Application of digital twins for simulation based tailoring of laser induced graphene.

In the era of man-machine interfaces, digital twins stand as a key technology, offering virtual representations of real-world objects, processes, and systems through computational models. They enable novel ways of interacting with, comprehending, and manipulating real-world entities within a virtual realm. The real implementation of graphene-based sensors and electronic devices remains challenging due to the integration complexities of high-quality graphene materials with existing manufacturing processes. To address this, scalable techniques for the in-situ fabrication of graphene-like materials are essential. One promising method involves using a CO2 laser to convert polyimide into graphene. Optimizing this graphitization process is hindered by complex parameter interactions and nonlinear terms. This article explores how these digital replicas can enhance the fabrication of laser-induced graphene (LIG) through laser simulation and machine learning methods to enable rapid single-step LIG patterning. This approach aims to create a universal simulation for all CO2 lasers, calculating optical energy flux and utilizing machine learning to control and predict LIG conductivity (ability to conduct current), morphology, and electrical resistance. The proposed procedure, integrating digital twins in the LIG production process, will avoid or reduce the preliminary tests required to determine the proper laser parameters to reach the desired LIG characteristics. Accordingly, this approach will reduce the time and costs associated with these tests and thus increase the efficiency and optimize the procedure.

ZnO Decorated Graphene-Based NFC Tag for Personal NO2 Exposure Monitoring during a Workday.

This paper presents the integration of a sensing layer over interdigitated electrodes and an electronic circuit on the same flexible printed circuit board. This integration provides an effective technique to use this design as a wearable gas measuring system in a target application, exhibiting high performance, low power consumption, and being lightweight for on-site monitoring. The wearable system proves the concept of using an NFC tag combined with a chemoresistive gas sensor as a cumulative gas sensor, having the possibility of holding the data for a working day, and completely capturing the exposure of a person to NO2 concentrations. Three different types of sensors were tested, depositing the sensing layers on gold electrodes over Kapton substrate: bare graphene, graphene decorated with 5 wt.% zinc oxide nanoflowers, or nanopillars. The deposited layers were characterized using FESEM, EDX, XRD, and Raman spectroscopy to determine their crystalline structure, morphological and chemical compositions. The gas sensing performance of the sensors was analyzed against NO2 (dry and humid conditions) and other interfering species (dry conditions) to check their sensitivity and selectivity. The resultant-built wearable NFC tag system accumulates the data in a non-volatile memory every minute and has an average low power consumption of 24.9 µW in dynamic operation. Also, it can be easily attached to a work vest.