journal article·2025-03Open access

Design Principles From Natural Olfaction for Electronic Noses

Haritosh Patel, Vicente Garrido Portilla, Anna V. Shneidman, Jacopo Movilli, Jack Alvarenga, Christophe Dupré, Michael Aizenberg, Venkatesh N. Murthy, Alexander Tropsha, Joanna Aizenberg

Advanced Science· Volume 12· Issue 12

Abstract

Abstract 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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Chemical Sensor Technologies · Insect Pheromone Research and Control · Olfactory and Sensory Function Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA 02134 USA

Department of Chemical Sciences University of Padova Padova 35131 Italy

Department of Molecular & Cellular Biology Harvard University Cambridge MA 02138 USA

Center for Brain Science Harvard University Cambridge MA 02138 USA

Kempner Institute Harvard University Boston MA 02134 USA

Department of Chemistry The University of North Carolina at Chapel Hill Chapel Hill NC 27516 USA

Department of Chemistry and Chemical Biology Harvard University Cambridge MA 02138 USA

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