Network for Computational Nanotechnology - Hierarchical nanoMFG Node

计算纳米技术网络 - 分层 nanoMFG 节点

• 批准号: 1720701
• 负责人: Elif Ertekin
• 金额: $ 400万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1720701&HistoricalAwards=false
• 关键词: Network; Computational; Nanotechnology; Hierarchical; nanoMFG

To become economically viable and accessible at large scales, nanomanufacturing critically depends on achieving control over complex process parameters and a thorough understanding of the underlying driving scientific phenomena. The goal of this collaborative effort will be to establish a Hierarchical Nanomanufacturing Node for computational tools development aimed at creating smart, model- and data-driven nanomanufacturing. By leveraging emerging advances in computing and cyberinfrastructure, the vision of this node is to simulate every step of the manufacturing process of a nano-enabled product. Simulation tools developed will be optimized and tested for utility by stakeholders including researchers in nanotechnology and related industrial sectors. In addition, the impact of this 5-year activity on the nanomanufacturing industry will be critically assessed. The node will be part of the established Network for Computational Nanotechnology (NCN) Cyber Platform. The mission of the nanoMFG Node is to be the engine for design, simulation, planning, and optimization of highly relevant nano-manufacturing growth and patterning processes. Both working with the existing cyber framework (nanoHUB) and facilitating development of human resources that will broadly impact nanomanufacturing and nanotechnology are essential to this mission. To help achieve this mission, computational tools for nanomanufacturing aimed at multiscale theory, modeling, and simulation (TM&S) will be developed and broadly disseminated. The intellectual merit of this 5-year activity is the development of nanomanufacturing simulation tools, validated by experimental data, and integrated with data-driven uncertainty quantification. The framework for the effort is based on a layered computational tool infrastructure comprising the creation of the following: (1) nanoscale transport phenomena models, (2) process models, (3) uncertainty quantification framework, (4) application and empirical validation of process models, (5) tools for multiscale transport phenomena, and (6) tools for nanoscale self-assembly. Beyond nanomanufacturing, the burgeoning field of nanoscience will significantly benefit from free, open-source computational tools that have been validated by experiments. The collaboration will bring researchers, educators, industries, national labs, and high schools together to form a cohesive framework to advance nanoscience and resulting nanotechnologies, promote STEM education, and build the human resources necessary to make nanomanufacturing an economically viable engine for society. This will have several impacts on realizing the varied promises promulgated by nanotechnology as it pertains to: accelerating the high-tech economy, improving human health, and positively impacting an array of manufacturing industry domains including aviation, automotive, agricultural, construction machinery, and many others. Efforts to promote workforce diversification are tightly interwoven into this effort to both help increase participation from underrepresented groups and institutions and also to add to the richness of the tools being developed. Fellowships will be competitively awarded annually to help train the next generation workforce not only on simulation and modeling tool development but also on the value of community building and sharing of resources and expertise, thus building a nationwide innovation ecosystem for economic development of the U.S. and its leadership worldwide.

为了在大范围内变得经济可行且可访问，纳米制造在很大程度上取决于对复杂过程参数的控制以及对潜在驱动科学现象的彻底理解。这项协作工作的目的是为计算工具开发建立一个层级纳米制造节点，旨在创建智能，模型和数据驱动的纳米制造。通过利用计算和网络基础结构的新兴进步，该节点的愿景是模拟启用纳米产品的制造过程的每个步骤。 利益相关者（包括纳米技术和相关工业领域的研究人员）将对开发的仿真工具进行优化和测试。此外，将对这项5年活动对纳米制造业的影响进行严格评估。该节点将成为已建立的计算纳米技术网络（NCN）网络平台的一部分。 NanomFG节点的任务是成为高度相关的纳米制造增长和模式化过程的设计，模拟，计划和优化的引擎。都与现有的网络框架（NanoHub）一起工作，以及促进将广泛影响纳米制造和纳米技术的人力资源发展的发展，对这一任务至关重要。为了帮助实现这一任务，将开发和广泛传播针对多尺度理论，建模和模拟（TM＆S）的纳米制造的计算工具。这项5年活动的智力优点是通过实验数据验证的纳米制造模拟工具的开发，并与数据驱动的不确定性量化集成。努力的框架基于一个分层计算工具基础架构，包括以下创建：（1）纳米级传输现象模型，（2）过程模型，（3）不确定性定量框架，（4）过程模型的应用和经验验证，（5）（5）用于NAN Moscale Sypsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemsemmemsemsemmemsemsemsemmemsemmemsemmemsemmemsemmemsemmemmemmemmemmemmem。 除了纳米制造外，纳米科学的新兴领域将从实验验证的免费，开源计算工具中受益匪浅。该合作将使研究人员，教育者，行业，国家实验室和高中聚集在一起，形成一个具有凝聚力的框架，以推动纳米科学并产生的纳米技术，促进STEM教育，并建立人力资源，使纳米制造为社会提供经济上可行的引擎。这将对实现纳米技术颁布的各种诺言具有多种影响，因为它与：加速高科技经济，改善人类健康，并积极影响一系列制造业领域，包括航空，汽车，汽车，农业，建筑，建筑机械以及许多其他领域。促进劳动力多样化的努力紧密地交织在一起，以帮助增加代表性不足的群体和机构的参与，并增加开发工具的丰富性。 奖学金将每年获得竞争性奖励，以帮助培训下一代劳动力，不仅在模拟和建模工具开发方面，而且还培训社区建设以及资源和专业知识的价值，从而为美国及其领导层的全国经济发展建立一个全国性的创新生态系统。

项目成果

Atmospheric-Pressure Flame Vapor Deposition of Nanocrystalline Diamonds: Implications for Scalable and Cost-Effective Coatings

• DOI: 10.1021/acsanm.2c02059
• 发表时间: 2022-07-21
• 期刊: ACS APPLIED NANO MATERIALS
• 影响因子: 5.9
• 作者: Manjarrez, Adrian;Zhou, Kai;Cai, Lili
• 通讯作者: Cai, Lili

Optical reflectance imaging reveals interlayer coupling in mechanically stacked MoS 2 and WS 2 bilayers

光学反射成像揭示了机械堆叠的 MoS 2 和 WS 2 双层中的层间耦合

• DOI: 10.1364/oe.473397
• 发表时间: 2023
• 期刊: Optics Express
• 影响因子: 3.8
• 作者: Nguyen, Vu;Li, Wan;Ager, Joel;Xu, Ke;Taylor, Hayden
• 通讯作者: Taylor, Hayden

Crowd-Sourced Data and Analysis Tools for Advancing the Chemical Vapor Deposition of Graphene: Implications for Manufacturing

促进石墨烯化学气相沉积的众包数据和分析工具：对制造业的影响

• DOI: 10.1021/acsanm.0c02018
• 发表时间: 2020
• 期刊: ACS Applied Nano Materials
• 影响因子: 5.9
• 作者: Schiller, Joshua A.;Toro, Ricardo;Shah, Aagam;Surana, Mitisha;Zhang, Kaihao;Robertson, Matthew;Miller, Kristina;Cruse, Kevin;Liu, Kevin;Seong, Bomsaerah
• 通讯作者: Seong, Bomsaerah

Automated image segmentation of scanning electron microscopy images of graphene using U-Net Neural Network

使用 U-Net 神经网络对石墨烯扫描电子显微镜图像进行自动图像分割

• DOI: 10.1016/j.mtcomm.2023.106127
• 发表时间: 2023
• 期刊: Materials Today Communications
• 影响因子: 3.8
• 作者: Shah, Aagam;Schiller, Joshua A.;Ramos, Isiah;Serrano, James;Adams, Darren K.;Tawfick, Sameh;Ertekin, Elif
• 通讯作者: Ertekin, Elif

The global impact of science gateways, virtual research environments and virtual laboratories

• DOI: 10.1016/j.future.2018.12.026
• 发表时间: 2019-06-01
• 期刊: FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
• 影响因子: 7.5
• 作者: Barker, Michelle;Olabarriaga, Silvia Delgado;Costa, Alessandro
• 通讯作者: Costa, Alessandro