Substrate-Integrated Hollow Waveguides: A New Level of Integration in Mid-Infrared Gas Sensing

A new generation of hollow waveguide (HWG) gas cells of unprecedented compact dimensions facilitating low sample volumes suitable for broad- and narrow-band mid-infrared (MIR; 2.5–20 μm) sensing applications is reported: the substrate-integrated hollow waveguide (iHWG). iHWGs are layered structures providing light guiding channels integrated into a solid-state substrate material, which are competitive if not superior in performance to conventional leaky-mode fiber optic silica HWGs having similar optical pathlengths. In particular, the provided flexibility in device and optical design and the wide variety of manufacturing strategies, substrate materials, access to the optical channel, and optical coating options highlight the advantages of iHWGs in terms of robustness, compactness, and cost-effectiveness. Finally, the unmatched modularity of this novel waveguide approach facilitates tailoring iHWGs to almost any kind of gas sensor technology providing adaptability to the specific demands of a wide range of sensing scenarios. Device fabrication is demonstrated for the example of a yin-yang-shaped gold-coated iHWG fabricated within an aluminum substrate with a footprint of only 75 mm × 50 mm × 12 mm (L × W × H), yet providing a nominal optical absorption path length of more than 22 cm. The analytical utility of this device for advanced MIR gas sensing applications is demonstrated for the gaseous constituents butane, carbon dioxide, cyclopropane, isobutylene, and methane

Preclassification of broadband and sparse infrared data by multiplicative signal correction approach

Abstract Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied

Preprocessing strategies for sparse infrared spectroscopy:a case study on cartilage diagnostics

Abstract The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm⁻¹, followed by peak normalization at 850 cm⁻¹ and preprocessing by MSC

Fiber-Coupled Substrate-Integrated Hollow Waveguides: An Innovative Approach to Mid-infrared Remote Gas Sensors

In this study, an innovative approach based on fiberoptically coupled substrate-integrated hollow waveguide (iHWG) gas cells for the analysis of low sample volumes suitable for remote broad- and narrow-band mid-infrared (MIR; 2.5–20 μm) sensing applications is reported. The feasibility of remotely addressing iHWG gas cells, configured in a double-pass geometry via a reflector, by direct coupling to a 7-around-1 mid-infrared fiber bundle is demonstrated, facilitating low-level hydrocarbon gas analysis. For comparison studies, two iHWGs with substrate dimensions of 50 × 50 × 12 mm (L × W × H) and geometric channel lengths of 138 and 58.5 mm, serving as miniature light-guiding gas cells, were fiber-coupled to a Fourier transform infrared spectrometer enabling broadband MIR sensing. In addition to the fundamental feasibility of this concept, the achievable sensitivity toward several gaseous hydrocarbons and the reproducibility of assembling the fiber-iHWG interface were investigated

Generation of Surface Plasmons at Waveguide Surfaces in the Mid-Infrared Region

A technique is proposed to extend the application of surface-plasmon-based spectroscopy into the mid-infrared spectral regime, which is of substantial interest in the field of chemical analysis and biosensing. Surface plasmons can be excited for wavelengths of the order of 6 μm at corrugated waveguides for a given combination of materials and thicknesses, and for refractive indices of the surrounding medium corresponding to those of organic solvents. This approach can easily be extrapolated to other values of any of these parameters. Based on these considerations, a new generation of mid-IR SPR sensors can be developed with a diverse range of potential applications in chem/bio sensing