DMREF/Collaborative Research: Nitride Discovery - Creating the Knowledge Base for Hard Coating Synthesis

DMREF/合作研究：氮化物发现 - 创建硬涂层合成知识库

基本信息

批准号：
1629230
负责人：
Daniel Gall
金额：
$ 42.97万
依托单位：
Rensselaer Polytechnic Institute
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
起止时间：
2016-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629230&HistoricalAwards=false
关键词：
DMREF Collaborative Research Nitride Discovery

项目摘要

In applications where moving parts must be protected from excessive wear, hard coatings are often applied to components. The class of hard ceramic materials known as nitrides are often used for these coatings, but the performance of nitride coatings is limited and often insufficient, leading to breakdown of important components. New nitride materials have been predicted that are expected to have the significantly improved properties for wear resistance and corrosion resistance. Fabrication of these materials, however, remains a challenge. This award supports research to systematically determine the conditions that will enable the manufacture of these desired new nitride materials. The results of this research will accelerate discovery of hard, wear-resistant and corrosion-resistant coatings through a targeted synthesis approach, yielding new coating materials for emerging applications including fuel-efficient jet engines and gas turbines, environmentally-friendly lubricant-free cutting tools, high-temperature concentrating solar power plants, and wind turbines.The coatings-by-design approach in this work facilitates the introduction of promising new nitrides, which have remained unexplored because the traditional approach of simultaneously optimizing composition, microstructure, and deposition process is too costly and slow. This method enables a deterministic pathway to ascertain the processing parameter space to achieve the desired composition and microstructure from thermodynamic and kinetic parameters, and then synthesize the coating experimentally. This method is based on an iterative combination of experiments and density functional theory calculations, to determine nitrogen incorporation kinetics and phase formation during deposition of transition metal nitride coatings. The research effort focuses primarily on the systematic determination of reaction and diffusion rates and local equilibria at the surface of growing transition metal nitride layers during coating synthesis. A quantitative model will be developed that predicts composition and phase of deposited nitrides as a function of incident ion energetics and growth temperature and pressure, using both measured and calculated values. The model will be verified by experimental coating deposition. Deposition of new super-hard, tough, and vacancy-stabilized nitride phases will be explored. Data collected in this research will be made publicly available through an open online database.

在必须保护运动部件免受过度磨损的应用中，通常将硬涂层应用于组件。这些涂料经常使用的硬陶瓷材料通常用于这些涂料，但是氮化物涂层的性能有限且通常不足，导致重要组件的分解。已经预测，新氮化物材料有望具有显着改善的耐磨性和耐腐蚀性的性能。但是，这些材料的制造仍然是一个挑战。该奖项支持研究，以系统地确定能够制造这些所需的新氮化物材料的条件。这项研究的结果将通过有针对性的合成方法加速发现硬磨损，耐腐蚀和耐腐蚀的涂料，从而为新兴应用提供新的涂料材料，包括燃油效率的喷气发动机和燃气轮机，环保型润滑的润滑剂无润滑的切割工具，启动式式式涂层，介绍了涂层。硝酸盐尚未得到探索，因为传统的同时优化组成，微观结构和沉积过程的方法太昂贵且缓慢。该方法使确定性途径可以确定处理参数空间，以从热力学和动力学参数中实现所需的组成和微观结构，然后通过实验合成涂层。该方法基于实验和密度功能理论计算的迭代组合，以确定过渡金属氮化金属涂层沉积过程中氮的掺入动力学和相结合。研究工作主要集中于在涂层合成过程中的反应和扩散速率和局部平衡的系统确定。将开发一个定量模型，该模型使用测量值和计算值都可以预测氮化物的组成和相位，这是入射离子能量和生长温度和压力的函数。该模型将通过实验涂料沉积来验证。将探索新的超硬，坚韧和空缺的氮化物阶段的沉积。这项研究收集的数据将通过开放的在线数据库公开提供。