Quantitative Spectrum Analysis Research Articles

Corrosion of 316L stainless steel produced by laser powder bed fusion and powder metallurgy in pressurized water reactor primary coolant

Corrosion mechanism of 316 L stainless steel produced by laser powder bed fusion-hot isostatic pressing (LPBF-HIP) and powder metallurgy-hot isostatic pressing (PM-HIP) is studied with in-situ electrochemical impedance measurements coupled to detailed oxide film characterization. Quantitative analysis of impedance spectra using the Mixed-Conduction Model and estimation of local kinetic and transport parameters by interpretation of in-depth elemental composition profiles indicated lower corrosion and oxidation rates of LPBF-HIP and PM-HIP materials in comparison to conventional wrought 316 L. This owes to a higher fraction of low-angle grain boundaries, smaller grain size, the presence of nano-sized oxide particles and elevated Cr and Ni contents.

Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra.

Machine learning (ML)-enabled diffuse reflectance spectroscopy (DRS) is increasingly used as an alternative to the computation-intensive inverse Monte Carlo (MCI) simulation to predict tissue's optical properties, including the absorption coefficient, and reduced scattering coefficient, . We aim to develop a use-error-robust ML algorithm for optical property prediction from DRS spectra. We developed a wavelength-independent regressor (WIR) to predict optical properties from DRS data. For validation, we generated 1520 simulated DRS spectra with the forward Monte Carlo model, where to , and to . We introduced common use-errors, such as wavelength miscalibrations and intensity fluctuations. Finally, we collected 882 experimental DRS images from 170 tissue-mimicking phantoms and compared performances of the WIR model, a dense neural network, and the MCI model. When compounding all use-errors on simulated data, the WIR model best balanced accuracy and speed, yielding errors of 1.75% for and 1.53% for , compared to the MCI's 50.9% for and 24.6% for . Regarding experimental data, WIR model had mean errors of 13.2% and 6.1% for and , respectively. The errors for MCI were about eight times higher. The WIR model presents reliable use-error-robust optical property predictions from DRS data.

HYDROGEN BONDING EFFECT ON OPTICAL PROPERTIES OF SUPRAMOLECULAR LIQUID CRYSTALS IN UV VISIBLE REGION

This study utilizes spectral analysis techniques and various computational methods to examine the impact of hydrogen bonding on the optical characteristics of supramolecular liquid crystals within the UV-visible spectrum.This investigation involves the computational method of spectral denoising for quantitative analysis of UV-visible spectra, and then spectral analysis is carried out for the measurement of the optical properties of supramolecular liquid crystals. Other computational methods, like the texture absorbance method, are also employed for colorimetric studies of compounds.The investigation will focus on supramolecular systems:p,n- alkoxy benzoic acid: Do-decyl hydroxybenzoate (nOBA:12HB), where n = 7, 8, and 9. The supramolecular system is formed via hydrogen bond development between the OH acceptor of the non-liquid crystalline substance Do-decyl hydroxyl benzoate (12HB) and the COOH donor of the liquid crystal p-n alkoxy benzoic acid. Here, optical properties: such as absorption coefficient, optical energy band gap, Urbach energy, and refractive index of supramolecular liquid crystals are measured for the denoised spectral data. The influence of hydrogen bonding and alkoxy chain length on the optical properties of liquid crystals has been deliberated upon. Further, the attempt at computation studies on the spectral data will yield important insights for subsequent investigations into UV-visible spectral data measurements.

Luminescence features of Ho3+ , Er3+ and Tm3+ ions in red lead added non-conventional antimony oxide glass system.

In this study, a glass system based on heavy metal oxides, namely Pb3 O4 -Sb2 O3 -SiO2 , was prepared with Ho2 O3 , Er2 O3 , and Tm2 O3 as dopants. The prepared glass were characterized using X-ray diffraction (XRD) and infrared (IR) studies. The luminescence efficiencies of the three rare earth ions viz., Ho3+ , Er3+ , and Tm3+ in the glass system were investigated, specifically focusing on green (Ho3+ , Er3+ ), blue (Tm3+ ), and near-infrared (NIR) emissions. The optical absorption (OA) and photoluminescence (PL) spectra of RE mixed glass displayed strong bands in the visible and NIR regions, which were analyzed using the Judd-Ofelt (J-O) theory. The J-O coefficients (Ωλ ) were found to be in the order of Ω2 > Ω4 > Ω6 for all the three glass. Notably, the Tm3+ -doped glass exhibited the lowest Ω2 value, which was attributed to a larger degree of disorder in the glass network due to lower fractions of Sb5+ and Pb4+ ions that take part in the glass network forming. This conclusion was arrived based on the IR studies. Furthermore, the quantitative analysis of PL spectra and decay curves indicated that SbHo and SbEr glass were suitable for efficient green emission, while SbTm glass is a promising choice for blue laser emission.

Quantitative alignment parameter estimation for analyzing X-ray photoelectron spectra.

The interpretation of X-ray photoelectron spectroscopy (XPS) data relies on measurement models that depend on several parameters, including the photoelectron attenuation length and X-ray photon flux. However, some of these parameters are not known, because they are not or cannot be measured. The unknown geometrical parameters can be lumped together in a multiplicative factor, the alignment parameter. This parameter characterizes the ability of the exciting light to interact with the sample. Unfortunately, the absolute value of the alignment parameter cannot be measured directly, in part because it depends on the measurement model. Instead, a proxy for the experimental alignment is often estimated, which is closely related to the alignment parameter. Here, a method for estimating the absolute value of the alignment parameter based on the raw XPS spectra (i.e. non-processed photoelectron counts), the geometry of the sample and the photoelectron attenuation length is presented. The proposed parameter estimation method enables the quantitative analysis of XPS spectra using a simplified measurement model. All computations can be executed within the open and free Julia language framework PROPHESY. To demonstrate feasibility, the alignment parameter estimation method is first tested on simulated data with known acquisition parameters. The method is then applied to experimental XPS data and a strong correlation between the estimated alignment parameter and the typically used alignment proxy is shown.

Chemometrics Approach Based on Wavelet Transforms for the Estimation of Monomer Concentrations from FTIR Spectra.

Fourier-transform infrared (FTIR) spectroscopy can detect the presence of functional groups and molecules directly from a mixed solution of organic molecules. Although it is quite useful to monitor chemical reactions, quantitative analysis of FTIR spectra becomes difficult when various peaks of different widths overlap. To overcome this difficulty, we propose a chemometrics approach to accurately predict the concentration of components in chemical reactions, yet interpretable by humans. The proposed method first decomposes a spectrum into peaks with various widths by the wavelet transform. Subsequently, a sparse linear regression model is built using the wavelet coefficients. Models by the method are interpretable using the regression coefficients shown on Gaussian distributions with various widths. The interpretation is expected to reveal the relation of broad regions in spectra to the model prediction. In this study, we conducted the prediction of monomer concentration in copolymerization reactions of five monomers against methyl methacrylate by various chemometric approaches including conventional methods. A rigorous validation scheme revealed that the proposed method overall showed better predictive ability than various linear and non-linear regression methods. The visualization results were consistent with the interpretation obtained by another chemometric approach and qualitative evaluation. The proposed method is found to be useful for calculating the concentrations of monomers in copolymerization reactions and for the interpretation of spectra.

Enhancement of weak magnetism of 3C–SiC by 3d-transition metal doping

Defects seem to play a very decisive role in setting up long-range magnetic order in 3C–SiC. The feeble ferromagnetic response exhibited by SiC at room temperature, known as d0 magnetism, has been attributed to defects in the system. We present the strengthening of d0 magnetism through hole doping by 3d-transition metal (TM) substitution at the Si site. The valence state of the Mn, Co, and Ni has been found to be 2+ while Cr takes a 3+ valence state. The Mn-doped system exhibits the highest Curie temperature, saturation magnetization, and spontaneous magnetization value as compared to samples with other dopants. Detailed quantitative analysis of magnetization data and electron spin resonance spectra suggests the enhancement of the d0 magnetism due to the generation of holes caused by the TM doping at the Si site. The room temperature ferromagnetic response observed in the doped samples could be explained using modified Bound Magnetic Polaron (BMP) model.

User-independent nonlinear modeling using adjusted spline-interpolated knots (UNMASK) and indirect hard modeling for deriving compositions from spectra with background signals

Backgroundsignals of different shapes and intensities pose a major challenge for the quantitative analysis of mixture spectra from Raman-, Mid-IR- or NMR-spectroscopy. Commonly used approaches for the treatment of such background signals are the subtraction of the background before the spectral analysis, derivative methods and the modeling of the background during the spectral analysis. Only the latter enables the unambiguous distinction between background and spectral features without any distortion of the spectral features. Indirect Hard Modeling (IHM) allows for such joint modeling of spectral features and background signals. However, so far, complex background signals containing distinctive peaks remain challenging for IHM. Furthermore, the user chooses and parametrizes the background treatment method for IHM on a case-by-case basis depending on the spectral properties (e.g. shapes and intensities of the backgrounds or signal-to-noise-ratio). We present a new method called User-independent Nonlinear Modeling using Adjusted Spline-interpolated Knots (UNMASK) that is parametrized by minimizing the prediction error of the calibration spectra, allowing to omit any user-decisions. Besides, unlike other background treatment methods, UNMASK is suited for many cases regardless of the spectral properties, and can even be applied for mixtures with complex background signals, e.g. caused by unknown/unspecified components. We validate UNMASK by applying it to three Raman mixture spectra, covering different application scenarios with varying spectral properties. By comparing the results from UNMASK with different commonly used methods, we show that UNMASK is the only method that consistently leads to low prediction errors for all application scenarios.

Peak-fitting of Cu 2p photoemission spectra in Cu0, Cu1+, and Cu2+ oxides: A method for discriminating Cu0 from Cu1+

The photoemission spectra of Cu 2p are one of the most studied. Its analysis is especially challenging because, among others, the peaks for Cu0 and Cu1+ overlap, and Cu1+ and Cu2+ coexist under many conditions. The chemical state determination of metallic copper and the initial oxidation mechanism of copper require state-of-the-art peak fitting analysis and background modeling. This work presents a detailed and quantitative analysis of the Cu 2p photoemission spectra for a metallic ultra-thin film and the initial oxidation stages. Additionally, annealed films at 200 °C for up to 5 min were analyzed to assess the full oxidation of the copper oxides and assert the validity of the proposed peak-fitting procedures. For the initial oxidation, it was possible to accurately identify Cu1+ and Cu2+ states and, alongside, quantify the corresponding O 1s species, resulting in compositions close to stoichiometric values. Using the active background, it was possible to achieve close reproduction of all the photoemission spectra; three coexisting copper species were resolved during the fitting procedure. An accurate peak-fitting analysis allowed for quantifying the relative proportion of Cu1+ to Cu0 states despite their strong overlap.

Deep learning baseline correction method via multi-scale analysis and regression

Due to the fluorescence or instrument error, the captured spectra are influenced by the baseline, and the baseline may impact the qualitative and quantitative analysis of spectra. Currently, numerous studies have attempted to remove the impact of spectra baseline, among which multi-scale analysis and regression methods are the most representative methods. It is worth noting that deep learning has made progress in data analysis, but remains rare for baseline correction. In this paper, a baseline correction method based on deep learning is proposed. Specifically, we use the mathematical and physical significance of multi-scale analysis and regression to design a deep learning model and propose a non-convex and non-smooth loss function. Furthermore, some deep learning modules are added to the designed network model. The experiments of simulation, real, and application demonstrate the proposed method achieves state-of-the-art performance.

Fast adaptive sampling with operation time control

The rational choice of sampling points is crucial for supervised machine learning algorithms. Adaptive sampling ensures optimal distribution of points in the multidimensional space and provides higher approximation accuracy with respect to the random sampling. Most approaches to the adaptive sampling maximize the function evaluating the compromise between local exploitation and global exploration. However, existing accurate methods suffer from long sampling time, while fast methods are not accurate. Only a few reported methods are available as open source implementations. We introduce a fast adaptive sampling algorithm with accuracy comparable to that of the best-known adaptive sampling methods and demonstrate a comparative study of different approaches to minimization of approximation errors. The method scales linearly with number of sampling points and supports batch generation of new points. The optimization function is the integral norm of an approximation error, the Lp norm, where the norm parameter p regulates sampling, inclining it either towards local exploitation or, conversely, global exploration. The small Lp norm is achieved by reducing the function approximation error and the size of the region with large variation after adding a new sampling point. Our solution is similar to kriging in terms of the choice between local exploitation and global exploration. The difference is in error estimation which depends on the values of the function in sampling points in contrast to homoscedastic variance estimate of kriging. Fast and accurate adaptive sampling is of interest to supervised machine learning tasks such as training a model interatomic potential, quantitative analysis of spectra, self-driving chemical laboratories, and many others. The source code for the new adaptive sampling algorithm MinLpE is available on Github and is distributed under a LGPL license.

Quantitative analysis of Cu XANES spectra using linear combination fitting of binary mixtures simulated by FEFF9

X-ray absorption near edge structure (XANES) is a powerful tool to probe the fingerprint of local structures, and when coupled with X-ray microscopy, the small spot size enables one to probe very specific regions of interest in a device or material, e.g. interfaces/bulk, different grains, good/bad electrical areas. In this work, we investigate the use of linear combination fitting (LCF) of XANES spectra for the particular case of Cu doping in CdTe. We show that the experimental data seem to be accurately represented by standards of Cu2Te and its substoichiometric counterpart, Cu1.43Te. We use Cu in CdTe as a case study to evaluate the accuracy of linear combination fitting using simulated standards, given that experimental standards for certain phases (e.g. Cu1.43Te) or defect structures (e.g. vacancies) cannot be readily obtained. We discuss how spectral features of the FEFF9-simulated standards, fitting ranges, and noise levels all dictate the accuracy of this type of analysis. We show that the greater the spectral difference between the two standards, the better the LCF is able to differentiate between the two structures and to tolerate experimental noise. Finally, we estimate the error of the fitted weights for different spectral features and noise levels and propose a framework to study local structures semi-quantitatively by using binary mixtures of FEFF9-simulated standards.

Micro-groove manufacturing via a femtosecond laser on optically clear adhesive films

In this study, the femtosecond laser ablation of optically clear adhesives (OCAs) was carried out to machine a smooth groove for optoelectronic module and chip integrated manufacturing. A series of microstructures (holes, channels and grooves) were machined and investigated with different incident laser energies. To design an appropriate array period and inverted trapezoidal shape for the channels, a smooth groove with average roughness 5.7μm was machined. After processing, a new method with visual detection was introduced to evaluate the tack of micro-areas. The results illustrate that both the tack and transmittance of the films are decreased after laser irradiation. With quantitative analysis of FTIR spectra, a massive loss of chemical bonds (hydrogen radicals) leads to performance degradation at the macro level. The partial preservation of the C-O,C=O and -OH bond maintains the stability of the irradiated structure with a small tack and transmittance.

Circular Dichroism Spectra of α-Chymotrypsin-SDS Solutions Depend on the Procedure of Their Preparation.

We recorded the far- and near-UV circular dichroism (CD) spectra of solutions of α-chymotrypsin and sodium dodecyl sulfate (SDS) with the final surfactant concentration significantly above the critical micellization concentration. Solutions were prepared using three different procedures. The reference procedure was to mix the chymotrypsin solution with the SDS solution once, immediately achieving the final SDS concentration. In alternative procedures, the protein solutions initially contained some SDS and were mixed with pure SDS solutions at a concentration to provide the same final surfactant as the reference mixing. We demonstrate that the supplementation to the selected final concentration of SDS of the pure chymotrypsin solution leads to different CD spectra than the supplementation to this final concentration of SDS in the chymotrypsin solution containing a small concentration of a few millimolar SDS. These differences disappear when the initial concentration of SDS in the protein solution, which we then supplement to the indicated final concentration, is higher. This suggests the irreversibility of the processes caused by the addition of SDS to chymotrypsin and the influence of the initial amount of this surfactant on the processes occurring with its further addition to the solution. For quantitative analysis of far-UV CD spectra in terms of populations of protein secondary structure elements, we used four well-established software packages. All programs consistently indicate that the observed differences in the far-UV CD spectra can be explained by the differences in the increase in the population of helical forms in chymotrypsin under the influence of SDS.

Quantitative study on the structure-bioavailability relationship of dissolved organic nitrogen in wastewater treatment plant effluent.

This study systematically investigated the relationship between the structure properties and biological characteristics of dissolved organic nitrogen (DON) in the effluents from municipal wastewater treatment plants. Ultrafiltration, Fourier transform infrared (FTIR) spectroscopy, ultraviolet (UV) spectroscopy, and excitation-emission matrix (EEM) fluorescence spectroscopy were used to characterize the structure of organic matters in the effluent samples, and the bioavailability of DON was determined by algal/bacterial-based bioassay. The quantitative analysis of EEM spectra conducted by fluorescence regional integration method showed that the organic portion of all samples was mainly consistent with fulvic acid and protein. Combined with the bioassay results, a positive correlation between the DON bioavailability and the protein content (sum of region I and region II) (r = 0.80, P < 0.02) and soluble microbial byproduct-like materials (region IV) (r = 0.76, P < 0.03) were observed. Nevertheless, the humic substances content represented by the region III and V would negatively affect the DON bioavailability. High humification degree (high HIX value) (r = - 0.77, P < 0.03) was related to low bioavailability. Furthermore, according to UV spectroscopy results, strong aromaticity (high UV254 values) (r = - 0.78, P < 0.03) suggested low DON bioavailability. One protein-like component (C3) and two humic-like components (C1 and C2) were identified via fluorescence excitation-emission matrices-parallel factor analysis (EEM-PARAFAC), and component C3 values were positively correlated to the BAN/DON ratio (r = 0.74, P < 0.03). The ultrafiltration showed that the low molecular weight DON (< 3kDa) accounted for 30-73% of the total DON, and no notable relationship was observed for DON molecular weight and its bioavailability.

Reliable and broad-range layer identification of Au-assisted exfoliated large area MoS2 and WS2 using reflection spectroscopic fingerprints

The emerging Au-assisted exfoliation technique provides a wealth of large-area and high-quality ultrathin two-dimensional (2D) materials compared with traditional tape-based exfoliation. Fast, damage-free, and reliable determination of the layer number of such 2D films is essential to study layer-dependent physics and promote device applications. Here, an optical method has been developed for simple, high throughput, and accurate determination of the layer number for Au-assisted exfoliated MoS$_2$ and WS$_2$ films in a broad thickness range. The method is based on quantitative analysis of layer-dependent white light reflection spectra, revealing that the reflection peak intensity can be used as a clear indicator for determining the layer number. The simple yet robust method will facilitate the fundamental study on layer-dependent optical, electrical, and thermal properties and device applications of 2D materials. The technique can also be readily combined with photoluminescence and Raman spectroscopies to study other layer-dependent physical properties of 2D materials.

SpectraNet–53: A deep residual learning architecture for predicting soluble solids content with VIS–NIR spectroscopy

This work presents a new deep learning architecture, SpectraNet–53, for quantitative analysis of fruit spectra, optimized for predicting Soluble Solids Content (SSC, in °Brix). The novelty of this approach resides in being an architecture trainable on a very small dataset, while keeping a performance level on-par or above Partial Least Squares (PLS), a time-proven machine learning method in the field of spectroscopy. SpectraNet–53 performance is assessed by determining the SSC of 616 Citrus sinensi L. Osbeck ‘Newhall’ oranges, from two Algarve (Portugal) orchards, spanning two consecutive years, and under different edaphoclimatic conditions. This dataset consists of short-wave near-infrared spectroscopic (SW-NIRS) data, and was acquired with a portable spectrometer, in the visible to near infrared region, on-tree and without temperature equalization. SpectraNet–53 results are compared to a similar state-of-the-art architecture, DeepSpectra, as well as PLS, and thoroughly assessed on 15 internal validation sets (where the training and test data were sampled from the same orchard or year) and on 28 external validation sets (training/test data sampled from different orchards/years). SpectraNet–53 was able to achieve better performance than DeepSpectra and PLS in several metrics, and is especially robust to training overfit. For external validation results, on average, SpectraNet–53 was 3.1% better than PLS on RMSEP (1.16 vs. 1.20 °Brix), 11.6% better in SDR (1.22 vs. 1.10), and 28.0% better in R2 (0.40 vs. 0.31).

Sintesis Komposit ZnO/Ca3(PO4)2 menggunakan metode Sol-gel sebagai Material Fotokatalis Limbah Cair Industri (Metilen Biru)

This study use composite ZnO/Ca 3 (PO 4 ) 2 material was synthesized by using the sol-gel method. Composite material ZnO/Ca 3 (PO 4 ) 2 is made from beef tailbone which is heated at a temperature of 300 for 10, 90, 120, and 240 minutes. Samples were characterized using X-ray diffraction ( XRD ), Fourier transforms infrared ( FTIR ) dan UV - Visible ( UV-Vis ). Quantitative analysis of XRD spectra showed the lowest crystal size with a diameter of 21.71 nm on heating for 90 m. The results of the FTIR spectrum show the vibration of the PO 4 and ZnO bond at wavenumber 1039 cm -1 and 400-500 cm -1 . The best percentage of degradation is indicated by small crystal size and high infrared absorption by O-H bonds. The degradation results reached 93.98% in just 5 minutes with an energy gap of 3.84 eV. Based on these results, the ZnO/Ca3(PO4)2 composite is one of the promising and potential catalyst materials to solve the problem of industrial waste pollution, especially methylene blue.

Multicomponent Alkane IR Measurement System Based on Dynamic Adaptive Moving Window PLS

Infrared (IR) spectroscopy for multicomponent alkane measurement has shown excellent potentials in unconventional oil and gas exploration. However, the cross-sensitivity of alkane IR absorption and the complexity of unconventional oil and gas components place extreme demands on quantitative IR spectroscopy models. To cope with these problems, an adaptive moving partial least square (AMW-PLS) modeling method for the quantitative analysis of IR spectra is proposed in this paper. Based on the designed moving control function and dual endpoint independent moving strategy, AMW-PLS can quickly determine the optimal local modeling interval location and width by tracking the accuracy of the calibration model dynamically. By transposing the proposed AMW-PLS into the designed infrared gas logging system in this paper, 88 samples of 2 different mixture types were tested and compared with the other three typical analytical methods. The experimental results show that AMW-PLS can dynamically track the IR characteristic shifts caused by the changes in the gas mixture component complexity and concentration distribution, and thus achieves the best performance. The instrument achieved an average relative error of 3.3651% for five alkane substances in three standard industrial gas mixtures under strong interference, which provides new technical support for early discovery of oil and gas reservoirs.

Effect of accelerating gas temperature on microstructure and properties of cold sprayed Al coating on polyether ether ketone (PEEK)

Cold spray (CS) is an emerging deposition technique for the metallization of polymer material to improve its thermal and electrical properties. In this study, pure Al coatings were deposited on polyether ether ketone (PEEK) substrate by CS. Further, the influence of accelerating gas temperature on microstructure and properties (i.e. deposition efficiency, adhesive strength, electrical conductivity, thermal diffusivity and hardness distribution) of the prepared coatings was systematically investigated. Energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), the quantitative analysis of oxygen content and Raman spectra results revealed that the prepared coatings were composed of crystalline metallic Al without obvious oxide phases. Moreover, the polymer structure of PEEK substrate remained intact after high-velocity and high-temperature impact during the CS process. The adhesive strength, electrical conductivity and thermal diffusivity of the Al coating increased with increasing accelerating gas temperature. At the accelerating gas temperature of 380 °C, a dense and thick Al coating with high adhesive strength was obtained in just two deposition passes. Moreover, multi-pass tests (at high nozzle traverse speed) were performed to understand the deposition mechanism of Al coating on PEEK substrate through analyzing cross-section/surface morphology of samples with different thicknesses. Finally, the deposition mechanism of cold sprayed Al coating on PEEK was proposed.