Exploring Scent Distinction with Polymer Brush Arrays

Abstract

The ability to distinguish scents, volatile organic compounds (VOCs), and their mixtures is critical in agriculture, food safety, and public health. This study introduces a proof-of-concept approach for VOC and scent distinction, leveraging polymer brush arrays with diverse chemical compositions designed to interact with various VOCs and scents. When VOCs or scents are exposed to the brush array, they produce distinct mass absorption patterns for different polymer brushes, effectively creating "fingerprints". Scents can be recognized without having to know the absorption of their individual components. This allows for a scent distinction technique, mimicking scent recognition within a mammalian olfactory system. To demonstrate the scent distinction, we synthesized different polymer brushes, zwitterionic, hydrophobic, and hydrophilic, using surface-initiated photoinduced electron transfer-reversible addition-fragmentation chain-transfer polymerization with eosin Y and triethanolamine as catalysts. The polymer brushes were then exposed to vapors of different single-compound VOCs and complex scents consisting of many VOCs, such as the water-ethanol mixture, rosemary oil, lavender oil, and whiskey scents. Quartz crystal microbalance measurements with dissipation monitoring (QCM-D) show a clear difference in brush absorption for these diverse VOC vapors such that distinct fingerprints can be identified. Our proof-of-concept study aims to pave the way for universal electronic nose sensors that distinguish scents by combining mass absorption patterns from polymer brush-coated surfaces.

Scheme 1. General Scheme for Distinguishing Scents and Vapors Using a Polymer Brush Array

The diagram begins with the human nose on the left, which contains various receptors that detect specific scents. In the center, the scent of lavender oil consisting of VOCs and their molecular components are shown, with each molecule represented by distinct shapes and colors. On the right, our polymer brush electronic nose is depicted. The polymer brush coatings work by adsorbing different molecules, such as those found in lavender oil. The polymer brush coatings capture these molecules based on their mass, producing a unique “fingerprint” pattern when identifying the lavender oil smell

Scheme 2. Overview of the SI-PET-RAFT Polymer Brush Coating Synthesis Process

Polymer brushes composed of MeOEGMA, CBMA, HPMA, and BMA were synthesized through a four-step procedure: (1) plasma cleaning of silicon surfaces, (2) immobilization of APTES, (3) attachment of the NHS-RAFT agent, and (4) polymer brush synthesis facilitated by EY and TEOA

Figure 1

Kinetics of poly(BMA) brush growth. To the best of our knowledge, this study is the first to demonstrate the SI-PET-RAFT procedure for BMA monomer, utilizing EY and TEOA as photocatalysts. The kinetics and evolution of polymer brush growth were analyzed to confirm the control over the polymerization process further.

Figure 2

Narrow C 1s XPS spectra of poly(BMA) (a), poly(HPMA) (b), poly(MeOEGMA) (c), and poly(CBMA) (d), along with the corresponding computed spectra for these monomers (e–h). The narrow C 1s spectra were analyzed to validate the chemical composition of the polymer brushes, with experimental results corroborated by simulated XPS spectra for each monomer. This combination of experimental and simulated data provides robust confirmation of the polymer brush compositions.

Scheme 3. Schematic Representation of the QCM-D Vapor Absorption Setup

The schematic depiction illustrates a bubbler system containing the liquid that generates vapor (e.g., lavender oil, ethanol). Air, flowing at a controlled rate of 7 l·h^–1, was passed through the target liquid to produce vapor. The vapor concentration was measured using an Ion Science MiniPID2 sensor for single VOC compounds. Subsequently, the vapor was directed over the QCM-D chip coated with the corresponding polymer brushes to assess vapor absorption properties. All the components of the setup were at 20 °C to prevent condensation

Figure 3

Representative sensograms and QCM-D sensorgrams of poly(MeOEGMA) exposure to ethanol vapor (EtOH) (12 ppt). (a) Poly(CBMA) exposed to vapor of Jack Daniels whiskey, (b) poly(HPMA) exposed to α-pinene vapor (1.2 ppt), (c) and poly(BMA) to lavender oil vapor. (d) The Δ(f) is the change frequency in third harmonics (f3), and Δ(D) is the change in dissipation in third harmonics (D3) in the QCM-D sensogram. Please also see for a complete overview of sensorgrams Supporting Information Table S8–S17 and S19–S24 for each of the vapors and brushes.

Figure 4

Measured added mass for poly(BMA), poly(CBMA), poly(HPMA), and poly(MeOEGMA) brushes after exposure to the different VOCs and scents given in the graph and ratio between the mass of absorbed vapor and mass of the polymer brush coatings. The overview of absorbed mass on all four polymer brushes showed each scent’s possible “fingerprint”.

Scheme 4. Single-Component VOCs Are Arranged Based on Their Polarity, from Most to Least Polar

Figure 5

Representative sensorgram of reusability of polymer brush poly(MeOEGMA) coatings exposed to the vapor of ethanol, Jack Daniels whiskey, and lavender oil. The error margins for absorption of corresponding vapors separately on poly(MeOEGMA) brush are shown with blue (ethanol), brown (Jack Daniels whiskey), and purple (lavender oil). Please also see Supporting Information Table S18 for a full overview of all sensorgrams on reusability of different polymer brushes.

Figure 6

Adsorbed mass and ratio between absorbed mass and mass of the polymer brush coating of different vapor and vapor diluted with dry air (∼80% of initial concentration), ethanol(12 ppt) α-pinene(1.2 ppt) ethanol (diluted) (10 ppt) α-pinene (diluted) (0.9 ppt) on the surfaces of poly(BMA), poly(CBMA), poly(HPMA), and poly(MeOEGMA) brushes.