Sniffing speeds up chemical detection by controlling air-flows near sensors

Design Principles From Natural Olfaction for Electronic Noses

Natural olfactory systems possess remarkable sensitivity and precision beyond what is currently achievable by engineered gas sensors. Unlike their artificial counterparts, noses are capable of distinguishing scents associated with mixtures of volatile molecules in complex, typically fluctuating environments and can adapt to changes. This perspective examines the multifaceted biological principles that provide olfactory systems their discriminatory prowess, and how these ideas can be ported to the design of electronic noses for substantial improvements in performance across metrics such as sensitivity and ability to speciate chemical mixtures. The topics examined herein include the fluid dynamics of odorants in natural channels; specificity and kinetics of odorant interactions with olfactory receptors and mucus linings; complex signal processing that spatiotemporally encodes physicochemical properties of odorants; active sampling techniques, like biological sniffing and nose repositioning; biological priming; and molecular chaperoning. Each of these components of natural olfactory systems are systmatically investigated, as to how they have been or can be applied to electronic noses. While not all artificial sensors can employ these strategies simultaneously, integrating a subset of bioinspired principles can address issues like sensitivity, drift, and poor selectivity, offering advancements in many sectors such as environmental monitoring, industrial safety, and disease diagnostics.

Sniffing Like a Wine Taster: Multiple Overlapping Sniffs (MOSS) Strategy Enhances Electronic Nose Odor Recognition Capability

As highly promising devices for odor recognition, current electronic noses are still not comparable to human olfaction due to the significant disparity in the number of gas sensors versus human olfactory receptors. Inspired by the sniffing skills of wine tasters to achieve better odor perception, a multiple overlapping sniffs (MOSS) strategy is proposed in this study. The MOSS strategy involves rapid and continuous inhalation of odorants to stimulate the sensor array to generate feature-rich temporal signals. Computational fluid dynamics simulations are performed to reveal the mechanism of complex dynamic flows affecting transient responses. The proposed strategy shows over 95% accuracy in the recognition experiments of three gaseous alkanes and six liquors. Results demonstrate that the MOSS strategy can accurately and easily recognize odors with a limited sensor number. The proposed strategy has potential applications in various odor recognition scenarios, such as medical diagnosis, food quality assessment, and environmental surveillance.

Rhythms in Speech

Speech can be defined as the human ability to communicate through a sequence of vocal sounds. Consequently, speech requires an emitter (the speaker) capable of generating the acoustic signal and a receiver (the listener) able to successfully decode the sounds produced by the emitter (i.e., the acoustic signal). Time plays a central role at both ends of this interaction. On the one hand, speech production requires precise and rapid coordination, typically within the order of milliseconds, of the upper vocal tract articulators (i.e., tongue, jaw, lips, and velum), their composite movements, and the activation of the vocal folds. On the other hand, the generated acoustic signal unfolds in time, carrying information at different timescales. This information must be parsed and integrated by the receiver for the correct transmission of meaning. This chapter describes the temporal patterns that characterize the speech signal and reviews research that explores the neural mechanisms underlying the generation of these patterns and the role they play in speech comprehension.

Ethorobotic rats for rodent behavioral research: design considerations

The development of robots as tools for biological research, sometimes termed "biorobotics", has grown rapidly in recent years, fueled by the proliferation of miniaturized computation and advanced manufacturing techniques. Much of this work is focused on the use of robots as biomechanical models for natural systems. But, increasingly, biomimetic robots are being employed to interact directly with animals, as component parts of ethology studies in the field and behavioral neuroscience studies in the laboratory. While it has been possible to mechanize and automate animal behavior experiments for decades, only recently has there been the prospect of creating at-scale robotic animals containing the sensing, autonomy and actuation necessary for complex, life-like interaction. This not only opens up new avenues of enquiry, but also provides important ways to improve animal welfare, both by reducing or replacing the use of animal subjects, and by minimizing animal distress (if robots are used judiciously). This article will discuss the current state of the art in robotic lab rats, providing perspective on where research could be directed to enable the safe and effective use of biorobotic animals.

Active Sensing in Bees Through Antennal Movements Is Independent of Odor Molecule

When sampling odors, many insects are moving their antennae in a complex but repeatable fashion. Previous studies with bees have tracked antennal movements in only two dimensions, with a low sampling rate and with relatively few odorants. A detailed characterization of the multimodal antennal movement patterns as function of olfactory stimuli is thus wanted. The aim of this study is to test for a relationship between the scanning movements and the properties of the odor molecule. We tracked several key locations on the antennae of bumblebees at high frequency and in three dimensions while stimulating the insect with puffs of 11 common odorants released in a low-speed continuous flow. Water and paraffin were used as negative controls. Movement analysis was done with the neural network Deeplabcut. Bees use a stereotypical oscillating motion of their antennae when smelling odors, similar across all bees, independently of the identity of the odors and hence their diffusivity and vapor pressure. The variability in the movement amplitude among odors is as large as between individuals. The main type of oscillation at low frequencies and large amplitude is triggered by the presence of an odor and is in line with previous work, as is the speed of movement. The second oscillation mode at higher frequencies and smaller amplitudes is constantly present. Antennae are quickly deployed when a stimulus is perceived, decorrelate their movement trajectories rapidly, and oscillate vertically with a large amplitude and laterally with a smaller one. The cone of airspace thus sampled was identified through the 3D understanding of the motion patterns. The amplitude and speed of antennal scanning movements seem to be function of the internal state of the animal, rather than determined by the odorant. Still, bees display an active olfactory sampling strategy. First, they deploy their antennae when perceiving an odor. Second, fast vertical scanning movements further increase the odorant capture rate. Finally, lateral movements might enhance the likelihood to locate the source of odor, similarly to the lateral scanning movement of insects at odor plume boundaries.

Canine-inspired Unidirectional Flows for Improving Memory Effects in Machine Olfaction

A dog's nose differs from a human's in that air does not change direction but flows in a unidirectional path from inlet to outlet. Previous simulations showed that unidirectional flow through a dog's complex nasal passageways creates stagnant zones of trapped air. We hypothesize that these zones give the dog a "physical memory," which it may use to compare recent odors to past ones. In this study, we conducted experiments with our previously built Gaseous Recognition Oscillatory Machine Integrating Technology (GROMIT) and performed corresponding simulations in two dimensions. We compared three settings: a control setting that mimics the bidirectional flow of the human nose; a short-circuit setting where odors exit before reaching the sensors; and a unidirectional configuration using a dedicated inlet and outlet that mimics the dog's nose. After exposure to odors, the sensors in the unidirectional setting showed the slowest return to their baseline level, indicative of memory effects. Simulations showed that both short-circuit and unidirectional flows created trapped recirculation zones, which slowed the release of odors from the chamber. In the future, memory effects such as the ones found here may improve the sensitivity and utility of electronic noses.

Nonequilibrium sensing of volatile compounds using active and passive analyte delivery

Although sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch, the development of artificial noses is significantly behind their biological counterparts. This largely stems from the sophistication of natural olfaction, which relies on both fluid dynamics within the nasal anatomy and the response patterns of hundreds to thousands of unique molecular-scale receptors. We designed a sensing approach to identify volatiles inspired by the fluid dynamics of the nose, allowing us to extract information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) rather than relying on a large sensor array. By accentuating differences in the nonequilibrium mass-transport dynamics of vapors and training a machine learning algorithm on the sensor output, we clearly identified polar and nonpolar volatile compounds, determined the mixing ratios of binary mixtures, and accurately predicted the boiling point, flash point, vapor pressure, and viscosity of a number of volatile liquids, including several that had not been used for training the model. We further implemented a bioinspired active sniffing approach, in which the analyte delivery was performed in well-controlled 'inhale-exhale' sequences, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. Our results outline a strategy to build accurate and rapid artificial noses for volatile compounds that can provide useful information such as the composition and physical properties of chemicals, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.

Nasal anatomy and sniffing in respiration and olfaction of wild and domestic animals

Animals have been widely utilized as surrogate models for humans in exposure testing, infectious disease experiments, and immunology studies. However, respiratory diseases affect both humans and animals. These disorders can spontaneously affect wild and domestic animals, impacting their quality and quantity of life. The origin of such responses can primarily be traced back to the pathogens deposited in the respiratory tract. There is a lack of understanding of the transport and deposition of respirable particulate matter (bio-aerosols or viruses) in either wild or domestic animals. Moreover, local dosimetry is more relevant than the total or regionally averaged doses in assessing exposure risks or therapeutic outcomes. An accurate prediction of the total and local dosimetry is the crucial first step to quantifying the dose-response relationship, which in turn necessitates detailed knowledge of animals' respiratory tract and flow/aerosol dynamics within it. In this review, we examined the nasal anatomy and physiology (i.e., structure-function relationship) of different animals, including the dog, rat, rabbit, deer, rhombus monkey, cat, and other domestic and wild animals. Special attention was paid to the similarities and differences in the vestibular, respiratory, and olfactory regions among different species. The ventilation airflow and behaviors of inhaled aerosols were described as pertinent to the animals' mechanisms for ventilation modulation and olfaction enhancement. In particular, sniffing, a breathing maneuver that animals often practice enhancing olfaction, was examined in detail in different animals. Animal models used in COVID-19 research were discussed. The advances and challenges of using numerical modeling in place of animal studies were discussed. The application of this technique in animals is relevant for bidirectional improvements in animal and human health.

The scaling of olfaction: Moths have relatively more olfactory surface area than mammals

Body size affects nearly every aspect of locomotion and sensing, but little is known how body size influences olfaction. One reason for this missing link is that olfaction differs fundamentally from vision and hearing in that molecules are advected by fluid before depositing on olfactory sensors. This critical role of fluid flow in olfaction leads to complexities and trade-offs. For example, a greater density of hairs and sensory neurons may lead to greater collection, but can also lead to reduced flow through hairs and additional weight and drag due to a larger olfactory organ. In this study, we report the surface area and sensory neuron density in olfactory organs of 95 species of moths and mammals. We find that approximately 12-14 percent of an olfactory system's surface area is devoted to chemosensors. Furthermore, total olfactory surface area and olfactory sensing surface area scale with body mass to the 0.49 and 0.38 powers respectively, indicating that moths have a higher proportion of olfactory surface area than mammals. The density of olfactory neurons appears to be near the limit, at 10,000 to 100,000 neurons per square mm across both insects and mammals. This study demonstrates the need for future work detailing how scaling of olfaction and other senses vary across taxa.

On the design of particle filters inspired by animal noses

Passive filtering is a common strategy to reduce airborne disease transmission and particulate contaminants across scales spanning orders of magnitude. The engineering of high-performance filters with relatively low flow resistance but high virus- or particle-blocking efficiency is a non-trivial problem of paramount relevance, as evidenced in the variety of industrial filtration systems and face masks. Next-generation industrial filters and masks should retain sufficiently small droplets and aerosols while having low resistance. We introduce a novel 3D-printable particle filter inspired by animals' complex nasal anatomy. Unlike standard random-media-based filters, the proposed concept relies on equally spaced channels with tortuous airflow paths. These two strategies induce distinct effects: a reduced resistance and a high likelihood of particle trapping by altering their trajectories with tortuous paths and induced local flow instability. The structures are tested for pressure drop and particle filtering efficiency over different airflow rates. We have also cross-validated the observed efficiency through numerical simulations. We found that the designed filters exhibit a lower pressure drop, compared to commercial masks and filters, while capturing particles bigger than approximately 10 μm. Our findings could facilitate a novel and scalable filter concept inspired by animal noses.

Semiquantitative Classification of Two Oxidizing Gases with Graphene-Based Gas Sensors

Miniature and low-power gas sensing elements are urgently needed for a portable electronic nose, especially for outdoor pollution monitoring. Hereby we prepared chemiresistive sensors based on wide-area graphene (grown by chemical vapor deposition) placed on Si/Si3N4 substrates with interdigitated electrodes and built-in microheaters. Graphene of each sensor was individually functionalized with ultrathin oxide coating (CuO-MnO2, In2O3 or Sc2O3) by pulsed laser deposition. Over the course of 72 h, the heated sensors were exposed to randomly generated concentration cycles of 30 ppb NO2, 30 ppb O3, 60 ppb NO2, 60 ppb O3 and 30 ppb NO2 + 30 ppb O3 in synthetic air (21% O2, 50% relative humidity). While O3 completely dominated the response of sensors with CuO-MnO2 coating, the other sensors had comparable sensitivity to NO2 as well. Various response features (amplitude, response rate, and recovery rate) were considered as machine learning inputs. Using just the response amplitudes of two complementary sensors allowed us to distinguish these five gas environments with an accuracy of ~ 85%. Misclassification was mostly due to an overlap in the case of the 30 ppb O3, and 30 ppb O3 + 30 ppb NO2 responses, and was largely caused by the temporal drift of these responses. The addition of recovery rates to machine learning input variables enabled us to very clearly distinguish different gases and increase the overall accuracy to ~94%.