Federal University of Uberlândia (UFU), Institute of Chemistry, Uberlândia, MG, Brazil.
Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology, 08028, Barcelona, Spain.
Anal Chim Acta. 2021 Jan 25;1143:1-8. doi: 10.1016/j.aca.2020.11.012. Epub 2020 Nov 13.
Commercial printers based on fused deposition modeling (FDM) are widely adopted for 3D printing applications. This method consists of the heating of polymeric filaments over the melting point followed by their deposition onto a solid base to create the desirable 3D structure. Prior investigation using chromatographic techniques has shown that chemical compounds (e.g. VOCs), which can be harmful to users, are emitted during the printing process, producing adverse effects to human health and contributing to indoor air pollution. In this study, we present a simple, inexpensive and disposable paper-based optoelectronic nose (i.e. colorimetric sensor array) to identify the gaseous emission fingerprint of five different types of thermoplastic filaments (ABS, TPU, PETG, TRITAN and PLA) in the indoor environment. The optoelectronic nose is comprised of selected 15 dyes with different chemical properties deposited onto a microfluidic paper-based device with spots of 5 mm in diameter each. Digital images were obtained from an ordinary flatbed scanner, and the RGB information collected before and after air exposure was extracted by using an automated routine designed in MATLAB, in which the color changes provide a unique fingerprint for each filament in 5 min of printing. Reproducibility was obtained in the range of 2.5-10% (RSD). Hierarchical clustering analysis (HCA) and principal component analysis (PCA) were successfully employed, showing suitable discrimination of all studied filaments and the non-polluted air. Besides, air spiked with vapors of the most representative VOCs were analyzed by the optoelectronic nose and visually compared to each filament. The described study shows the potential of the paper-based optoelectronic nose to monitor possible hazard emissions from 3D printers.
基于熔融沉积建模(FDM)的商用打印机广泛应用于 3D 打印领域。该方法包括将高分子长丝加热至熔点以上,然后将其沉积到固体基底上,以创建所需的 3D 结构。先前使用色谱技术的研究表明,在打印过程中会释放出对用户有害的化学化合物(例如 VOC),从而对人体健康产生不利影响,并导致室内空气污染。在这项研究中,我们提出了一种简单、廉价且一次性的基于纸张的光电鼻(即比色传感器阵列),以识别五种不同类型热塑性长丝(ABS、TPU、PETG、TRITAN 和 PLA)在室内环境中释放的气态排放指纹。光电鼻由 15 种具有不同化学性质的选定染料组成,这些染料沉积在具有 5 毫米直径斑点的微流控纸质设备上。从普通平板扫描仪获取数字图像,并使用 MATLAB 中设计的自动程序提取暴露于空气前后的 RGB 信息,其中颜色变化为每种长丝在 5 分钟的打印过程中提供独特的指纹。在 2.5-10%(RSD)的范围内获得了重现性。成功地采用了层次聚类分析(HCA)和主成分分析(PCA),显示出对所有研究长丝和未污染空气的适当区分。此外,通过光电鼻分析并与每种长丝进行视觉比较,对空气中带有最具代表性 VOC 蒸气的空气进行了分析。所描述的研究表明,基于纸张的光电鼻具有监测 3D 打印机可能产生的危险排放的潜力。