The use of molecularly modified Si nanowire field effect transistors (SiNW FETs) for selective detection in the liquid phase has been successfully demonstrated. In contrast, selective detection of chemical species in the gas phase has been rather limited. In this paper, we show that the application of artificial intelligence on deliberately controlled SiNW FET device parameters can provide high selectivity toward specific volatile organic compounds (VOCs). The obtained selectivity allows identifying VOCs in both single-component and multicomponent environments as well as estimating the constituent VOC concentrations. The effect of the structural properties (functional group and/or chain length) of the molecular modifications on the accuracy of VOC detection is presented and discussed. The reported results have the potential to serve as a launching pad for the use of SiNW FET sensors in real-world counteracting conditions and/or applications.

Abstract:Two of the biggest challenges in medicine today are the need to detect diseases in a non-invasive manner, and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularlymodified Silicon Nanowire Field Effect Transistors (SiNW FETs) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma and Chronic Obstructive Pulmonary Disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlated their sensitivity and selectivity towards volatile organic compounds (VOCs) linked with diseased states. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples showed that the optimized SiNW FETs can detectand discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct positive way, which can reassure patients and prevent numerous negative investigations. 3Physicians are always challenged by the need to give the correct diagnosis as early in the onset of a disease is possible, whether the disease-related symptoms are absent or not evident.1 Symptoms are not always characteristic of one particular disease; overlap of symptoms is common in, for example, lung diseases. 2 Patients with different respiratory diseases, such as malignant or benign tumors, or substantially less severe diseases, may have similar symptoms, e.g. cough, chest pain, difficulty to breathe, etc. These symptoms may be characteristic of lung cancer (LC), pneumonia, asthma, and chronic obstructive pulmonary disease (COPD). 1,2Therefore, it is of particular clinical importance to find a diagnostic tool capable of distinguishing between these diseases. A diagnostic tool that involves no needle, surgery and/or active materials and/or radioactive exposure would have a benefit.A highly promising approach that could meet the aforementioned need is based on the detection and classification of the disease breathprint, viz. the chemical profiles of highly-and semi-VOCs in exhaled breath linked with disease. [3][4][5][6][7][8][9][10][11][12][13][14][15] The rationale behind this approach relies on the fact that VOCs generated by cellular metabolic pathways during a specific disease circulate in the blood stream and diffuse into exhaled breath, which is easily sampled. 4,16,17 In certain instances, analysis of breathprints offers several potential advantages, such as: (a) breath samples are non-invasive and easy to obtain; (b) breath contains less complicated mixtures than either serum or urine; and (c) breath testing has the potential for direct and real-time diagnosis and monitoring. 3,18-21Several mass-spectrometry and spectroscopy studies have shown that the breathprint of a specific disease differs from that of healthy control...

An ambipolar poly(diketopyrrolopyrrole-terthiophene)-based field-effect transistor (FET) sensitively detects xylene isomers at low ppm levels with multiple sensing features. Combined with pattern-recognition algorithms, a sole ambipolar FET sensor, rather than arrays of sensors, can discriminate highly similar xylene structural isomers from one another.

Statistical models have been used to estimate the refractive index of 72 imidazolium-based ionic liquids using the electronic polarisability of their ions as the data for two different mathematical approaches: artificial neural networks, in the form of multi-layer perceptrons, and multiple linear regression models. Although the artificial neural networks and linear models have been able to accomplish this task, the multi-layer perceptron model has been shown to be a more accurate method, thanks to its ability of determining non-linear relationships between different dependent variables. Additionally, it is clear that the multiple linear regression presents a systematic deviation in the estimated refractive index values, which confirms that it is an inappropriate model for this system.

Tuberculosis (TB) is an infectious disease that threatens >10 million people annually. Despite advances in TB diagnostics, patients continue to receive an insufficient diagnosis as TB symptoms are not specific. Many existing biodiagnostic tests are slow, have low clinical performance, and can be unsuitable for resource‐limited settings. According to the World Health Organization (WHO), a rapid, sputum‐free, and cost‐effective triage test for real‐time detection of TB is urgently needed. This article reports on a new diagnostic pathway enabling a noninvasive, fast, and highly accurate way of detecting TB. The approach relies on TB‐specific volatile organic compounds (VOCs) that are detected and quantified from the skin headspace. A specifically designed nanomaterial‐based sensors array translates these findings into a point‐of‐care diagnosis by discriminating between active pulmonary TB patients and controls with sensitivity above 90%. This fulfills the WHO's triage test requirements and poses the potential to become a TB triage test.

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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.

Multiple sclerosis (MS) is the most common chronic neurological disease affecting young adults. MS diagnosis is based on clinical characteristics and confirmed by examination of the cerebrospinal fluids (CSF) or by magnetic resonance imaging (MRI) of the brain or spinal cord or both. However, neither of the current diagnostic procedures are adequate as a routine tool to determine disease state. Thus, diagnostic biomarkers are needed. In the current study, a novel approach that could meet these expectations is presented. The approach is based on noninvasive analysis of volatile organic compounds (VOCs) in breath. Exhaled breath was collected from 204 participants, 146 MS and 58 healthy control individuals. Analysis was performed by gas-chromatography mass-spectrometry (GC-MS) and nanomaterial-based sensor array. Predictive models were derived from the sensors, using artificial neural networks (ANNs). GC-MS analysis revealed significant differences in VOC abundance between MS patients and controls. Sensor data analysis on training sets was able to discriminate in binary comparisons between MS patients and controls with accuracies up to 90%. Blinded sets showed 95% positive predictive value (PPV) between MS-remission and control, 100% sensitivity with 100% negative predictive value (NPV) between MS not-treated (NT) and control, and 86% NPV between relapse and control. Possible links between VOC biomarkers and the MS pathogenesis were established. Preliminary results suggest the applicability of a new nanotechnology-based method for MS diagnostics.