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.

为了使纳米制造在经济上可行并可大规模实现，纳米制造关键取决于实现对复杂工艺参数的控制以及对潜在驱动科学现象的透彻理解。这项合作的目标是建立一个分层纳米制造节点，用于计算工具的开发，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设施的新兴进步，该节点的愿景是模拟纳米产品制造过程的每一步。开发的仿真工具将被优化和测试，以供包括纳米技术和相关工业部门的研究人员在内的利益相关者使用。此外，这项为期5年的活动对纳米制造业的影响将被严格评估。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节点的任务是成为设计、模拟、规划和优化高度相关的纳米制造增长和图案工艺的引擎。与现有的网络框架（nanoHUB）合作以及促进将广泛影响纳米制造和纳米技术的人力资源的发展对这一使命至关重要。为了帮助实现这一使命，纳米制造的多尺度理论、建模和仿真（TM&amp；S）计算工具将得到开发和广泛传播。这项为期5年的活动的智力价值在于纳米制造模拟工具的开发，通过实验数据验证，并与数据驱动的不确定性量化相结合。这项工作的框架是基于一个分层计算工具基础设施，包括以下内容的创建：(1)纳米尺度输运现象模型，(2)过程模型，(3)不确定性量化框架，(4)过程模型的应用和经验验证，(5)多尺度输运现象工具，(6)纳米尺度自组装工具。除了纳米制造，新兴的纳米科学领域将显著受益于免费的、开源的、经过实验验证的计算工具。这项合作将把研究人员、教育工作者、行业、国家实验室和高中聚集在一起，形成一个有凝聚力的框架，以推进纳米科学和由此产生的纳米技术，促进STEM教育，并建立必要的人力资源，使纳米制造成为社会经济上可行的引擎。这将对实现纳米技术所带来的各种承诺产生若干影响，因为它涉及：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、农业、建筑机械等。促进劳动力多样化的努力与这一努力紧密交织在一起，以帮助增加代表性不足的群体和机构的参与，并增加正在开发的工具的丰富性。奖学金将每年进行竞争性颁发，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，而且在社区建设和资源和专业知识共享的价值方面，从而为美国的经济发展及其在全球的领导地位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

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

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

Kirigami-inspired strain-insensitive sensors based on atomically-thin materials

• DOI: 10.1016/j.mattod.2019.08.013
• 发表时间: 2020-04-01
• 期刊: MATERIALS TODAY
• 影响因子: 24.2
• 作者: Yong, Keong;De, Subhadeep;Nam, SungWoo
• 通讯作者: Nam, SungWoo

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