC Press/Balkema, P.O. Box 11320, 2301 EH Leiden, The Netherlands, e-mail: Pub.NL@taylorandfrancis.com, www.crcpress.com – www.taylorandfrancis.com: CRC Press, 2016. http://dx.doi.org/10.1201/9781315375021-54.

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2

Lee, Kwangyong, Woojin Lee, Juil Kim, and Kiwon Chong. "A Technique for Code Generation of USN Applications Based on Nano-Qplus." In Computational Science – ICCS 2006, 902–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758549_120.

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3

Šuvakov, Milovan, and Bosiljka Tadić. "Simulation of the Electron Tunneling Paths in Networks of Nano-particle Films." In Computational Science – ICCS 2007, 641–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72586-2_93.

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4

Klaedtke, A., J. Hamm, and O. Hess. "5. Simulation of Active and Nonlinear Photonic Nano-Materials in the Finite-Difference Time-Domain (FDTD) Framework." In Computational Materials Science, 75–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39915-5_5.

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5

Dubey, Atul, and M. Silvina Tomassone. "Viral Protein Nano-Actuators, Computational Studies of Bio-nanomachines." In Encyclopedia of Complexity and Systems Science, 9749–63. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_577.

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6

Frijns, A. J. H., S. V. Nedea, A. J. Markvoort, A. A. van Steenhoven, and P. A. J. Hilbers. "Molecular Dynamics and Monte Carlo Simulations for Heat Transfer in Micro and Nano-channels." In Computational Science - ICCS 2004, 661–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25944-2_85.

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7

Yanovsky, Yu G. "Nano-Modeling Structure and Micromechanical Properties of Mesoscopic Composite Systems." In Computational Methods in Engineering & Science, 129–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-48260-4_12.

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8

Solov’yov, Ilia A., Andrey V. Korol, and Andrey V. Solov’yov. "Introduction to Computational Meso-Bio-Nano (MBN) Science and MBN Explorer." In Multiscale Modeling of Complex Molecular Structure and Dynamics with MBN Explorer, 1–41. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56087-8_1.

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9

Huang, D., and J. S. Zhuo. "Molecular Dynamics Simulation of Length Size Effect on Mechanical Properties of Nano-Metal." In Computational Methods in Engineering & Science, 267. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-48260-4_113.

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10

Ambrosi, Davide, Pasquale Ciarletta, Elena Danesi, Carlo de Falco, Matteo Taffetani, and Paolo Zunino. "A Multiscale Modeling Approach to Transport of Nano-Constructs in Biological Tissues." In Lecture Notes in Computational Science and Engineering, 109–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73371-5_6.

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Тези доповідей конференцій з теми "Computational Nano Science"

1

Junpeng, Yuan, Zhu Donghua, Huang Jin, Bao Hailong, and Yang Chunning. "A Text Mining Framework to Support Nano Science and Technology Management." In Multiconference on "Computational Engineering in Systems Applications. IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.4281982.

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2

Etminan, Maryam, George Maroulis, and Theodore E. Simos. "The Decomposition of Doubly Charged Silver Nano Clusters." In COMPUTATIONAL METHODS IN SCIENCE AND ENGINEERING: Advances in Computational Science: Lectures presented at the International Conference on Computational Methods in Sciences and Engineering 2008 (ICCMSE 2008). AIP, 2009. http://dx.doi.org/10.1063/1.3225438.

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3

Norlina, M. S., P. Mazidah, N. D. Md Sin, and M. Rusop. "Computational intelligence technique in optimization of nano-process deposition parameters." In 2015 7th Computer Science and Electronic Engineering (CEEC). IEEE, 2015. http://dx.doi.org/10.1109/ceec.2015.7332722.

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4

Sayed, Ahmed, and Hisham El-Shishiny. "Computational Experience with Nano-material Science Quantum Monte Carlo Modeling on BlueGene/L." In 2009 Fifth International Conference on MEMS NANO, and Smart Systems. IEEE, 2009. http://dx.doi.org/10.1109/icmens.2009.32.

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5

Maroulis, George, Demetrios Xenides, Panaghiotis Karamanis, George Maroulis, and Theodore E. Simos. "Computational Quantum Chemistry: From Atoms and Molecules to Clusters and Nano-objects." In COMPUTATIONAL METHODS IN SCIENCE AND ENGINEERING: Advances in Computational Science: Lectures presented at the International Conference on Computational Methods in Sciences and Engineering 2008 (ICCMSE 2008). AIP, 2009. http://dx.doi.org/10.1063/1.3225298.

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6

Yuan, Junpeng, Huang Jin, Donghua Zhu, Hailong Bao, and Chunning Yang. "A Text Mining Framework to Support Nano Science and Technology Management." In The Proceedings of the Multiconference on "Computational Engineering in Systems Applications". IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.313470.

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7

Machowski, Lukasz, and Tshilidzi Marwala. "Nano Version Control and "Robots of Robots" – Data Driven, Regenerative Production Code." In 2021 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2021. http://dx.doi.org/10.1109/csci54926.2021.00360.

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8

Fey, Florian, Alexander Gerwing, and Sergei Gorlatch. "Towards a Generic Framework for GPU-Parallelized Simulations of Light-Driven Nano-Particles." In 2022 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2022. http://dx.doi.org/10.1109/csci58124.2022.00245.

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9

Wang, Hao, Xiwen Chen, Abolfazl Razi, Michael Kozicki, Rahul Amin, and Mark Manfredo. "Nano-Resolution Visual Identifiers Enable Secure Monitoring in Next-Generation Cyber-Physical Systems." In 2022 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2022. http://dx.doi.org/10.1109/csci58124.2022.00155.

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10

Almatrood, Amjad F., Aby K. George, and Harpreet Singh. "On the Development of Multi-input Multi-output Nano Digital Circuits for Molecular Medicine." In 2015 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2015. http://dx.doi.org/10.1109/csci.2015.149.

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