Analysis of exhaled breath for diagnosing head and neck squamous cell carcinoma: a feasibility study

BACKGROUND

Squamous cell carcinoma of the head and neck (HNSCC) are wide-spread cancers that often lead to disfigurement and loss of important functions such as speech and ingestion. To date, HNSCC has no adequate method for early detection and screening.

METHODS

Exhaled breath samples were collected from 87 volunteers; 62 well-defined breath samples from 22 HNSCC patients (larynx and pharynx), 21 patients with benign tumours (larynx and pharynx) and 19 healthy controls were analysed in a dual approach: (i) chemical analysis using gas chromatography/mass spectrometry (GC-MS) and (ii) breath-print analysis using an array of nanomaterial-based sensors, combined with a statistical algorithm.

RESULTS

Gas chromatography/mass spectrometry identified ethanol, 2-propenenitrile and undecane as potential markers for HNSCC and/or benign tumours of the head and neck. The sensor-array-based breath-prints could clearly distinguish HNSCC both from benign tumours and from healthy states. Within the HNSCC group, patients could be classified according to tumour site and stage.

CONCLUSIONS

We have demonstrated the feasibility of a breath test for a specific, clinically interesting application: distinguishing HNSCC from tumour-free or benign tumour states, as well as for staging and locating HNSCC. The sensor array used here could form the basis for the development of an urgently needed non-invasive, cost-effective, fast and reliable point-of-care diagnostic/screening tool for HNSCC.

Impact of hemodialysis on exhaled volatile organic compounds in end-stage renal disease: a pilot study

Aim: To demonstrate the feasibility of nanomaterial-based sensors for identifying patterns of exhaled volatile organic compound of end-stage renal disease (ESRD) and study the impact of hemodialysis (HD) on these patterns. Patients & methods: Exhaled breath samples were collected from a group of 37 volunteers (26 ESRD HD patients; 11 healthy controls); a third of the samples were randomly blinded for determining the sensitivity/specificity of the method. Discriminant function analysis was used to build a model for discriminating ESRD patients and healthy controls (classification accuracy for blind samples: 80%), based on the signals of the nanomaterial sensors. Results & conclusion: The breath pattern of the ESRD patients approached the healthy pattern during the HD treatment, without reaching it completely. Gas chromatography/mass spectrometry identified four volatile organic compounds as potential ESRD biomarkers. Although this pilot study has yielded encouraging results, additional large-scale clinical studies are required to develop a fast, noninvasive breath test for monitoring HD adequacy in real time.

Original submitted 23 September 2012; Revised submitted 26 February 2013

Volatile organic compounds and the potential for a lung cancer breath test

SUMMARY Detecting exhaled volatile organic compounds (VOCs) that are associated with lung cancer (LC) has realistic potential for becoming an integral part of population-based LC screening and monitoring in the near future. Here, we review the main three approaches for profiling VOCs in LC patients and their advantages and pitfalls: first, mass spectrometry techniques for the identification and/or quantification of a wide variety of separate breath VOCs; second, canines that are trained to sniff out LC; and third, cross-reactive chemical sensors in combination with statistical methods for identifying disease-specific patterns. We estimate that the latter would be most suitable for clinical practice. In the short run, breath testing could provide a critically needed adjunct method for detecting nodule malignancy with high specificity during low-dose computed tomography screening. In the long run, breath testing holds potential for entirely revolutionizing LC screening, diagnosis and management.

Geographical variation in the exhaled volatile organic compounds

Breath-gas analysis has demonstrated that concentration profiles of volatile organic compounds (VOCs) could be used for detecting a variety of diseases, among them gastric cancer (GC) and peptic ulcer disease (PUD). Here, we explore how geographical variation affects the disease-specific changes in the chemical composition of breath samples, as compared to control states (less severe gastric conditions). Alveolar exhaled breath samples from 260 patients were collected at two remotely different geographic locations (China and Latvia), following similar breath-collection protocols. Each cohort included 130 patients that were matched in terms of diagnosis (37 GC/32 PUD/61 controls), average age, gender ratio and smoking habits. Helicobacter Pylori infection, which is a major cause for GC and PUD, was found in part of the patients, as well as in part of the controls, at both locations. The breath samples were analyzed by gas chromatography/mass spectrometry, using the same equipment and protocol-of-experiment. We observed similar characteristic differences in the chemical composition of the breath samples between the study groups at the two locations, even though the exact composition of the breath samples differed. Both in China and Latvia, the GC patients and controls could be distinguished by differences in the average levels of 6-methyl-5-hepten-2-one; PUD patients were distinguished from controls by the levels of aromatic compounds and alcohols; GC and PUD patients could not be distinguished at either site. This pilot study indicates the limitations of chemical breath-gas analysis alone for identifying gastric diseases based on the concentration profiles of separate VOCs in international patient cohorts. We assume that these limitations would apply to other diseases as well. The presented data could potentially be useful for developing an alternative, universally applicable diagnostic method that relies on the detection of changes in the collective patterns of the disease-specific classes of exhaled VOCs.

A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions

Background:

Upper digestive endoscopy with biopsy and histopathological evaluation of the biopsy material is the standard method for diagnosing gastric cancer (GC). However, this procedure may not be widely available for screening in the developing world, whereas in developed countries endoscopy is frequently used without major clinical gain. There is a high demand for a simple and non-invasive test for selecting the individuals at increased risk that should undergo the endoscopic examination. Here, we studied the feasibility of a nanomaterial-based breath test for identifying GC among patients with gastric complaints.

Methods:

Alveolar exhaled breath samples from 130 patients with gastric complaints (37 GC/32 ulcers / 61 less severe conditions) that underwent endoscopy/biopsy were analyzed using nanomaterial-based sensors. Predictive models were built employing discriminant factor analysis (DFA) pattern recognition, and their stability against possible confounding factors (alcohol/tobacco consumption; Helicobacter pylori) was tested. Classification success was determined (i) using leave-one-out cross-validation and (ii) by randomly blinding 25% of the samples as a validation set. Complementary chemical analysis of the breath samples was performed using gas chromatography coupled with mass spectrometry.

Results:

Three DFA models were developed that achieved excellent discrimination between the subpopulations: (i) GC vs benign gastric conditions, among all the patients (89% sensitivity; 90% specificity); (ii) early stage GC (I and II) vs late stage (III and IV), among GC patients (89% sensitivity; 94% specificity); and (iii) ulcer vs less severe, among benign conditions (84% sensitivity; 87% specificity). The models were insensitive against the tested confounding factors. Chemical analysis found that five volatile organic compounds (2-propenenitrile, 2-butoxy-ethanol, furfural, 6-methyl-5-hepten-2-one and isoprene) were significantly elevated in patients with GC and/or peptic ulcer, as compared with less severe gastric conditions. The concentrations both in the room air and in the breath samples were in the single p.p.b._v range, except in the case of isoprene.

Conclusion:

The preliminary results of this pilot study could open a new and promising avenue to diagnose GC and distinguish it from other gastric diseases. It should be noted that the applied methods are complementary and the potential marker compounds identified by gas-chromatography/mass spectrometry are not necessarily responsible for the differences in the sensor responses. Although this pilot study does not allow drawing far-reaching conclusions, the encouraging preliminary results presented here have initiated a large multicentre clinical trial to confirm the observed patterns for GC and benign gastric conditions.

Volatile fingerprints of cancer specific genetic mutations

We report on a new concept for profiling genetic mutations of (lung) cancer cells, based on the detection of patterns of volatile organic compounds (VOCs) emitted from cell membranes, using an array of nanomaterial-based sensors. In this in-vitro pilot study we have derived a volatile fingerprint assay for representative genetic mutations in cancer cells that are known to be associated with targeted cancer therapy. Five VOCs were associated with the studied oncogenes, using complementary chemical analysis, and were discussed in terms of possible metabolic pathways. The reported approach could lead to the development of novel methods for guiding treatments, so that patients could benefit from safer, more timely and effective interventions that improve survival and quality of life while avoiding unnecessary invasive procedures. Studying clinical samples (tissue/blood/breath) will be required as next step in order to determine whether this cell-line study can be translated into a clinically useful tool.

FROM THE CLINICAL EDITOR

In this novel study, a new concept for profiling genetic mutations of (lung) cancer cells is described, based on the detection of patterns of volatile organic compounds emitted from cell membranes, using an array of nano-gold based sensors.

Detection of Alzheimer’s and Parkinson’s disease from exhaled breath using nanomaterial-based sensors

Aim: To study the feasibility of a novel method in nanomedicine that is based on breath testing for identifying Alzheimer’s disease (AD) and Parkinson’s disease (PD), as representative examples of neurodegenerative conditions. Patients & methods: Alveolar breath was collected from 57 volunteers (AD patients, PD patients and healthy controls) and analyzed using combinations of nanomaterial-based sensors (organically functionalized carbon nanotubes and gold nanoparticles). Discriminant factor analysis was applied to detect statistically significant differences between study groups and classification success was estimated using cross-validation. The pattern identification was supported by chemical analysis of the breath samples using gas chromatography combined with mass spectrometry. Results: The combinations of sensors could clearly distinguish AD from healthy states, PD from healthy states, and AD from PD states, with a classification accuracy of 85, 78 and 84%, respectively. Gas chromatography combined with mass spectrometry analysis showed statistically significant differences in the average abundance of several volatile organic compounds in the breath of AD, PD and healthy subjects, thus supporting the breath prints observed with the sensors. Conclusion: The breath prints that were identified with combinations of nanomaterial-based sensors have future potential as cost-effective, fast and reliable biomarkers for AD and PD.

Original submitted 29 January 2012; Revised submitted 8 May 2012; Published online 15 October 2012

A nanomaterial-based breath test for short-term follow-up after lung tumor resection

In this case study, we demonstrate the feasibility of nanomaterial-based sensors for identifying the breath-print of early-stage lung cancer (LC) and for short-term follow-up after LC-resection. Breath samples were collected from a small patient cohort prior to and after lung resection. Gas-chromatography/mass-spectrometry showed that five volatile organic compounds were significantly reduced after LC surgery. A nanomaterial-based sensor-array distinguished between pre-surgery and post-surgery LC states, as well as between pre-surgery LC and benign states. In contrast, the same sensor-array could neither distinguish between pre-surgery and post-surgery benign states, nor between LC and benign states after surgery. This indicates that the observed pattern is associated with the presence of malignant lung tumors. The proof-of-concept presented here has initiated a large-scale clinical study for post-surgery follow-up of LC patients.

FROM THE CLINICAL EDITOR

Monitoring for tumor recurrence remains very challenging due to post-surgical and radiation therapy induced changes in target organs, which often renders standard radiological identification of recurrent malignancies inaccurate. In this paper a novel nanotechnology-based sensor array is used for identification of volatile organic compounds in exhaled air that enable identification of benign vs. malignant states.

The scent fingerprint of hepatocarcinoma: in-vitro metastasis prediction with volatile organic compounds (VOCs)

BACKGROUND

Hepatocellular carcinoma (HCC) is a common and aggressive form of cancer. Due to a high rate of postoperative recurrence, the prognosis for HCC is poor. Subclinical metastasis is the major cause of tumor recurrence and patient mortality. Currently, there is no reliable prognostic method of invasion.

AIM

To investigate the feasibility of fingerprints of volatile organic compounds (VOCs) for the in-vitro prediction of metastasis.

METHODS

Headspace gases were collected from 36 cell cultures (HCC with high and low metastatic potential and normal cells) and analyzed using nanomaterial-based sensors. Predictive models were built by employing discriminant factor analysis pattern recognition, and the classification success was determined using leave-one-out cross-validation. The chemical composition of each headspace sample was studied using gas chromatography coupled with mass spectrometry (GC-MS).

RESULTS

Excellent discrimination was achieved using the nanomaterial-based sensors between (i) all HCC and normal controls; (ii) low metastatic HCC and normal controls; (iii) high metastatic HCC and normal controls; and (iv) high and low HCC. Several HCC-related VOCs that could be associated with biochemical cellular processes were identified through GC-MS analysis.

CONCLUSION

The presented results constitute a proof-of-concept for the in-vitro prediction of the metastatic potential of HCC from VOC fingerprints using nanotechnology. Further studies on a larger number of more diverse cell cultures are needed to evaluate the robustness of the VOC patterns. These findings could benefit the development of a fast and potentially inexpensive laboratory test for subclinical HCC metastasis.

Detection of asymptomatic nigrostriatal dopaminergic lesion in rats by exhaled air analysis using carbon nanotube sensors

The ante-mortem diagnosis of Parkinson's disease (PD) still relies on clinical symptoms. Biomarkers could in principle be used for the early detection of PD-related neuronal damage, but no validated, inexpensive, and simple biomarkers are available yet. Here we report on the breath-print of presymptomatic PD in rats, using a model with 50% lesion of dopaminergic neurons in substantia nigra. Exhaled breath was collected from 19 rats (10 lesioned and 9 sham operated) and analyzed using organically functionalized carbon nanotube sensors. Discriminant factor analysis detected statistically significant differences between the study groups and a classification accuracy of 90% was achieved using leave-one-out cross-validation. The sensors' breath-print was supported by determining statistically significant differences of several volatile organic compounds in the breath of the lesioned rats and the sham operated rats, using gas chromatography combined with mass spectrometry. The observed breath-print shows potential for cost-effective, fast, and reliable early PD detection.

Gold nanoparticle sensors for detecting chronic kidney disease and disease progression

AIM

To study the feasibility of a novel nanomedical method that utilizes breath testing for identifying chronic kidney disease (CKD) and disease progression.

MATERIALS & METHODS

Exhaled breath samples were collected from 62 volunteers. The breath samples were analyzed using sensors based on organically functionalized gold nanoparticles, combined with support vector machine analysis. Sensitivity and specificity with reference to CKD patient classification according to estimated glomerular filtration rate were determined using cross-validation. The chemical composition of the breath samples was studied using gas chromatography linked with mass spectrometry.

RESULTS

A combination of two to three gold nanoparticles sensors provided good distinction between early-stage CKD and healthy states (accuracy of 79%) and between stage 4 and 5 CKD states (accuracy of 85%). A single sensor provided a distinction between early and advanced CKD (accuracy of 76%). Several substances in the breath were identified and could be associated with CKD-related biochemical processes or with the accumulation of toxins through kidney function loss.

CONCLUSION

Breath testing using gold nanoparticle sensors holds future potential as a cost-effective, fast and reliable diagnostic test for early detection of CKD and monitoring of disease progression.

Abstract A36: Breath Biomarkers in the Post NLST-era for the Discrimination between Malignant from Benign Pulmonary Nodules

Background: The recent National Lung Cancer Screening Trial (NLST) has proven that screening for lung cancer by low dose CT (LDCT) scans reduces the related mortality rate by 20%. However, in this program, the false positive rate was extremely high: 96% out of the 24% positive CT findings were non-cancerous. A high rate of false positives leads to unnecessary invasive procedures, which are both costly and associated with significant morbidity and mortality. It is now widely anticipated that LDCT screening programs will be launched in many countries in the near future. This will lead to a dramatically increased detection of small solitary pulmonary nodules (SPNs) for invasive investigation. Consequently, additional non-invasive biomarker approach to distinguish between benign and cancerous conditions is necessary. For that purpose, we evaluated in this study the role of exhale breath analysis as a potential non-invasive biomarker to discriminate between benign and malignant SPNs in the post NLST-era.

Objectives: Developing a signature of volatile organic compounds (VOCs) in the exhaled breath that aids to distinguishes between benign and cancerous SPNs, and therefore also aids in 1) Early detection of lung cancer, 2) Improves the specificity of the NLST screening protocol in high risk cohorts, 3) helps prevent unnecessary invasive procedures, 4) results in the early treatment of lung cancer, and 5) Improves the costeffectiveness of the NLST screening program. Specifically, in this study we report the VOCs signature that discriminates benign from malignant SPNs, between NSCLC and SCLC and between early and advanced NSCLC.

Methods: Cross-sectional comparative survey from 74 patients with solitary pulmonary nodules (SPNs) attending the University of Colorado Cancer Center or Denver Veterans Affairs Medical Center; USA. Breath samples were taken and the VOC profiles for malignant and benign lung nodules were determined by gas-chromatography/mass-spectrometry (GCMS), and the corresponding collective VOCs patterns were identified by a nanomaterialbased array of sensors.

Results: Among the 74 high risk patients with SPNs on their LDCTs, 53 were malignant and 21 were benign. Age, smoking history, co-morbidity and medications were similar in both groups. Nodule sizes were 2.7±1.7 cm vs. 1.9±1.1 cm accordingly (NS). Within the malignant group, 47 were NSCLC and 6 were SCLC. Thirty had early disease (stage I-II/limited) and 23 had advanced disease (stage III-IV/extensive).

GC-MS analysis identified two VOCs in the exhaled breath of nodule positive patients that showed statistically significant differences in concentration for benign and malignant lung nodules (N=11, 28 respectively; Benzene, 1-methyl-4-(1-methylethyl) and 1-Octene). The sensor array could distinguish between the corresponding collective VOCs patterns with an accuracy of 89.4%. Among the malignant SPNs we could further distinguish between SCLC and NSCLC (accuracy 93.9%) and between early and advanced disease (89.7%).

Conclusions: An array of nanomaterial-based sensors could discriminate significantly between benign and malignant SPNs in a high-risk cohort with positive LDCTs based on lung cancer related VOCs profiles. Further, it was able to discriminate between NSCLC and SCLC, as well as between early and advanced disease. These results could promote the development of a non-invasive, fast and potentially cost-effective diagnostic tool for the management of nodule-positive patients that could help to eliminate risky invasive procedures in patients with benign nodules in the post NLST-era.

Molecular gating of silicon nanowire field-effect transistors with nonpolar analytes

Silicon nanowire field-effect transistors (Si NW FETs) have been used as powerful sensors for chemical and biological species. The detection of polar species has been attributed to variations in the electric field at the conduction channel due to molecular gating with polar molecules. However, the detection of nonpolar analytes with Si NW FETs has not been well understood to date. In this paper, we experimentally study the detection of nonpolar species and model the detection process based on changes in the carrier mobility, voltage threshold, off-current, off-voltage, and subthreshold swing of the Si NW FET. We attribute the detection of the nonpolar species to molecular gating, due to two indirect effects: (i) a change in the dielectric medium close to the Si NW surface and (ii) a change in the charged surface states at the functionality of the Si NW surface. The contribution of these two effects to the overall measured sensing signal is determined and discussed. The results provide a launching pad for real-world sensing applications, such as environmental monitoring, homeland security, food quality control, and medicine.

Classification of lung cancer histology by gold nanoparticle sensors

We propose a nanomedical device for the classification of lung cancer (LC) histology. The device profiles volatile organic compounds (VOCs) in the headspace of (subtypes of) LC cells, using gold nanoparticle (GNP) sensors that are suitable for detecting LC-specific patterns of VOC profiles, as determined by gas chromatography-mass spectrometry analysis. Analyzing the GNP sensing signals by support vector machine allowed significant discrimination between (i) LC and healthy cells; (ii) small cell LC and non-small cell LC; and between (iii) two subtypes of non-small cell LC: adenocarcinoma and squamous cell carcinoma. The discriminative power of the GNP sensors was then linked with the chemical nature and composition of the headspace VOCs of each LC state. These proof-of-concept findings could totally revolutionize LC screening and diagnosis, and might eventually allow early and differential diagnosis of LC subtypes with detectable or unreachable lung nodules.

FROM THE CLINICAL EDITOR

In this study, a nanomedical device that profiles volatile organic compounds (VOCs) in lung cancer cells is investigated, using a matrix of gold nanoparticle (GNP) sensors that are suitable for detecting lung cancer (LC) specific patterns of VOC profiles. This device might eventually allow early differential diagnosis of LC subtypes including unreachable lung nodules.

Diagnosis of head-and-neck cancer from exhaled breath

Background:

Head-and-neck cancer (HNC) is the eighth most common malignancy worldwide. It is often diagnosed late due to a lack of screening methods and overall cure is achieved in <50% of patients. Head-and-neck cancer sufferers often develop a second primary tumour that can affect the entire aero-digestive tract, mostly HNC or lung cancer (LC), making lifelong follow-up necessary.

Methods:

Alveolar breath was collected from 87 volunteers (HNC and LC patients and healthy controls) in a cross-sectional clinical trial. The discriminative power of a tailor-made Nanoscale Artificial Nose (NA-NOSE) based on an array of five gold nanoparticle sensors was tested, using 62 breath samples. The NA-NOSE signals were analysed to detect statistically significant differences between the sub-populations using (i) principal component analysis with ANOVA and Student's t-test and (ii) support vector machines and cross-validation. The identification of NA-NOSE patterns was supported by comparative analysis of the chemical composition of the breath through gas chromatography in conjunction with mass spectrometry (GC–MS), using 40 breath samples.

Results:

The NA-NOSE could clearly distinguish between (i) HNC patients and healthy controls, (ii) LC patients and healthy controls, and (iii) HNC and LC patients. The GC–MS analysis showed statistically significant differences in the chemical composition of the breath of the three groups.

Conclusion:

The presented results could lead to the development of a cost-effective, fast, and reliable method for the differential diagnosis of HNC that is based on breath testing with an NA-NOSE, with a future potential as screening tool.

Classification of breast cancer precursors through exhaled breath

Certain benign breast diseases are considered to be precursors of invasive breast cancer. Currently available techniques for diagnosing benign breast conditions lack accuracy. The purpose of this study was to deliver a proof-of-concept for a novel method that is based on breath testing to identify breast cancer precursors. Within this context, the authors explored the possibility of using exhaled alveolar breath to identify and distinguish between benign breast conditions, malignant lesions, and healthy states, using a small-scale, case-controlled, cross-sectional clinical trial. Breath samples were collected from 36 volunteers and were analyzed using a tailor-made nanoscale artificial NOSE (NA-NOSE). The NA-NOSE signals were analyzed using two independent methods: (i) principal component analysis, ANOVA and Student's t-test and (ii) support vector machine analysis to detect statistically significant differences between the sub-populations. The NA-NOSE could distinguish between all studied test populations. Breath testing with a NA-NOSE holds future potential as a cost-effective, fast, and reliable diagnostic test for breast cancer risk factors and precursors, with possible future potential as screening method.

Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors

Background:

Tumour growth is accompanied by gene and/or protein changes that may lead to peroxidation of the cell membrane species and, hence, to the emission of volatile organic compounds (VOCs). In this study, we investigated the ability of a nanosensor array to discriminate between breath VOCs that characterise healthy states and the most widespread cancer states in the developed world: lung, breast, colorectal, and prostate cancers.

Methods:

Exhaled alveolar breath was collected from 177 volunteers aged 20–75 years (patients with lung, colon, breast, and prostate cancers and healthy controls). Breath from cancerous subjects was collected before any treatment. The healthy population was healthy according to subjective patient's data. The breath of volunteers was examined by a tailor-made array of cross-reactive nanosensors based on organically functionalised gold nanoparticles and gas chromatography linked to the mass spectrometry technique (GC-MS).

Results:

The results showed that the nanosensor array could differentiate between ‘healthy’ and ‘cancerous’ breath, and, furthermore, between the breath of patients having different cancer types. Moreover, the nanosensor array could distinguish between the breath patterns of different cancers in the same statistical analysis, irrespective of age, gender, lifestyle, and other confounding factors. The GC-MS results showed that each cancer could have a unique pattern of VOCs, when compared with healthy states, but not when compared with other cancer types.

Conclusions:

The reported results could lead to the development of an inexpensive, easy-to-use, portable, non-invasive tool that overcomes many of the deficiencies associated with the currently available diagnostic methods for cancer.

Diagnosing lung cancer in exhaled breath using gold nanoparticles

Conventional diagnostic methods for lung cancer are unsuitable for widespread screening because they are expensive and occasionally miss tumours. Gas chromatography/mass spectrometry studies have shown that several volatile organic compounds, which normally appear at levels of 1-20 ppb in healthy human breath, are elevated to levels between 10 and 100 ppb in lung cancer patients. Here we show that an array of sensors based on gold nanoparticles can rapidly distinguish the breath of lung cancer patients from the breath of healthy individuals in an atmosphere of high humidity. In combination with solid-phase microextraction, gas chromatography/mass spectrometry was used to identify 42 volatile organic compounds that represent lung cancer biomarkers. Four of these were used to train and optimize the sensors, demonstrating good agreement between patient and simulated breath samples. Our results show that sensors based on gold nanoparticles could form the basis of an inexpensive and non-invasive diagnostic tool for lung cancer.

Spongelike structures of hexa-peri-hexabenzocoronene derivatives enhance the sensitivity of chemiresistive carbon nanotubes to nonpolar volatile organic compounds of cancer

Cancer is a leading health hazard, and lung cancer is its most common form. Breath testing is a fast, noninvasive diagnostic method which links specific volatile organic compounds (VOCs) in exhaled breath to medical conditions. Arrays of sensors based on carbon nanotubes (CNTs) could in principle detect cancer by differentiating between the VOCs found in the breath of healthy and sick persons, but the notoriously low sensitivity of CNT sensors to nonpolar VOCs limits their accuracy. In this study, we have achieved a marked improvement of the sensitivity and selectivity of random networks (RNs) of CNT chemiresistors to nonpolar VOCs by functionalizing them with self-assembled, spongelike structures of discotic hexa-peri-hexabenzocoronene (HBC) derivatives. We observed swelling of the organic film by monitoring the changes of organic film thickness during exposure and propose that the expansion of the spongelike organic overlayer creates scattering centers in the underlying RN-CNTs by physically distancing the CNTs at their intersections. The results presented here could lead to the development of robust sensors for nonpolar VOCs of cancer breath, which have hitherto been difficult to trace.

Detection of nonpolar molecules by means of carrier scattering in random networks of carbon nanotubes: toward diagnosis of diseases via breath samples

Field effect transistors (FETs) based on random networks (RNs) of single-wall carbon nanotubes (CNTs) have several technological advantages. However, the low sensitivity (or no sensitivity) of RN-CNT sensors to nonpolar molecules is a problematic, negative feature that limits their applications in the detection of a wide variety of diseases via breath samples. In this paper, we show experimental evidence for the detection of both individual nonpolar molecules and patterns of nonpolar molecules, even in the presence of polar molecules in the same environment. We do so by preparing RN-CNT FETs and functionalizing them with organic films that exhibit distinctive electrical and physical (or mechanical) characteristics. Exposing the functionalized RN-CNTs to representative nonpolar breath biomarkers, and, for comparison, to polar molecules in the gas phase, and monitoring the changes in conductance, work function, and organic film thickness show sensitivity toward nonpolar molecules. We explain this observation by carrier scattering as a result of swelling of the organic film upon exposure to (nonpolar) chemical agents. Hence, the sensitivity towards nonpolar molecules can be tailored, even in the presence of polar molecules, by controlling the scattering of charge carrier through deliberate functionalization of CNTs. As examples for the technological impact of our findings, we describe ways to detect lung cancer and kidney disease using specially designed RN-CNT sensor arrays.

Annealing-induced interfacial toughening using a molecular nanolayer

Self-assembled molecular nanolayers (MNLs) composed of short organic chains and terminated with desired functional groups are attractive for modifying surface properties for a variety of applications. For example, organosilane MNLs are used as lubricants, in nanolithography, for corrosion protection and in the crystallization of biominerals. Recent work has explored uses of MNLs at thin-film interfaces, both as active components in molecular devices, and as passive layers, inhibiting interfacial diffusion, promoting adhesion and toughening brittle nanoporous structures. The relatively low stability of MNLs on surfaces at temperatures above 350-400 degrees C (refs 12, 13), as a result of desorption or degradation, limits the use of surface MNLs in high-temperature applications. Here we harness MNLs at thin-film interfaces at temperatures higher than the MNL desorption temperature to fortify copper-dielectric interfaces relevant to wiring in micro- and nano-electronic devices. Annealing Cu/MNL/SiO2 structures at 400-700 degrees C results in interfaces that are five times tougher than pristine Cu/SiO2 structures, yielding values exceeding approximately 20 J m(-2). Previously, similarly high toughness values have only been obtained using micrometre-thick interfacial layers. Electron spectroscopy of fracture surfaces and density functional theory modelling of molecular stretching and fracture show that toughening arises from thermally activated interfacial siloxane bridging that enables the MNL to be strongly linked to both the adjacent layers at the interface, and suppresses MNL desorption. We anticipate that our findings will open up opportunities for molecular-level tailoring of a variety of interfacial properties, at processing temperatures higher than previously envisaged, for applications where microlayers are not a viable option-such as in nanodevices or in thermally resistant molecular-inorganic hybrid devices.