CAREER: Blueprint for Unlocking New Energy Conversion Functionality in Chalcogenide Frameworks through Precisely Designed Composition, Electronic Structure and Surface Coordination

职业：通过精确设计的成分、电子结构和表面配位解锁硫族化物框架中新能源转换功能的蓝图

• 批准号: 2044403
• 负责人: Jesus Velazquez
• 金额: $ 52.09万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044403&HistoricalAwards=false
• 关键词: CAREER; Blueprint; Unlocking; New; Energy

Non-Technical Summary: An entirely new palette of materials is required to enable a sustainable terawatt-scale energy infrastructure. The development of functional materials with properties that facilitate renewable energy conversion and storage represents a promising approach that takes direct aim at this problem, one of the defining challenges of our time. This project, which is supported jointly by the Solid State and Materials Chemistry program in the Division of Materials Research and the Catalysis program in the Division of Chemical, Bioengineering, Environmental and Transport Systems, has far-reaching impact on both national and global sustainability, the accelerated discovery of economically viable energy conversion materials. The classes of materials being explored in this effort have properties such as electrical conductance, efficacy of solar-energy absorption, and electrochemical reactivity towards carbon dioxide reduction that are programmable as a function of their structure and elemental make-up. PI Velazquez’s study is comprised of an iterative combination of experimental research and computational modeling. The focus of this project is to combine state-of-the-art materials synthesis with experimental evaluation of electronic and structural properties in tailor-made solid-sate materials such that their properties of relevance to energy conversion can be accurately mapped to their composition and structure. This effort is augmented by a predictive computational modeling approach where favorable material properties are predicted for entirely new elemental compositions, thereby yielding new candidate materials in a closed-loop manner. In parallel with the experimental effort, undergraduate and graduate students are trained to solve problems at the interface between solid-state materials chemistry and chemical engineering; the training program specifically supports underrepresented minority and first-generation students. A major thrust of the educational plan involves curriculum supplementation through remote integration of modern solid-state chemistry and engineering research into high schools, thereby providing aspiring scientists with the tools required to successfully engage in STEM research. Technical Summary: This research systematically elucidates fundamental understanding of versatile classes of multi-dimensional metal chalcogenides, revealing material property descriptors through ex situ, in-situ, and operando measurements of atomistic and electronic structure in order to inform predictive models for electrochemical and photoelectrochemical reactivity. The PI hypothesizes that fine control over local metal-chalcogen coordination, intercalant charge density, and binary/ternary stoichiometry engenders desirable material properties that underpin energy conversion and storage functionality, including binding-site carbophilicity/oxophilicity, framework ionicity/covalency, and band gap/positions. To evaluate this hypothesis, the composition and local geometry of small-molecule adsorption sites in solid-state chalcogenide frameworks are systematically modified across the span of a substantial design space in order to experimentally and computationally elucidate trends in functionality that correlate with chemical and physical properties. Experimental results borne out of these modifications inform predictive machine learning-density functional theory models, which in turn generate candidate framework compositions that will engender optimal properties. This approach effectively closes a feedback loop between experiment and theory; as such, the anticipated products of this study are new metal chalcogenide materials and heterostructures from the 1D M2Mo6X6 (M = K, Rb, Cs; X = S, Se, Te), 2D MX2 (M = Ti, Mo, W; X = S, Se, Te), and 0D/3D MyMo6X8 (M = alkali, alkaline, transition, and/or post transition metal; y = 0–4; X = O, S, Se, Te) composition spaces that predictably yield oxygenated fuels, mediate bulk and interfacial charge-transport, and enable tunable photon absorption. More importantly, this research is expected to yield a materials design toolset that illustrates the importance of successfully iterative interplay between theory, synthesis, structure elucidation, and electronic structure studies when it comes to generating desirable solid-state properties. The intellectual merit of this work stems from its integrative approach to material design, where a foundational understanding regarding the effects of composition and structure on emergent physical properties, chemical reactivity, and energy conversion-related functionality is unraveled.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.

非技术概述：需要一种全新的材料调色板来实现可持续的太瓦级能源基础设施。开发具有促进可再生能源转换和储存的性能的功能材料是一种有希望的方法，它直接针对这个问题，这是我们这个时代的决定性挑战之一。该项目由材料研究部的固态和材料化学计划以及化学、生物工程、环境和运输系统部的催化计划联合支持，对国家和全球的可持续性、加速发现经济上可行的能源转换材料具有深远的影响。这项工作中探索的材料类别具有导电性、太阳能吸收效率以及二氧化碳还原的电化学反应能力等特性，这些特性可根据其结构和元素组成进行编程。Pi Velazquez的研究是由实验研究和计算模型的迭代组合组成的。该项目的重点是将最先进的材料合成与量身定做的固体材料的电子和结构性能的实验评估相结合，以便能够准确地将与能量转换相关的性能映射到其组成和结构。预测计算建模方法加强了这一努力，其中为全新的元素组成预测有利的材料性质，从而以闭环方式产生新的候选材料。在进行实验的同时，对本科生和研究生进行培训，以解决固态材料化学和化学工程之间的接口问题；培训计划专门为代表不足的少数族裔和第一代学生提供支持。该教育计划的一个主要目的是通过将现代固态化学和工程研究远程整合到高中来补充课程，从而为有抱负的科学家提供成功从事STEM研究所需的工具。技术综述：这项研究系统地阐明了对多种多维金属硫化物的基本认识，通过原子和电子结构的非原位、原位和操纵面测量揭示了材料性质描述符，从而形成了电化学和光电化学反应的预测模型。PI假设，对局部金属-硫族配位、插层电荷密度和二元/三元化学计量比的精细控制可以产生支持能量转换和存储功能的理想材料属性，包括结合位亲碳性/亲氧性、骨架离子/共价性和带隙/位置。为了评估这一假设，对固态硫化物骨架中小分子吸附中心的组成和局部几何形状进行了系统的修改，以便从实验和计算上阐明与化学和物理性质相关的功能趋势。从这些修改中得到的实验结果提供了预测性机器学习密度泛函理论模型，这些模型反过来生成将产生最佳性能的候选框架组合物。这种方法有效地闭合了实验和理论之间的反馈回路；因此，本研究的预期产品是新的金属硫系材料和来自一维M2Mo6X6(M=K，Rb，Cs；X=S，Se，Te)、二维MX2(M=Ti，Mo，W；X=S，Se，Te)和0D/3DMyMo6X8(M=碱，碱，过渡和/或后过渡金属；Y=0-4；X=O，S，Se，Te)组成空间的异质结构，可预测地产生含氧燃料，中间体相和界面电荷输运，并实现可调光子吸收。更重要的是，这项研究有望产生一个材料设计工具集，说明在产生理想的固态性能时，理论、合成、结构说明和电子结构研究之间成功迭代相互作用的重要性。这项工作的智力价值来自于它对材料设计的综合方法，其中关于组成和结构对紧急物理性质、化学反应和与能量转换相关的功能的影响的基础性理解被打破。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Machine Learning Guided Synthesis of Multinary Chevrel Phase Chalcogenides

机器学习引导多元 Chevrel 相硫属化物的合成

• DOI: 10.1021/jacs.1c02971
• 发表时间: 2021
• 期刊: Journal of the American Chemical Society
• 影响因子: 15
• 作者: Singstock, Nicholas R.;Ortiz-Rodríguez, Jessica C.;Perryman, Joseph T.;Sutton, Christopher;Velázquez, Jesús M.;Musgrave, Charles B.
• 通讯作者: Musgrave, Charles B.

Ln 10 S 14 O (Ln = La, Pr, Nd, Sm) Oxysulfides: A Series of Direct n-Type Semiconductors

Ln 10 S 14 O (Ln = La, Pr, Nd, Sm) 硫氧化物：一系列直接 n 型半导体

• DOI: 10.1021/acs.chemmater.2c01244
• 发表时间: 2022
• 期刊: Chemistry of Materials
• 影响因子: 8.6
• 作者: Wuille Bille, Brian A.;Kundmann, Anna C.;Osterloh, Frank E.;Velázquez, Jesús M.
• 通讯作者: Velázquez, Jesús M.

Promoting Inclusive and Culturally responsive Teaching using Co-classes for General Chemistry

利用普通化学共同课程促进包容性和文化响应式教学

• DOI: 10.1021/acs.jchemed.1c00339
• 发表时间: 2021
• 期刊: Journal of chemical education
• 影响因子: 3
• 作者: Ortiz-Rodríguez, J. C.;Brinkman, H.;Nglankong, L.;Enderle, B.*;Velázquez, J. M.*
• 通讯作者: Velázquez, J. M.*

Synthesis and thermodynamics of uranium-incorporated α-Fe2O3 nanoparticles

• DOI: 10.1016/j.jnucmat.2021.153172
• 发表时间: 2021-07-14
• 期刊: JOURNAL OF NUCLEAR MATERIALS
• 影响因子: 3.1
• 作者: Lam, Andy;Hyler, Forrest;Navrotsky, Alexandra
• 通讯作者: Navrotsky, Alexandra