reduce the size of these sensors, [4][5][6][7][8][9][10][11][12] since it exhibits a unique surface condition with tailored spectral properties and strong electric field enhancement, which results in a high sensitivity to the surroundings. [13][14][15][16][17][18][19][20][21] To date, most approaches toward metamaterial inspired sensors are based on perfect light absorption. [17,19,22,23] However, the measurement of their reflectance makes both optical alignment and chip integration challenging. [24] Furthermore, metamaterial sensors that are designed for the M-IR are rarely discussed because robust sources, detectors, and efficient components are limited. [25] In addition, despite metamaterial sensors exhibiting excellent sensitivity at their designed frequency, demonstrations of wideband or frequency-swept detection are uncommon. This is because the metamaterial optical response is usually fixed by its dimensions and dielectric properties. [26] Consequently, measurements at different wavelengths using a single metamaterial device are impossible. Recently, metamaterials that are tuned by integrating graphene, [27] vanadium dioxide, [28] liquid crystals, [29] and metal hydrides [30] were investigated. Particularly, the graphene [27] and vanadium dioxide [28] based metasurfaces are promising for dynamically tuning plasmonic induced transparency.Note, these tuning mechanisms tend to be impractical in the M-IR region because the materials involved have a strong Drude contribution to the dielectric function. The M-IR region is, however, an important spectral band where there exists an atmospheric transparency window and molecular vibrational fingerprints. [31][32][33] Chalcogenide phase change materials (PCMs) have a remarkable portfolio of properties. [34][35][36] In particular, unlike silica glasses, the low phonon energies of chalcogenides allow them to be transparent in the M-IR. [37] The fast switching between two structural states of the phase change data storage material, Ge 2 Sb 2 Te 5 (GST), make it ideal for active photonic devices. [34,[38][39][40][41][42][43][44][45][46][47][48] Notably, in the M-IR region GST exhibits a pronounced contrast in the real part of the permittivity (ε r ), and a negligibly small ratio of the imaginary (ε i ) to the ε r , indicating low absorptive losses. [49,50] In addition, it is now possible to design the nanostructure of GST to achieve specific switching characteristics. [51,52] Thus, there is evidence that chalcogenide PCMs can enable practical spectroscopically programmable M-IR metamaterials.In this work, we demonstrate a phase change material tuned transmissive M-IR metasurface. The metasurface consists of an array of Au square pillars stacked above a GST switchable layer. Contrary to other metal-dielectric-metal trilayer reflective metamaterials, our design works in transmission mode by avoidingThe intense light-matter interaction of plasmonic metasurfaces provides an appealing platform for optical sensing. To date, most metasurface sensors are not spectrally tuned. Mo...

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Fourier transform-infrared (FT-IR) spectroscopy has gained considerable attention among the forensic scientists because it shows high sensitivity and selectivity and offers near real time detection of analyzed samples. However, the amount of obtained information due to complexity of the measured spectra forces the use of additional data processing. Application of the multivariate statistical techniques for the analysis of the FT-IR data seems to be necessary in order to enable feature extraction, proper evaluation, and identification of obtained spectra. In this article, an attempt to develop a feasible procedure for characterization of spectroscopic signatures of the explosive materials in the remnants after explosion has been made. All spectra were derived after analysis of samples from debris after especially prepared and performed blasts with the use of three various highly explosive materials: C-4, 2,4,6-trinitrotoluene (TNT), and pentaerythritol tetranitrate (PETN). Two well-known multivariate statistical methods, hierarchical cluster analysis (HCA) and principal component analysis (PCA), were tested in order to classify the samples into separate classes using a broad wavelength data range (4000-600 cm(-1)) on collected spectra sets. After many trials it seems that PCA is the best choice for the mentioned earlier tasks. It was found that only three principal components carry over 99.6% of variance within the sample set. The results show that FT-IR spectroscopy in combination with multivariate methods is well-suited for identification and differentiation purposes even in quite large data sets, and for that reason forensic laboratories could employ these methods for rapid screening analysis.