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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Accounts of Chemical...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Accounts of Chemical Research
Article . 2020 . Peer-reviewed
License: STM Policy #29
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The Optoelectronic Nose

Authors: Zheng Li; Kenneth S. Suslick;

The Optoelectronic Nose

Abstract

How does one tell the difference between one molecule or mixture of molecules from another? Chemical sensing seeks to probe physical or chemical properties of molecular or ionic species (i.e., analytes) and transform that information into a useful and distinguishable output. The olfactory system of animals is the prototype of chemical sensing. Even for human beings (who are generally more visual than olfactory creatures), the sense of smell is one of our most basic capabilities, and we can discriminate among many thousands, and possibly even billions, of different odors. The chemical specificity of the olfactory system does not come from specific receptors for specific analytes (i.e., the traditional lock-and-key model of enzyme-substrate interactions), but rather olfaction uses pattern recognition of the combined responses of several hundred olfactory receptors.In analogy to olfaction, colorimetric sensor arrays provide high dimensional data from the color changes of chemically responsive colorants as they are exposed to analytes. These colorants include pH responsive dyes, Lewis acid/base indicators, redox dyes, vapochromics, and surface-modified silver nanoparticles. The color difference maps so created provide chemical sensing with high sensitivity (often down to ppb levels), impressive discrimination among very similar analytes, and exquisite fingerprinting of extremely similar mixtures over a wide range of analyte types, both in the gas and liquid phases. Such colorimetric arrays probe a wide range of the chemical reactivity of analytes, rather than the limited dimensionality of physical properties (e.g., mass) or physisorption (e.g., traditional electronic noses). Our sensor arrays are disposable and simple to produce by either inkjet or robotic dip-pen printing onto the surface of porous polymer membranes or even paper.Design of both sensor arrays and optical readers for their analysis has advanced to a fully self-contained pocket-sized instrument, the optoelectronic nose. Quantitative analysis requires appropriate chemometric methods for pattern recognition of data with inherently high dimensionality, e.g., hierarchical cluster analysis and support vector machines. A wide range of applications for the colorimetric sensor arrays has been developed, including personal dosimetry of toxic industrial chemicals, detection of explosives or fire accelerants, monitoring pollutants for artwork and cultural heritage preservation, quality control of foods and beverages, rapid identification of bacteria and fungi, and detection of disease biomarkers in breath or urine. The development of portable, high-accuracy instrumentation using standard imaging devices with the capability of onboard, real-time analysis has had substantial progress and increasingly meets the expectations for real-world use.

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Keywords

Principal Component Analysis, Silver, Support Vector Machine, Metal Nanoparticles, Beverages, Limit of Detection, Cluster Analysis, Humans, Colorimetry, Environmental Pollutants, Gases, Coloring Agents, Electronic Nose, Biomarkers, Food Analysis

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
126
Top 1%
Top 10%
Top 1%
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