We present the first implementation of complex-valued classical least squares (CLS) regression in spectroscopy. Although the results indicate that complex-valued CLS does not outperform methods that utilize only the more suitable part of the complex refractive index spectra, it includes an error detection feature that enables a self-correction mechanism. This mechanism decreases the mean absolute error (MAE) to approximately 26% relative to using only the mid-infrared (MIR) absorption index (k) spectra for CLS, and to about 46% relative to using only the MIR refractive index (n) spectra of benzene-toluene mixtures. For benzene-cyclohexane mixtures, the MAE was reduced to approximately 75% relative to the k spectra and 58% relative to the n spectra. In contrast, for benzene-carbon tetrachloride (CCl4) mixtures, i.e., a system that exhibits particularly large deviations from Beer's law, no improvement over the n spectra was observed; the n-based MAE was 81% relative to the k spectra. These percentages may further vary based on the complexity of the system, the spectral regions selected for CLS and the corresponding deviations from Beer's approximation.

Sensing with undetected photons has enabled new, unconventional approaches to Fourier transform infrared spectroscopy (FT-IR). Leveraging properties of non-degenerate entangled photon pairs, mid-infrared (mid-IR) information can be accessed in the near-infrared (near-IR) spectral domain to perform mid-IR spectroscopy with silicon-based detection schemes. Here, we address practical aspects of vibrational spectroscopy with undetected photons using a quantum FT-IR (QFT-IR) implementation. The system operates in the spectral range from around 3000 cm-1 to 2380 cm-1 (detection at around 12 500 cm-1) and possesses only 68 pW of mid-IR probing power for spectroscopic measurements with a power-dependence of the signal-to-noise ratio of 1.5 × 105 mW-1/2. We evaluate the system's short- and long-term stability and experimentally compare it to a commercial FT-IR instrument using Allan-Werle plots to benchmark our QFT-IR implementation's overall performance and stability. In addition, comparative qualitative spectroscopic measurements of polymer thin films are performed using the QFT-IR spectrometer and a commercial FT-IR with identical resolution and integration times. Our results show under which conditions QFT-IR can practically be competitive or potentially outperform conventional FT-IR technology.

The measurement of thin films with a thickness in the nanometer range is challenging because it requires extensive sample preparation, vacuum condition, long measurement times or using test inks that additionally contaminate the surface. The detection of those films is crucial for production processes that rely on a boundary layer to create a proper interface like adhesive bonding, coating, or lithography in various industries like automotive, solar, energy storage and semiconductor manufacturing. Consequently, there is a need for quick, reliable measurement techniques with high sensitivity to ensure the technical cleanliness of the opaque surface. In this paper the feasibility of epi-detection with coherent Raman scattering (CRS) Imaging is investigated on different substrate materials and demonstrated to be a method for fast scanning of large nontransparent surfaces including chemical fingerprinting of the substances atop. Therefore, various samples with low surface energy filmic contaminations from polysiloxanes are produced and investigated with CRS Imaging, a technique mostly applied to biological samples with the novel use demonstrated here for surface contamination monitoring in material sciences.

Polymer films with a thickness in the two-digit micrometer range are coated with nanometer-thin oxide layers in roll-to-roll coating systems. The coating improves the properties of the film, such as gas or water permeation. Maintaining a sufficiently large coating thickness is crucial to ensure its barrier function; thus, inline quality control of the thickness is indispensable. For this purpose, we have developed a sensing principle that addresses specific absorption bands of the coating via a reflection measurement in the infrared spectral range. However, for thin and weakly absorbing polymer substrates, light is reflected not only by the coating and the surface of the polymer. Partly it is also transmitted and reflected by the backside of the film, leading to interference effects that significantly affect the measurement signal. As industrial films vary in thickness by several percent and their exact values are unknown, determining the thickness of an oxide coating is hindered. In this paper, we demonstrate an approach for measuring coating thickness on such varying polymer films by averaging the interferences obtained at multiple angles of incidence. Calculations and measurements on industrial film samples indicate the effectiveness of our approach. It produces results with nm precision and nm accuracy for a thickness in the range of 5-100 nm. Furthermore, we discuss a possible implementation of this approach in an inline measurement system by fulfilling its requirements, for example, versatility and compactness.

In this study, Raman spectra (3700-10 cm-1) and attenuated total reflection infrared-far-infrared (ATR-IR/FIR) spectra (4000-50 cm-1) including low-frequency region were measured for amorphous rocks, which were five types of obsidians whose formation ages and sources are different and pitchstone to clarify the differences in water content (free and bound water species), their Si-O bonds and possible linkage with a metal ion, and the mean atomic volume. In order to explore these points, we focused on infrared (IR) absorptions of hydroxyl (OH) groups that is observed in the 4000-3000 cm-1 region, those of Si-O bond that is identified in the 1300-850 cm-1 region and a Boson peak that appears in a low-frequency region of Raman spectra, respectively. IR absorption of Si-O stretching was detected for all samples and that of OH stretching and H-O-H bending was also detected in some rocks. Therefore, using IR spectroscopy was useful to discriminate each rock based on the water content and the environment of Si-O bonds. On the other hands, a Boson peak could be detected for the low-frequency region below 60 cm-1 of Raman spectra, which appears in amorphous solids. This study is the first finding that the Raman shift of Boson peak was different among similar natural glassy rocks from multiple sources and it means that the mean atomic volume of samples was different. In addition, sharp bands of Raman scattering which came from inorganic substances such as feldspar helped to identify ingredients in samples. As a results, we made clear that using both IR and Raman including low-frequency regions is effective to identify the same types of natural amorphous rocks.

Raman spectroscopy is a powerful characterization technique with increasing applications that would greatly benefit from data harmonization. Several standards deal with calibration in Raman spectroscopy, but no detailed procedure covers the complete calibration of an instrument, including both spectral axes, from reference material spectra generation to data processing. Moreover, the type of reference materials, the quality of the recorded spectra and the choice of the fitting functions are critical for obtaining precise and reliable reference data for calibration. This report describes the challenges and importance of peak fitting for Raman signal calibration based on an interlaboratory study with 10 different instruments. Spectra of neon emission, silicon, calcite, and polystyrene were fitted using common peak shapes, observing that Gaussian, Pearson IV, Voigt, and Voigt shapes are preferred for these materials, respectively. An analysis of the effect on the fitting of the signal-to-noise ratio (S/N) recommends a minimum value of 100 for a Raman peak if it should be used to calibrate a Raman instrument. Some factors that might affect the peak shape of the Raman signal, such as the physical and chemical properties of the sample, the nature of the electronic transitions, the instrument response and the spectral resolution are discussed. The results highlight the role of peak fitting analysis in improving the quality and reliability of Raman spectra calibration and, thus, enhancing data transfer and comparability, especially for handheld and portable Raman analyzers, as well as applications based on quantification, multivariate data analysis, and other complex processing steps.

In the present work, appropriate machine learning techniques coupled with LIBS have been proposed for the effective classification of multielement rock samples. To obtain the best classification efficiency most suitable emission lines were selected. Plasma on the surface of seventeen rock samples was generated using a 532 nm Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser, and optical emission spectra were collected via an Avantes spectrometer. Well-isolated signature emission lines corresponding to detected elements (Ca, Mg, Na, K, Fe, Ba, Sr, Si, Al, and Li) were chosen as input for the machine learning algorithms. Three machine learning techniques, including analysis of variance (ANOVA), principal component analysis (PCA), and PCA coupled with standard normal variate (SVM), were utilized on normalized intensities of selected spectral lines of detected elements. ANOVA testing on the selected lines was employed to assess the normality and suitability of data for further machine learning techniques. The combination of laser-induced breakdown spectroscopy (LIBS) with PCA enabled a comprehensive classification of rock samples. The linearity and efficiency of PCA were enhanced by utilizing the support vector machine (SVM), resulting in the accurate classification of rock samples. This study demonstrates that to assess the effective classification of multielement rock samples the appropriate emission lines and machine learning techniques are crucial. Using this methodology results become more reliable as compared to conventional machine learning techniques.

Physiological systems are not at equilibrium and undergo time-dependent fluctuations, making it challenging to relate in vitro studies to in vivo biomolecular behavior. To bridge this gap, enzyme dynamics can be studied in the presence of controlled perturbations that recapitulate the intracellular environment. Here, we report an approach to the study of reactive oxygen species (ROS) based on the in situ manipulation of hydrogen peroxide (H2O2) levels in functionalized nanopore-based electrochemical zero-mode waveguide (EZMW) arrays, with each nanopore presenting small numbers of immobilized horseradish peroxidase (HRP) enzyme molecules. H2O2 is generated or consumed within the attoliter volume of the EZMW nanopores by poising an embedded ring electrode to suitable potentials, and the resulting effect on apparent turnover of HRP under non-equilibrium conditions is monitored using the enzymatically accelerated conversion of the non-fluorescent probe Amplex Red to fluorescent resorufin. A Nafion membrane is placed on the top surface of the EZMW array, providing a cation permselective barrier to transport in, or out, of the EZMW nanopores, thereby improving the sensitivity of the experiment by sequestering enzymatically generated resorufin in the attoliter volume of the EZMW nanopores. By fabricating arrays presenting 441 individual reaction volumes in parallel, distinct changes in population dynamics in the presence of in situ H2O2 generation or consumption are characterized with respect to temporal evolution and magnitude of the H2O2 aliquot delivered. This approach presents a promising avenue for studying biomolecular reactions in spatiotemporally controlled chemical environments that can mimic the non-equilibrium conditions encountered in vivo.