课题基金 / 基金详情

NRT-DESE: Data Intensive Research Enabling Clean Technologies (DIRECT)

NRT-DESE: Data Intensive Research Enabling Clean Technologies (DIRECT)
NRT-DESE:数据密集型研究支持清洁技术(直接)
批准号:
1633216
负责人:
Jim Pfaendtner
金额:
$300.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-01 至 2022-09-30

项目摘要

项目成果

Jim Pfaendtner的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
Discovering new materials that will generate and store renewable energy in a low cost, environmentally benign, and scalable fashion is perhaps the most important technological challenge facing society today. All phases of this scientific process (design, synthesis, and characterization) are routinely stymied by the same challenge: researchers are not equipped to handle the deluge of data coming from our labs and high performance computers. This National Science Foundation Research Traineeship (NRT) award to the University of Washington will create, test and evaluate a new training model for graduate students in the area of data intensive research in materials for clean energy. The University of Washington program, DIRECT: Data Intensive Research Enabling Clean Technologies, addresses these challenges by training a new generation of energy researchers who are equipped to handle the massive data sets arising from all stages of materials discovery. This project anticipates 72 trainees (18 MS and 54 PhD students), including 16 funded trainees, from Chemical Engineering, Chemistry, Materials Science & Engineering, Molecular Engineering and Human Centered Design & Engineering. DIRECT creates a new training modality comprised of three phases: 1) new graduate coursework at the nexus of data science and advanced materials for energy, 2) a project-based learning scheme to apply new skills and work on challenging real world problems in a team-based setting, and 3) capstone experiences that leverage broad networks spanning industry, national labs and several international partners. The thematic focus of the research is next-generation materials for batteries and photovoltaics. We will use an ethnographic approach rooted in the social sciences to understand why some methods are successfully deployed while others are not, and learn how to apply data science tools in a contextualized manner to materials science to maximize usability. The project-based learning component of the traineeship will provide graduate students the chance to teach and practice leadership and management skills, a unique opportunity most trainees would not otherwise receive. DIRECT trainees will be equipped for many new career options that require data science training and will be prepared with the skills needed to thrive in the economy of the 21st century. The project will also provide unique information about the effectiveness of project-based learning in the acquisition of advanced technical skills and disciplinary knowledge in graduate education.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.
期刊论文(21)
专著(0)
科研奖励(0)
会议论文
Data Science in Chemical Engineering: Applications to Molecular Science
化学工程中的数据科学:在分子科学中的应用
DOI: 10.1146/annurev-chembioeng-101220-102232
发表时间: 2021
期刊: Annual Review of Chemical and Biomolecular Engineering
影响因子: 8.4
作者: [Ashraf, Chowdhury, Joshi, Nisarg, Beck, David A.C., Pfaendtner, Jim]
通讯作者: Pfaendtner, Jim
Enrichment Of Student Learning And Homework Management With Use Of GitHub In An Introductory Cross-Disciplinary Engineering Course Series On Software Engineering And Data Science
在软件工程和数据科学的跨学科工程入门课程系列中使用 GitHub 丰富学生的学习和作业管理
DOI: 10.18260/2-1-370.660-119316
发表时间: 2020
期刊: Chemical Engineering Education
影响因子: --
作者: [Curtis, Chad]
通讯作者: Curtis, Chad
Unsupervised machine learning for unbiased chemical classification in X-ray absorption spectroscopy and X-ray emission spectroscopy
用于 X 射线吸收光谱和 X 射线发射光谱中无偏差化学分类的无监督机器学习
DOI: 10.1039/d1cp02903g
发表时间: 2021
期刊: Physical Chemistry Chemical Physics
影响因子: 3.3
作者: [Tetef, Samantha, Govind, Niranjan, Seidler, Gerald T.]
通讯作者: Seidler, Gerald T.
DOI: 10.1038/s41524-022-00940-2
发表时间: 2022-12
期刊: npj Computational Materials
影响因子: 9.7
作者: [Nicholas R. Lewis;Yicheng Jin;Xiuyu Tang;Vidit Shah;Christina Doty;B. Matthews;Sarah Akers;S. Spurgeon]
通讯作者: Nicholas R. Lewis;Yicheng Jin;Xiuyu Tang;Vidit Shah;Christina Doty;B. Matthews;Sarah Akers;S. Spurgeon
16
    Collaborative Research: Mechanisms of Catalytic Enhancement of Immobilized Lipases by Tunable Polymer Materials
    • 批准号:
      2103613
    • 项目类别:
      Standard Grant
    • 资助金额:
      $32.84万
    • 财政年份:
      2021
    • 负责人:
      Jim Pfaendtner
    • 依托单位:
    Collaborative Research: Experimental and computational methods to study chemical transformations of solid xylose into useful compounds
    • 批准号:
      1703638
    • 项目类别:
      Standard Grant
    • 资助金额:
      $22.5万
    • 财政年份:
      2017
    • 负责人:
      Jim Pfaendtner
    • 依托单位:
    Combined molecular simulation and experimental study to discover, predict and control enzyme immobilization in polymeric nanoparticles
    • 批准号:
      1703438
    • 项目类别:
      Standard Grant
    • 资助金额:
      $33.13万
    • 财政年份:
      2017
    • 负责人:
      Jim Pfaendtner
    • 依托单位:
    NSF-DFG: Combining Simulation and Spectroscopy to Determine the Structure and Dynamics of Adsorbed Proteins - Application to Biomass Conversion
    • 批准号:
      1264459
    • 项目类别:
      Standard Grant
    • 资助金额:
      $30.78万
    • 财政年份:
      2013
    • 负责人:
      Jim Pfaendtner
    • 依托单位:
    海外基金