Gas Sensing Based on a Nonlinear Response: Discrimination between Hydrocarbons and Quantification of Individual Components in a Gas Mixture

Phospholipid Molecular Layer that Enhances Distinction of Odors Based on Artificial Sniffing

We propose a novel odor-sensing system based on the dynamic response of phospholipid molecular layers for artificial olfaction. Organisms obtain information about their surroundings based on multidimensional information obtained from sniffing, i.e., periodic perturbations. Semiconductor- and receptor-based odor sensors have been developed previously. However, these sensors predominantly identify odors based on one-dimensional information, which limits the type of odor molecule they can identify. Therefore, the development of odor sensors that mimic the olfactory systems of living organisms is useful to overcome this limitation. In this study, we developed a novel odor-sensing system based on the dynamics of phospholipids that responds delicately to chemical substances at room temperature using multidimensional information obtained from periodic perturbations. Odor molecules are periodically supplied to the phospholipid molecular layer as an input sample. The waveform of the surface tension of the phospholipid molecular layer changes depending on the odor molecules and serves as an output. Such characteristic responses originating from the dynamics of odor molecules on the phospholipid molecular layer can be reproduced numerically. The phospholipid molecular layer amplified the information originating from the odor molecule, and the mechanism was evaluated by using surface pressure-area isotherms. This paper offers a platform for an interface-chemistry-based artificial sniffing system as an active sensor and a novel olfactory mechanism via physicochemical responses of the receptor-independent membranes of the organism.

Carbon Dioxide Sensing-Biomedical Applications to Human Subjects

Carbon dioxide (CO2) monitoring in human subjects is of crucial importance in medical practice. Transcutaneous monitors based on the Stow-Severinghaus electrode make a good alternative to the painful and risky arterial "blood gases" sampling. Yet, such monitors are not only expensive, but also bulky and continuously drifting, requiring frequent recalibrations by trained medical staff. Aiming at finding alternatives, the full panel of CO2 measurement techniques is thoroughly reviewed. The physicochemical working principle of each sensing technique is given, as well as some typical merit criteria, advantages, and drawbacks. An overview of the main CO2 monitoring methods and sites routinely used in clinical practice is also provided, revealing their constraints and specificities. The reviewed CO2 sensing techniques are then evaluated in view of the latter clinical constraints and transcutaneous sensing coupled to a dye-based fluorescence CO2 sensing seems to offer the best potential for the development of a future non-invasive clinical CO2 monitor.

Smart Multi-Sensor Platform for Analytics and Social Decision Support in Agriculture

Smart agriculture based on new types of sensors, data analytics and automation, is an important enabler for optimizing yields and maximizing efficiency to feed the world’s growing population while limiting environmental pollution. The aim of this paper is to describe a multi-sensor Internet of Things (IoT) system for agriculture consisting of a soil probe, an air probe and a smart data logger. The implementation details will focus of the integration element and the innovative Artificial Intelligence based gas identification sensor. Furthermore, the paper focuses on the analytics and decision support system implementation that provides farming recommendations and is enhanced with a feedback loop from farmers and a social trust index that will increase the reliability of the system.

Gallium Nitride (GaN) Nanostructures and Their Gas Sensing Properties: A Review

In the last two decades, GaN nanostructures of various forms like nanowires (NWs), nanotubes (NTs), nanofibers (NFs), nanoparticles (NPs) and nanonetworks (NNs) have been reported for gas sensing applications. In this paper, we have reviewed our group’s work and the works published by other groups on the advances in GaN nanostructures-based sensors for detection of gases such as hydrogen (H₂), alcohols (R-OH), methane (CH₄), benzene and its derivatives, nitric oxide (NO), nitrogen dioxide (NO₂), sulfur-dioxide (SO₂), ammonia (NH₃), hydrogen sulfide (H₂S) and carbon dioxide (CO₂). The important sensing performance parameters like limit of detection, response/recovery time and operating temperature for different type of sensors have been summarized and tabulated to provide a thorough performance comparison. A novel metric, the product of response time and limit of detection, has been established, to quantify and compare the overall sensing performance of GaN nanostructure-based devices reported so far. According to this metric, it was found that the InGaN/GaN NW-based sensor exhibits superior overall sensing performance for H₂ gas sensing, whereas the GaN/(TiO₂–Pt) nanowire-nanoclusters (NWNCs)-based sensor is better for ethanol sensing. The GaN/TiO₂ NWNC-based sensor is also well suited for TNT sensing. This paper has also reviewed density-functional theory (DFT)-based first principle studies on the interaction between gas molecules and GaN. The implementation of machine learning algorithms on GaN nanostructured sensors and sensor array has been analyzed as well. Finally, gas sensing mechanism on GaN nanostructure-based sensors at room temperature has been discussed.

Adsorption/Combustion-type Micro Gas Sensors: Typical VOC-sensing Properties and Material-design Approach for Highly Sensitive and Selective VOC Detection

Highly sensitive and selective detection of various volatile organic compounds (VOCs) has been most needed in a wide range of fields, such as medical diagnosis, health supervision, industry-process control, and environmental monitoring. Since a semiconductor-type gas sensor is a typical promising candidate among various portable VOC-sensing devices, many efforts on developing these gas sensors are introduced in this article for the first time. Through some development stages, it has been well known that the temperature-modulated operation of gas sensors is one of effective ways to improve the magnitude of VOC responses. On the other hand, catalytic combustion-type gas sensors operated with a mode of pulse-driven heating were developed in the early 2000s, and they are named as "adsorption/combustion-type gas sensors" after their gas-sensing mechanism, based on the combustion of VOC adsorbates on the sensing material. The representative VOC-sensing properties of the adsorption/combustion-type gas sensors and recent material-design approach to achieve highly sensitive and selective VOC detection are summarized in this article.

Realtime Gas Emission Monitoring at Hazardous Sites Using a Distributed Point-Source Sensing Infrastructure

This paper describes a distributed point-source monitoring platform for gas level and leakage detection in hazardous environments. The platform, based on a wireless sensor network (WSN) architecture, is organised into sub-networks to be positioned in the plant's critical areas; each sub-net includes a gateway unit wirelessly connected to the WSN nodes, hence providing an easily deployable, stand-alone infrastructure featuring a high degree of scalability and reconfigurability. Furthermore, the system provides automated calibration routines which can be accomplished by non-specialized maintenance operators without system reliability reduction issues. Internet connectivity is provided via TCP/IP over GPRS (Internet standard protocols over mobile networks) gateways at a one-minute sampling rate. Environmental and process data are forwarded to a remote server and made available to authenticated users through a user interface that provides data rendering in various formats and multi-sensor data fusion. The platform is able to provide real-time plant management with an effective; accurate tool for immediate warning in case of critical events.

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.

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.

Real-time gas identification by analyzing the transient response of capillary-attached conductive gas sensor

In this study, the ability of the Capillary-attached conductive gas sensor (CGS) in real-time gas identification was investigated. The structure of the prototype fabricated CGS is presented. Portions were selected from the beginning of the CGS transient response including the first 11 samples to the first 100 samples. Different feature extraction and classification methods were applied on the selected portions. Validation of methods was evaluated to study the ability of an early portion of the CGS transient response in target gas (TG) identification. Experimental results proved that applying extracted features from an early part of the CGS transient response along with a classifier can distinguish short-chain alcohols from each other perfectly. Decreasing time of exposition in the interaction between target gas and sensing element improved the reliability of the sensor. Classification rate was also improved and time of identification was decreased. Moreover, the results indicated the optimum interval of the early transient response of the CGS for selecting portions to achieve the best classification rates.

Selectivity Enhancement in Multisensor Systems Using Flow Modulation Techniques

In this paper, the use of a new technique to obtain transient sensor information is introduced and its usefulness to improve the selectivity of metal oxide gas sensors is discussed. The method is based on modulating the flow of the carrier gas that brings the species to be measured into the sensor chamber. In such a way, the analytes' concentration at the surface of the sensors is altered. As a result, reproducible patterns in the sensor response develop, which carry important information for helping the sensor system, not only to discriminate among the volatiles considered but also to semi-quantify them. This has been proved by extracting features from sensor dynamics using the discrete wavelet transform (DWT) and by building and validating support vector machine (SVM) classification models. The good results obtained (100% correct identification among 5 volatile compounds and nearly a 89% correct simultaneous identification and quantification of these volatiles), which clearly outperform those obtained when the steady-state response is used, prove the concept behind flow modulation.

Distinction of gases with a semiconductor sensor depending on the scanning profile of a cyclic temperature

A gas-sensing system based on a dynamic nonlinear response is reported to improve the selectivity in the sensor response toward sample gases. A cyclic temperature composed of fundamental and second harmonics was applied to a SnO(2) semiconductor gas sensor and the resulting conductance of the sensor was analyzed by fast Fourier transformation (FFT). The dynamic nonlinear responses to the gas species were further characterized depending on the scanning profile of the temperature. These characteristic sensor responses under the application of second-harmonic perturbation were theoretically considered based on a reaction-diffusion model for the semiconductor surface.

Frequency Encoding of Resonant Mass Sensors for Chemical Vapor Detection

Chemical vapors can be detected by a resonant mass sensor array with selective absorption coatings implementing a frequency encoding method. The sensor array consists of sensor elements with different frequencies for their identifications in the frequency response obtained with a pulse Fourier transform detection scheme. Zero-loading resonance frequencies are chosen so that frequency shift due to absorption is bounded within a predefined region so that there is no overlap of peaks and all peaks can be assigned to the correct elements at any operation conditions. Mechanical oscillations of all or selected numbers of the sensor elements are excited by application of an excitation signal. Free oscillation decay signals from all or selectively excited sensor elements are detected and digitized. The free oscillation decay signal is subjected to a spectral analysis routine converting into a frequency spectrum, in which frequency shifts due to absorption of chemical vapors can be obtained. The implementation of the frequency encoding method with pulse Fourier transform detection to resonant mass sensors allows simultaneous multisensor detection, fast data acquisition speed, high signal-to-noise ratio by coaddition of raw data, flexible excitation, reduced complexity of electronic hardware, application of advanced data/spectral analysis algorithms, and realization of many other advantages by the introduction of the pulse Fourier transform method. A practical chemical vapor sensing system is demonstrated experimentally by use of nine frequency-encoded and polymer-coated sensors.

Chemical sensor based on nonlinearity: principle and application

Novel chemical sensors based on a time-dependent nonlinear response are reviewed. The strategy is to artificially mimic information transduction in living organisms. In taste and olfaction, information of chemical structure and concentration is transformed into nervous impulses in the nervous cell, i.e., time-dependent multi-dimensional information. Because the excitation and pulse generation in the nervous cell are typically nonlinear phenomena, it may be worthwhile to utilize the nonlinearity as the multi-dimensional information for molecular recognition. The principle of a "nonlinear" sensor is that a sinusoidal modulation is applied to a system, and the output signal is analyzed. The output signal of the sensor is characteristically deformed from the sinusoidal input depending on the chemical structure and concentration of the chemical stimuli. The characteristic nonlinear responses to chemical stimuli are discussed in relation to the kinetics of chemical compounds on the sensor surface. As a practical application, we introduced electrochemical sensors based on the differential capacitance, semiconductor gas sensors under the application of sinusoidal temperature or diffusion change, and a chemical sensor based on the spatio-temporal information. We demonstrated that mutli-dimensional information based on nonlinearity can provide quite useful information for the analysis of chemical species, even in the presence of another analyte or an interference with a single detector.

Dynamic information processing in natural and artificial olfactory systems

A new strategy for building artificial gas sensing systems is suggested based on knowledge of the dynamic response mechanism of the olfactory system. Difficulties with the processing of time-dependent inputs by neural networks are discussed.