CAREER: Data-driven design of graphene oxide for environmental applications enabled by natural language processing and machine learning techniques
职业:通过自然语言处理和机器学习技术实现氧化石墨烯环境应用的数据驱动设计
基本信息
- 批准号:2238415
- 负责人:
- 金额:$ 50.1万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-06-15 至 2028-05-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Graphene oxide (GO) has emerged as a promising functional nanomaterial and building block for various environmental technologies including antimicrobial coatings for drinking water filtration membranes, sorbents for the removal of pollutants from air and water, and photocatalysts for the removal and destruction of organic pollutants from contaminated water. However, the current approaches used to design, synthesize, and optimize GO-based nanomaterials for targeted environmental applications suffer from a lack of standardization leading to numerous trial and error runs with prohibitive material development costs. The overarching goal of this CAREER project is to explore the utilization of data-driven approaches to characterize and unravel critical correlations between the synthesis conditions of GO and the material properties that will enable the rationale design and development of GO-based water purification and environmental remediation technologies. To advance this goal, the Principal Investigator (PI) proposes to use natural language processing and machine learning techniques to 1) extract peer-reviewed information from hundreds of thousands of scientific papers devoted to GO synthesis, characterization, and applications, 2) structure this knowledge into robust datasets, and 3) leverage these datasets to develop and experimentally validate structure-property relationships between the synthesis conditions and properties of GO. The successful completion of this project will benefit society through the generation of new fundamental knowledge and the creation of curated datasets and associated computational tools to advance the design and development of GO-based environmental technologies. Additional benefits to society will be achieved through student education and training including the mentoring of a graduate student at the University of Florida.The design and synthesis of tailored graphene oxide (GO)-based functional nanomaterials for water purification and environmental remediation will require a knowledge of the relationships between the material synthesis input parameters and the resulting material properties. The structural, physicochemical, and functional surface/bulk properties of GO depend on several parameters including the nature and characteristics of the precursor graphite, the synthesis conditions, and the post-synthesis treatment protocols. In this CAREER project, the Principal Investigator (PI) proposes to combine natural language processing (NLP) with machine learning (ML) and targeted materials synthesis and characterization experiments to develop and validate structure-property relationships between the synthesis conditions and properties of GO to advance the rationale design and development of GO-based water purification and environmental remediation technologies. The specific objectives of the research are to: 1) use NLP tools (e.g., latent semantic analysis and named-entity recognition) to automatically extract relevant information about the synthesis of GO (e.g., synthesis conditions, precursor materials, and post-fabrication treatments) and the resulting material properties of GO (e.g., size of sheets, and various physical/chemical properties); 2) structure this information into datasets and use classical frequentist approaches and other data analysis techniques (e.g., principal component analysis, linear discriminant analysis, and T-distributed stochastic neighbor embedding) to find correlations between the synthesis input parameters and the resulting material properties of GO, and 3) validate these correlations using targeted experiments including material synthesis, characterization, and performance evaluation of the synthesized GO nanomaterials as antimicrobial coatings for drinking water filtration membranes and sorbents/photocatalysts for the removal and destruction of organic pollutants from contaminated water. The successful completion of this project has the potential for transformative impact through the generation of fundamental knowledge and structured datasets to advance the rationale design of GO-based nanomaterials for water purification and environmental remediation. To implement the educational and training goals of this CAREER project, the PI proposes to develop and teach a graduate course on data-driven design of nanomaterials with a focus on environmental applications. In addition, the PI plans to 1) recruit and mentor undergraduate students from underrepresented groups and 2) develop an online seminar series with the goal of contributing to the training of students at the University of Florida to work on important problems at the interface of materials sciences, data analysis, and environmental engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
氧化石墨烯(GO)已成为一种有前途的功能性纳米材料和各种环境技术的基石,包括用于饮用水过滤膜的抗菌涂层,用于从空气和水中去除污染物的吸附剂,以及用于从污染水中去除和破坏有机污染物的光催化剂。然而,目前用于设计、合成和优化用于目标环境应用的GO基纳米材料的方法缺乏标准化,导致大量试错运行,材料开发成本过高。该CAREER项目的总体目标是探索利用数据驱动的方法来表征和揭示GO合成条件与材料性质之间的关键相关性,这将使基于GO的水净化和环境修复技术的合理设计和开发成为可能。为了推进这一目标,主要研究者(PI)建议使用自然语言处理和机器学习技术来1)从数十万篇致力于GO合成,表征和应用的科学论文中提取同行评审的信息,2)将这些知识结构化为强大的数据集,以及3)利用这些数据集来开发和实验验证GO的合成条件和性质之间的结构-性质关系。该项目的成功完成将通过产生新的基础知识和创建策划的数据集和相关的计算工具来促进基于GO的环境技术的设计和开发,从而使社会受益。通过学生教育和培训,包括指导佛罗里达大学的一名研究生,将为社会带来额外的好处。设计和合成用于水净化和环境修复的定制氧化石墨烯(GO)基功能纳米材料将需要了解材料合成输入参数与所得材料性质之间的关系。GO的结构、物理化学和功能表面/本体性质取决于若干参数,包括前体石墨的性质和特征、合成条件和合成后处理方案。在本CAREER项目中,主要研究者(PI)提出将联合收割机自然语言处理(NLP)与机器学习(ML)和目标材料合成和表征实验相结合,开发和验证GO合成条件与性质之间的结构-性质关系,以推进基于GO的水净化和环境修复技术的合理设计和开发。研究的具体目标是:1)使用NLP工具(例如,潜在语义分析和命名实体识别)以自动提取关于GO合成的相关信息(例如, 合成条件、前体材料和制造后处理)和所得GO的材料性质(例如,片的尺寸和各种物理/化学性质); 2)将该信息构造成数据集并使用经典的频率论方法和其它数据分析技术(例如,主成分分析、线性判别分析和T分布随机近邻嵌入),以找到合成输入参数与GO的所得材料性质之间的相关性,以及3)使用包括材料合成,表征,合成的GO纳米材料作为用于饮用水过滤膜和吸附剂的抗微生物涂层的性能评价/光催化剂用于从污染水中去除和破坏有机污染物。该项目的成功完成有可能通过生成基础知识和结构化数据集来产生变革性影响,以推进用于水净化和环境修复的GO基纳米材料的合理设计。为了实现这个职业项目的教育和培训目标,PI建议开发和教授一门关于纳米材料数据驱动设计的研究生课程,重点是环境应用。此外,PI计划1)从代表性不足的群体中招募和指导本科生,2)开发在线研讨会系列,目标是为佛罗里达大学的学生培训做出贡献,以解决材料科学,数据分析,该奖项反映了NSF的法定使命,并被认为是值得通过评估使用基金会的智力价值和更广泛的支持。影响审查标准。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Andreia Fonseca de Faria其他文献
Andreia Fonseca de Faria的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
相似国自然基金
多源数据融合的内外激励耦合下电驱动系统非平稳非高斯服役载荷谱高保
- 批准号:
- 批准年份:2025
- 资助金额:0.0 万元
- 项目类别:省市级项目
数据驱动的航行体构型/润湿性协同调控入水动力学研究
- 批准号:
- 批准年份:2025
- 资助金额:0.0 万元
- 项目类别:省市级项目
区块链与人工智能驱动的大数据隐私保护技术研究
- 批准号:
- 批准年份:2025
- 资助金额:0.0 万元
- 项目类别:省市级项目
成渝交通一体化背景下的高速公路智慧管控系统:大数据驱动、AI预警与数智决策
- 批准号:
- 批准年份:2025
- 资助金额:0.0 万元
- 项目类别:省市级项目
空天地数字农业:无人机(UAV)集群+大数据驱动赋能贵妃枇杷全息农场系统构建与关键技术应用研究
- 批准号:
- 批准年份:2025
- 资助金额:0.0 万元
- 项目类别:省市级项目
面向电力系统复杂数据的深度变分推断方法
- 批准号:
- 批准年份:2025
- 资助金额:0.0 万元
- 项目类别:省市级项目
数据驱动的随机交通网络均衡研究
- 批准号:
- 批准年份:2025
- 资助金额:0.0 万元
- 项目类别:省市级项目
基于数据驱动的新能源汽车全生命周期低碳协同智能优化技术创新路径研究
- 批准号:
- 批准年份:2025
- 资助金额:0.0 万元
- 项目类别:省市级项目
联邦学习驱动下成渝地区职业教育AI产教协同的跨区域数据共享机制与培养方案优化要素机理研究
- 批准号:
- 批准年份:2025
- 资助金额:0.0 万元
- 项目类别:省市级项目
融合动态图神经网络与大模型的区域科技安全态势分析方法研究
- 批准号:
- 批准年份:2025
- 资助金额:0.0 万元
- 项目类别:省市级项目
相似海外基金
CAREER: Data-Driven Hardware and Software Techniques to Enable Sustainable Data Center Services
职业:数据驱动的硬件和软件技术,以实现可持续的数据中心服务
- 批准号:
2340042 - 财政年份:2024
- 资助金额:
$ 50.1万 - 项目类别:
Continuing Grant
CAREER: A Universal Framework for Safety-Aware Data-Driven Control and Estimation
职业:安全意识数据驱动控制和估计的通用框架
- 批准号:
2340089 - 财政年份:2024
- 资助金额:
$ 50.1万 - 项目类别:
Standard Grant
CAREER: Design of Cellular Mechanical Metamaterials under Uncertainty with Physics-Informed and Data-Driven Machine Learning
职业:利用物理信息和数据驱动的机器学习在不确定性下设计细胞机械超材料
- 批准号:
2236947 - 财政年份:2023
- 资助金额:
$ 50.1万 - 项目类别:
Standard Grant
CAREER: Data-driven Multiscale Modeling of Complex Traffic Systems Utilizing Networked Driving Simulators
职业:利用网络驾驶模拟器对复杂交通系统进行数据驱动的多尺度建模
- 批准号:
2238359 - 财政年份:2023
- 资助金额:
$ 50.1万 - 项目类别:
Standard Grant
CAREER: A Holistic Developer-Centered Approach to Enhance Privacy for Data-Driven Applications
职业:以开发人员为中心的整体方法来增强数据驱动应用程序的隐私
- 批准号:
2238047 - 财政年份:2023
- 资助金额:
$ 50.1万 - 项目类别:
Continuing Grant
CAREER: Machine Learning for Data-Driven Fault-Tolerant Control of Complex Systems
职业:用于复杂系统数据驱动容错控制的机器学习
- 批准号:
2426614 - 财政年份:2023
- 资助金额:
$ 50.1万 - 项目类别:
Standard Grant
CAREER: Data-driven dynamic adaptive optimization for next generation power system operation
职业:数据驱动的下一代电力系统运行的动态自适应优化
- 批准号:
2316675 - 财政年份:2023
- 资助金额:
$ 50.1万 - 项目类别:
Standard Grant
CAREER: Building long-term climate resilience in 21st-century regional urban land systems through integrated data-driven research and education
职业:通过综合数据驱动的研究和教育,在 21 世纪区域城市土地系统中建立长期的气候适应能力
- 批准号:
2239859 - 财政年份:2023
- 资助金额:
$ 50.1万 - 项目类别:
Continuing Grant
CAREER: Data-Driven Control of High-Rate Dynamic Systems
职业:高速动态系统的数据驱动控制
- 批准号:
2237696 - 财政年份:2023
- 资助金额:
$ 50.1万 - 项目类别:
Continuing Grant
CAREER: DeepCertify: Data-driven Formal Approach to Safe Autonomy
职业:DeepCertify:数据驱动的安全自治正式方法
- 批准号:
2238030 - 财政年份:2023
- 资助金额:
$ 50.1万 - 项目类别:
Continuing Grant