5.2 Applications inspired by natural Chemo-sensory Systems
There are many potential applications for chemo-sensory systems. Some of these include
- Health industry to understand our own biology
- Commercial food industry to understand sense of taste
- Commercial perfume industry to understand sense of smell
- Commercial pesticide/herbicide industry to understand insect gustation
- Governments to monitor air and water and find sources of pollution
- Military to trace chemical trails to hidden explosives, etc.
As with photo-sensory and mechano-sensory system applications, the rest of this chapter represents a sample of contributions of scientists and researchers attempting to demonstrate or build chemo-sensory systems based on biological inspiration.
5.2.1 A model nose demonstrating discrimination capability [Persaud82]
This work demonstrates the coarse-coding capability of an olfactory system. The ORC types have different response levels for the basic odor components, and specific odors are believed to be perceived as a combination of the ORC type responses.
A model for an artificial olfactory system simulating biological ones was pursued with a focus on selecting odorant detectors that respond to a wide variety of chemical types and combining the responses so that different odorants can be identified in parallel. A ratio of sensor responses was used to discriminate between different stimulating odorants.
Gas sensors made by using n-type semiconductors is convenient, as the dopant and intensity of the doping could be adjusted to achieve a desired biomimetic ORC type. This would be advantageous as semiconductor technology is well suited to make a wide variety of n-type semiconductors with very uniform responses, each representing an ORC type. A set of such commercially available semiconductor gas sensors were used and gave specific response patterns for specific stimulants, but the response times were not as fast as natural olfactory systems.
The model nose was completed by using three commercially-available sensors from Figaro (www.figarosensor.com), including one intended as a general purpose combustible gas sensor, one more sensitive to alcohols, and one more sensitive to carbon monoxide. The results showed that the responses to over 20 different odorants were consistent and unique. The researchers point out that as in biological systems, such an artificial olfactory system would have to be trained to recognize specific patterns as specific odors.
5.2.2 Integrating a sniff pump in an artificial olfactory sensor [White02]
The Tuft Medical School Nose (TMSN) was designed to improve sensitivity and discrimination ability with respect to previous artificial nose efforts. A fan and valving system were arranged so that odorant molecules were drawn over the olfactory sensor array in short bursts, mimicking inhalation patterns.
A deviation from biology includes the use of polymer and dye mixtures in LEDs whose fluorescence changes based on the present odorants. So electric energy is used to illuminate LED whose spectra change with input odorants, and then the photonic energy is converted to analog electronic for further processing. Presumably, this is done to help meet the desired sensitivity for a specific application, the one here being land mine detection. This device included 32 sensors whose responses were broad across the various odorants, which included TNT, DNT, and other such compounds. This course-coding of the input resembles natural olfactory sensors.
This project (funded by ONR) illustrates the different uses of biology for inspired design. One purpose is to emulate biology to better understand how biology does what it does, so it is very important to make every effort to not deviate from biology. Another purpose involves a separate problem that needs to be solved (detecting land mines) where biology can give some incredible insights into novel designs, but to meet the objectives other technology may be integrated int the design that moves it away from true emulation of biology.
5.2.3 Integrating spike-based processing into artificial olfactory sensor [Liu18]
This effort contributes the integration of spike-based signal processing which is a known characteristic in natural olfactory sensors. The first sensing stage is an array of virtual olfactory receptor neurons (VORNs) that convert the odorant response into a spatio-temporal pattern of spikes. As in biological ORs the array is composed of groups of similar receptors with overlapping responses. The next sensing state is the bionic olfactory bulb (BOB) composed of processing elements named for their biological counterparts, the mitral cell layer which feedforwards to the granule cell layer. Inhibitory responses are fed back from the granule layer to the mitral layer, which is also known in biology. This is another example of lateral inhibition, or the suppression of continued responses once the cell is stimulated.
The task is to discern one of seven Chinese liquors which come from different geographical locations with their own unique combination of odorants. Little is known in biology concerning how the natural olfactory systems process the spike signals for specific odor detection. The researchers here used two traditional methods for electronic nose data processing, namely linear discriminant analysis (LDA) and support vector machine (SVM), as well as backpropagation artificial neural network (BP-ANN). The latter has significant semblance to biological information processing and performed better than the other two.
5.2.4 Integrating insect olfactory receptors for biohybrid gas flow sensor [Yam20]
In this effort biology is used to create chemical sensing since the natural sensor is sensitive and selective. Insect DNA is used to synthesize olfactory receptors which are brought into an artificial cell membrane. The difficulty is getting the input gas into a soluble form for chemical detection of the artificial olfactory receptor. Microscopic slits were designed into the gas flow path and modified with hydrophobic (water-repelling) coating to create microchannels for chemical detection.
Since the odorant detection was sporadic for the given stimulants the design was scaled to monitor 16 channels. This appears to give the detection response that is desired. Biology also relies on multiple channels or opportunities for a successful chemical detection. For example, the male silkworm moth discussed earlier can detect a single molecule of the female pheromone due to antennas each having over 10,000 sensilla (each 100 microns long and 2 microns in diameter).
5.2.5 Robotic lobster chemotaxis in turbulent chemical sources [Grasso02]
The Robo-Lobster experiment is motivated by a desire for autonomy for underwater vehicles. Acoustics is primarily used and sometimes optics, but many biological species make strong use of chemo-sensing. The lobster has long antennas that sample the water chemistry for purposes such as eating, mating, spawning, and avoiding predators. A challenge to locating an odorant source is the turbulent nature of underwater chemical plumes that cause discontinuities in chemical trails; gradient descent will not work. If moving toward a food source that is detected, the lobster antennas meander back and forth in an attempt to catch samples of the odorant and the lobster adjusts its orientation and movement direction in response to what is detected. Numerous underwater chemical source detection applications exist in the scientific, environmental, commercial, and military industries.
The emphasis of the effort was more on the autonomous acquisition of the chemical source. The ability of the lobster to crawl on the bottom was simplified to an underwater wheeled robot in a fish tank. The tank measured 10m by 2m and was filled to 44 cm deep with moving seawater. A chemical source was introduced that brought odorant molecules to the robot in slow-moving turbulent patterns. The robot would move forward when the chemical was detected (when the sensor conductivity exceeding a threshold) and oriented itself so that the responses to the two artificial antennas was more balanced. Sensor responses were converted to digital values and a Motorola microcontroller programmed in C was used to implement the wheel-movement algorithm.
A robot designed to imitate a particular species and attempting to perform a task done by that species can illuminate our understanding of biology. The authors express this by suggesting “construct a robot that is competent to test a hypothesis or set of hypotheses that have been suggested by the biology and then allow the robot’s behavior to inform you of the acceptability of that hypothesis”.