Optimized Feature Extraction for Temperature-Modulated Gas Sensors

One of the most serious limitations to the practical utilization of solid-state gas sensors is the drift of their signal. Even if drift is rooted in the chemical and physical processes occurring in the sensor, improved signal processing is generally considered as a methodology to increase sensors stability. Several studies evidenced the augmented stability of time variable signals elicited by the modulation of either the gas concentration or the operating temperature. Furthermore, when time-variable signals are used, the extraction of features can be accomplished in shorter time with respect to the time necessary to calculate the usual features defined in steady-state conditions. In this paper, we discuss the stability properties of distinct dynamic features using an array of metal oxide semiconductors gas sensors whose working temperature is modulated with optimized multisinusoidal signals. Experiments were aimed at measuring the dispersion of sensors features in repeated sequences of a limited number of experimental conditions. Results evidenced that the features extracted during the temperature modulation reduce the multidimensional data dispersion among repeated measurements. In particular, the Energy Signal Vector provided an almost constant classification rate along the time with respect to the temperature modulation

Understanding odor information segregation in the olfactory bulb by means of mitral and tufted cells

Odor identification is one of the main tasks of the olfactory system. It is performed almost independently from the concentration of the odor providing a robust recognition. This capacity to ignore concentration information does not preclude the olfactory system from estimating concentration itself. Significant experimental evidence has indicated that the olfactory system is able to infer simultaneously odor identity and intensity. However, it is still unclear at what level or levels of the olfactory pathway this segregation of information occurs. In this work, we study whether this odor information segregation is performed at the input stage of the olfactory bulb: the glomerular layer. To this end, we built a detailed neural model of the glomerular layer based on its known anatomical connections and conducted two simulated odor experiments. In the first experiment, the model was exposed to an odor stimulus dataset composed of six different odorants, each one dosed at six different concentrations. In the second experiment, we conducted an odor morphing experiment where a sequence of binary mixtures going from one odor to another through intermediate mixtures was presented to the model. The results of the experiments were visualized using principal components analysis and analyzed with hierarchical clustering to unveil the structure of the high-dimensional output space. Additionally, Fisher's discriminant ratio and Pearson's correlation coefficient were used to quantify odor identity and odor concentration information respectively. Our results showed that the architecture of the glomerular layer was able to mediate the segregation of odor information obtaining output spiking sequences of the principal neurons, namely the mitral and external tufted cells, strongly correlated with odor identity and concentration, respectively. An important conclusion is also that the morphological difference between the principal neurons is not key to achieve odor information segregation

MEMRISTOR BASED SENSOR

A sensor comprises a plurality of sensor elements arranged in an array . Each sensor element is memristive and has an electrical resistance characteristic related to exposure to a species to be sensed . The sensor elements are arranged to be connectable such that at least one sensor element is connected in parallel with at least one other sensor element . By using appropriate connections , the array of sensor elements can be read

Porphyrin-Based Nanostructures for Sensing Applications

The construction of nanosized supramolecular hosts via self-assembly of molecular components is a fascinating field of research. Such intriguing class of architectures, beside their intrinsic intellectual stimuli, is of importance in many fields of chemistry and technology, such as material chemistry, catalysis, and sensor applications. Within this wide scenario, tailored solid films of porphyrin derivatives are structures of great potential for, among others, chemical sensor applications. The formation ofsupramoleculesrelays on noncovalent interactions (electrostatic, hydrogen bond, , or coordinative interactions) driven by the chemical information stored on the assembling molecules, such as shape and functional groups. This allows, for example, the formation of large well-defined porphyrin aggregates in solution that can be spontaneously transferred onto a solid surface, so achieving a solid system with tailored features. These films have been used, covering the bridge between nanostructures and microsystems, for the construction of solid-state sensors for volatiles and metal ion recognition and detection. Moreover, the variation of peripheral substituents of porphyrins, such as, for example, chiral appended functionalities, can result in the formation of porphyrin aggregates featuring high supramolecular chirality. This would allow the achievement of porphyrin layers characterised by different chiroptical and molecular recognition properties

The skeleton counts! A study of the porphyrinoid structure’s influence on sensing properties

A series of porphyrinoids has been tested as sensing layers for the development of nanogravimetric chemical sensors using quartz crystal microbalances (QMB) as transducers. The macrocycles have been studied as Ni complexes, Cu in the case of corrole, to elucidate the influence of the molecular skeleton on the sensing properties of the related sensors. For the first time, subphthalocyanines have been tested in sensor applications. The study has been carried out by testing different volatile organic compounds chosen as model analytes. The results obtained demonstrate that the exploitation of different porphyrinoids offers useful insights for the development of cross-sensitive sensor arrays and can open novel perspectives for their applications in the sensor field

An Investigation on the Role of Spike Latency in an Artificial Olfactory System

Experimental studies have shown that the reactions to external stimuli may appear only few hundreds of milliseconds after the physical interaction of the stimulus with the proper receptor. This behavior suggests that neurons transmit the largest meaningful part of their signal in the first spikes, and than that the spike latency is a good descriptor of the information content in biological neural networks. In this paper this property has been investigated in an artificial sensorial system where a single layer of spiking neurons is trained with the data generated by an artificial olfactory platform based on a large array of chemical sensors. The capability to discriminate between distinct chemicals and mixtures of them was studied with spiking neural networks endowed with and without lateral inhibitions and considering as output feature of the network both the spikes latency and the average firing rate. Results show that the average firing rate of the output spikes sequences shows the best separation among the experienced vapors, however the latency code is able in a shorter time to correctly discriminate all the tested volatile compounds. This behavior is qualitatively similar to those recently found in natural olfaction, and noteworthy it provides practical suggestions to tail the measurement conditions of artificial olfactory systems defining for each specific case a proper measurement time

Recent advances in chemical sensors using porphyrin-carbon nanostructure hybrid materials

Porphyrins and carbon nanomaterials are among the most widely investigated and applied compounds, both offering multiple options to modulate their optical, electronic and magnetic properties by easy and well-established synthetic manipulations. Individually, they play a leading role in the development of efficient and robust chemical sensors, where they detect a plethora of analytes of practical relevance. But even more interesting, the merging of the peculiar features of these single components into hybrid nanostructures results in novel materials with amplified sensing properties exploitable in different application fields, covering the areas of health, food, environment and so on. In this contribution, we focused on recent examples reported in literature illustrating the integration of different carbon materials (i.e., graphene, nanotubes and carbon dots) and (metallo)porphyrins in heterostructures exploited in chemical sensors operating in liquid as well as gaseous phase, with particular focus on research performed in the last four years

Sensor array and gas chromatographic detection of the blood serum volatolomic signature of COVID-19

Volatolomics is gaining consideration as a viable approach to diagnose several diseases, and it also shows promising results to discriminate COVID-19 patients via breath analysis. This paper extends the study of the relationship between volatile compounds (VOCs) and COVID-19 to blood serum. Blood samples were collected from subjects recruited at the emergency department of a large public hospital. The VOCs were analyzed with a gas chromatography mass spectrometer (GC/MS). GC/MS data show that in more than 100 different VOCs, the pattern of abundances of 17 compounds identifies COVID-19 from non-COVID with an accuracy of 89% (sensitivity 94% and specificity 83%). GC/MS analysis was complemented by an array of gas sensors whose data achieved an accuracy of 89% (sensitivity 94% and specificity 80%)

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer's disease with magnetoencephalography

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer's disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships