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This article is an overview of the present and ongoing developments in the field of nanomaterial-based sensors for enabling fast, relatively inexpensive and minimally (or non-) invasive diagnostics of health conditions with follow-up by detecting volatile organic compounds (VOCs) excreted from one or combination of human body fluids and tissues (e.g., blood, urine, breath, skin). Part of the review provides a didactic examination of the concepts and approaches related to emerging sensing materials and transduction techniques linked with the VOC-based non-invasive medical evaluations. We also present and discuss diverse characteristics of these innovative sensors, such as their mode of operation, sensitivity, selectivity and response time, as well as the major approaches proposed for enhancing their ability as hybrid sensors to afford multidimensional sensing and information-based sensing. The other parts of the review give an updated compilation of the past and currently available VOC-based sensors for disease diagnostics. This compilation summarizes all VOCs identified in relation to sickness and sampling origin that links these data with advanced nanomaterial-based sensing technologies. Both strength and pitfalls are discussed and criticized, particularly from the perspective of the information and communication era. Further ideas regarding improvement of sensors, sensor arrays, sensing devices and the proposed workflow are also included.

This chapter introduces the topic of detection of volatile organic compounds (VOCs) via nanomaterial-based sensors for health applications. Sensors are fast and relatively inexpensive tools for noninvasive diagnostics of health status by the detection of VOCs excreted from human breath or different body fluids and tissues, and they can be used to determine the volatilomic print of an individual. Utilization of the knowledge on different chemical families toward tuning sensor approaches is demonstrated. The chapter introduces the approach of semiselective/cross-reactive sensors for VOC detection and its advantages compared to selective sensor approaches. Different sensor technologies are reviewed, and it is shown that they are capable of easy adaptation in the field. As sensor systems provide high-dimensional outputs when applied to volatilomic analysis, predictive models for diagnosis are an integral part of the analysis. The chapter reviews current developments and future advancements of nanomaterial-based sensors and assesses the hidden difficulties in developing machine learning models for volatilome applications, offering recommended best practices for model validation and assessment.

Biointerfaced sensors have emerged as a new paradigm for medical applications that require an interface and/or intimate contact with biological components/systems such as cells, tissues, and whole organs. This article provides a review of the concept, design, and device characteristics of biointerfaced sensors needed for successful implementation of biodiagnostics and monitoring. It begins by presenting and discussing the different considerations that arise from artificial interfaces with different biological environments. It then explores the main strategies for sensor and material design, while highlighting the required chemistry, structure, and mechanical properties needed to maintain an unperturbed interface with the surrounding biological environment. Finally, the review discusses successful state‐of‐the‐art demonstrations of body monitoring and biodiagnostics, focusing on the brain, heart, muscles, skin, teeth, and other tissues for medical purposes. Insights, perspectives, and recommendations for future research are presented.

Chemical sensors based on programmable molecularly modified gold nanoparticles are tailored for the detection and discrimination between the breathprint of irritable bowel syndrome (IBS) and inflammatory bowel diseases (IBD). The sensors are examined in both lab- and real-world clinical conditions. The results reveal a discriminative power accuracy of 81% between IBD and IBS and 75% between Crohn's and Colitis states.

Temperature and relative humidity are two of the most important variables in breath analysis. Several studies have investigated the temperature range of exhaled breath, mostly in the context of respiratory disease diagnostics, but none of them inspected the correlation with clinical parameters, nor the effect of different geographic locations. In this study, we have built a tailor-made device for measuring temperature and relative humidity in exhaled breath. With it, we have carried out 340 measurements (265 in Paris/France and 75 in Haifa/Israel) from 31 participants. The results show that the temperature range of exhaled breath is 31.4-35.4 °C for Haifa’s participants and 31.4-34.8 °C for Parisian participants and the range of exhaled breath relative humidity is 65.0-88.6% and 41.9-91.0% for Haifa and Paris participants, respectively. Clinical and environmental effects were also inspected to give more information on the confounding factors. The results presented in this study contribute to the definition of the ranges of temperature and relative humidity of exhaled breath in individuals, in addition to their correlation with clinical and environmental factors such as gender, BMI and age. These factors must be taken into consideration in order to increase the reproducibility and reliability of a wide variety of measurements in this particular field.

Delayed diagnosis of many diseases is caused by technology limitations of currently available methods. Therefore, there is a need for novel technology that would allow efficient early detection, population stratification for personalized therapy, and for rapid assessment of treatment efficacy. Such an emerging approach is based on artificially intelligent sensing arrays of solid-state or flexible sensors that detect the so-called "volatolomics", viz. profiles of volatile organic compounds (VOCs) which are by-products of metabolic processes and emit from various body fluids.

The detection of many diseases is missed because of delayed diagnoses or the low efficacy of some treatments. This emphasizes the urgent need for inexpensive and minimally invasive technologies that would allow efficient early detection, stratifying the population for personalized therapy, and improving the efficacy of rapid bed-side assessment of treatment. An emerging approach that has a high potential to fulfill these needs is based on so-called "volatolomics", namely, chemical processes involving profiles of highly volatile organic compounds (VOCs) emitted from body fluids, including breath, skin, urine and blood. This article presents a didactic review of some of the main advances related to the use of nanomaterial-based solid-state and flexible sensors, and related artificially intelligent sensing arrays for the detection and monitoring of disease with volatolomics. The article attempts to review the technological gaps and confounding factors related to VOC testing. Different ways to choose nanomaterial-based sensors are discussed, while considering the profiles of targeted volatile markers and possible limitations of applying the sensing approach. Perspectives for taking volatolomics to a new level in the field of diagnostics are highlighted.

Despite recent dramatic development of materials with self-healing ability, fabrication of a self-healing devices remains challenging. In this paper, truly self-healing composites consisting polymers and carbon nanostructures are reported. Using a modified fabrication technique – called “layer-by-layer stamping” – chemical sensors are produced and characterized by infrared spectroscopy and various microscopy techniques. As a demonstration of the ability of the developed sensors to coexist with harsh operation conditions, the performance of the sensors under exposure to volatile organic compounds that are derived from human breath/skin is evaluated under different conditions of cutting and self-healing cycles. The results show a discrimination ability of the selected sensors to return to an operation level of 70%–100% after cutting and 24 h healing of the composite film. From this analysis, the discriminative power of the self-healing sensor array remains high for the majority of analyte discrimination even after mechanical injury, thus strengthening the potential of such arrays for use in health monitoring applications based on detection of analyte patterns.