The chemical signatures of volatile organic compounds (VOCs) in humans can be utilized for point-of-care (POC) diagnosis. Apart from toxic exposure studies, VOCs generated in humans can provide insights into one's healthy and diseased metabolic states, acting as a biomarker for identifying numerous diseases noninvasively. VOC sensors and the technology of e-nose have received significant attention for continuous and selective monitoring of various physiological and pathophysiological conditions of an individual. Noninvasive detection of VOCs is achieved from biomatrices of breath, sweat and saliva. Among these, detection from sweat and saliva can be continuous in real-time. The sensing approaches include optical, chemiresistive and electrochemical techniques. This article provides an overview of such techniques. These, however, have limitations of reliability, precision, selectivity, and stability in continuous monitoring. Such limitations are due to lack of sensor stability and complexity of samples in a multivariate environment, which can lead to false readings. To overcome selectivity barriers, sensor arrays enabling multimodal sensing, have been used with pattern recognition techniques. Stability and precision issues have been addressed through advancements in nanotechnology. The use of various forms of nanomaterial not only enhance sensing performance, but also plays a major role in detection on a miniaturized scale. The rapid growth in medical Internet of Things (IoT) and artificial intelligence paves a pathway for improvements in human theranostics.

Potential applications of thin film metamaterials are diverse and their realization to offer miniaturized waveguides, antennas and shielding patterns are on anvil. These artificially engineered structures can produce astonishing electromagnetic responses because of their constituents being engineered at much smaller dimensions than the wavelength of the incident electromagnetic wave, hence behaving as artificial materials. Such micro-nano dimensions of thin film metamaterial structures can be customized for various applications due to their exclusive responses to not only electromagnetic, but also to acoustic and thermal waves that surpass the natural materials' properties. In this paper, the recent major advancements in the emerging fields of diagnostics (sensors) and therapeutics involving thin film metamaterials have been reviewed and underlined; discussing their edge over conventional counterpart techniques; concentrating on their design considerations and feasible ways of achieving them. Challenges faced in sensitivity, precision, accuracy and factors that interfere with the degree of performance of the sensors are also dealt with, herein.

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This paper presented the fabrication and calibration of a clad-modified evanescent based plastic optical fiber (POF) sensor for the detection of ammonia in both stagnant and dynamic aqueous media. This optochemical sensor was based on Oxazine 170 perchlorate (sensing material) and polydimethylsiloxane (PDMS) (protective material) thin layers. A special chemical solution was developed for the etching removal of cladding and a methodology for trapping moisture was exercised. Experimental results on dissolved ammonia detection exhibited short response time (≤10 s), low detection limit (minimum detection limit 1.4 ppm), high sensitivity, and excellent reversibility (over 99%).

Elevated level of acetone in breath or sweat is an indication of type-I diabetes, which can turn into 'ketoacidosis'a serious hyperglycemic condition. Continuous monitoring is a challenge among the conventional sensing methods. Though real-time detection of acetone from different biofluids is promising, signal interference from other biomarkers remains an issue. A minor fluctuation of the signals in micro-ampere range causes substantial overlapping in linear/polynomial calibration fittings. To address the above in non-invasive detection, principal component analysis (PCA) was demonstrated which generated specific pattern for the different concentration points of acetone in the subspace. It improves the overlapping of the signals in between of two or different concentration points of the data sets and eliminate dimensionality and redundancy of data variables. An algorithm following PCA was incorporated into the microcontroller (nRF51822) for the functionality of wearable device in a selective manner in the physiological range (0.5 ppm to 4 ppm) of acetone.

Existing fuel cell alcohol sensors suffer from humidity interference and signal instability which renders them useless for transdermal ethanol detection in a non-ideal environment. To address these insufficiencies in fuel cell sensors, various electrode designs, along with the catalysts that would best improve response, were explored in this work. The designs include both two and three electrode setups with Nafion as the proton exchanging membrane. The three-electrode setup provided a more stable signal compared to the two-electrode setup. Considering the development of a potential transdermal ethanol sensor, the ethanol exposed area of the working electrode was optimized to be 1cm2. Catalysts such as Cu, stainless steel, and Ni were studied in these experiments and it was determined that Ni demonstrated the best catalytic activity for ethanol oxidation and oxygen reduction; providing 300 times greater current response than Fe and 3 times greater than Cu. The lower detection limit of the sensor is 1 part per million (ppm) and the sensitivity of the fuel cell sensor using the Ni catalyst was found to be 0.02 nAppm-1 in this study.

We envision unmanned aerial vehicles (UAV) for rapid evacuation of critically-ill patients from hazardous locations to health care facilities in safe zones. For safety, medical teams accompany patients to monitor vital signs and titrate anesthesia dose during transport. UAV transports would require continuous automated remote monitoring of both vital signs and of sedative dose to be feasible and safe. Volatile anesthetics (isoflurane) are the only anesthetic agents that can be monitored continuously with infrared spectroscopy (IR) devices; but unsuitable for transport. Our objective is to devise a safe UAV transport protocol incorporating novel technology for gas monitoring. Our group has developed and tested a miniaturized wearable fuel cell sensor that can detect isoflurane gas vapors as low as 40 ppm (within therapeutic range) with a sensitivity of 0.0112 nA ppm −1 cm −2 . Ambient signal interference was resolved by principal component analysis (PCA). Data variance of 1 st and 2 nd principal components was 88.68% and 11.31%, respectively. The PCA regression model reported here can determine accurate Isoflurane concentrations. Electronic IoT platform has been built constituting micro-fuel cell, miniaturized electronic components with Bluetooth. This wearable sensor can be incorporated into a comprehensive life support system for casualty evacuation in conjunction with autonomous UAV emergency medical operations.

A wearable anesthetic sensor combined with principal component regression as a new approach to improve accuracy in the analytical field.