Conjugated Polymers and Composites for Energy, Electronics and Sensing

用于能源、电子和传感的共轭聚合物和复合材料

• 批准号: RGPIN-2019-04671
• 负责人: Freund, Michael
• 金额: $ 3.5万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=747862
• 关键词: Conjugated; Polymers; Composites; Energy; Electronics

This research program will focus on developing conducting polymer composites to impact the fields of renewable energy, electronics and sensing. Given the scale of projected energy needs as well as the rapid climate change associated with growing carbon dioxide levels in the atmosphere, there is a major push by governments to increase the rate of innovation and discovery in the area of carbon-neutral solar fuel production. This program will help address this need by developing membranes that will be a key component of standalone artificial photosynthetic devices to directly produce solar fuels. Currently, the production of membranes for these devices represents a technology gap that must be addressed so that catalysts and light absorbers being developed by researchers around the world can be integrated into a working system. Existing collaborations with world-renowned researchers at Caltech and MIT with programs developing light absorbers and catalysts will allow rapid translation of advances in membrane design into proof of concept devices and will therefore have a significant impact in this quickly advancing field. Trainees involved in these studies will gain unique skills and will be highly desirable in the rapidly growing renewable energy sector of the Canadian economy. Organic-based electronics provide new mechanisms of operation that promise to overcome current limitations of silicon-based devices, including cost and miniaturization. However, there remain significant challenges for integrating organic materials into devices such as memory and transistors. The proposed research program will focus on the development of new conducting polymer composites for use in dynamic random-access memory (DRAM), commonly used in electronic devices. In collaboration with electrical and computer engineers, the efforts will focus on increasing read/write speeds, memory retention (i.e., reducing volatility) and scaling laws (i.e., how small can they be made). In addition, issues ranging from device fabrication to integration into existing electronic platforms and processes will be addressed. The invention of the charge coupled device (CCD) chip has revolutionized the interface between technology and its environment. By pixelating optical images of its surroundings, devices can use sophisticated image processing and pattern recognition algorithms to perform increasingly complex tasks associated with visual perception. The proposed research will focus on creating a chemically diverse sensor array chip that mimics the olfactory system to provide the next revolution in sensory input for technology. Working in collaboration with electrical and computer engineers, integrated circuit sensor arrays will be developed, novel methods for creating large numbers of chemically diverse polymer sensing materials on these chips (i.e., chemical programming) will be demonstrated, and new machine learning algorithms will be explored for processing olfactory data streams.

该研究计划将着重于开发导电聚合物复合材料，以影响可再生能源，电子和感应的领域。鉴于预计能源需求的规模以及与大气中二氧化碳水平增长相关的快速气候变化，政府在碳中性太阳能燃料生产领域的创新和发现率很大。该程序将通过开发膜来帮助满足这一需求，这些膜将是直接生产太阳能燃料的独立人工光合设备的关键组成部分。当前，这些设备的膜的生产代表了必须解决的技术差距，以便将世界各地的研究人员开发的催化剂和光吸收器可以集成到一个工作系统中。与加州理工学院和麻省理工学院的世界知名研究人员与计划开发光吸收器和催化剂的现有合作将使膜设计的进步快速转化为概念验证验证，因此将在这个快速前进的领域中产生重大影响。参与这些研究的学员将获得独特的技能，并且在加拿大经济的快速增长的可再生能源领域将是非常可取的。基于有机的电子设备提供了新的操作机制，该机制有望克服基于硅设备的当前局限性，包括成本和微型化。但是，将有机材料集成到内存和晶体管等设备中仍然存在重大挑战。拟议的研究计划将重点介绍用于在电子设备中常用的动态随机记忆（DRAM）的新导电聚合物复合材料的开发。与电气和计算机工程师合作，这些努力将集中在提高读/写速度，保留记忆率（即降低波动率）和缩放定律（即可以使它们的规模较小）上。此外，将解决从设备制造到集成到现有电子平台和流程中的问题。电荷耦合设备（CCD）芯片的发明彻底改变了技术与其环境之间的界面。通过将其周围环境的光学图像像素化，设备可以使用复杂的图像处理和模式识别算法来执行与视觉感知相关的越来越复杂的任务。拟议的研究将着重于创建一个化学多样的传感器阵列芯片，该芯片模拟了嗅觉系统，以提供技术的下一个技术输入革命。将与电气和计算机工程师合作，将开发集成电路传感器阵列，将展示这些芯片上的化学多样性聚合物传感材料（即化学编程）的新颖方法，并将探索新的机器学习算法用于处理嗅觉数据流。