Clear aligners have transformed orthodontic care by providing an aesthetic, removable alternative to traditional braces. However, their significant environmental footprint, contributing to approximately 15,000 tons of plastic waste annually, poses a critical challenge. To address this issue, advancements in 4D printing have introduced “smart” aligners with shape memory properties, enabling reshaping and reducing the number of aligners required per treatment. This study focuses on ClearX aligners, an innovative 4D-printed solution aimed at extending usage duration and minimizing environmental impact. Using a comprehensive suite of tests, including morphological, optical, and mechanical evaluations conducted via scanning electron microscopy, UV-Vis spectroscopy, infrared spectroscopy, and bending and strain assessments, we evaluated the optical and mechanical stability of the ClearX material before and after thermal activation. Our results demonstrate that ClearX aligners retain their structural and functional properties after reshaping. Temporary changes in transparency, observed only under prolonged treatment durations exceeding manufacturer recommendations, are fully reversible within 12 h and do not compromise the aligner’s usability. These findings support the potential of ClearX aligners to effectively combine patient-centered, high-quality orthodontic care with sustainable practices.

Proper nutrition is a fundamental aspect to maintaining overall health and well-being, influencing both physical and social aspects of human life; an unbalanced or inadequate diet can lead to various nutritional deficiencies and chronic health conditions. In today’s fast-paced world, monitoring nutritional intake has become increasingly important, particularly for those with specific dietary needs. While smartphone-based applications using image recognition have simplified food tracking, they still rely heavily on user interaction and raise concerns about practicality and privacy. To address these limitations, this paper proposes a novel, compact spectroscopic sensing platform for automatic beverage recognition. The system utilizes the AS7265x commercial sensor to capture the spectral signature of beverages, combined with a K-Nearest Neighbors (KNN) machine learning algorithm for classification. The approach is designed for integration into everyday objects, such as smart glasses or cups, offering a noninvasive and user-friendly alternative to manual tracking. Through optimization of both the sensor configuration and KNN parameters, we identified a reduced set of four wavelengths that achieves over 96% classification accuracy across a diverse range of common beverages. This demonstrates the potential for embedding accurate, low-power, and cost-efficient sensors into Internet of Things (IoT) devices for real-time nutritional monitoring, reducing the need for user input while enhancing accessibility and usability.

Brain ischemia is a severe condition caused by reduced cerebral blood flow, leading to the disruption of ion gradients in brain tissue. This imbalance triggers spreading depolarizations, which are waves of neuronal and glial depolarization propagating through the gray matter. Microelectrode arrays (MEAs) are essential for real-time monitoring of these electrophysiological processes both in vivo and in vitro, but their sensitivity and signal quality are critical for accurate detection of extracellular brain activity. In this study, we evaluate the performance of a flexible microelectrode array based on gold-coated zinc oxide nanorods (ZnO NRs), referred to as nano-fMEA, specifically for high-fidelity electrophysiological recording under pathological conditions. Acute mouse brain slices were tested under two ischemic models: oxygen–glucose deprivation (OGD) and hyperkalemia. The nano-fMEA demonstrated significant improvements in event detection rates and in capturing subtle fluctuations in neural signals compared to flat fMEAs. This enhanced performance is primarily attributed to an optimized electrode–tissue interface that reduces impedance and improves charge transfer. These features enabled the nano-fMEA to detect weak or transient electrophysiological events more effectively, making it a valuable platform for investigating neural dynamics during metabolic stress. Overall, the results underscore the promise of ZnO NRs in advancing electrophysiological tools for neuroscience research.

This work focuses on the synthesis and the comprehensive characterization of polydianiline (PDANI) polymer, obtained via oxidative polymerization of dianiline, a low-toxicity and more environmentally friendly starting monomer for polyaniline (PANI) formation. Despite the structural similarity to PANI, PDANI remains underexplored, especially regarding the effect of different synthesis conditions. Here, we investigate how chloride, sulfate, and camphor sulfonate dopants, combined with green solvents such as water and DMSO, modulate the final properties of PDANI in the emeraldine salt configuration. The produced materials were extensively characterized using a multi-technique approach. FTIR, Raman, EPR, and UV-Vis spectroscopies provided insights into chemical structure, molecular order, and polaron population. Electrical conductivity was disclosed via current-voltage (I-V) measurements, while cyclic voltammetry (CV) and coulovoltammetry (QV) were employed to evaluate redox activity and charge reversibility. The resulting PDANI displays structural and functional features comparable to those of PANI synthesized under similar conditions. Notably, the nature of the dopant and acidic medium was found to crucially govern the oxidation level, molecular organization, and electrochemical performance, boosting PDANI as a tunable and sustainable alternative for applications ranging from electronics to energy storage.