Abusultan Ahmed, Abunahla Heba, Halawani Yasmin, Mohammad Baker, Alamoodi Nahla, Alazzam Anas
System on Chip Lab (SoCL), Khalifa University, Abu Dhabi, UAE.
Department of Mechanical Engineering, Khalifa University, Abu Dhabi, UAE.
Nanoscale Res Lett. 2022 Sep 12;17(1):89. doi: 10.1186/s11671-022-03727-y.
The adverse effect of ultraviolet (UV) radiation on human beings has sparked intense interest in the development of new sensors to effectively monitor UV and solar exposure. This paper describes a novel low-cost and flexible graphene oxide (GO)-based paper sensor capable of detecting the total amount of UV or sun energy delivered per unit area. GO is incorporated into the structure of standard printing paper, cellulose, via a low-cost fabrication technique. The effect of UV and solar radiation exposure on the GO paper-based sensor is investigated using a simple color change analysis. As a result, users can easily determine the amount of ultraviolet or solar energy received by the sensor using a simple color analysis application. A neural network (ANN) model is also explored to learn the relation between UV color intensity and exposure time, then digitally display the results. The accuracy for the developed ANN reached 96.83%. The disposable, cost-effective, simple, biodegradable, safe, and flexible characteristics of the paper-based UV sensor make it an attractive candidate for a variety of sensing applications. This work provides new vision toward developing highly efficient and fully disposable GO-based photosensors.
紫外线(UV)辐射对人类的不利影响引发了人们对开发新型传感器以有效监测紫外线和太阳辐射暴露的浓厚兴趣。本文描述了一种新型的低成本、柔性氧化石墨烯(GO)基纸质传感器,它能够检测单位面积所传递的紫外线或太阳能总量。通过一种低成本制造技术,将氧化石墨烯融入标准打印纸纤维素的结构中。利用简单的颜色变化分析来研究紫外线和太阳辐射暴露对基于氧化石墨烯的纸质传感器的影响。结果,用户可以使用简单的颜色分析应用程序轻松确定传感器接收到的紫外线或太阳能的量。还探索了一种神经网络(ANN)模型来学习紫外线颜色强度与暴露时间之间的关系,然后以数字方式显示结果。所开发的神经网络的准确率达到了96.83%。基于纸质的紫外线传感器具有一次性使用、成本效益高、简单、可生物降解、安全且柔性的特点,使其成为各种传感应用的有吸引力的候选者。这项工作为开发高效且完全一次性使用的基于氧化石墨烯的光传感器提供了新的视角。