Spectra Of Mixtures Research Articles

OxPAPC stabilizes liquid-ordered domains in biomimetic membranes

Long-chain polyunsaturated fatty acids (PUFAs) are prone to nonenzymatic oxidation in response to differing environmental stressors and endogenous cellular sources. There is increasing evidence that phospholipids containing oxidized PUFA acyl chains control the inflammatory response. However, the underlying mechanism(s) of action by which oxidized PUFAs exert their functional effects remain unclear. Herein, we tested the hypothesis that replacement of 1-palmitoyl-2-arachidonyl-phosphatidylcholine (PAPC) with oxidized 1-palmitoyl-2-arachidonyl-phosphatidylcholine (oxPAPC) regulates membrane architecture. Specifically, with solid-state 2H NMR of biomimetic membranes, we investigated how substituting oxPAPC for PAPC modulates the molecular organization of liquid-ordered (Lo) domains. 2H NMR spectra for bilayer mixtures of 1,2-dipalmitoylphosphatidylcholine-d62 (an analog of DPPC deuterated throughout sn-1 and -2 chains) and cholesterol to which PAPC or oxPAPC was added revealed that replacing PAPC with oxPAPC disrupted molecular organization, indicating that oxPAPC does not mix favorably in a tightly packed Lo phase. Furthermore, unlike PAPC, adding oxPAPC stabilized 1,2-dipalmitoylphosphatidylcholine-d6-rich/cholesterol-rich Lo domains formed in mixtures with 1,2-dioleoylphosphatidylcholine while decreasing the molecular order within 1,2-dioleoylphosphatidylcholine-rich liquid-disordered regions of the membrane. Collectively, these results suggest a mechanism in which oxPAPC stabilizes Lo domains—by disordering the surrounding liquid-disordered region. Changes in the structure, and thereby functionality, of Lo domains may underly regulation of plasma membrane-based inflammatory signaling by oxPAPC.

TSMC-Net: Deep-Learning Multigas Classification Using THz Absorption Spectra.

The identification of gas mixture speciation from a complex multicomponent absorption spectrum is a problem in gas sensing that can be addressed using machine-learning approaches. Here, we report on a deep convolutional neural network for multigas classification using terahertz (THz) absorption spectra, THz spectra mixture classifier network or TSMC-Net. TSMC-Net has been developed to identify eight volatile organic compounds in mixtures based on their fingerprint rotational absorption spectra in the 220-330 GHz frequency range. A data set consisting of simulated absorption spectra for randomly generated mixtures, with absorption greater than thresholds representing detectable limits and annotated with multiple labels, was prepared for model development. The supervised multilabel classification problem, i.e., the identification of individual gases in a mixture, is converted to a supervised multiclass classification problem via label powerset conversion. The trained model is validated and tested against simulated spectra for gas mixtures, with and without white Gaussian noise. The trained model exhibits high precision, recall, and accuracy for each pure compound. Class activation maps illustrate the complex decision-making process of the model and highlight relevant frequency regions that are needed to identify unique mixtures. Finally, the model was demonstrated against measured THz absorption spectra for pure species and mixtures, acquired using a microelectronics-based THz absorption spectrometer. The data set generation strategy and deep convolutional neural network approach are generalized and can be extrapolated to other spectroscopy types, frequency ranges, and sensors.

A Nuclear Magnetic Resonance Solvent Impurity Jigsaw-Style Activity for Undergraduate Organic Chemistry Students

A cooperative-learning jigsaw-style activity was developed to create opportunities for introductory organic chemistry students to discuss and decipher 1H NMR spectra. Students cooperatively interpreted spectra that contained both pure and mixed solvents, building consensus through explanation and argumentation on what each spectrum represented. Through participating in this cooperative learning activity, students refined their ability to analyze complex, crude spectra that typically contained contaminants. The activity was designed to engage students in decoding data that are more realistic and applicable to research laboratories, where spectra are oftentimes difficult to interpret due to impurities such as unevaporated solvents and byproducts along with their intended product. Student perceptions of the activity were captured from reflective discussion in class and an end of course survey. Most students reported a shift in thinking about how they approached analyzing NMR spectra and found the activity helpful in learning to interpret spectra of complex mixtures.

Reflection, emission, and polarization properties of surfaces made of hyperfine grains, and implications for the nature of primitive small bodies

Solar System small bodies were the first objects to accrete inside the protoplanetary disk, giving insights into its composition and structure. The P-/D-type asteroids are particularly interesting because of the similarity of their spectra, at visible and near infrared wavelengths (Vis-NIR), with cometary nuclei, suggesting that they are the most primitive types of small bodies. There are various indications that (1) their low albedo in the visible (Vis) and mid-infrared (MIR) wavelength ranges seems mainly controlled by the presence of opaque minerals (iron sulfides, FeNi alloys etc.) (Quirico et al., 2016; Rousseau et al., 2018); and (2) their surfaces are made of intimate mixtures of these opaque minerals and other components (silicates, carbonaceous compounds, etc.) in the form of sub-micrometre-sized grains, smaller than the wavelength at which they are observed, so-called “hyperfine” grains. Here, we investigate how the Vis-NIR-MIR (0.55–25 μm) spectral and V-band (0.53 μm) polarimetric properties of surfaces made of hyperfine grains are influenced by the relative abundance of such hyperfine materials, having strongly different optical indexes. Mixtures of grains of olivine and iron sulfide (or anthracite), as analogues of silicates and opaque minerals present on small bodies, were prepared at different proportions. The measurements reveal that these mixtures of hyperfine grains have spectral and polarimetric Vis-NIR properties varying in strongly nonlinear ways. When present at even a few percent, opaque components dominate the Vis-NIR spectral and polarimetric properties, and mask the silicate bands at these wavelengths. The Vis-NIR spectral slope ranges from red (positive slope), for pure opaque material, to blue (negative slope) as the proportion of silicates increases, which is reminiscent of the range of spectral slopes observed on P/D/X/C- and B-types asteroids. The spectra of the darkest mixtures in the Vis-NIR exhibit the absorption bands of SiO in olivine around 10 μm in the MIR, which is observed in emission for several small bodies. The samples studied here have macro- and micro-porosities lower than 78%, indicating that surfaces more compact than “fairy castle” hyperporous (80–99%) ones can also exhibit a blue spectral slope or a silicate signature at 10 μm. Remarkably, some mixtures exhibit altogether a red spectral slope in the Vis-NIR, a 10-μm feature in the MIR, and a V-band polarimetric phase curve similar (but not identical) to P-/D-type asteroids, reinforcing the hypothesis that these bodies are made of powdery mixtures of sub-micrometre-sized grains having contrasted optical indexes. This work shows that both the contrasted optical indexes of the components, and the dispersion or aggregation −depending on their relative proportions− of their hyperfine grains, induce different light scattering regimes in the Vis-NIR and MIR, as observed for primitive small bodies. The optical separation of hyperfine grains seems to be a major parameter controlling the optical properties of these objects.

Collision-induced absorption spectra (CIA) for monatomic gas mixtures of Helium-Xenon

The lineshapes of collision-induced absorption (CIA) at room temperature are computed quantum mechanically for gaseous binary mixtures of helium with xenon using theoretical induced dipole moment and interatomic potentials. Empirical and literature models of the induced dipole moments which reproduce the experimental spectra and the first few spectral moments are given. Good agreement between the computed and experimental lineshapes of absorption is obtained when the potential parameters which are fitted well to the vibrational energy levels, thermophysical and transport properties are used.

Simultaneous Determination for Lansoprazole and Simvastatin drugs via Ultraviolet Spectrophotometry

A semi-novel, accurate, sensitive, simple, requiring no prior separation and economical procedures have been developed for the simultaneous analysis of binary mixture of Lansoprazole (Lanso) and Simvastatin (Semva). The first method is the Derivative of ratio spectra, in which the absorption spectrum of one drug as interfering drug were subtracted from the absorption spectrum of the mixture and then the net spectrum derivative, the first derivative (for Lanso) and second derivative (for Semva). Beer’s law was obeyed in the concentration ranges of (1-90) and (4-70) μg.ml-1 and the results showed that the method was precise and accurate with RSD% of (0.0161-0.0235), (0.0360-0.0525) and R.E% (0.3125-0.4545), (-0.0909-0.0312) for Lanso and Semva respectively. The second method based on ratio difference, where the absorption spectrum of mixtures the drugs was divided by the on the absorption spectrum of a suitable the interfering drug. The difference in peak amplitudes between the two selected wavelengths in the ratio spectra were recorded for each drug. Beer’s law was obeyed in the concentration ranges of (0.1-30) and (1-40) μg.ml-1 and the results of the method were precise and accurate with RSD% of (0.2880-0.4580), (0.1294-0.9880) and R.E% of (-0.0090-0.0909), (-0.0651-0.0710) for Lanso and Semva respectively of this method.
 The proposed methods were successfully applied for the determination of the two compounds.

ConInceDeep: A novel deep learning method for component identification of mixture based on Raman spectroscopy

For mixture component identification, the methods based on deep learning are becoming prevalent due to their end-to-end characteristic, being completely data-driven and reducing the demand for prior knowledge. The most used CNN model currently, however, relies only on the original one-dimensional (1D) spectral data. When faced with complex mixtures, the features of overlapping peaks and weak peaks are probably not sufficient mined by 1D CNN model, which would reduce the identification accuracy. Thus, a novel deep learning method entitled ConInceDeep for component identification of mixture by Raman spectroscopy was proposed in this study. The ConInceDeep firstly performed continuous wavelet transform on 1D spectral data using the Lorentz4 wavelet to fully reveal the detailed characteristics of weak and overlapping spectral peaks in the mixture and then employed Inception modules consisting of multiple-size kernels to construct two-dimensional CNN model to improve the model's adaptability to different Raman peaks. The proposed ConInceDeep model was trained and validated entirely by the spectra of virtual mixtures which were generated by simple manipulation of substances' spectra in the database. Three mixture datasets were used for verifying the identification performance of the ConInceDeep, including 191 liquid mixtures, 33 powder mixtures and two kinds of real samples. In liquid mixture set, ConInceDeep was compared with other three experimental methods, and achieved an increase of Acc from 90.32% to 96.60% compared with the traditional 1D-CNN. In powder mixtures set, ConInceDeep reached 99.66% Acc and 0.35% FPR by 54 models. Additionally, it was also applied to real samples to demonstrate its value. Summarily, the ConInceDeep provides a new reference for mixture identification based on Raman spectroscopy.

Thermodynamic and spectral studies of molecular interactions in binary liquid mixtures of 1-butoxy-2-propanol with amines at different temperatures

ABSTRACT Binary liquid mixtures of 1-butoxy-2-propanol with amines namely diethylamine, dipropylamine and triethylamine have been discussed in the present discussion. Measurements of density and speed of sound of pure liquids and their binary mixtures have been carried out over the entire composition range and temperatures (298.15, 303.15 and 308.15) K. To study the thermodynamic behaviour of these liquid mixtures, various parameters were calculated using the experimentally measured data. Strong intermolecular interactions among the components of binary liquid mixtures were confirmed by calculated parameters. Various excess and deviation properties were correlated to composition using the Redlich–Kister polynomial. Further, density and speed of sound data were correlated with composition using Jouyban–Acree model. The experimental results have been analysed by using Prigogine–Flory–Patterson (PFP) theory. 1H-NMR spectra of binary mixtures at equimolar composition has also been studied.

Raman spectroscopy for the identification of body fluid traces: Semen and vaginal fluid mixture

Raman spectroscopy is a versatile research tool that has found numerous practical applications recently. High specificity, non-destructive nature, and potential for on-field application make Raman spectroscopy an attractive tool for forensic science. The great potential of Raman spectroscopy for confirmatory identification of all main body fluids has been recently reported (Vyas et al. Forensic Chemistry 2020, 100247). Bringing the developed methodology from the laboratory to the crime scene requires further method development and validation for realistic, practical samples subjected to environmental conditions, contamination, substrate interference, aging, etc. This work targets the validation of the developed methodology for body fluid mixtures, semen, and vaginal fluid in particular, which are critical for sexual assault cases. Near-infrared (NIR) Raman spectra of dry semen-vaginal fluid mixtures were acquired in automatic scanning mode and subjected to advanced statistical analysis. Support Vector Machine Discriminant Analysis (SVMDA) was used to develop three binary classification models, including vaginal fluid vs mixture, semen vs mixture, and semen vs vaginal fluid. These binary models were combined to build a single tertiary model, which resulted in the 100% correct classification on the sample level for all examined mixtures (1.5% to 100% for vaginal fluid and 3% to 100% for semen). The developed analytical approach could be used for various other applications when complex binary mixtures need to be detected and analyzed.

Machine Learning Spectroscopy Using a 2-Stage, Generalized Constituent Contribution Protocol.

A corrected group contribution (CGC)-molecule contribution (MC)-Bayesian neural network (BNN) protocol for accurate prediction of absorption spectra is presented. Upon combination of BNN with CGC methods, the full absorption spectra of various molecules are afforded accurately and efficiently-by using only a small dataset for training. Here, with a small training sample (<100), accurate prediction of maximum wavelength for single molecules is afforded with the first stage of the protocol; by contrast, previously reported machine learning (ML) methods require >1,000 samples to ensure the accuracy of prediction. Furthermore, with <500 samples, the mean square error in the prediction of full ultraviolet spectra reaches <2%; for comparison, ML models with molecular SMILES for training require a much larger dataset (>2,000) to achieve comparable accuracy. Moreover, by employing an MC method designed specifically for CGC that properly interprets the mixing rule, the spectra of mixtures are obtained with high accuracy. The logical origins of the good performance of the protocol are discussed in detail. Considering that such a constituent contribution protocol combines chemical principles and data-driven tools, most likely, it will be proven efficient to solve molecular-property-relevant problems in wider fields.

Infrared Spectroscopy of Polymers X: Polyacrylates

Acrylate polymers are derivatives of acrylic acid, but contain a plethora of different functional groups and are best discussed by themselves. In this column, we study the spectrum of an important polymer, polymethyl methacrylate (PMMA), which is otherwise known as plexiglass, and is found in windows, car parts, and paint. We then study the spectra of PMMA mixtures and copolymers in detail.

Midinfrared Spectroscopic Analysis of Aqueous Mixtures Using Artificial-Intelligence-Enhanced Metamaterial Waveguide Sensing Platform.

As miniaturized solutions, mid-infrared (MIR) waveguide sensors are promising for label-free compositional detection of mixtures leveraging plentiful absorption fingerprints. However, the quantitative analysis of liquid mixtures is still challenging using MIR waveguide sensors, as the absorption spectrum overlaps for multiple organic components accompanied by strong water absorption background. Here, we present an artificial-intelligence-enhanced metamaterial waveguide sensing platform (AIMWSP) for aqueous mixture analysis in the MIR. With the sensitivity-improved metamaterial waveguide and assistance of machine learning, the MIR absorption spectra of a ternary mixture in water can be successfully distinguished and decomposed to single-component spectra for predicting concentration. A classification accuracy of 98.88% for 64 mixing ratios and 92.86% for four concentrations below the limit of detection (972 ppm, based on 3σ) with steps of 300 ppm are realized. Besides, the mixture concentration prediction with root-mean-squared error varying from 0.107 vol % to 1.436 vol % is also achieved. Our work indicates the potential of further extending this sensing platform to MIR spectrometer-on-chip aiming for the data analytics of multiple organic components in aqueous environments.

Unexpected Photocatalytic Degeneration of NAD + for Inducing Apoptosis of Hypoxia Cancer Cells

Unexpected Photocatalytic Degeneration of NAD ⁺ for Inducing Apoptosis of Hypoxia Cancer Cells

Synthesis and Characterization of Yttrium Methanediide Silanide Complexes

The salt metathesis reactions of the yttrium methanediide iodide complex [Y(BIPM)(I)(THF)2] (BIPM = {C(PPh2NSiMe3)2}) with the group 1 silanide ligand-transfer reagents MSiR3 (M = Na, R3 = tBu2Me or tBu3; M = K, R3 = (SiMe3)3) gave the yttrium methanediide silanide complexes [Y(BIPM)(SitBu2Me)(THF)] (1), [Y(BIPM)(SitBu3)(THF)] (2), and [Y(BIPM){Si(SiMe3)3}(THF)] (3). Complexes 1-3 provide rare examples of structurally authenticated rare earth metal-silicon bonds and were characterized by single-crystal X-ray diffraction, multinuclear NMR and ATR-IR spectroscopies, and elemental analysis. Density functional theory calculations were performed on 1-3 to probe their electronic structures further, revealing predominantly ionic Y-Si bonding. The computed Y-Si bonds show lower covalency than Y═C bonds, which are in turn best represented by Y+-C- dipolar forms due to the strong σ-donor properties of the silanide ligands investigated; these observations are in accord with experimentally obtained 13C{1H} and 29Si{1H} NMR data for 1-3 and related Y(III) BIPM alkyl complexes in the literature. Preliminary reactivity studies were performed, with complex 1 treated separately with benzophenone, azobenzene, and N,N'-dicyclohexyl-carbodiimide. 29Si{1H} and 31P{1H} NMR spectra of these reaction mixtures indicated that 1,2-migratory insertion of the unsaturated substrate into the Y-Si bond is favored, while for the latter substrate, a [2 + 2]-cycloaddition reaction also occurs at the Y═C bond to afford [Y{C(PPh2NSiMe3)2[C(NCy)2]-κ4C,N,N',N'}{C(NCy)2(SitBu2Me)-κ2N,N'}] (4); these reactivity profiles complement and contrast with those of Y(III) BIPM alkyl complexes.

Computational chromatography: A machine learning strategy for demixing individual chemical components in complex mixtures

Surface-enhanced Raman spectroscopy (SERS) holds exceptional promise as a streamlined chemical detection strategy for biological and environmental contaminants compared with current laboratory methods. Priority pollutants such as polycyclic aromatic hydrocarbons (PAHs), detectable in water and soil worldwide and known to induce multiple adverse health effects upon human exposure, are typically found in multicomponent mixtures. By combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of machine learning (ML), we examine whether individual PAHs can be identified through an analysis of the SERS spectra of multicomponent PAH mixtures. We have developed an unsupervised ML method we call Characteristic Peak Extraction, a dimensionality reduction algorithm that extracts characteristic SERS peaks based on counts of detected peaks of the mixture. By analyzing the SERS spectra of two-component and four-component PAH mixtures where the concentration ratios of the various components vary, this algorithm is able to extract the spectra of each unknown component in the mixture of unknowns, which is then subsequently identified against a SERS spectral library of PAHs. Combining the molecular fingerprinting capabilities of SERS with the signal separation and detection capabilities of ML, this effort is a step toward the computational demixing of unknown chemical components occurring in complex multicomponent mixtures.

Ultra-high resolution near-infrared spectrum by wavelet packet transform revealing the hydrogen bond interactions

Resolution is always an obstacle to analyzing the fine structure of a spectrum. The problem is particularly serious in the analysis of the near-infrared (NIR) spectra of aqueous solutions, because the spectrum is generally composed of overlapping broad peaks making the understanding of the structures and the interactions notoriously difficult. In this work, wavelet packet transform (WPT) was adopted to enhance the resolution of the NIR spectra of aqueous mixtures. Due to the microscopic ability of WPT in both position and frequency, the fine details of a spectrum can be observed in the spectral components of different frequencies obtained by WPT decomposition. Ultra-high resolution spectrum can be obtained from the high-frequency component representing the spectral features. Spectral features of different hydrogen-bonded OH, as well as the OH in HOH and HOD, were identified from the high-resolution NIR spectra of water and heavy water mixtures and validated by the variation of the spectral intensity with the mole ratio of H2O and D2O. The high-resolution spectrum was further applied in analyzing the interaction of amine and water. The spectral features of the hydrogen bonding between CH/NH in tert-butylamine (TBA) and OH in water were observed. The structures of CH bonded to one water molecule, and the structures of NH connecting with one and two water molecules were identified.

R Shiny App for the Automated Deconvolution of NMR Spectra to Quantify the Solid-State Forms of Pharmaceutical Mixtures.

Bioavailability and chemical stability are important characteristics of drug products that are strongly affected by the solid-state structure of the active pharmaceutical ingredient (API). In pharmaceutical development and quality control activities, solid-state NMR (ssNMR) has proved to be an excellent tool for the detection and accurate quantification of undesired solid-state forms. To obtain correct quantitative outcomes, the resulting spectrum of an analytical sample should be deconvoluted into the individual spectra of the pure components. However, the ssNMR deconvolution is particularly challenging due to the following: the relatively large line widths that may lead to severe peak overlap, multiple spinning sidebands as a result of applying Magic Angle Spinning (MAS), and highly irregular peak shapes commonly observed in mixture spectra. To address these challenges, we created a tailored and automated deconvolution approach of ssNMR mixture spectra that involves a linear combination modelling (LCM) of previously acquired reference spectra of pure solid-state components. For optimal model performance, the template and mixture spectra should be acquired under the same conditions and experimental settings. In addition to the parameters controlling the contributions of the components in the mixture, the proposed model includes terms for spectral processing such as phase correction and horizontal shifting that are all jointly estimated via a non-linear, constrained optimisation algorithm. Finally, our novel procedure has been implemented in a fully functional and user-friendly R Shiny webtool (hence no local R installation required) that offers interactive data visualisations, manual adjustments to the automated deconvolution results, and the traceability and reproducibility of analyses.

Thermodynamic Properties of Binary Liquid Mixtures Containing Furfural with Chlorobenzene, Nitromethane, Diethylmalonate and 1-Butanol at 308.15K and 318.15K Supported by FTIR Spectral Studies

A study on thermodynamic properties of the liquid mixture is used in the industrial process, which often extends to solution chemistry. Density(ρ), Viscosity(η), and Ultrasonic velocity(U) were determined for binary liquid mixtures of furfural with chlorobenzene, nitromethane, diethyl malonate and 1- butanol at temperature of 308.15K and 318.15K at atmospheric pressure over the whole range of mole fractions. The calculated thermodynamic properties and some excess parameters such as Excess Volume(VE), Deviation in Isentropic Compressibility(∆KS), Deviation in Viscosity(∆η), Deviation in Intermolecular Free Length(∆LF), Deviation in Intermolecular Free Volume(∆VF), Deviation in Internal Pressure(∆π) and Deviation in Acoustic Impedance(∆Z) were calculated and applied to the Redlich-Kister type polynomial equation to determine the appropriate coefficients. The effects of composition and temperature on thermodynamic parameters have been studied in terms of molecule interaction in these liquid mixtures. Further, IR spectra of these liquid mixtures were recorded and the data were utilized to examine the mixing behavior of the components.

Quantitative analysis of aqueous biomolecular mixtures by THz spectroscopy based on high-power LiNbO3 radiation source

The rapid and accurate identification of the types and contents of early pathological markers by THz technology are of particular importance for the prevention and treatment of major diseases. Nevertheless, these markers usually contain interference from water and other non-target molecules, resulting in low signal-to-noise ratio (SNR) and making identification and quantitative analysis difficult. Here, based on THz spectroscopy from a high-power THz source radiated by LiNbO3, we perform quantitative and real-time THz detection of mixtures (α-lactose monohydrate and 4-aminobenzoic acid) in liquids. The results demonstrate that the absorption spectra of the aqueous biomolecular mixtures exhibit an accumulation of THz features of each pure product, i.e., the amplitude of the absorption peaks is proportional to the mixing ratio, while the corresponding absorption baseline increases with decreasing concentration. Furthermore, the content of the target substance can be calculated from the linear relationship between the absorption spectra of pure and mixed samples. This technology will support the future application of THz-TDS in early disease diagnosis under complex states and environments.

The rotational spectrum of HD broadened by H2 at temperatures between 100 – 296 K

Laboratory measurements of the R(0) – R(3) pure rotational transitions of hydrogen deuteride (HD) have been performed at low pressures (< 1 bar) and temperatures between 98 and 296 K. The measurements were conducted using a Fourier transform spectrometer coupled to the Soleil synchrotron far-infrared source to obtain pure HD and HD-H2 mixture spectra. The observed spectra have been fitted using a non-linear least-squares multispectral fitting program. For each transition the line position, intensity, pressure broadening, pressure-induced frequency shift, and the temperature dependencies of these quantities have been determined simultaneously. These spectroscopic line parameters are compared to previous measurements made at higher gas pressures. Notably the present, and previous, measurements of the self- and H2-broadened linewidths are smaller by a factor of five compared with those listed in the HITRAN and GEISA databases. This work is applicable to the low pressure, low temperature regimes found in the atmospheres of the giant gaseous planets.