Alexander Vergara

Multifrequency interrogation of nanostructured gas sensor arrays : a tool for analyzing response kinetics

This paper presents a unique perspective on enhancing the physicochemical mechanisms of two distinct highly sensitive nanostructured metal oxide micro hot plate gas sensors by utilizing an innovative multifrequency interrogation method. The two types of sensors evaluated here employ an identical silicon transducer geometry but with a different morphological structure of the sensitive film. While the first sensing film consists of self-ordered tungsten oxide nanodots, limiting the response kinetics of the sensor-chemical species pair only to the reaction phenomena occurring at the sensitive film surface, the second modality is a three-dimensional array of tungsten oxide nanotubes, which in turn involves both the diffusion and adsorption of the gas during its reaction kinetics with the sensitive film itself. By utilizing the proposed multifrequency interrogation methodology, we demonstrate that the optimal temperature modulation frequencies employed for the nanotubes-based sensors to selectively detect hydrogen, carbon monoxide, ethanol, and dimethyl methyl phosphonate (DMMP) are significantly higher than those utilized for the nanodot-based sensors. This finding helps understand better the amelioration in selectivity that temperature modulation of metal oxides brings about, and, most importantly, it sets the grounds for the nanoengineering of gas-sensitive films to better exploit their practical usage.

Optimized temperature modulation of micro-hotplate gas sensors through pseudorandom binary sequences

In recent years, modulating the working temperature of metal-oxide gas sensors has been one of the most widely used methods to enhance sensor selectivity. When the working temperature of a gas sensor is modulated, the kinetics of the gas-sensor interaction are altered, and this leads to characteristic response patterns. Many works have shown that it is possible to identify and determine the concentration of gases in simple mixtures, even using a single temperature-modulated metal-oxide gas sensor. However, the selection of the frequencies used to modulate temperature remains an empirical process. In this paper, we introduce a method, borrowed from the field-of-system identification, to systematically determine the optimal set of modulating frequencies to solve a given gas-analysis application. The method consists of using maximum-length pseudorandom binary sequences to modulate the working temperature of metal-oxide gas sensors. Since these signals have a flat power spectrum (i.e., like white noise) in a wide frequency range, an estimate of the impulse response of each gas-sensor pair can be computed by the cross correlation of the excitatory and response sequences. Studying the impulse response estimates, the set of modulating frequencies that are useful to discriminate between different gases and to estimate gas concentration, is obtained in a systematic way. The method is demonstrated with tungsten oxide micro-hotplate gas sensors applied to detect ammonia, nitrogen dioxide, and their binary mixtures at different concentrations. It is shown that it is possible to find temperature-modulating frequencies to obtain high gas identification and quantification rates (95.55% and 100%, respectively).

Chemical Discrimination in Turbulent Gas Mixtures with MOX Sensors Validated by Gas Chromatography-Mass Spectrometry

Chemical detection systems based on chemo-resistive sensors usually include a gas chamber to control the sample air flow and to minimize turbulence. However, such a kind of experimental setup does not reproduce the gas concentration fluctuations observed in natural environments and destroys the spatio-temporal information contained in gas plumes. Aiming at reproducing more realistic environments, we utilize a wind tunnel with two independent gas sources that get naturally mixed along a turbulent flow. For the first time, chemo-resistive gas sensors are exposed to dynamic gas mixtures generated with several concentration levels at the sources. Moreover, the ground truth of gas concentrations at the sensor location was estimated by means of gas chromatography-mass spectrometry. We used a support vector machine as a tool to show that chemo-resistive transduction can be utilized to reliably identify chemical components in dynamic turbulent mixtures, as long as sufficient gas concentration coverage is used. We show that in open sampling systems, training the classifiers only on high concentrations of gases produces less effective classification and that it is important to calibrate the classification method with data at low gas concentrations to achieve optimal performance.

Sensor Selection and Chemo-Sensory Optimization: Toward an Adaptable Chemo-Sensory System

Over the past two decades, despite the tremendous research on chemical sensors and machine olfaction to develop micro-sensory systems that will accomplish the growing existent needs in personal health (implantable sensors), environment monitoring (widely distributed sensor networks), and security/threat detection (chemo/bio warfare agents), simple, low-cost molecular sensing platforms capable of long-term autonomous operation remain beyond the current state-of-the-art of chemical sensing. A fundamental issue within this context is that most of the chemical sensors depend on interactions between the targeted species and the surfaces functionalized with receptors that bind the target species selectively, and that these binding events are coupled with transduction processes that begin to change when they are exposed to the messy world of real samples. With the advent of fundamental breakthroughs at the intersection of materials science, micro- and nano-technology, and signal processing, hybrid chemo-sensory systems have incorporated tunable, optimizable operating parameters, through which changes in the response characteristics can be modeled and compensated as the environmental conditions or application needs change. The objective of this article, in this context, is to bring together the key advances at the device, data processing, and system levels that enable chemo-sensory systems to “adapt” in response to their environments. Accordingly, in this review we will feature the research effort made by selected experts on chemical sensing and information theory, whose work has been devoted to develop strategies that provide tunability and adaptability to single sensor devices or sensory array systems. Particularly, we consider sensor-array selection, modulation of internal sensing parameters, and active sensing. The article ends with some conclusions drawn from the results presented and a visionary look toward the future in terms of how the field may evolve.

Estimation of the limit of detection using information theory measures.

Definitions of the limit of detection (LOD) based on the probability of false positive and/or false negative errors have been proposed over the past years. Although such definitions are straightforward and valid for any kind of analytical system, proposed methodologies to estimate the LOD are usually simplified to signals with Gaussian noise. Additionally, there is a general misconception that two systems with the same LOD provide the same amount of information on the source regardless of the prior probability of presenting a blank/analyte sample. Based upon an analogy between an analytical system and a binary communication channel, in this paper we show that the amount of information that can be extracted from an analytical system depends on the probability of presenting the two different possible states. We propose a new definition of LOD utilizing information theory tools that deals with noise of any kind and allows the introduction of prior knowledge easily. Unlike most traditional LOD estimation approaches, the proposed definition is based on the amount of information that the chemical instrumentation system provides on the chemical information source. Our findings indicate that the benchmark of analytical systems based on the ability to provide information about the presence/absence of the analyte (our proposed approach) is a more general and proper framework, while converging to the usual values when dealing with Gaussian noise.

Two-dimensional wavelet transform feature extraction for porous silicon chemical sensors

Designing reliable, fast responding, highly sensitive, and low-power consuming chemo-sensory systems has long been a major goal in chemo-sensing. This goal, however, presents a difficult challenge because having a set of chemo-sensory detectors exhibiting all these aforementioned ideal conditions are still largely un-realizable to-date. This paper presents a unique perspective on capturing more in-depth insights into the physicochemical interactions of two distinct, selectively chemically modified porous silicon (pSi) film-based optical gas sensors by implementing an innovative, based on signal processing methodology, namely the two-dimensional discrete wavelet transform. Specifically, the method consists of using the two-dimensional discrete wavelet transform as a feature extraction method to capture the non-stationary behavior from the bi-dimensional pSi rugate sensor response. Utilizing a comprehensive set of measurements collected from each of the aforementioned optically based chemical sensors, we evaluate the significance of our approach on a complex, six-dimensional chemical analyte discrimination/quantification task problem. Due to the bi-dimensional aspects naturally governing the optical sensor response to chemical analytes, our findings provide evidence that the proposed feature extractor strategy may be a valuable tool to deepen our understanding of the performance of optically based chemical sensors as well as an important step toward attaining their implementation in more realistic chemo-sensing applications.

A unified framework for Volatile Organic Compound classification and regression

Motivated by the insect olfactory system, which resolves both the identity and the quantity of a nectar in parallel based on the same sensory cue, we address the problem of Volatile Organic Compound (VOC) classification and regression in a unified setting. We derive a maximum margin formulation for minimizing the empirical regression error and the classification error jointly, and then call the sequential minimal optimization procedure for solution. The solution yields a pool of support vectors that achieves both tasks almost equally accurately as individual performances of a support vector machine classifier and a support vector regressor designed independently. We investigate empirically the advantages and inconveniences of handling these two problems under a single formulation for odor identification and quantification. We demonstrate the method on an extensive dataset acquired by an array metal-oxide sensors for five VOC identities and a wide range of concentrations.

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.

Demonstration of fast and accurate discrimination and quantification of chemically similar species utilizing a single cross-selective chemiresistor.

Performance characteristics of gas-phase microsensors will determine the ultimate utility of these devices for a wide range of chemical monitoring applications. Commonly employed chemiresistor elements are quite sensitive to selected analytes, and relatively new methods have increased the selectivity to specific compounds, even in the presence of interfering species. Here, we have focused on determining whether purposefully driven temperature modulation can produce faster sensor-response characteristics, which could enable measurements for a broader range of applications involving dynamic compositional analysis. We investigated the response speed of a single chemiresitive In2O3 microhotplate sensor to four analytes (methanol, ethanol, acetone, 2-butanone) by systematically varying the oscillating frequency (semicycle periods of 20–120 ms) of a bilevel temperature cycle applied to the sensing element. It was determined that the fastest response (≈ 9 s), as indicated by a 98% signal-change metric, occurred for a period of 30 ms and that responses under such modulation were dramatically faster than for isothermal operation of the same device (>300 s). Rapid modulation between 150 and 450 °C exerts kinetic control over transient processes, including adsorption, desorption, diffusion, and reaction phenomena, which are important for charge transfer occurring in transduction processes and the observed response times. We also demonstrate that the fastest operation is accompanied by excellent discrimination within a challenging 16-category recognition problem (consisting of the four analytes at four separate concentrations). This critical finding demonstrates that both speed and high discriminatory capabilities can be realized through temperature modulation.

Optimizing the Operating Temperature for an array of MOX Sensors on an Open Sampling System

Chemo‐resistive transduction is essential for capturing the spatio‐temporal structure of chemical compounds dispersed in different environments. Due to gas dispersion mechanisms, namely diffusion, turbulence and advection, the sensors in an open sampling system, i.e. directly exposed to the environment to be monitored, are exposed to low concentrations of gases with many fluctuations making, as a consequence, the identification and monitoring of the gases even more complicated and challenging than in a controlled laboratory setting. Therefore, tuning the value of the operating temperature becomes crucial for successfully identifying and monitoring the pollutant gases, particularly in applications such as exploration of hazardous areas, air pollution monitoring, and search and rescue1. In this study we demonstrate the benefit of optimizing the sensor’s operating temperature when the sensors are deployed in an open sampling system, i.e. directly exposed to the environment to be monitored.