We explored the potential of different nanoparticles (TiO₂, CaCO₃, and Al₂O₃), considering their pure form and modified with cinnamon essential oil (CEO). These materials were characterized using various techniques, including FTIR spectroscopy, XRD analysis, TGA, and SEM. The interaction between CEO and nanoparticles changed depending on the nanoparticle type. Al₂O₃ nanoparticles exhibited the strongest interaction with CEO, increasing their antioxidant capacity by around 40% and their transfer of antimicrobial properties, particularly against Gram-negative bacteria. In contrast, TiO₂ and CaCO₃ nanoparticles showed limited interaction with CEO, resulting in lower antioxidant capacity and antimicrobial activity. Incorporating pure and CEO-modified nanoparticles into polylactic acid (PLA) films improved their mechanical and thermal properties, which are suitable for applications requiring greater strength. This research highlights the potential of metal oxide nanoparticles to enhance the antimicrobial and antioxidant capabilities of polymers. In addition, incorporating cinnamon essential oil can increase the antioxidant and antimicrobial effectiveness of the metal oxide nanoparticles and improve the mechanical and thermal properties of PLA films. Thus, these PLA films exhibit favorable characteristics for active packaging applications.

PostCOVID-19 is a condition affecting approximately 10% of individuals infected with SARS-CoV-2, presenting significant challenges in diagnosis and clinical management. Portable neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), offer real-time insights into cerebral hemodynamics and represent a promising tool for studying postCOVID-19 in naturalistic settings. This study investigates the integration of fNIRS with machine learning to identify neural correlates of postCOVID-19. A total of six machine learning classifiers—Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), XGBoost, Logistic Regression, and Multi-Layer Perceptron (MLP)—were evaluated using a stratified subject-aware cross-validation scheme on a dataset comprising 29,737 time-series samples from 37 participants (9 postCOVID-19, 28 controls). Four different feature representation strategies were compared: raw time-series, PCA-based dimensionality reduction, statistical feature extraction, and a hybrid approach that combines time-series and statistical descriptors. Among these, the hybrid representation demonstrated the highest discriminative performance. The SVM classifier trained on hybrid features achieved strong discrimination ($\mathit{ROC}\text{-}\mathit{AUC}$ = 0.909) under subject-aware CV5; at the default threshold, $\mathit{Sensitivity}$ was moderate and $\mathit{Specificity}$ was high, outperforming all other methods. In contrast, models trained on statistical features alone exhibited limited $\mathit{Sensitivity}$ despite high $\mathit{Specificity}$. These findings highlight the importance of temporal information in the fNIRS signal and support the potential of machine learning combined with portable neuroimaging for postCOVID-19 identification. This approach may contribute to the development of non-invasive diagnostic tools to support individualized treatment and longitudinal monitoring of patients with persistent neurological symptoms.