Gabriele Magna

The influence of film morphology and illumination conditions on the sensitivity of porphyrins-coated ZnO nanorods.

ZnO and porphyrins have complementary properties that make their combination attractive for diverse applications such as photovoltaic and chemical sensing. Among the other features, the organic layer morphology is supposed to influence both the chemical sensitivity and the charge transfer processes. In this paper, we studied the influence of the film morphology on the sensing properties by comparing porphyrins coated ZnO nanorods obtained with two different methods. In the first approach, each porphyrin unit is grafted onto preformed ZnO nanorods by a carboxylic group as linker. The second method is a one-pot procedure, where ZnO nanorods growth occurs in the presence of the water soluble tetrakis-(4-sulfonatophenyl)porphyrin. In both cases the macrocycles share the same Zn-tetraphenylporphyrin core structure, but decorated with different peripheral groups, necessary to comply with the material growth conditions. The adsorption of volatile organic molecules has been monitored measuring the contact potential difference between the sensitive surface and a gold electrode, by means of a Kelvin probe setup. Sensitive signals have been measured both in dark and under visible light. The results show that material preparation affects both the sensitivities to gases and light. A chemometric analysis of four sensors (first and second growth method, measured in dark and in light) shows two main evidences: (a) the interaction between volatile compounds and the sensing layer is largely dominated by non-specific dispersion interaction and (b) the signal of the four sensors becomes rather uncorrelated when the contribution of the dispersion interaction is removed. These results indicate that the differences due to film morphology are enough to differentiate the sensor behaviour, even when the same porphyrin nucleus is used as sensing element. This feature provides an additional degree of freedom for the development of gas sensor arrays.

Identification of mammography anomalies for breast cancer detection by an ensemble of classification models based on artificial immune system

The interpretation of diagnostic images is often conditioned by the specific properties of the instrument that generated the image. This makes particularly complicated to develop universal recognition algorithms that can facilitate the diagnosis in case of massive population screenings. Mammography is a typical example where such an algorithm is required. Although the technological advances in medical imaging increases the accuracy of interpretation of images, the improved resolution may not facilitate the identification of breast cancer at a very early stage due to the many confounding factors related, for instance, to differences in instrument settings or breast positioning by the operator. Being impossible an exact standardization, the problem of reducing the effects of different instruments and operators can be faced with a proper algorithm that can extract from each image the relevant information in an unsupervised manner, thus limiting the influence of instrumental and positioning issues. For this scope, in this paper we investigated the properties of a classifier based on an ensemble of Adaptive Artificial Immune Networks (A2INET) applied to original mammography image indicators aimed at diagnosing bilateral asymmetry that is known to be correlated with increased breast cancer risk. Classification models were trained using a set of descriptors measuring the degree of similarity of paired regions of the left and right breasts. Noteworthy, the ensemble of A2INET models achieved very high classification rates even when training and testing were made on two completely independent and heterogeneous datasets. The obtained results are promising (maximum accuracy level of 0.90, sensitivity of 0.93 and specificity of 0.87) and they prefigure to apply automatic diagnostic tools in clinical practice exploiting a network of different instrument databases.

The influence of gas adsorption on photovoltage in porphyrin coated ZnO nanorods

Recent studies evidence the interplay between the photosensitivity and the gas sensitivity in porphyrin-functionalized ZnO nanorods. These effects are critically governed by the transport phenomena of electronic charge across the interfaces of organic and inorganic structures. Then the surface potential is a useful quantity to investigate the mutual relationship between these two phenomena. For this scope, Kelvin probe measurements of porphyrin-ZnO structures were performed in the dark, under visible light and exposed to organic vapors. Results show a synergic effect of gas sensitivity and photosensitivity for free base porphyrins, while in the case of metalloporphyrins, the coordinated metal ion alters the cooperation between the two effects, as observed in the Zn complex, where the exposure to light increases the adsorption of gases, but the photovoltage is reduced during the exposure to the gas.

Combining porphyrins and pH indicators for analyte detection

AbstractHigh sensitivity and cross-selectivity are mandatory properties for sensor arrays. Although metalloporphyrins and pH indicators are among the most common and appropriate choices for the preparation of optical sensor arrays, the sensitivity spectrum of these dyes is limited to those analytes able to induce an optical response. To extend the receptive field of optical sensors, we explore the design of composite materials, where the molecular interaction among the subunits enriches their sensing working mechanisms. We demonstrate that blends of single metalloporphyrins and pH indicators, tested with a transduction apparatus based on ubiquitous and easily available hardware, can be endowed with sensing properties wider than those of single constituents, enabling the recognition of a broad range of volatiles. Graphical abstractBlends of single metalloporphyrins and pH indicators, tested with a transduction apparatus based on ubiquitous and easily available hardware, can be endowed with sensing properties wider than those of single constituents, enabling the recognition of a broad range of volatiles.

Stable Odor Recognition by a neuro-adaptive Electronic Nose

Sensitivity, selectivity and stability are decisive properties of sensors. In chemical gas sensors odor recognition can be severely compromised by poor signal stability, particularly in real life applications where the sensors are exposed to unpredictable sequences of odors under changing external conditions. Although olfactory receptor neurons in the nose face similar stimulus sequences under likewise changing conditions, odor recognition is very stable and odorants can be reliably identified independently from past odor perception. We postulate that appropriate pre-processing of the output signals of chemical sensors substantially contributes to the stability of odor recognition, in spite of marked sensor instabilities. To investigate this hypothesis, we use an adaptive, unsupervised neural network inspired by the glomerular input circuitry of the olfactory bulb. Essentially the model reduces the effect of the sensors’ instabilities by utilizing them via an adaptive multicompartment feed-forward inhibition. We collected and analyzed responses of a 4 × 4 gas sensor array to a number of volatile compounds applied over a period of 18 months, whereby every sensor was sampled episodically. The network conferred excellent stability to the compounds’ identification and was clearly superior over standard classifiers, even when one of the sensors exhibited random fluctuations or stopped working at all.

Conductive Photo-Activated Porphyrin-ZnO Nanostructured Gas Sensor Array

Chemoresistors working at room temperature are attractive for low-consumption integrated sensors. Previous studies show that this feature can be obtained with photoconductive porphyrins-coated ZnO nanostructures. Furthermore, variations of the porphyrin molecular structure alter both the chemical sensitivity and the photoconductivity, and can be used to define the sensor characteristics. Based on these assumptions, we investigated the properties of an array of four sensors made of a layer of ZnO nanoparticles coated with porphyrins with the same molecular framework but different metal atoms. The array was tested with five volatile organic compounds (VOCs), each measured at different concentrations. Results confirm that the features of individual porphyrins influence the sensor behavior, and the differences among sensors are enough to enable the discrimination of volatile compounds disregarding their concentration.

Porphyrins for olfaction mimic: The Rome Tor Vergata approach

The impressive chemistry shown by porphyrins in natural systems is particularly attractive for exploitation in chemical sensors. In these devices the sensing mechanisms can mimic most of the porphyrin biological reactivity, such as reversible binding, activation of small molecules, redox activity, and photoactivated processes. The simultaneous presence of multiple binding mechanisms allows porphyrins to interact with a large variety of analytes. This feature reduces the selectivity, but prompts the development of sensor arrays, where the cross-selectivity of more sensors is used to classify and identify samples characterized by a complex composition. Since 1995 the Sensors Group of the University of Rome Tor Vergata has exploited these features to prepare sensor arrays based on different transducers and aimed at several applications. These kinds of devices have been reported as electronic noses (gaseous phase analytes) and electronic tongues (liquid phase analytes) to underline that their working mechanis...

Adaptive classification model based on artificial immune system for breast cancer detection

Early stage asymmetric signs in breast that can be captured by the screening-digital mammography can be used for a precocious diagnosis of breast cancer. Conventional mammography screening fails to detect subtle anomalies, so computer-aided methods are studied in order to improve the accuracy of image analysis. To classify the images into asymmetric and normal cases, in this paper we investigated the performance of an Adaptive Artificial Immune System (A2INET) classifier. To test the efficiency of the algorithm, two public datasets have been considered: 32 pairs of mammographic images including MLO projection retrieved from Digital Database for Screening mammographic (DDSM) and 30 ones from Mammographic Image Analysis Society (mini-MIAS) databases. Results show that A2INET yields best results with respect to the other more conventional classifiers.

4.1.1 Kelvin probe study of the gas sensing properties of porphyrins-coated ZnO nanorods

Hybrid materials formed by layers of porphyrins onto ZnO surface show an interesting combination of photonic and gas sensing properties. In this paper, we investigate, with the Kelvin probe technique, the changes of the surface potential of porphyrins coated ZnO nanowires induced by concurrent adsorption of gas and visible light illumination.

Detection of breath alcohol concentration using a gas sensor array

In this work a system based on an array of five quartz microbalances (QMBs) coated with different metalloporphyrins has been proposed for measuring the Breath Alcohol Concentration (BrAC). Four of these sensors were functionalized with widely selective coatings and one with a metalloporphyrins modified in order to enhanced the sensitivity to alcohols. The results obtained for the BrAC estimation show that the system performances, in terms of accuracy, are absolutely adequate for the legal scopes of breath alcohol measurement.