Ultrasensitive silicon nanowire for real-world gas sensing: noninvasive diagnosis of cancer from breath volatolome.

We report on an ultrasensitive, molecularly modified silicon nanowire field effect transistor that brings together the lock-and-key and cross-reactive sensing worlds for the diagnosis of (gastric) cancer from exhaled volatolome. The sensor is able to selectively detect volatile organic compounds (VOCs) that are linked with gastric cancer conditions in exhaled breath and to discriminate them from environmental VOCs that exist in exhaled breath samples but do not relate to the gastric cancer per se. Using breath samples collected from actual patients with gastric cancer and from volunteers who do not have cancer, blind analysis validated the ability of the reported sensor to discriminate between gastric cancer and control conditions with >85% accuracy, irrespective of important confounding factors such as tobacco consumption and gender. The reported sensing approach paves the way to use the power of silicon nanowires for simple, inexpensive, portable, and noninvasive diagnosis of cancer and other disease conditions.

Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath.

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) 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 correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. 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 show that the optimized SiNW FETs can detect and 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 harmless way, which can reassure patients and prevent numerous unpleasant investigations.

Field-effect transistors based on silicon nanowire arrays: effect of the good and the bad silicon nanowires.

Aligned arrays of silicon nanowires (aa-Si NWs) allow the exploitation of Si NWs in a scalable way. Previous studies explored the influence of the Si NWs' number, doping density, and diameter on the related electrical performance. Nevertheless, the origin of the observed effects still not fully understood. Here, we aim to provide an understanding on the effect of channel number on the fundamental parameters of aa-Si NW field effect transistors (FETs). Toward this end, we have fabricated and characterized 87 FET devices with varied number of Si NWs, which were grown by chemical vapor deposition with gold catalyst. The results show that FETs with Si NWs above a threshold number (n > 80) exhibit better device uniformity, but generally lower device performance, than FETs with lower number of Si NWs (3 ≤ n < 80). Complementary analysis indicates that the obtained discrepancies could be explained by a weighted contribution of two main groups of Si NWs: (i) a group of gold-free Si NWs that exhibit high and uniform electrical characteristics; and (ii) a group of gold-doped Si NWs that exhibit inferior electrical characteristics. These findings are validated by a binomial model that consider the aa-Si NW FETs via a weighted combination of FETs of individual Si NWs. Overall, the obtained results suggest that the criterions used currently for evaluating the device performance (e.g., uniform diameter, length, and shape of Si NWs) do not necessarily guarantee uniform or satisfying electrical characteristics, raising the need for new growth processes and/or advanced sorting techniques of electrically homogeneous Si NWs.

Sniffing chronic renal failure in rat model by an array of random networks of single-walled carbon nanotubes.

In this study, we use an experimental model of bilateral nephrectomy in rats to identify an advanced, yet simple nanoscale-based approach to discriminate between exhaled breath of healthy states and of chronic renal failure (CRF) states. Gas chromatography/mass spectroscopy (GC-MS) in conjugation with solid-phase microextraction (SPME) of healthy and CRF breath, collected directly from the trachea of the rats, identified 15 common volatile organic compounds (VOCs) in all samples of healthy and CRF states and 27 VOCs that appear in CRF but not in healthy states. Online breath analysis via an array of chemiresistive random network of single-walled carbon nanotubes (SWCNTs) coated with organic materials showed excellent discrimination between the various breath states. Stepwise discriminate analysis showed that enhanced discrimination capacity could be achieved by decreasing the humidity prior to their analysis with the sensors' array. Furthermore, the analysis showed the adequacy of using representative simulated VOCs to imitate the breath of healthy and CRF states and, therefore, to train the sensors' array the pertinent breath signatures. The excellent discrimination between the various breath states obtained in this study provides expectations for future capabilities for diagnosis, detection, and screening various stages of kidney disease, especially in the early stages of the disease, where it is possible to control blood pressure and protein intake to slow the progression.