Advancements in Microfabricated Gas Sensors and Microanalytical Tools for the Sensitive and Selective Detection of Odors

Abstract

In recent years, advancements in micromachining techniques and nanomaterials have enabled the fabrication of highly sensitive devices for the detection of odorous species. Recent efforts done in the miniaturization of gas sensors have contributed to obtain increasingly compact and portable devices. Besides, the implementation of new nanomaterials in the active layer of these devices is helping to optimize their performance and increase their sensitivity close to humans' olfactory system. Nonetheless, a common concern of general-purpose gas sensors is their lack of selectivity towards multiple analytes. In recent years, advancements in microfabrication techniques and microfluidics have contributed to create new microanalytical tools, which represent a very good alternative to conventional analytical devices and sensor-array systems for the selective detection of odors. Hence, this paper presents a general overview of the recent advancements in microfabricated gas sensors and microanalytical devices for the sensitive and selective detection of volatile organic compounds (VOCs). The working principle of these devices, design requirements, implementation techniques, and the key parameters to optimize their performance are evaluated in this paper. The authors of this work intend to show the potential of combining both solutions in the creation of highly compact, low-cost, and easy-to-deploy platforms for odor monitoring.

Keywords: gas chromatography; gas sensors; lab-on-a-chip (LOC); microelectromechanical systems (MEMS); microfluidic devices; nanomaterials; volatile organic compounds (VOCs).

Figure 1

General framework with the different families of gas sensors that exist for the monitoring of VOCs. Gas sensors can be classified according to two basic principles: (i) the functional materials used to interact with the different compounds or (ii) the transduction mechanism employed for sensing.

Figure 2

Schematic representation of different components in a PID optical gas sensor for the detection of different VOCs (color dots). Reprinted from ref [47]. Copyright 2018, Elsevier.

Figure 3

Schematic representation of a SAW gas sensor with two arrays or reflectors and a piezoelectric substrate. Reprinted from ref [7]. Copyright 2019, Sensors by MDPI.

Figure 4

Schematic design of a standard amperometric gas sensor. Reprinted with permission from ref [84]. Copyright 2017, American Chemical Society (ACS).

Figure 5

Schematic of a calorimetric H₂O₂ sensor. It consists of two Pt-meander structures: one passive and the other catalytically activated by a MnO₂ layer. Reprinted with permission from ref [112]. Copyright 2017, Wiley-VCH GmbH.

Figure 6

Model of the intergrain potential barrier of a n-type ZnO metal oxide semiconductor in (a) the absence of a target specie and (b) the presence of a reducing VOC (R). Reproduced and modified with permission from ref. [127]. Copyright 2015, Elsevier.

Figure 7

Working principle of an IDE-based gas sensor with a hybrid polymer composite: (a) top view of the gas sensor and the active layer; (b) representation of the polymer matrix with carbon fillers and its electrical characteristics prior to detection; and (c) polymer swell-effect due to the adsorption of VOCs, altering the distribution of fillers and overall impedance/conductivity of the composite [134].

Figure 8

Schematic representation of a typical bioelectronic sensor device for the detection of odorous species (VOCs). These devices are generally constituted of a primary sensing element (i.e., biological element) and a secondary element used to capture and amplify the responses of bioreceptors [153]. Olfactory receptors (ORs) can be deployed onto these devices by means of cells or tissues, lipid layers, and nanovesicles [178].

Figure 9

SEM images of different microchromatographic columns layouts on a chip-based configuration: (a) semipacked column with embedded microposts, (b) circular spiral, (c) square spiral, and (d) radiator. Reprinted with permission from ref. [234] (a), ref. [230] (b), and ref. [244] (c,d). Copyright 2013 Elsevier (part a), Copyright 2009 IEEJ (part b) and Copyright 2006 Elsevier (part c, d).

Figure 10

Schematic representation of an automated 3D-µGC system. It consists of a 1 × 2 × 4 channel adaptive configuration with three different levels of separation. The initial vapor mixture consists of eight different VOCs. After each separation column, there is a nondestructive detector connected to a computer-controller flow routing system that directs each vapor peak to the next column [246].

Figure 11

(A) Schematic diagram of the 3D-printed microfluidic device with a square-based cross-section. (B) Schematic representation of the diffusion and physisorption effect of gas molecules along the microfluidic channel (red dots). (C) Normalized transient response of the microfluidic-based device towards three different VOCs (ethanol, methanol, and acetone). The output signals are shifted onwards due to the selectivity provided by the microfluidic channel. (D) 3D feature space representation of six different analytes at eight different concentration levels (from 250 to 4000 ppm). Reprinted and modified with permission from ref. [254]. Copyright 2016, Elsevier.

Figure 12

Multilayer coating of a microfluidic channel: (A) three-layer coating with chromium, gold, and Parylene C and (B) four-layer coating adding Cytonix, a highly hydrophobic material that enhances interaction with nonpolar analytes. Reprinted from ref. [262]. Copyright 2019, Springer Nature.