The Surface-Enhanced Raman Scattering (SERS) technique is utilized to fabricate sensors for gas detection due to its rapid detection speed and high sensitivity. However, gases with similar molecular structures are difficult to directly discriminate using SERS gas sensors because there are characteristic peak overlaps in the Raman spectra. Here, we proposed a multiple SERS gas sensor matrix via a spin-coating functional polymer to enhance the gas recognition capability. Poly (acrylic acid) (PAA), Poly (methyl methacrylate) (PMMA) and Polydimethylsiloxane (PDMS) were employed to fabricate the polymer film. The high design flexibility of the two-layer film was realized by the layer-by-layer method with 2 one-layer films. The SERS gas sensor coated by different polymer films showed a distinct affinity to target gases. The principle component analysis (PCA) algorithm was used for the further clustering of gas molecules. Three target gases, phenethyl alcohol, acetophenone and anethole, were perfectly discriminated, as the characteristic variables in the response matrix constructed by the combination of gas responses obtained 3 one-layer and 3 two-layer film-coated sensors. This research provides a new SERS sensing approach for recognizing gases with similar molecular structures.

The spatial distribution of gas emitted from an odor source provides valuable information regarding the composition, size, and localization of the odor source. Surface-enhanced Raman scattering (SERS) gas sensors exhibit ultra-high sensitivity, molecular specificity, rapid response, and large-area detection. In this paper, a SERS gas sensor array was developed for visualizing the spatial distribution of gas evaporated from benzaldehyde and 4-ethylbenzaldehyde odor sources. The SERS spectra of the gas were collected by scanning the sensor array using an automatic detection system. The non-negative matrix factorization algorithm was employed to extract feature and concentration information at each spot on the sensor array. A heatmap image was generated for visualizing the gas spatial distribution using concentration information. Gaussian fitting was applied to process the image for localizing the odor source. The size of the odor source was estimated using the processed image. Moreover, the spectra of benzaldehyde, 4-ethylbenzaldehyde, and their gas mixture were simultaneously detected using one SERS sensor array. The feature information was recognized using a convolutional neural network with an accuracy of 98.21%. As a result, the benzaldehyde and 4-ethylbenzaldehyde odor sources were identified and visualized. Our research findings have various potential applications, including odor source localization, environmental monitoring, and healthcare.

The visualization of the spatial distributions of gases from various sources is essential to understanding the composition, localization, and behavior of these gases. In this study, an inkjet-printed localized surface plasmon resonance (LSPR) subpixel gas sensor array was developed to visualize the spatial distributions of gases and to differentiate between acetic acid, geraniol, pentadecane, and cis-jasmone. The sensor array, which integrates gold nanoparticles (AuNPs), silver nanoparticles (AgNPs), and fluorescent pigments, was positioned 3 cm above the gas source. Hyperspectral imaging was used to capture the LSPR spectra across the sensor array, and these spectra were then used to construct gas information matrices. Principal component analysis (PCA) enabled effective classification of the gases and localization of their sources based on observed spectral differences. Heat maps that visualized the gas concentrations were generated using the mean squared error (MSE) between the sensor responses and reference spectra. The array identified and visualized the four gas sources successfully, thus demonstrating its potential for gas localization and detection applications. The study highlights a straightforward, cost-effective approach to gas sensing and visualization, and in future work, we intend to refine the sensor fabrication process and enhance the detection of complex gas mixtures.

Localized surface plasmon resonance (LSPR) offers rapid gas detection but faces selectivity and fabrication complexity challenges. This letter reports a method of fabricating LSPR gas sensor arrays with pixel patterns by inkjet printing. We developed Au/Ag nanoparticles ink with different compositions to collect spectrum information for accurate gas discrimination. In addition, principal component analysis (PCA) is used to realize the selectivity among four common gases that we often encounter in our daily lives. For the first time, this work demonstrates the fabrication of LSPR gas sensors through inkjet printing. Based on the LSPR phenomenon for gas detection, it shows great potential for rapid, sensitive, and selective gas detection for future research.

We synthesized gold (Au) nano-urchins (AuNUs) and deposited these AuNUs on indium tin oxide (ITO) glass. We compared the influence of the resistance of ITO glass and the deposition density of NUs on refractive index sensitivity (RIS) of these ITO substrates, and found that ITO glass with resistance of 8–12 /sq and substrates with many AuNU dimers gave the highest RIS which was as high as 455 nm/RIU (refractive index unit), which is the highest on substrate as we known. Compared with AuNUs deposited on quartz glass, the extinction peak intensity and RIS were enhanced on ITO glass. The RIS enhancement is mainly attributed to the considerably enhanced electric field at the tips of AuNUs, electrical hot spots generated by AuNU aggregates (such as dimers), and the repulsive forces decrease in each AuNU by the ITO layer. It is thought that further shape, distribution, and size change of AuNUs and dimers on ITO glass will greatly affect the RIS and spectral characteristics. AuNU substrate is then proposed for heparin detection through Au etching. The results showed that heparin detection was realized in a linear range of 0.05 to $5~\mu \text{g}$ /mL with a detection limit of 8 ng/mL, which has potential to be applied in the practical environment.