CAREER: Nitride Synthesis via Controlled Decomposition of Precursors under Moderate Pressure

职业：通过中压下前体的受控分解合成氮化物

• 批准号: 2046468
• 负责人: Siddha Pimputkar
• 金额: $ 50万
• 依托单位: Lehigh University
• 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: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046468&HistoricalAwards=false
• 关键词: CAREER; Nitride; Synthesis; via; Controlled

NON-TECHNICAL DESCRIPTION: Nitrides are a fascinating class of functional materials that remain largely unexplored due to demanding synthesis constraints: of the 447 predicted stable ternary nitrides, less than half have been synthesized. This research develops and applies a novel synthesis approach based on controlled decomposition of a nitrogen-containing precursor thereby driving the crystallization of the remaining nitride materials. This decomposition-based approach is hitherto unexplored and offers a paradigm shift in single crystal nitride synthesis with a clear path towards inexpensive scaling of the method. Application of an extensive library of existing precursor materials allows for synthesis of a wide range of ternary and even more complex nitrides. Technologically important nitrides will be synthesized providing materials for super-hard materials, catalysis and (opto-)electronic devices. Students are being trained in equipment development, crystal synthesis, and machine learning techniques preparing them for employment in (single crystal) material synthesis and semiconductor industries. To promote recruitment and retention of female, underrepresented minority and at-risk students in science and engineering, the Boldly Utilizing Innovation to Lead in Developing Engineers for Research and Science (BUILDERS) initiative is engaging and connecting middle/high school and undergraduate students via synthesis workshops providing hierarchical mentorship and educational opportunities for the broader audience. TECHNICAL DETAILS: Many computationally predicted stable nitrides with intriguing properties have been proposed. Their single crystal synthesis from solution is challenged due to limited synthesis process parameter windows and insufficient solubility of nitrogen or cations at temperatures below their decomposition temperature. These limitations are overcome using a new synthesis pathway based on decomposition of lithiated nitride precursor from a melt. Liquification and controlled decomposition of the precursor results in the deposition of a crystalline nitride and is achieved via controlling the lithium vapor pressure, nitrogen overpressure and temperature gradients, while alloy composition is achieved via control of the melt composition. The newly developed synthesis equipment is suitable to operate up to at least 100 atm and 1000 C. In situ data is fed through a machine learning algorithm to continuously and automatically control synthesis conditions to yield targeted material compositions. This approach does not rely on dissolution and diffusion of nitrogen through the flux from the gas phase leading to transformative opportunities in rapid, scalable synthesis of nitride single crystals. Students are obtaining in-depth, hands-on experience about the synergistic relationships between equipment development, in situ technologies, machine learning algorithms and resulting targeted material synthesis.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.

非技术描述：氮化物是一类引人入胜的功能材料，由于苛刻的合成约束，它们在很大程度上尚未探索：在447个预测的稳定三元氮化物中，不到一半的人已经合成了一半。这项研究开发并采用了一种基于含氮前体的受控分解的新型合成方法，从而驱动其余氮化物材料的结晶。迄今为止，这种基于分解的方法是尚未探索的，并提供了单晶氮化物合成的范式转移，并具有通往该方法廉价缩放的清晰路径。现有前体材料的广泛库的应用允许综合各种三元，甚至更复杂的氮化物。在技​​术上重要的氮化物将合成，为超硬材料，催化和（光学）电子设备提供材料。正在接受学生开发，晶体合成和机器学习技术的培训，以准备就业（单晶）材料合成和半导体行业。为了促进女性，代表性不足的少数群体和高危学生的招聘和保留科学与工程学的学生，大胆地利用创新来领导开发研究与科学工程师（建筑商）倡议（Builders）倡议（Builders）倡议，正在通过合成工作坊来吸引中/高中，并连接中学和本科生，从而为更广阔的受众提供层次的教育机会。技术细节：已经提出了许多具有有趣特性的计算预测稳定的硝酸盐。由于合成过程参数窗口有限，氮或阳离子在分解温度以下的温度下，氮或阳离子的溶解度不足，因此对溶液的单晶合成质疑。使用新的合成途径基于基于熔融熔融的氮化物前体的分解来克服这些局限性。 前体的液化和受控分解会导致结晶氮化物的沉积，并通过控制锂蒸气压，氮过压和温度梯度来实现，而合金组成是通过控制熔体组合物来实现的。新开发的合成设备适用于至少100个atm和1000 C的操作。原位数据通过机器学习算法进行馈送，以连续并自动控制合成条件以产生目标材料组成。这种方法不依赖于从气相的通量中溶解和扩散，从而导致氮化物单晶的快速，可扩展合成的转化机会。学生正在获得有关设备开发，原位技术，机器学习算法和产生的目标材料合成之间协同关系的深入，实践经验。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查审查标准来通过评估来通过评估来获得支持的。