Controlling the Properties of Laser-Induced Graphene by Machine Learning

通过机器学习控制激光诱导石墨烯的特性

• 批准号: 576808-2022
• 负责人: Grau, GerdGFMN
• 金额: $ 1.82万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 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=752401
• 关键词: Controlling; Properties; Laser; Induced; Graphene

The goal of this project is to improve the fabrication of laser-induced graphene (LIG) using machine learning (ML). LIG is a recently discovered material that has received a lot of attention due to its simple fabrication and favorable properties. LIG can be created by exposing a polymer, for example a flexible plastic foil or a 3D printed object, to a scanned laser beam. Wherever the polymer is exposed to the laser, it is converted to LIG. No additional steps such as chemical synthesis, deposition of graphene, or photolithography are needed. This makes the process very simple, fast, and low-cost. The resulting LIG is electrically conductive and has a porous microstructure with properties that can be tuned by varying the laser parameters or the chemical properties of the polymer precursor. In order to achieve optimal LIG properties for a particular application, extensive experimentation or complex simulations are necessary due to the complex underlying processes. This limits the performance of LIG devices and how easily LIG can be adapted to a new application. Here, we propose to train a ML model to automatically optimize the properties of LIG as desired. A major strength of ML is to be able to map complex relationships between inputs and outputs even if it is difficult to do so using traditional analytical models. We will train the ML model using experimental results in our lab. There will be a constant communication between the experimental and ML parts of the project to ensure that the newly collected data improves regions of the model that require further training. This project is an international collaboration between researchers in Canada who are experts for LIG and researchers in the United States who are experts for ML. This project will benefit Canada in multiple ways. LIG can be employed in a variety of devices ranging from physical and biomedical sensors to supercapacitors and batteries for energy storage and this project will be beneficial for Canadian manufacturers of such products. More generally, ML is becoming increasingly important to optimize advanced manufacturing processes and this project will add to Canada's capacity in this area both in terms of technical results and student training.

该项目的目的是使用机器学习（ML）改善激光诱导的石墨烯（LIG）。 LIG是一种最近发现的材料，由于其简单的制造和有利的特性，它引起了很多关注。可以通过暴露聚合物（例如柔性塑料箔或3D印刷物体）将LIG创建，并在扫描的激光束上。无论将聚合物暴露于激光器，都可以转换为LIG。不需要其他步骤，例如化学合成，石墨烯的沉积或光刻。这使得过程非常简单，快速和低成本。所得的LIG是导电性的，具有多孔微观结构，其性能可以通过改变激光参数或聚合物前体的化学性质来调整。为了实现特定应用的最佳地带特性，由于复杂的基础过程，需要进行广泛的实验或复杂的模拟。这限制了LIG设备的性能以及如何轻松地适应新应用程序。在这里，我们建议训练ML模型，以根据需要自动优化LIG的性质。 ML的主要优势是能够在输入和输出之间绘制复杂的关系，即使使用传统的分析模型很难这样做。我们将使用实验室中的实验结果训练ML模型。项目的实验和ML部分之间将不断进行沟通，以确保新收集的数据改善了需要进一步培训的模型区域。该项目是加拿大研究人员之间的一项国际合作，他们是LIG的专家和美国研究人员的专家，他们是ML的专家。该项目将以多种方式使加拿大受益。 LIG可用于从物理和生物医学传感器到超级电容器和电池以存储能源的各种设备中，该项目将对加拿大产品的加拿大制造商有益。更普遍的是，ML对于优化先进的制造流程变得越来越重要，就技术结果和学生培训而言，该项目将增加加拿大在这一领域的能力。