Radu Ionescu

Multicomponent gas mixture analysis using a single tin oxide sensor and dynamic pattern recognition

A new method, which is based on the discrete wavelet transform, is presented for extracting important features from the response transients of a micromachined, tin oxide-based gas sensor. It is shown that two components in a mixture can be simultaneously and accurately quantified by processing the response dynamics of a single sensor operated in a temperature-modulated mode. The discrete wavelet transform outperforms the fast Fourier transform (classical approach) because it is more appropriate for the non- linear frequency-time problem encountered here.

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 Alzheimers disease (AD) and Parkinsons 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.

Nanoarray of Polycyclic Aromatic Hydrocarbons and Carbon Nanotubes for Accurate and Predictive Detection in Real-World Environmental Humidity

In the present work, we introduce a cross-reactive array of synthetically designed polycyclic aromatic hydrocarbons (PAH) and single-walled carbon nanotube (SWCNT) bilayers and demonstrate the huge potential of the array in discriminating between polar and nonpolar volatile organic compounds (VOCs), as well as between the different VOCs from each subgroup. Using appropriate combinations of PAH/SWCNT sensors, we demonstrate that high sensitivity and accuracy values can be obtained for discriminating polar and nonpolar VOCs in samples with variable humidity levels (5-80% RH). The same array of sensors exhibited self-learning capabilities that facilitated exchanging information about environmental properties under observation. The results presented here could lead to the development of a cost-effective, lightweight, low-power, and non-invasive tool for a widespread detection of VOCs in real-world environmental, security, food, health, and other applications.

Detection of Multiple Sclerosis from Exhaled Breath Using Bilayers of Polycyclic Aromatic Hydrocarbons and Single-Wall Carbon Nanotubes

A cross-reactive array of polycyclic aromatic hydrocarbons and single wall carbon nanotube bilayers was designed for the detection of volatile organic compounds (tentatively, hexanal and 5-methyl-undecane) that identify the presence of disease in the exhaled breath of patients with multiple sclerosis. The sensors showed excellent discrimination between hexanal, 5-methyl-undecane, and other confounding volatile organic compounds. Results obtained from a clinical study consisting of 51 volunteers showed that the sensors could discriminate between multiple sclerosis and healthy states from exhaled breath samples with 85.3% sensitivity, 70.6% specificity, and 80.4% accuracy. These results open new frontiers in the development of a fast, noninvasive, and inexpensive medical diagnostic tool for the detection and identification of multiple sclerosis. The results could serve also as a launching pad for the discrimination between different subphases or stages of multiple sclerosis as well as for the identification of multiple sclerosis patients who would respond well to immunotherapy.

Wavelet transform and fuzzy ARTMAP-based pattern recognition for fast gas identification using a micro-hotplate gas sensor

Abstract It is shown that a single thermally-modulated tin oxide-based resistive microsensor can discriminate between two different pollutant gases (CO and NO2) and their mixtures. The method employs a novel feature-extraction and pattern classification method, which is based on a 1-D discrete wavelet transform and a Fuzzy adaptive resonant theory map (ARTMAP) neural network. The wavelet technique is more effective than FFT in terms of data compression and is highly tolerant to the presence of additive noise and drift in the sensor responses. Furthermore, Fuzzy ARTMAP networks lead to a 100% success rate in gas recognition in just two training epochs, which is significantly lower than the number of epochs required to train the back-propagation network.

Carbon nanotubes randomly decorated with gold clusters: from nano2hybrid atomic structures to gas sensing prototypes.

Carbon nanotube surfaces, activated and randomly decorated with metal nanoclusters, have been studied in uniquely combined theoretical and experimental approaches as prototypes for molecular recognition. The key concept is to shape metallic clusters that donate or accept a fractional charge upon adsorption of a target molecule, and modify the electron transport in the nanotube. The present work focuses on a simple system, carbon nanotubes with gold clusters. The nature of the gold-nanotube interaction is studied using first-principles techniques. The numerical simulations predict the binding and diffusion energies of gold atoms at the tube surface, including realistic atomic models for defects potentially present at the nanotube surface. The atomic structure of the gold nanoclusters and their effect on the intrinsic electronic quantum transport properties of the nanotube are also predicted. Experimentally, multi-wall CNTs are decorated with gold clusters using (1) vacuum evaporation, after activation with an RF oxygen plasma and (2) colloid solution injected into an RF atmospheric plasma; the hybrid systems are accurately characterized using XPS and TEM techniques. The response of gas sensors based on these nano(2)hybrids is quantified for the detection of toxic species like NO(2), CO, C(2)H(5)OH and C(2)H(4).

Wavelet transform-based fast feature extraction from temperature modulated semiconductor gas sensors

We demonstrate that a single, thermally modulated tungsten oxide-based resistive sensor can discriminate between different vapours. The method uses a novel feature extraction and pattern classification method, which is based on the discrete wavelet transform (DWT). It was found that DWT outperformed fast Fourier transform (FFT) in the extraction of important features from the sensor response and, allowed for straightforward gas recognition in feature space.

Volatile fingerprints of cancer specific genetic mutations

UNLABELLED 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.

Assessment of ovarian cancer conditions from exhaled breath.

We present a pilot study that aims to examine the possibility to easily and noninvasively detect and discriminate females with ovarian cancer (OC) from females that have no tumor(s) and from females that have benign genital tract neoplasia, using exhaled breath samples. The study is based on clinical samples and data from 182 females, as follows: 48 females with OC, 48 tumor‐free controls and 86 females with benign gynecological neoplasia. Analysis of the breath samples with gas chromatography linked with mass spectrometry shows that decanal, nonanal, styrene, 2‐butanone and hexadecane could serve as potential volatile markers for OC. Analysis of the same samples with tailor‐made nanoarrays shows good discrimination between females with OC and females that have either no tumor or benign genital tract neoplasia (71% for accuracy, sensitivity and specificity). Conversely, the nanoarray output shows excellent discrimination between the OC patients and the tumor‐free controls (79% sensitivity, 100% specificity and 89% accuracy). These results suggest that the nanoarray approach might be useful to avoid unnecessary complicated or expensive tests for tumor‐free females in case of a negative result. In the case of positive result, the test will indicate with high probability the presence of OC.

Response model for thermally modulated tin oxide-based microhotplate gas sensors

Abstract To gain some insight into the conductance response of temperature-modulated metal oxide gas sensors, we introduce a model for the physicochemical processes involved in the sensing operation. For this, we consider the interactions that take place at the sensor surface in the presence of reducing and oxidising species. Then we validate the model against experimental responses in the presence of ppm levels of CO and NO 2 in air. A sinusoidal voltage drives a resistive platinum heater and modulates the temperature of a micromachined tin oxide gas sensor; the resulting variation in conductance is analysed. Excellent agreement between theoretical and experimental responses is achieved. The model developed was used to compute the conductance response of a temperature-modulated sensor in the presence of different concentrations of CO and NO 2 . Features from the simulated response transients were extracted using the discrete wavelet transform and classified using a principal component analysis. A linear separation between CO and NO 2 was obtained, which is in good agreement with our previous experimental results.