Raman Spectroscopy Data Research Articles

Interaction between dirhodium(II) tetraacetate and PAMAM dendrimer grafted onto magnetite nanoparticles: Effects on magnetic properties

In this study, we report on fabrication and characterization of a novel magnetic nanocarrier based on association of magnetite nanoparticles (NPs) with poly(amidoamine) (PAMAM) dendrimers loading dirhodium(II) tetraacetate complex (DiRh(II)). The pristine magnetite NP was coated with ATPS (3-aminopropyltriethoxysilane). The interaction between PAMAM dendrimer and DiRh(II) and its effects on magnetic properties were investigated. The magnetite spinel phase is confirmed via x-ray diffraction (XRD) and Raman spectroscopy data analysis. A mean crystallite size of 9.5 ± 0.3 nm of magnetite particles is assessed from XRD data, which is close to the values obtained via dynamic light scattering (DLS) and transmission electron microscopy micrographs. Our findings strongly suggest that the Rh(II) ions interact with lone electron pairs of nitrogen atoms in amide-II and primary amines of PAMAM surface-terminated moieties. The onset of an absorption peak at 283 nm in DiRh(II) containing sample, assigned to σRh-L→σ∗Rh-Rh electronic transition, revealed a direct interaction between Rh(II) ions and L moieties of PAMAM dendrimer. Magnetization results indicate that the presence of DiRh(II) attached to PAMAM dendrimer leads to the strong weakening of magnetic dipole–dipole interactions in ATPS- and ATPS + PAMAM coated magnetite nanoparticles.

Dry ball-milling preparation and characterization of graphite–phthalocyanine composites

The hybrids of phthalocyanines with carbon nanotubes, graphene and graphene oxide were demonstrated previously to be promising for different catalytical, electrocatalytical and other applications. Nevertheless, the above carbon nanomaterials are toxic and relatively expensive. We attempted to substitute them by low-cost and harmless graphite, using dry ball-milling of graphite–phthalocyanine mixtures under mild conditions. According to scanning electron microscopy imaging and powder X-ray diffraction, the composites obtained exhibit the absence of crystalline phthalocyanine phase, which implies high efficiency of phthalocyanine milling. According to Raman spectroscopy data, the mechanochemical treatment does not provoke the formation of large amounts of defects in graphite, but instead helps its exfoliation. The composites were characterized by UV-visible spectroscopy and thermal gravimetric analysis, which showed that all of them exhibit a reduced thermal stability compared to unprocessed graphite by roughly 100–250 °C. The strength of interaction of phthalocyanines with a graphene sheet (as a graphite surface model), geometries and selected electronic parameters (frontier orbital and spin density distribution, HOMO-LUMO gap energies) of the resulting noncovalent complexes were analyzed at the PBE-D2/DNP theoretical level. The interactions of phthalocyanines with the graphene sheet are strong, with the calculated formation energies (absolute values) for the noncovalent complexes of 62.1–70.1 kcal/mol.

Crystal Growth and Complex Characterization of Novel Gallium- and Germanium-Rich Tourmalines: Refinement of the Crystal Structure, Cation Distribution, and Raman and Mössbauer Spectroscopy

The crystal growth and characterization of novel tourmaline compounds are important in fundamental and applied fields as new tourmaline species and promising piezoelectric materials. Single crystals of Ga,Ge-rich tourmalines (up to 12.43 wt % GeO2 and 11.03 wt % Ga2O3) were grown on a seed as a newly formed, up to 1.2 mm thick layer in hydrothermal solution at 650 °C and 100 MPa. Two crystallizing schemes were developed with quartz and corundum nutrients in the condition of a normal temperature solubility factor and with topaz and quartz nutrients using a reverse temperature solubility factor. The chemical composition and crystal structure of grown tourmalines as well as the distribution of gallium and germanium between crystallographic sites were studied by electron microprobe analysis (EMPA), single-crystal X-ray analysis (SCXRD), and Mössbauer and Raman spectroscopy. The unit cell parameters and refined crystal-chemical formulae of two high-quality single-crystal samples are as follows: a = 16.0518(7) Å, c = 7.1874(4) Å, V = 1603.80(13), and X(Na0.66□0.34) Y(Fe1.782+Al0.5Fe0.363+Ni0.19Ti0.17) Z(Al4.91Ga1.09) T(Si4.78Ge0.94Al0.28O18) (BO3)3 V(OH)3 W[(OH)0.42O0.58]; a = 16.0083(8), c = 7.1508(4) Å, V = 1587.00(14), and X(Na0.17□0.83) Y(Fe1.312+Al1.35Fe0.273+Ni0.07) Z(Al5.43Ga0.57) T(Si5.15Ge0.55Al0.30O18) (BO3)3 V(OH)3 W[(OH)0.51O0.49]. Based on the Raman spectroscopy data, it is now possible to indicate the predominance of either divalent or trivalent cations in the Y site for tourmalines, whereas the Z site is fully occupied by trivalent cations.

Reconstruction of Raman Spectra of Biochemical Mixtures Using Group and Basis Restricted Non-Negative Matrix Factorization.

Raman spectroscopy is a useful tool for obtaining biochemical information from biological samples. However, interpretation of Raman spectroscopy data in order to draw meaningful conclusions related to the biochemical make up of cells and tissues is often difficult and could be misleading if care is not taken in the deconstruction of the spectral data. Our group has previously demonstrated the implementation of a group- and basis-restricted non-negative matrix factorization (GBR-NMF) framework as an alternative to more widely used dimensionality reduction techniques such as principal component analysis (PCA) for the deconstruction of Raman spectroscopy data as related to radiation response monitoring in both cellular and tissue data. While this method provides better biological interpretability of the Raman spectroscopy data, there are some important factors which must be considered in order to provide the most robust GBR-NMF model. We here evaluate and compare the accuracy of a GBR-NMF model in the reconstruction of three mixture solutions of known concentrations. The factors assessed include the effect of solid versus solutions bases spectra, the number of unconstrained components used in the model, the tolerance of different signal to noise thresholds, and how different groups of biochemicals compare to each other. The robustness of the model was assessed by how well the relative concentration of each individual biochemical in the solution mixture is reflected in the GBR-NMF scores obtained. We also evaluated how well the model can reconstruct original data, both with and without the inclusion of an unconstrained component. Overall, we found that solid bases spectra were generally comparable to solution bases spectra in the GBR-NMF model for all groups of biochemicals. The model was found to be relatively tolerant of high levels of noise in the mixture solutions using solid bases spectra. Additionally, the inclusion of an unconstrained component did not have a significant effect on the deconstruction, on the condition that all biochemicals in the mixture were included as bases chemicals in the model. We also report that some groups of biochemicals achieve a more accurate deconstruction using GBR-NMF than others, likely due to similarity in the individual bases spectra.

Crystal structure, far-infrared spectroscopy, and microwave dielectric properties of two novel vanadates CaRECeV3O12 (RE = Nd, Sm)

Two novel low-εr CaRECeV3O12 (RE = Nd, Sm) microwave dielectric ceramics were prepared to utilize the solid-state method. According to XRD and Raman spectroscopy data, the CaRECeV3O12 (RE = Nd, Sm) ceramics had a tetragonal zircon-type with an I41/amd space group. Dense CaNdCeV3O12 and CaSmCeV3O12 ceramics sintered at 1225 °C and 1200 °C showed εr ∼11.85 ± 0.1 and 11.33 ± 0.1, Q × f ∼40,100 ± 500 GHz and 43,290 ± 500 GHz, τf ∼ −27.2 ± 0.5 ppm/°C and −22.4 ± 0.5 ppm/°C, along with αL of 9.4 ppm/°C and 8.5 ppm/°C, respectively. The measured εr and corrected εr(corr) values were larger than εr(C-M) (9.41 and 9.47) in CaNdCeV3O12 and CaSmCeV3O12 due to the cations being, on average, under-bonded and the overall structures being in a slightly expanded state. The slightly higher |<d>| value of CaNdCeV3O12 led to its higher εr(corr) value, even though it had a lower αD/Vm value. The sign and magnitude of τf values for CaNdCeV3O12 and CaSmCeV3O12 were mainly determined by their positive temperature coefficients of dielectric polarizability ταm of +36.2 ppm/°C and +32.1 ppm/°C, respectively. The inherent dielectric constant and dielectric loss were examined using far-infrared reflectivity spectra, Raman spectra, and the packing fraction.

High-temperature behavior of the UV-luminescent sodalite-type natural compound Na8(Al6Si6O24)(HS)2: Comparative study by in situ single-crystal X-ray diffraction and Raman spectroscopy

The in situ single-crystal X-ray diffraction and Raman spectroscopy were performed for a sapozhnikovite Na8(Al6Si6O24)(HS)2, a rare mineral with sodalite-type crystal structure. The crystal chemical analysis and calculation of thermal expansion coefficients based on the refinement of Na8(Al6Si6O24)(HS)2 crystal structures in the temperature range of 300–1273 K show that this sodalite-type compound undergoes: (1) reversible phase transition P4‾ 3n ↔ Pm3‾n at 1223 K, (2) drastic increase of linear expansion coefficient at 1173 K, (3) non-elastic behavior of sapozhnikovite framework after cooling. The temperature dependencies of the unit-cell parameters were divided into two parts and described by quadratic a = 8.911(12) – 0.000018(33)T + 0.000000138(22)T2 (from 300 to 1173 K) and linear a = 7.765(13) + 0.001130(11)T (from 1173 to 1273 K) polynomial functions. Raman spectroscopy data reveal the partial transformations of the extra-framework HS− anion by follows reactions: 6HS–→ S3•– + 3H2S (gas) and 2HS– – 2e → S22− + 2H+. These transformations may cause the observed discontinuity in the thermal expansion curve for sapozhnikovite at 1173 K with drastic increase of linear expansion coefficient and not fully elastic behavior of framework after removal of temperature exposure. After the heat treatment experiments (300–1273 K) the HS− anion restores in the β-cage of sapozhnikovite crystal structure. In the synthetic analogue of sapozhnikovite the heat treatment leads to the irreversible transformation according to the reactions: 2HS– + 2.5O2 (gas) → SO42− + 0.25S4 + H2O (gas) and 2HS– + 3.5O2 (gas) → SO42− + SO2 (gas) + H2O (gas). The orange luminescence of Na8(Al6Si6O24)(HS)2 is stable up to the 1073 K.

Non-Perturbative Identification and Subtyping of Amyloidosis in Human Kidney Tissue with Raman Spectroscopy and Machine Learning.

Amyloids are proteins with characteristic beta-sheet secondary structures that display fibrillary ultrastructural configurations. They can result in pathologic lesions when deposited in human organs. Various types of amyloid protein can be routinely identified in human tissue specimens by special stains, immunolabeling, and electron microscopy, and, for certain forms of amyloidosis, mass spectrometry is required. In this study, we applied Raman spectroscopy to identify immunoglobulin light chain and amyloid A amyloidosis in human renal tissue biopsies and compared the results with a normal kidney biopsy as a control case. Raman spectra of amyloid fibrils within unstained, frozen, human kidney tissue demonstrated changes in conformation of protein secondary structures. By using t-distributed stochastic neighbor embedding (t-SNE) and density-based spatial clustering of applications with noise (DBSCAN), Raman spectroscopic data were accurately classified with respect to each amyloid type and deposition site. To the best of our knowledge, this is the first time Raman spectroscopy has been used for amyloid characterization of ex vivo human kidney tissue samples. Our approach, using Raman spectroscopy with machine learning algorithms, shows the potential for the identification of amyloid in pathologic lesions.

Applying Material Science Principles to Chemical Stability: Modelling Solid State Autoxidation in Mifepristone Containing Different Degrees of Crystal Disorder

Ball-milling and harsh manufacturing processes often generate crystal disorder which have practical implications on the physical and chemical stabilities of solid drugs during subsequent storage, transport, and handling. The impact of the physical state of solid drugs, containing different degrees/levels of crystal disorder, on their autoxidative stability under storage has not been widely investigated. This study investigates the impact of differing degrees of crystal disorder on the autoxidation of Mifepristone (MFP) to develop a predictive (semi-empirical) stability model. Crystalline MFP was subjected to different durations of ambient ball milling, and the resulting disorder/ amorphous content was quantified using a partial least square (PLS) regression model based on Raman spectroscopy data. Samples of MFP milled to generate varying levels of disorder were subjected to a range of (accelerated) stability conditions, and periodically sampled to examine their recrystallization and degradation extents. Crystallinity was monitored by Raman spectroscopy, and the degradation was evaluated by liquid chromatography. The analyses of milled samples demonstrated a competition between recrystallization and degradation via autoxidation of MFP, to different extents depending on stability conditions/exposure time. The degradation kinetics were analyzed by accounting for the preceding amorphous content, and fitted with a diffusion model. An extended Arrhenius equation was used to predict the degradation of stored samples under long-term (25°C/60% RH) and accelerated (40°C/75% RH, 50°C/75% RH) stability conditions. This study highlights the utility of such a predictive stability model for identifying the autoxidative instability in non-crystalline/partially crystalline MFP, owing to the degradation of the amorphous phases. This study is particularly useful for identifying drug-product instability by leveraging the concept of material sciences.

Identification of three medically important mosquito species using Raman spectroscopy

AbstractAccurate identification of disease vectors is crucial when collecting epidemiological data. In mosquitoes, which transmit diseases like malaria, yellow fever, chikungunya, and dengue fever, identification mainly relies on the observation of external morphological features at different life cycle stages. This process is tedious and labor‐intensive. In this paper, the utility of Raman spectroscopy to discriminate and classify three mosquito species, namely,Aedes aegypti,Anopheles gambiae,andCulex quinquefasciatus,is presented. The three species were chosen to represent two subfamilies of medically important mosquitoes, that is, the Anophelinae and the Culicinae. The study is primarily a proof of concept on the potential of Raman spectroscopy in mosquito taxonomy. A dispersive Raman microscope was used to record spectra from the legs (femur and tibia) of fresh anesthetized laboratory‐bred mosquitoes. Broad peaks centered around 1400, 1590, and 2060 cm−1dominated the spectra. These peaks, attributed to cuticular melanin, were important in mosquito discrimination. Variance threshold (VT) and principal component analysis (PCA) were used for feature selection and feature extraction, respectively. The extracted features were then used to train and test linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) classifiers. VT/PCA/QDA achieved an overall accuracy of 94%, a sensitivity of 87%, and a specificity of 96%, whereas VT/PCA/LDA attained an accuracy of 85%, a sensitivity of 69%, and a specificity of 90%. The success of these relatively simple classification models on Raman spectroscopy data lays the ground for future development of machine learning models that may include discrimination of cryptic species.

Effect of Supersonic Nitrogen Flow on Ceramic Material Ta4HfC5–SiC

The behavior of the ceramic material Ta4HfC5-30 vol % SiC has been studied under the effect of supersonic flow of dissociated nitrogen, which is necessary to assess the potential application of these materials in oxygen-free gas environments at temperatures 1800°C. It has been found that as a result of heating the surface to ~2020°C in a few minutes there is a decrease to ~1915°C followed by a slow decrease to 188°C. This is probably due to the chemical processes occurring on the surface and the formation of an extremely rough microstructure. The ablation rate has been determined; it has been shown that neither at introduction of the sample into a high enthalpy nitrogen flow nor at sharp cooling (temperature drop to ~880°C in 9–10 s) cracking of the sample or detachment of the near-surface region has been observed. X-ray powder diffraction and Raman spectroscopy data allow us to conclude the complete removal of silicon carbide from the surface layer and the transformation of complex tantalum-hafnium carbide into the nitride.

Power‐Law‐Based Synthetic Minority Oversampling Technique on Imbalanced Serum Surface‐Enhanced Raman Spectroscopy Data for Cancer Screening

Surface‐enhanced Raman spectroscopy (SERS) has shown highly promising for existing cancer screening. However, previous “proof‐of‐concept” studies ignored the natural imbalance of cancer types in the population, leading the model to be biased toward learning more features in majority class during the learning process at the expense of ignoring minority class. Herein, a power‐law‐based synthetic minority oversampling technique (PL‐SMOTE) method is proposed to guide the resampling of multiclass serum SERS data by analyzing the long‐tailed (power‐law) distribution of cancer prevalence in the population. The proposed PL‐SMOTE method balances the number of minorities to resample and the number of overlaps between classes by introducing modulating factor. Modeling on resampled datasets synthesized by PL‐SMOTE verifies the effectiveness of proposed PL‐SMOTE method. After further fine‐tuning, the parameters of the deep neural network model and PL‐SMOTE method, an optimal cancer screening model with an optimal macroaveraged Recall score of 97.24% and an optimal macroaveraged F2‐Score of 97.38% is obtained. A new method for multiclass imbalanced resampling is provided, which has significant improvement on model performance in terms of SERS cancer screening. The method also inspires in other multiclass imbalanced scenario, such as biological medicine, abnormal detection, and disaster prediction.

Spherical DNA for Probing Wettability of Microplastics.

Wettability of microplastics may change due to chemical or physical transformations at their surface. In this work, we studied the adsorption of spherical nucleic acids (SNAs) with a gold nanoparticle core and linear DNA of the same sequence to probe the wettability of microplastics. Soaking microplastics in water at room temperature for 3 months resulted in the enhancement of SNA adsorption capacity and affinity, whereas linear DNA adsorption was the same on the fresh and soaked microplastics. Drying of the soaked microplastics followed by rehydration decreased the adsorption of the SNA, suggesting that the effect of soaking was reversible and related to physical changes instead of chemical changes of the microplastics. Raman spectroscopy data also revealed no chemical transformations of the soaked microplastics. Heating of microplastics over a short period induced a similar effect to long-term soaking. We propose that soaking or heating removes air entrapped in the nanosized pores at the water-plastic interface, increasing the contact surface area of the SNA to afford stronger adsorption. However, such wetted porosity would not change the adsorption of linear DNA because of its much smaller size.

A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning.

Raman spectroscopy has been widely used to provide the structural fingerprint for molecular identification. Due to interference from coexisting components, noise, baseline, and systematic differences between spectrometers, component identification with Raman spectra is challenging, especially for mixtures. In this study, a method entitled DeepRaman has been proposed to solve those problems by combining the comparison ability of a pseudo-Siamese neural network (pSNN) and the input-shape flexibility of spatial pyramid pooling (SPP). DeepRaman was trained, validated, and tested with 41,564 augmented Raman spectra from two databases (pharmaceutical material and S.T. Japan). It can achieve 96.29% accuracy, 98.40% true positive rate (TPR), and 94.36% true negative rate (TNR) on the test set. Another six data sets measured on different instruments were used to evaluate the performance of the proposed method from different aspects. DeepRaman can provide accurate identification results and significantly outperform the hit quality index (HQI) method and other deep learning models. In addition, it performs well in cases of different spectral complexity and low-content components. Once the model is established, it can be used directly on different data sets without retraining or transfer learning. Furthermore, it also obtains promising results for the analysis of surface-enhanced Raman spectroscopy (SERS) data sets and Raman imaging data sets. In summary, it is an accurate, universal, and ready-to-use method for component identification in various application scenarios.

Murphyite, Pb(TeO4), the Te-Analogue of Raspite, a New Mineral from Tombstone, Arizona, USA

Abstract A new mineral species, murphyite (IMA 2021-107), ideally Pb(TeO4), has been found from the Grand Central mine, Tombstone, Arizona, USA. It occurs as bladed or prismatic crystals on top of a quartz matrix. Associated minerals include chlorargyrite, emmonsite, ottoite, stolzite, scheelite, schieffelinite, quartz, and jarosite. Individual crystals of murphyite are up to 0.20 × 0.05 × 0.05 mm in size. Twinning is common on {100}. Murphyite is colorless to very pale yellow in transmitted light, transparent with white streak and adamantine luster. It is brittle and has a Mohs hardness of ∼3½, with perfect cleavage on {100}. The calculated density is 7.579 g/cm3. Murphyite is insoluble in water or hydrochloric acid. An electron microprobe analysis yielded the empirical formula (based on 4 O apfu): (Pb0.96Fe0.03Mn0.02)Σ1.01[(Te0.61W0.38)Σ0.99O4], which can be simplified to Pb[(Te,W)O4]. Murphyite is the Te-analogue of raspite, Pb(WO4), and represents the first mineral with Te6+ substituting for W6+ over 50%. It is monoclinic with space group P21/a and unit-cell parameters a = 13.6089(3), b = 5.01750(10), c = 5.5767(2) Å, β = 107.9280(10)°, V = 362.302(17) Å3, and Z = 4. Its crystal structure consists of distorted MO6 (M = Te + W) octahedra sharing edges to form zigzag chains running parallel to [010]. These chains are linked together by PbO7 polyhedra. Compared to raspite, the substitution of W6+ by Te6+, which has a smaller ionic radius, results in a noticeable structural change: a significant decrease in MO6 octahedral angle distortion, with a concomitant increase in both MO6 octahedral volume and average Pb–O bond length. The unit-cell volume increases from 358.72(4) Å3 for raspite to 362.302(17) Å3 for murphyite. Raman spectroscopic data show that the major peak ascribable to M–O symmetrical stretching vibrations within the MO6 octahedron is centered at 870 cm−1 for raspite but at 881 cm−1 for murphyite.

Comparison of three cyclodextrins to optimize bisphenol A extraction from source water: Computational, spectroscopic, and analytical studies.

Computationally and spectroscopically assisted analytical comparative investigation into the extraction of bisphenol A using three cyclodextrins, that is, α, β, and γ respectively, were performed. A simple, self-tailored μ-solid-phase extraction podium was used to extract bisphenol A from water samples, and high-performance liquid chromatography-ultraviolet was used for the qualitative and quantitative analysis of bisphenol A. Density functional theory first principle calculations, attenuated total reflectance Fourier-transform infrared spectroscopy and Fourier-transform Raman spectroscopy data supports the analytical selection of β-cyclodextrin as the adsorbent for bisphenol A extraction. Analytical optimization of various parameters including sample volume, sample pH, eluting solvent and its volume was performed to discover the most proper conditions for maximum extraction. Under the optimized conditions, a limit of detection value of 0.70ng/ml and a limit of quantification value of 2.31ng/ml was achieved with β-cyclodextrin, with recovery (%) values over 98.40-102.50 in real source water samples. Overall, well assisted by comprehensive computational and spectroscopic studies, a novel, simple, sensitive and economic analytical method was developed for the extraction of bisphenol A from source water using cyclodextrin.

Insights into the metamorphic history and origin of flake graphite mineralization at the Graphite Creek graphite deposit, Seward Peninsula, Alaska, USA

Graphite Creek is an unusual flake graphite deposit located on the Seward Peninsula, Alaska, USA. We present field observations, uranium-lead (U–Pb) monazite and titanite geochronology, carbon (C) and sulfur (S) stable isotope geochemistry, and graphite Raman spectroscopy data from this deposit that support a new model of flake graphite ore genesis in high-grade metamorphic environments. The Graphite Creek deposit is within the second sillimanite metamorphic zone of the Kigluaik Mountains gneiss dome. Flake graphite, hosted in sillimanite-gneiss and quartz-biotite paragneiss, occurs as disseminations and in sets of very high grade (up to 50 wt.% graphite), semi-massive to massive graphite lenses 0.2 to 1 m wide containing quartz, sillimanite, inclusions of garnet porphyroblasts, K-feldspar, and tourmaline. Restitic garnet, sillimanite, graphite, and biotite accumulations indicate a high degree of anatexis and melt loss. Strong yttrium depletion in monazite, high europium ratios (Eu/Eu*), and excursions of high strontium and thorium concentrations are consistent with biotite dehydration melting. Monazite and titanite U–Pb ages record peak metamorphism from ~ 97 to 92 million years ago (Ma) and a retrograde event at ~ 85 Ma. Raman spectroscopy confirms the presence of carbonaceous material and highly ordered, crystalline graphite. Graphite δ13CVPDB values of − 30 to − 12‰ and pyrrhotite δ34SVCDT values of − 14 to 10‰ are consistent with derivation from organic carbon and sulfur in sedimentary rocks, respectively. These data collectively suggest that formation of massive graphite lenses occurred approximately synchronously with high-temperature metamorphism and anatexis of a highly carbonaceous pelitic protolith. Melt extraction and fluid release associated with anatexis were likely crucial for concentrating graphite. High-temperature, graphitic migmatite sequences within high-strain shear zones may be favorable for the occurrence of high-grade flake graphite deposits.

Cold sintering of bioglass and bioglass/polymer composites

Cold sintering of bioglass and bioglass/polymer composites

Regulation and prediction of defect-related properties in ZnO nanosheets: synthesis, morphological and structural parameters, DFT study and QSPR modelling

The work is aimed to consider photocatalytic efficiency and photoluminescence dependence on oxygen vacancies and other crystal lattice defects in ZnO nanosheets in order to provide their regulation and perdictionA synthesis via precipitation method under different conditions followed by hydrothermal treatment under different temperatures to obtain series of nanosheet samples with various amounts of defects was performed. Obtained samples were fully characterized (SEM, XRD, FTIR, XPS, Raman, absorbance spectra). A novel approach has been proposed and applied to determine the amount of oxygen vacancies and defects from XPS and Raman spectroscopy data. Quantum-chemical calculations (DFT) were performed to obtain density of states and band structure and to study the impact of lattice parameters and oxygen vacancies on electronic structure.The photocatalytic properties under UV and visible light irradiation were studied. Relations between morphological, structural parameters and functional properties (photoluminescent and photocatalytic) of ZnO nanosheets using quantitative structure–property relationship (QSPR) method were established. The impact of defects in combinations with other parameters on functional properties was demonstrated. Revealed dependencies and QSPR model provide a possibility to predict functional properties of nanosheets can be used for the development of new materials.

Raman Spectroscopic and DFT Study of COA-Cl and Its Analogues.

COA-Cl is a newly synthesized adenosine analogue that exhibits various physiological activities. Its angiogenic, neurotropic, and neuroprotective potencies make it promising for the development of medicines. In this study, we show Raman spectroscopic study of COA-Cl to elucidate molecular vibrations and related chemical properties. Density functional theory calculations were combined with the Raman spectroscopic data to understand the details of each vibrational mode. Comparative analysis with adenine, adenosine, and other nucleic acid analogues enabled identification of unique Raman peaks originating from the cyclobutane moiety and chloro group of COA-Cl. This study provides fundamental knowledge and crucial insights for further development of COA-Cl and related chemical species.

Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids.

Standardized data processing approaches are required in the field of bio-Raman spectroscopy to ensure information associated with spectral data acquired by different research groups, and with different systems, can be compared on an equal footing. An open-sourced data processing software package was developed, implementing algorithms associated with all steps required to isolate the inelastic scattering component from signals acquired using Raman spectroscopy devices. The package includes a novel morphological baseline removal technique (BubbleFill) that provides increased adaptability to complex baseline shapes compared to current gold standard techniques. Also incorporated in the package is a versatile tool simulating spectroscopic data with varying levels of Raman signal-to-background ratios, baselines with different morphologies, and varying levels of stochastic noise. Application of the BubbleFill technique to simulated data demonstrated superior baseline removal performance compared to standard algorithms, including iModPoly and MorphBR. The data processing workflow of the open-sourced package was validated in four independent in-human datasets, demonstrating it leads to inter-systems data compatibility. A new open-sourced spectroscopic data pre-processing package was validated on simulated and real-world in-human data and is now available to researchers and clinicians for the development of new clinical applications using Raman spectroscopy.