We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.

This article reports on a noninvasive approach in detecting and following-up individuals who are at-risk or have an existing COVID-19 infection, with a potential ability to serve as an epidemic control tool. The proposed method uses a developed breath device composed of a nanomaterial-based hybrid sensor array with multiplexed detection capabilities that can detect disease-specific biomarkers from exhaled breath, thus enabling rapid and accurate diagnosis. An exploratory clinical study with this approach was examined in Wuhan, China, during March 2020. The study cohort included 49 confirmed COVID-19 patients, 58 healthy controls, and 33 non-COVID lung infection controls. When applicable, positive COVID-19 patients were sampled twice: during the active disease and after recovery. Discriminant analysis of the obtained signals from the nanomaterial-based sensors achieved very good test discriminations between the different groups. The training and test set data exhibited respectively 94% and 76% accuracy in differentiating patients from controls as well as 90% and 95% accuracy in differentiating between patients with COVID-19 and patients with other lung infections. While further validation studies are needed, the results may serve as a base for technology that would lead to a reduction in the number of unneeded confirmatory tests and lower the burden on hospitals, while allowing individuals a screening solution that can be performed in PoC facilities. The proposed method can be considered as a platform that could be applied for any other disease infection with proper modifications to the artificial intelligence and would therefore be available to serve as a diagnostic tool in case of a new disease outbreak.

Devices integrated with self-healing ability can benefit from long-term use as well as enhanced reliability, maintenance and durability. This progress report reviews the developments in the field of self-healing polymers/composites and wearable devices thereof. One part of the progress report presents and discusses several aspects of the self-healing materials chemistry (from non-covalent to reversible covalent-based mechanisms), as well as the required main approaches used for functionalizing the composites to enhance their electrical conductivity, magnetic, dielectric, electroactive and/or photoactive properties. The second and complementary part of the progress report links the self-healing materials with partially or fully self-healing device technologies, including wearable sensors, supercapacitors, solar cells and fabrics. Some of the strong and weak points in the development of each self-healing device are clearly highlighted and criticized, respectively. Several ideas regarding further improvement of soft self-healing devices are proposed.

Abstract

Artificial olfaction, i.e., e‐nose, plays a critical function in robotics by mimicking the human olfactory organ that can recognize different smells that correlate with a range of fields, including environment monitoring, disease diagnosis, public security affairs, agricultural production, food industry, etc. The advances in the artificial olfaction (electronic nose) technology and its applications are concisely reviewed herein. Three main elements are investigated and presented, with an emphasis on the emerging sensors and algorithm of the artificial neural network in the relevant fields. The first element is the diverse applications of e‐nose in medical care, food industry, environment monitoring, public security affairs, and agricultural production. The second element is the investigation of the sensors in e‐nose and representative and promising advances, which is the building block of e‐nose through mimicking the olfactory receptors. The third element is the introduction to the algorithm of the artificial neural network to serve in the recognition of the pattern of odors (i.e., their chemical profiles). Promises and challenges of the separately reviewed parts and the combined parts are presented and discussed. Ideas regarding further orientation and development of the e‐nose system are also considered.