There is an enormous demand for chemical sensors for many areas and disciplines. High sensitivity and ease of operation are two main issues for sensor development. Fluorescence techniques can easily fulfill these requirements and therefore fluorescent-based sensors appear as one of the most promising candidates for chemical sensing. However, the development of sensors is not trivial; material science, molecular recognition and device implementation are some of the aspects that play a role in the design of sensors. The development of fluorescent sensing materials is increasingly captivating the attention of the scientists because its implementation as a truly sensory system is straightforward. This critical review shows the use of polymers, sol–gels, mesoporous materials, surfactant aggregates, quantum dots, and glass or gold surfaces, combined with different chemical approaches for the development of fluorescent sensing materials. Representative examples have been selected and they are commented here.

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Design of fluorescent materials for chemical sensing

The development of a low-cost, sensitive colorimetric sensor array for the detection and identification of volatile organic compounds (VOCs) is reported. Using an array composed of chemoresponsive dyes, enormous discriminatory power is possible in a simple device that can be imaged easily with an ordinary flatbed scanner. Excellent differentiation of closely related organic compounds can be achieved, and a library of 100 VOCs is presented. The array discriminates among VOCs by probing a wide range of intermolecular interactions, including Lewis acid/base, Bronsted acid/base, metal ion coordination, hydrogen bonding, and dipolar interactions. Importantly, by proper choice of dyes and substrate, the array is essentially nonresponsive to changes in humidity.

Colorimetric sensor arrays for volatile organic compounds.

Bioinspired engineering of honeycomb structure – Using nature to inspire human innovation

Odors are sensations that occur when compounds (called odorants) stimulate receptors located in the olfactory epithelium at the roof of the nasal cavity. Odorants are hydrophobic, volatile compounds with a molecular weight of less than 300 daltons. Humans can recognize and distinguish up to 10 000 different substances on the basis of their odor quality. Odorant receptors (ORs) in the nasal cavity detect and discriminate among these thousands of diverse chemical ligands. An individual odorant can bind to multiple receptor types, and structurally different odorants can bind to a single receptor. Specific patterns of activation generate signals that allow us to discriminate between the vast number of distinct smells. The physicochemical attributes of odorants that induce specific odor sensations are not well understood. The genes that code for ORs have been cloned, and results from cloning studies indicate that ORs are members of a superfamily of hundreds of different G-protein-coupled receptors that possess seven transmembrane domains. A complete knowledge of structureodor relationships in olfaction awaits the three-dimensional analysis of this large family of ORs. Ultimately, simultaneous knowledge of the three-dimensional structure of ORs as well as odorants will allow us to develop a pattern recognition paradigm that can predict odor quality.

/pdf/handbook-of-machine-olfaction-4sqbr5ud63.pdf

Handbook of Machine Olfaction

We have developed a randomly ordered fiber-optic gene array for rapid, parallel detection of unlabeled DNA targets with surface immobilized molecular beacons (MB) that undergo a conformational change accompanied by a fluorescence change in the presence of a complementary DNA target. Microarrays are prepared by randomly distributing MB-functionalized 3-μm diameter microspheres in an array of wells etched in a 500-μm diameter optical imaging fiber. Using several MBs, each designed to recognize a different target, we demonstrate the selective detection of genomic cystic fibrosis related targets. Positional registration and fluorescence response monitoring of the microspheres was performed using an optical encoding scheme and an imaging fluorescence microscope system.

Screening unlabeled DNA targets with randomly ordered fiber-optic gene arrays.

A fiber optic bead-based sensor array platform has been employed to discriminate between six different odors and air carrier gas. Six different bead sensor types, with over 250 replicates of each, were monitored before, during, and after odor exposure to produce time-dependent fluorescence response patterns that were unique for each sensor−analyte combination. A total of 2683 sensors were analyzed with respect to changes in their fluorescence, and signals from identical sensor beads were averaged to improve signal-to-noise ratios. Analyte classification rates of 100% were achieved for three complex (coffee bean) odors and three pure (simple) odors (toluene, acetone, 1,3-dinitrotoluene) measured at their highest relative concentrations. When lower odor concentrations were employed, the system exhibited better than 85% classification rates for analyte discrimination. Sensor response repeatability to these odor stimuli has also been quantified statistically, which is vital in defining the detection limit of ...

Optical multibead arrays for simple and complex odor discrimination.

In this paper automatic sensor identification of sensor classes within a high-density randomized array, without a priori knowledge of sensor locations, is demonstrated. Two different fluorescence-based sensor types, with hundreds of replicates each, were randomly distributed into an optical imaging fiber array platform. The sensor element types were vapor-sensitive microspheres with the environmentally-sensitive fluorescent dye Nile Red adsorbed on their surface. Nile Red undergoes spectral changes when exposed to different microenvironmental polarity conditions, e.g. microsphere surface polarity or odor exposure. These reproducible sensor spectral changes, or sensor-response profiles, enable sensors within a randomized array to be grouped into categories by optical decoding methods. Two computational decoding methods (supervised and unsupervised) are introduced; equal classification rates were achieved for both. By comparing sensor responses from a randomized array with those obtained from known (control) arrays, 587 sensors were correctly classified with 99.32% accuracy. Although both methods were equally effective, the unsupervised method, which uses sensor response changes to odor exposure, is a better decoding model for the vapor-sensitive arrays studied, because it relies only on the odor-response profiles. Another decoding technique employed the emission spectra of the sensors and is more applicable to other types of multiplexed fluorescence-based arrays and assays. The sensor-decoding techniques are compared to demonstrate that sensors within high-density optical chemosensor arrays can be positionally-registered, or decoded, with no additional overhead in time or expense other than collecting the sensor-response profiles.

Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays.

Two recent experimental studies /20,21/ revealed that odorant-evoked activity-dependent competition is significant in the organisation and maintenance of the olfactory system. In this paper, we investigate the generation of a chemotopic sensory map in the olfactory bulb through three models driven by high-density optical chemosensor arrays which have similar properties to olfactory receptor neurons. By exposing the sensor arrays to various odours, these models were subjected to Hebbian learning to achieve self-organisation, potentially explaining the activity-dependent competition demonstrated by these recent studies. Our final model also predicts a role for periglomerular cells in the formation of the chemotopic sensory map in the olfactory bulb.

Wiring the olfactory bulb--activity-dependent models of axonal targeting in the developing olfactory pathway.

Two recent studies revealed that odorant-evoked activity-dependent competition is significant in the organisation and maintenance of the olfactory system. In this paper, we investigate the generation of a chemotopic sensory map in the olfactory bulb through three models driven by high-density optical chemosensor arrays which have similar properties to olfactory receptor neurons. By exposing the sensor arrays to various odours and Hebbian learning these models achieve self-organisation, potentially explaining the activity-dependent competition demonstrated by recent experimental studies. Our final model also predicts a role for periglomerular cells in the formation of the chemotopic sensory map.

Daljeet S. Gill

Papers

Optical multibead arrays for simple and complex odor discrimination.

Automatic decoding of sensor types within randomly ordered, high-density optical sensor arrays.

Wiring the olfactory bulb--activity-dependent models of axonal targeting in the developing olfactory pathway.

Activity-Dependent Model of Axonal Targeting in the Developing Olfactory Bulb Driven by Population Coded Chemosensor Input