Figure 1 - uploaded by Michael Schmuker
Content may be subject to copyright.
Simplified schematic of the sensor boards. The ADC on each sensor board measures the voltage across the MOX sensing elements RS. A separate 5 V supply powers the heating elements RH. The sensor board communicates via I2C with a Cortex-M7 microcontroller that transmits the data to the host computer.
Source publication
Electronic olfaction can help detect and localize harmful gases and pollutants, but the turbulence of the natural environment presents a particular challenge: odor encounters are intermittent, and an effective electronic nose must therefore be able to resolve short odor pulses. The slow responses of the widely used metal oxide (MOX) gas sensors com...
Contexts in source publication
Context 1
... I2C bus operating at 800 kHz connects the boards to a microcontroller (Teensy 4.0, PJRC.COM) that reads out the data and transmits it to the host computer via USB (Figure 1). The system is set up so that the microcontroller can handle multiple sensors in parallel, for instance, left and right electronic noses in a stereoconfiguration. ...
Context 2
... gas sensors are connected to the ADC in the voltage divider configuration that is standard for this type of sensor, 7 with the sensing element RS in series with a load resistor RL (Figure 1). This configuration works well with the chosen ADC as it allows a ratiometric measurement relative to the power supply, free from common-mode noise. ...
Similar publications
An electronic nose (e‐nose) mimics the mammalian olfactory system in identifying odors and expands human olfaction boundaries by tracing toxins and explosives. However, existing feature‐based odor recognition algorithms rely on domain‐specific expertise, which may limit the performance due to information loss during the feature extraction process....
Citations
... For this reason, various studies have investigated sensor response times and tried to improve them. Recent advancements in both hardware [43,44] and software [45,46,21,47,48,49,50,51] have significantly reduced response and recovery times from the orders of hours or minutes [52] down to tens-of-seconds or seconds [49,51]. Nevertheless, those timescales remain orders-of-magnitudes slower than what's observed for olfactory sensing in animals, potentially stalling progress on critical challenges in tracking of greenhouse gas emissions [53], ecological and environmental monitoring [54,55], aerial-based wild fire detection [56] disaster management [57], and more. ...
... For this reason, various studies have investigated sensor response times and tried to improve them. Recent advancements in both hardware [43,44] and software [45,46,21,47,48,49,50,51] have significantly reduced response and recovery times from the orders of hours or minutes [52] down to tens-of-seconds or seconds [49,51]. Nevertheless, those timescales remain orders-of-magnitudes slower than what's observed for olfactory sensing in animals, potentially stalling progress on critical challenges in tracking of greenhouse gas emissions [53], ecological and environmental monitoring [54,55], aerial-based wild fire detection [56] disaster management [57], and more. ...
Animals have evolved to rapidly detect and recognise brief and intermittent encounters with odour packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has faced challenges in achieving comparable results -- existing solutions are either slow; or bulky, expensive, and power-intensive -- limiting applicability in real-world scenarios for mobile robotics. Here we introduce a miniaturised high-speed electronic nose; characterised by high-bandwidth sensor readouts, tightly controlled sensing parameters and powerful algorithms. The system is evaluated on a high-fidelity odour delivery benchmark. We showcase successful classification of tens-of-millisecond odour pulses, and demonstrate temporal pattern encoding of stimuli switching with up to 60 Hz. Those timescales are unprecedented in miniaturised low-power settings, and demonstrably exceed the performance observed in mice. For the first time, it is possible to match the temporal resolution of animal olfaction in robotic systems. This will allow for addressing challenges in environmental and industrial monitoring, security, neuroscience, and beyond.
... In terms of dynamical response (i.e., sensor response vs. time), chemiresistor-based gas sensing has potential in many areas of industrial and environmental monitoring and safety, where it can help detect and localize harmful gases or pollutants, even when odors are dispersed by turbulent plumes, thus requiring fast response and recovery times. While response time is usually short, recovery can be much longer; therefore, solutions are needed to tackle this issue, both in terms of signal and device engineering, or in terms of novel materials and architectures [12]. ...
With the emergence of novel sensing materials and the increasing opportunities to address safety and life quality priorities of our society, gas sensing is experiencing an outstanding growth. Among the characteristics required to assess performances, the overall speed of response and recovery is adding to the well-established stability, selectivity, and sensitivity features. In this review, we focus on fast detection with chemiresistor gas sensors, focusing on both response time and recovery time that characterize their dynamical response. We consider three classes of sensing materials operating in a chemiresistor architecture, exposed to the most investigated pollutants, such as NH3, NO2, H2S, H2, ethanol, and acetone. Among sensing materials, we first selected nanostructured metal oxides, which are by far the most used chemiresistors and can provide a solid ground for performance improvement. Then, we selected nanostructured carbon sensing layers (carbon nanotubes, graphene, and reduced graphene), which represent a promising class of materials that can operate at room temperature and offer many possibilities to increase their sensitivities via functionalization, decoration, or blending with other nanostructured materials. Finally, transition metal dichalcogenides are presented as an emerging class of chemiresistive layers that bring what has been learned from graphene into a quite large portfolio of chemo-sensing platforms. For each class, studies since 2019 reporting on chemiresistors that display less than 10 s either in the response or in the recovery time are listed. We show that for many sensing layers, the sum of both response and recovery times is already below 10 s, making them promising devices for fast measurements to detect, e.g., sudden bursts of dangerous emissions in the environment, or to track the integrity of packaging during food processing on conveyor belts at pace with industrial production timescales.
... A large part of the long-term response-recover period is due to a slow recovery phase, in which the conductance slowly returns to the baseline while the initial reaction of the volatiles with the electrode is reversed. 37 To reduce the response−recovery time, sensor purging, 38 pulsed heating, 39 and signal processing method 40 were used in the previous study. In this work, we demonstrated a fast-response electronic nose system by using a new kind of porous ceramic substrate, which significantly reduces sensor response time (evaluated by T 90 ). ...
Electronic noses are an artificial olfactory system that mimics the animal olfactory function. Currently, metal oxide semiconductor (MOS) gas sensors play a vital role in the development of high-performance electronic noses and have widespread applications across various fields. They are particularly valuable in ensuring food safety, monitoring air quality, and even detecting explosives for antiterrorism purposes. However, there is an increasing demand for electronic noses to exhibit faster response times in large-scale commercial applications. To address this challenge, we developed a novel MOS gas sensor with a porous ceramic substrate, specifically designed to facilitate rapid gas diffusion. The sensing performance of the sensor array was evaluated and the result showed that the T90 time of porous ceramic-assisted MOS sensor was significantly (57%) shorter than sensors with a normal substrate. Moreover, the electronic nose system had demonstrated remarkable capability in accurately distinguishing between five distinct types of hazardous gases, including VOCs as well as ammonia. Furthermore, a low-cost electronic system was developed and applied to cigarette brand identification; 2490 groups of data were collected for each individual test at only a cost of 20 s. By employing a machine learning algorithm to analyze the data, an accuracy higher than 95% was achieved (96.29% for K nearest neighbor and 96.32% for random forest). We found that our system can resolve the onset time of electronic nose measurement with enough precision, and it was expected that this special approach by using porous ceramic as an insulating substrate can provide a simple and reliable method to manufacture a fast-response electronic nose.
... In this case, a reduction in column length could be considered. In addition, signal processing can provide a better baseline separation as well (Eberheim, 2003;Drix and Schmuker, 2021). Therefore, signal processing will be implemented in the future. ...
A possible way to reduce the size and complexity of common gas chromatography (GC) systems is the economization of the column temperature regulation system. To this end, a temperature compensation method was developed and validated on a benchtop GC-PDD (pulsed discharge detector) with ethene. An in-house-
developed algorithm correlates the retention index of a test gas to the retention index of a previously selected reference gas. To investigate further methods of cost reduction, commercial gas sensors were tested as cheap, sensitive, and versatile detectors. Therefore, CO2 was chosen as a naturally occurring reference gas, while ethene was chosen as a maturity marker for climacteric fruits and hence as a test gas. A demonstrator, consisting of a simple syringe injection system, a PLOT (porous layer open tubular) column boxed in a polystyrene-foam housing, a commercial MOS (metal-oxide semiconductor) sensor for the test gas, and a CO2-specific IR (infrared) sensor, was used to set up a simple GC system and to apply this method on test measurements. Sorption parameters for ethene and CO2 were determined via a van ’t Hoff plot, where the entropy S was −11.982 J mol−1 K−1 (deltaS0 Ethene) and 1.351 J mol−1 K−1 (deltaS0 Carbon dioxide), and the enthalpy H was −20.622 kJ mol−1 (deltaH0 Ethene) and −14.792 kJ mol−1 (deltaH0 Carbon dioxide), respectively. Ethene (100 ppm) measurements revealed a system-specific
correction term of 0.652 min. Further measurements of ethene and interfering gases revealed a mean retention time for ethene of 3.093 min; the mean predicted retention time is 3.099 min. The demonstrator was able to identify the test gas, ethene, as a function of the reference gas, CO2, in a first approach, without a column heating system and in a gas mixture by applying a temperature compensation algorithm and a system-specific holdup time correction term.
... The use of temperature modulation provides a richer sensor response [42] at the expense of making the measurements slower. Signal processing techniques allow improving the response time by one order of magnitude, reaching sub-second levels [43][44][45]. ...
The recent availability of small drones equipped with cameras and other sensing instruments has led to a myriad of environmental applications. In this chapter we review the use of miniaturized chemical sensor systems in drones for monitoring environmental gases and volatile organic compounds (VOCs). This is of interest for emission monitoring, mapping pollutant distributions, or monitoring greenhouse gases. The use of chemical sensors in these uncrewed aerial platforms require a careful system design. We will shortly review the main sensor technologies and the main concepts and difficulties associated to the integration of chemical sensors in drones. In many cases, these sensor readings aim to map the spatial distribution of gases over an area of interest. The main algorithms and methods to estimate these maps will be briefly summarized.
... While mammals can discriminate temporal correlations of rapidly fluctuating odours at frequencies of up to 40 Hz [1], metal-oxide (MOx) gas sensor based olfactory systems usually have response times that are several orders of magnitude slower [14]. Methods for improving the response time have been investigated [8,9]. ...
... Zhang et al. [14] used an array of six commercial sensors and an artificial neural network for detection of mixed indoor hazardous gases. An array of commercial sensors is bulky and consumes much energy [15,16]. It is well-known that metal oxide gas sensors exhibit different trends in response to various gases at different temperatures depending on the sensing material [17,18]. ...
... Together with biologically realistic modeling of individual larva locomotion and chemotactic behavior [16] this will allow to reproduce behavioral [128][129][130][131][132] and optophysiological observations [64,133,134] and to generate testable hypothesis at the physiological and behavioral level. In the future this may inspire modeling virtual larvae exploring and adapting to their virtual environment in a closed loop scenario and the implementation of such mini brains on compact and low-power neuromorphic hardware for the spiking control of autonomous robots [7,28,135,136]. ...
Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small-sized, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution.
... Zhang et al. [14] used an array of six commercial sensors and an artificial neural network for detection of mixed indoor hazardous gases. An array of commercial sensors is bulky and consumes much energy [15,16]. It is well-known that metal oxide gas sensors exhibit different trends in response to various gases at different temperatures depending on the sensing material [17,18]. ...
Classification of different gases is important, and it is possible to use different gas sensors for this purpose. Electronic noses, for example, combine separated gas sensors into an array for detecting different gases. However, the use of separated sensors in an array suffers from being bulky, high-energy consumption and complex fabrication processes. Generally, gas sensing properties, including gas selectivity, of semiconductor gas sensors are strongly dependent on their working temperature. It is therefore feasible to use a single device composed of identical sensors arranged in a temperature gradient for classification of multiple gases. Herein, we introduce a design for simple fabrication of gas sensor array based on bilayer Pt/SnO2 for real-time monitoring and classification of multiple gases. The study includes design simulation of the sensor array to find an effective gradient temperature, fabrication of the sensors and test of their performance. The array, composed of five sensors, was fabricated on a glass substrate without the need of backside etching to reduce heat loss. A SnO2 thin film sensitized with Pt on top deposited by sputtering was used as sensing material. The sensor array was tested against different gases including ethanol, methanol, isopropanol, acetone, ammonia, and hydrogen. Radar plots and principal component analysis were used to visualize the distinction of the tested gases and to enable effective classification.
... Together with biologically realistic modeling of individual larva locomotion and chemotactic behavior [16] this will allow to reproduce behavioral [121][122][123][124][125] and optophysiological observations [62,126,127] and to generate testable hypothesis at the physiological and behavioral level. In the future this may inspire modeling virtual larvae exploring and adapting to their virtual environment in a closed loop scenario and the implementation of such mini brains on compact and low-power neuromorphic hardware for the spiking control of autonomous robots [7,27,128,129]. ...
Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of similar mechanism to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small-sized, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution.
