A Component-wise Model for Understanding Spin-Charge Interactions in Nanoparticle Solids Using Targeted Synthesis, Magnetometry, and Magnetoresistance

第一部分：非技术性总结在许多现代技术和材料科学的核心是理解和控制电子的电荷和自旋之间的相互作用的挑战。该项目由NSF材料研究部的固态和材料化学计划支持，通过关注一种称为磁阻（MR）的现象来应对这一挑战。对于磁阻材料，磁场可用于改变材料的电阻。与采用复杂层状结构作为活性MR材料的传统MR器件不同，该项目采用了一种独特、简单且适应性强的方法，即利用低成本、高纯度磁性纳米颗粒合成的进展，并将其组装成MR活性混合复合材料。这种方法有望更具容错性和可调性，使研究人员能够以前所未有的速度开发、测试和完善MR和自旋输运理论。此外，该项目通过致力于数据管理的透明度和可访问性来增强其研究的影响。在一个数据推动发现的时代，该团队将专注于嵌入FAIR原则的数据分析和软件开发，建立一种“开源”研究的文化，这种文化对于使研究成为可能的公共资助者来说是真正可发现的，可解释的，可互操作的和可重用的。技术总结这项研究得到了NSF材料研究部固态和材料化学项目的支持，探索了一种纳米级的自下而上的方法，电子自旋电荷相互作用的最重要的技术方法之一：磁阻（MR）。与传统的层状MR材料不同，加州大学圣地亚哥分校的Jeffrey Rinehart教授的研究团队专注于由特定成分和磁相要求合成的纳米颗粒组成的颗粒状MR材料。通过利用胶体纳米化学的进步，获得了对单个颗粒的磁结构的无与伦比的控制，并进行了彻底的表征，从而允许与复合材料结构的MR行为严格相关。这是研究人员第一次在定义明确的参数之间建立定量的结构-功能关系：粒子间相互作用强度，单粒子磁各向异性和粒子体积。阐明影响系统MR行为的这些关键因素，可以将MR响应的完整景观绘制为多维响应函数，提供比以前尝试的更全面的表征。从简单但重要的基于铁氧体的系统开始，研究扩展到高性能磁性材料，最终目标是为各种传感应用创建具有可调场灵敏度和伪自旋阀行为的MR系统。该研究包括广泛的数据组织和建模组件，重点是与FAIR数据原则保持一致，并使数据广泛用于研究。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

PART 1: NON-TECHNICAL SUMMARYAt the heart of much of modern technology and materials science lies the challenge of understanding and controlling the interaction between an electron’s charge and its spin. This project, which is supported by the Solid State and Materials Chemistry program in NSF’s Division of Materials Research, targets that challenge by focusing on a phenomenon known as magnetoresistance (MR). For a magnetoresistant material, a magnetic field can be used to change the material’s electrical resistance. Unlike traditional MR devices that employ intricate layered structures as the active MR material, this project embraces a unique, simpler, and highly adaptable approach, namely leveraging advances in low-cost, high-purity magnetic nanoparticle synthesis and assemble them into MR-active hybrid composites. This method promises to be more fault-tolerant and tunable, enabling researchers to develop, test, and refine theories of MR and spin transport at an unprecedented pace. Furthermore, this project enhances the impact of its research through a commitment to transparency and accessibility in data management. In an era where data fuels discovery, the team will focus on data analysis and software development embedded in FAIR principles, building a culture of “open-source” research that is genuinely Findable, Accessible, Interoperable, and Reusable to the public funders who make the research possible.PART 2: TECHNICAL SUMMARYThe research, which is supported by the Solid State and Materials Chemistry program in NSF’s Division of Materials Research, explores a nanoscale bottom-up approach to one of the most technologically important methods of electronic spin-charge interaction: magnetoresistance (MR). Instead of the traditional layered MR materials, the research team of Professor Jeffrey Rinehart at UC San Diego focuses on granular MR materials composed of nanoparticles synthesized with specific composition and magnetic phase requirements. By leveraging advancements in colloidal nanochemistry, unparalleled control over the magnetic structure of individual particles is obtained and thoroughly characterized, thereby allowing rigorous correlation with the MR behavior of composite material structures. This is the first time that researchers establish quantitative structure-function relationships between well-defined parameters: interparticle interaction strength, single-particle magnetic anisotropy, and particle volume. Elucidating these key factors influencing the MR behavior of the system allows mapping out the full landscape of MR response as multidimensional response function, providing a far more comprehensive characterization than has previously been attempted. Starting from the simple but important ferrite-based systems, research expands to high-performing magnetic materials with an ultimate goal of creating MR systems with adjustable field sensitivity and pseudo-spin valve behavior for a variety of sensing applications. The research includes an extensive data organization and modeling component with an emphasis on alignment with FAIR data principles and making data widely available for study.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.