Abstract
The digitization of human senses has driven innovation across various technologies and transformed our daily lives, yet the digitization of olfaction remains a challenging frontier. Artificial olfactory systems, or electronic noses (e-noses), offer great potential for environmental monitoring, food safety, healthcare, and the fragrance industry. However, integrating sensor arrays that mimic olfactory receptors remains difficult, typically requiring complex, repetitive, and costly fabrication processes. In this research, we report the development of a porous laser-induced graphene (LIG) sensor array with in situ-doped cerium oxide nanoparticles for the classification of odorant molecules. By adjusting the laser irradiation parameters, we achieve a high degree of physical and chemical diversity in both LIG and CeO(x). Consequently, a sensor array exhibiting diverse response patterns to different odorant molecules can be fabricated through one-step laser irradiation of a polymer precursor. Using t-distributed stochastic neighbor embedding (t-SNE) and support vector machine (SVM)-based machine learning, we accurately predict the type and concentration of nine odorant molecules used in perfumes and cosmetics, achieving a high accuracy exceeding 95%. This study provides a rapid and straightforward solution for creating functional olfactory receptor-mimicking arrays, advancing the development of artificial olfaction systems.