DMREF: Collaborative Research: Self-assembled peptide-pi-electron supramolecular polymers for bioinspired energy harvesting, transport and management
DMREF: Collaborative Research: Self-assembled peptide-pi-electron supramolecular polymers for bioinspired energy harvesting, transport and management
批准号:
1841807
负责人:
Andrew Ferguson
金额:
$52.52万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-07-01 至 2023-09-30
中文摘要
非技术描述:大自然在光合作用过程中巧妙地控制了关键色素和染料的空间排列,以利用太阳能。通过分子和分子排列的定制设计,可以实现合成材料中受控染料排列的模仿。然而,在关键的10-100纳米“中尺度”(比一毫米小数千倍)范围内,对工程分子材料的组装施加可靠的控制仍然是难以捉摸的。这种中尺度分子结构将结合电荷和能量转移活动,在生物溶液中组装的能力,以及与生物环境的相容性。考虑到分子设计的多种可能性,实验程序必须结合计算机建模和数据驱动筛选来指导实验设计和合成。计算与实验之间的紧密结合和相互强化的反馈可以揭示分子组装的基本设计规律,加速具有定制结构和功能的多分子组装的发现和发展。该项目将通过合作开发这些功能性分子超结构,包括分子合成、类似生物系统的自组装、组装的结构和电学特性建模,以及利用组装来管理光与电之间的相互作用。pi致力于在该项目中进行劳动力培训和发展,指导具有最先进工具的不同社会经济背景的下一代材料和数据科学家,并使他们接触到将定义未来研究的综合跨学科工作模式。技术描述:π共轭超分子体系的光物理和电学性质主要取决于分子间电子相互作用的明确性质。这些相互作用是由精确的分子结构、化学和涌现的超分子排列控制的。pi开发了一种肽结构,提供了一种途径,通过裁剪空间体积和组分序列的可变疏水性来控制涌现的超分子结构,从而影响分子间取向、高阶纤化和特定的电子结果。他们最初使用爱迪生的方法来揭示这些变化,但这个项目的目标是通过紧密集成的原子模拟和电子结构计算来施加明确的工程控制。研究活动建立在团队的基础上?我们为实现这些目标奠定了坚实的基础,这两个具体目标是:(i)开发具有先进光电功能的复杂肽半导体材料;(ii)开发新的组装范例,导致具有化学和电子梯度和局部电场的异质肽纳米材料。这项工作的执行将需要研究团队在以下方面的相互关联的努力:(i)合成新的pi电子单元和新的自组装肽,(ii)纳米材料聚集体及其高阶组装的分子和数据驱动建模,以及(iii)纳米材料内电传输的表征。该项目将为本科生、妇女和代表性不足的少数民族提供研究机会。pi将在最先进的实验和计算工具方面培训和指导研究人员,并使他们接触到综合跨学科的工作模式。K-12外展活动将激发对材料科学的兴奋和认识,并鼓励学生在科学、技术、工程和数学(STEM)领域接受高等教育。
英文摘要
Non-technical Description: Nature exquisitely controls the spatial arrangement of key pigments and dyes in the process of photosynthesis to harness solar energy. Mimicry of controlled dye arrangements in synthetic materials can be realized through tailored design of molecules and molecular arrangements. However, exerting reliable control over the assembly of engineered molecular materials in the crucial 10-100 nanometer "mesoscale" regime, thousands of times smaller than a millimeter, remains elusive. Such mesoscale molecular structures will combine charge and energy transfer activities with capabilities for assembly in biological solutions, and compatibility with biological environments. Given the multitude of molecular design possibilities, it is essential that experimental programs incorporate computer modeling and data-driven screening to guide experimental design and synthesis. Tight integration and mutually reinforcing feedback between computation and experiment can reveal fundamental design rules for molecular assembly, and accelerate the discovery and development of multi-molecule assemblies with tailored structure and function. This project will develop these functional molecular superstructures in a collaboration encompassing molecular synthesis, self-assembly analogous to biological systems, modeling of the structures and electrical properties of the assemblies, and utilizing the assemblies to manage interactions between light and electricity. The PIs are committed to workforce training and development within this project, guiding the next generation of materials and data scientists of diverse socio-economic background in state-of-the-art tools and exposing them to an integrated interdisciplinary mode of work that will define future research. Technical Description: The photophysical and electrical properties of pi-conjugated supramolecular systems depend critically on the explicit nature of the intermolecular electronic interactions. These interactions are governed by the precise molecular structure and chemistry and emergent supramolecular arrangements. The PIs developed a peptide construct that offer a pathway to exert such control over emergent supramolecular structure through tailoring of steric bulk and variable hydrophobicity of the component sequences to influence intermolecular orientations, higher-order fibrilization, and specific electronic outcomes. They initially used an Edisonian approach to uncover these variations, but the goals of this project are to wield explicit engineered control through tightly integrated atomistic simulations and electronic structure calculations. The research activities build upon the team?s strong foundation to accomplish these goals in two specific objectives: (i) the development of sophisticated peptidic semiconductor materials with advanced optoelectronic functionality and (ii) the development of new assembly paradigms leading to heterogeneous peptidic nanomaterials with chemical and electronic gradients and localized electric fields. The execution of this work will entail interconnected efforts by the research team in the (i) synthesis of new pi-electron units and new self-assembling peptides, (ii) molecular and data-driven modeling of the nanomaterial aggregates and their higher-order assemblies, and (iii) characterization of electrical transport within the nanomaterials. This project will make special provision for research opportunities for undergraduate students, women, and underrepresented minorities. The PIs will train and mentor researchers in state-of-the-art experimental and computational tools and expose them to an integrated interdisciplinary mode of work. K-12 outreach activities will inspire excitement and awareness of materials science and encourage students to pursue higher education in science, technology, engineering, and math (STEM) fields.
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Computational discovery of high charge mobility self-assembling π-conjugated peptides
高电荷迁移率自组装α-共轭肽的计算发现
DOI:
10.1039/d2me00017b
发表时间:
2022
期刊:
Molecular Systems Design & Engineering
影响因子:
3.6
作者:
[Shmilovich, Kirill, Yao, Yifan, Tovar, John D., Katz, Howard E, Schleife, Andre, Ferguson, Andrew L]
通讯作者:
Ferguson, Andrew L
Hybrid computational–experimental data-driven design of self-assembling π-conjugated peptides
自组装α-共轭肽的混合计算-实验数据驱动设计
DOI:
10.1039/d1dd00047k
发表时间:
2022
期刊:
Digital Discovery
影响因子:
--
作者:
[Shmilovich, Kirill, Panda, Sayak Subhra, Stouffer, Anna, Tovar, John D., Ferguson, Andrew L.]
通讯作者:
Ferguson, Andrew L.
DOI:
10.1021/acs.jpcb.0c00708
发表时间:
2020-05-14
期刊:
JOURNAL OF PHYSICAL CHEMISTRY B
影响因子:
3.3
作者:
[Shmilovich, Kirill, Mansbach, Rachael A., Ferguson, Andrew L.]
通讯作者:
Ferguson, Andrew L.
DOI:
10.1021/acs.jctc.3c00923
发表时间:
2023-12-27
期刊:
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
影响因子:
5.5
作者:
[Herringer,Nicholas S. M., Dasetty,Siva, Ferguson,Andrew L.]
通讯作者:
Ferguson,Andrew L.
DOI:
10.1021/acsami.0c02095
发表时间:
2020-05-06
期刊:
ACS APPLIED MATERIALS & INTERFACES
影响因子:
9.5
作者:
[Jira, Edward R., Shmilovich, Kirill, Schroeder, Charles M.]
通讯作者:
Schroeder, Charles M.
Collaborative Research: DMREF: Closed-Loop Design of Polymers with Adaptive Networks for Extreme Mechanics
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批准号:2323730
-
项目类别:Standard Grant
-
资助金额:$42.18万
-
财政年份:2023
-
负责人:Andrew Ferguson
-
依托单位:
Latent Space Simulators for the Efficient Estimation of Long-time Molecular Thermodynamics and Kinetics
-
批准号:2152521
-
项目类别:Standard Grant
-
资助金额:$38.79万
-
财政年份:2022
-
负责人:Andrew Ferguson
-
依托单位:
REU SITE: Research Experience for Undergraduates in Molecular Engineering
-
批准号:2050878
-
项目类别:Standard Grant
-
资助金额:$43.4万
-
财政年份:2021
-
负责人:Andrew Ferguson
-
依托单位:
EAGER: (ST1) Collaborative Research: Exploring the emergence of peptide-based compartments through iterative machine learning, molecular modeling, and cell-free protein synthesis
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批准号:1939463
-
项目类别:Standard Grant
-
资助金额:$14.99万
-
财政年份:2019
-
负责人:Andrew Ferguson
-
依托单位:
EAGER: Collaborative Research: Type II: Data-Driven Characterization and Engineering of Protein Hydrophobicity
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批准号:1844505
-
项目类别:Standard Grant
-
资助金额:$5.3万
-
财政年份:2019
-
负责人:Andrew Ferguson
-
依托单位:
Nonlinear dimensionality reduction and enhanced sampling in molecular simulation using auto-associative neural networks
-
批准号:1841805
-
项目类别:Standard Grant
-
资助金额:$30.45万
-
财政年份:2018
-
负责人:Andrew Ferguson
-
依托单位:
CAREER: Teaching Machines to Design Self-Assembling Materials
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批准号:1841800
-
项目类别:Continuing Grant
-
资助金额:$9.0万
-
财政年份:2018
-
负责人:Andrew Ferguson
-
依托单位:
Nonlinear Manifold Learning of Protein Folding Funnels from Delay-Embedded Experimental Measurements
-
批准号:1841810
-
项目类别:Standard Grant
-
资助金额:$16.2万
-
财政年份:2018
-
负责人:Andrew Ferguson
-
依托单位:
DMREF: Collaborative Research: Self-assembled peptide-pi-electron supramolecular polymers for bioinspired energy harvesting, transport and management
-
批准号:1729011
-
项目类别:Standard Grant
-
资助金额:$53.68万
-
财政年份:2017
-
负责人:Andrew Ferguson
-
依托单位:
Nonlinear dimensionality reduction and enhanced sampling in molecular simulation using auto-associative neural networks
-
批准号:1664426
-
项目类别:Standard Grant
-
资助金额:$38.01万
-
财政年份:2017
-
负责人:Andrew Ferguson
-
依托单位:
Nonlinear Manifold Learning of Protein Folding Funnels from Delay-Embedded Experimental Measurements
-
批准号:1714212
-
项目类别:Standard Grant
-
资助金额:$21.0万
-
财政年份:2017
-
负责人:Andrew Ferguson
-
依托单位:
CAREER: Teaching Machines to Design Self-Assembling Materials
-
批准号:1350008
-
项目类别:Continuing Grant
-
资助金额:$45.0万
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财政年份:2014
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负责人:Andrew Ferguson
-
依托单位:
Dimension theory of dynamically defined sets
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批准号:EP/I024328/1
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项目类别:Fellowship
-
资助金额:$29.58万
-
财政年份:2011
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负责人:Andrew Ferguson
-
依托单位:
Electrical identification of single dopant atoms
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批准号:EP/G062331/1
-
项目类别:Research Grant
-
资助金额:$41.72万
-
财政年份:2009
-
负责人:Andrew Ferguson
-
依托单位:
海外基金