Fourier Transform Infrared Spectroscopy Data Research Articles

XRF data-based statistical clustering of ancient ceramic artifacts and analysing provenance with thermo-mineralogical analysis

The chemical composition of a ceramic assemblage 48 sherds from the Museum of Asian Art (MoAA), Universiti Malaya, was determined using X-Ray Fluorescence (XRF) and the statistical analysis of the results was compared with the classification made by the museum curator on the basis of typology. The major objective of this study was to establish a link between the components used to produce Chinese porcelain from Jingdezhen, South China, throughout the different dynasties. A statistical analysis is applied based on 16 major and minor oxides employing hierarchical cluster analysis (HCA), principal component analysis (PCA), biplots, boxplots, and descriptive statistics. Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD), and Thermomechanical Analysis (TMA) techniques were used to analyse the material characterization of sherds for classification, resulting in conclusions consistent with provenance and dating. The HCA analysis separated 48 sherds into 5 clusters. Each cluster except cluster 5, lacked of anorthite, which typically forms above temperature 850 °C, indicating that the firing temperature was not exceed 850 °C for clusters 1–4. Similarly, the deformation point for clusters 1–4 were low temperature and high temperature for cluster 5. The observed chemical composition and SiO2/Al2O3 ratio (3.76–4.18) for clusters 1–4 and cluster 5 ratio of 2.92 indicates the Ming and Qing dynasty respectively with origin of Jingdezhen, South China of the examined ceramics. The HCA classification of sherds was based on XRF raw data without combining all experimental techniques like XRD, TMA, and FTIR data. The most representative sherds of each HCA cluster were analyzed thermo-mineralogically. A combination of strong evidence due to material characterization can therefore overcome the hindrance of unreliable sources that cause ambiguity of the sherd's originality and classification.

Synthesis and characterization of fluorenone derivatives with electrical properties explored using density functional theory (DFT)

This study provides thorough computational and experimental assessments of four types of novel synthesized thiosemicarbazones. The compounds were effectively synthesized using a condensation reaction between thiosemicarbazide and fluorenone, producing a remarkable range of 70–88%. Additional chemical structures were examined utilizing spectroscopic methods, including Fourier-transform infrared spectroscopy (FTIR), NMR spectroscopy, and ultraviolet-visible spectroscopy. The computational analyses utilized DFT using the M06/6-311G (d, p) technique. The electrical characteristics, including the stability of orbitals via energy exchange between a donor and acceptor, can be evaluated by natural bond orbital (NBO) analysis. The nonlinear optical (NLO) properties were analyzed to detect any prohibited energy gaps. FTIR and UV-visible data were computed using the identical M06/6–311G (d, p) level of theory. The NBO test has confirmed the occurrence of charge separation due to the efficient transfer of electrons from the donor to the acceptor unit over the π bridge. The molecular chemical softness and hardness are dependable indications of a molecule’s chemical stability. A significant magnitude of the absolute value of polarizability and hyper-polarizability indicates considerable dispersion of electric charge. The outcomes derived from Density Functional Theory (DFT) generally align well with experimental findings.

Efficient generation of HPLC and FTIR data for quality assessment using time series generation model: a case study on Tibetan medicine Shilajit.

The scarcity and preciousness of plateau characteristic medicinal plants pose a significant challenge in obtaining sufficient quantities of experimental samples for quality evaluation. Insufficient sample sizes often lead to ambiguous and questionable quality assessments and suboptimal performance in pattern recognition. Shilajit, a popular Tibetan medicine, is harvested from high altitudes above 2000m, making it difficult to obtain. Additionally, the complex geographical environment results in low uniformity of Shilajit quality. To address these challenges, this study employed a deep learning model, time vector quantization variational auto- encoder (TimeVQVAE), to generate data matrices based on chromatographic and spectral for different grades of Shilajit, thereby increasing in the amount of data. Partial least squares discriminant analysis (PLS-DA) was used to identify three grades of Shilajit samples based on original, generated, and combined data. Compared with the originally generated high performance liquid chromatography (HPLC) and Fourier transform infrared spectroscopy (FTIR) data, the data generated by TimeVQVAE effectively preserved the chemical profile. In the test set, the average matrices for HPLC, FTIR, and combined data increased by 32.2%, 15.9%, and 23.0%, respectively. On the real test data, the PLS-DA model's classification accuracy initially reached a maximum of 0.7905. However, after incorporating TimeVQVAE-generated data, the accuracy significantly improved, reaching 0.9442 in the test set. Additionally, the PLS-DA model trained with the fused data showed enhanced stability. This study offers a novel and effective approach for researching medicinal materials with small sample sizes, and addresses the limitations of improving model performance through data augmentation strategies.

In vitro spectroscopic analysis of the chemical interaction between calcium hypochlorite and chlorhexidine.

This study aimed to identify the chemical composition resulting from the chemical interaction between calcium hypochlorite [Ca(OCl)2] and chlorhexidine (CHX) using different 1H and 13C nuclear magnetic resonance spectrometry (NMR), correlated two-dimensional spectroscopy (2D COSY), heteronuclear single quantum coherence (HSQC), and Fourier Transform Infrared Spectroscopy (FTIR) experiments. The 5.25% Ca(OCl)2 was mixed with 2% CHX in a 1:1 ratio, obtaining an orange-brown precipitate that was filtered, washed in ultrapure water, dried and characterized by 1H and 13C NMR, 2D COSY, HSQC and FTIR. One-dimensional and two-dimensional NMR spectra demonstrated the chemical changes around the protons and carbon atoms of the initial CHX and after reacted with Ca(OCl)2. These techniques and FTIR show that the interaction generated two by-products from the degradation of CHX, none of which were parachloroaniline (PCA) despite both products obtained being substituted benzene compounds. Using NMR and FTIR data together with a previously proposed catalytic cleavage degradation mechanism for CHX, the products appear to be parachlorophenylurea (PCU) and parachlorophenylguanidyl-1,6-diguanidyl-hexane (PCGH). Therefore, this in vitro study demonstrated that the precipitate formed from the chemical interaction between Ca(OCl)₂ and CHX results in two by-products, PCU and PCGH, neither of which is PCA. Due to the absence of cytotoxicity studies on the products generated from the combination of Ca(OCl)₂ and CHX, an intermediate rinse with an inert solution is recommended to remove these compounds from the root canals.

Soil Organic Carbon Stocks Depend Differently on Physicochemical Features in Subtropical Seasonally Flooded Wetland and Non‐flooded Shoreland Forest

ABSTRACTIn recent years, an increasing number of ecosystems are threatened by seasonal flooding, changing non‐flooded shoreland (NF) into seasonally flooded wetland (SF), but the consequences of this hydrological change for soil organic carbon (SOC) dynamics remain unknown. In this study, we investigated how the SOC content was determined by flooding duration and soil physicochemical variables in adjacent SF and NF at six depths (0–10 cm, 10–20 cm, 20–30 cm, 30–50 cm, 50–70 cm, and 70–100 cm) at Shengjin Lake in subtropical China. Soil physicochemistry and SOC composition were analyzed, and Fourier‐transformed infrared spectroscopy (FTIR) was used to resolve the SOC composition. Neither SOC content nor the vertical distribution of SOC was distinguishable between the sites. However, FTIR data revealed that plant‐originated aliphatics and amides were higher at NF than SF sites, with the opposite pattern for aromatics. At SF sites, SOC content was positively affected by soil moisture and flooding duration and was negatively impacted by soil particle size at most soil layers. At NF sites, SOC content was mainly affected by silt and total Fe at the top 20 cm soil, while a higher fraction of plant‐derived labile C was positively correlated to SOC contents at 30–100 cm depth. The results hence indicated a strong effect of seasonal flooding on SOC dynamics in terrestrial ecosystems. SOC stabilization induced by low mineralization and high adsorption played a central role at SF sites, while SOC formation through plant input was more important at NF sites. Our findings suggest that management strategies designed to conserve SOC will need to be site‐specific.

Predicting Stability of Barley Straw-Derived Biochars Using Fourier Transform Infrared Spectroscopy.

In order to estimate the ability of biochar to sequester carbon as part of greenhouse gas removal technology, there is a need for rapid and accessible estimations of biochar stability. This study employs a novel method using Fourier transform infrared spectroscopy (FTIR) to predict common stability indicators, namely H:C and O:C molar ratios. Biochars derived from barley straw were produced at temperatures from 150 to 700 °C. The greatest compositional changes of the biochars occurred between 200 and 400 °C. All biochars produced at ≥400 °C achieved H:C < 0.7 and O:C < 0.4, indicative of biochars suitable for soil application. Regression models were built using FTIR data to predict H:C and O:C molar ratios. The H:C model produced a coefficient of determination (R 2) of 0.99, mean absolute percentage error (MAPE) 6.86%, and root-mean-square error (RMSE) of 0.07. The O:C model achieved the same R 2 (0.99), MAPE of 9.02%, and RMSE of 0.03. Our results demonstrate that combining FTIR data with modeling is a promising rapid and accessible method for attaining biochar stability data.

Bio-spectroscopic investigation linking changes of retinal structure with short-term administration of Amiodarone and revealing the ameliorative effect of vitamin E supplementation

Long term use of Amiodarone (AMIO) is associated with the development of ocular adverse effects. This study investigates the short term effects, and the ameliorative consequence of vitamin E on retinal changes that were associated with administration of AMIO. This is accomplished by investigating both retinal structural and conformational characteristics using Fourier transform infrared spectroscopy (FTIR) and Fundus examination. Three groups of healthy rabbits of both sexes were used; the first group served as control. The second group was orally treated with AMIO (160 mg /kg body weight) in a daily basis for two weeks. The last group orally received AMIO as the second group for two weeks then, oral administration of vitamin E (100 mg/kg body weight) for another two weeks as well. FTIR results revealed significant structural and conformational changes in retinal tissue constituents that include lipids and proteins due to AMIO administration. AMIO treatment was associated with fluctuated changes (increased/decreased) in the band position and bandwidth of NH, OH, and CH bonds. This was concomitant with changes in the percentage of retinal protein constituents in particularly α-helix and Turns. AMIO facilitates the formation of intra-molecular hydrogen bonding and turned retinal lipids to be more disordered structure. In conclusion, the obtained FTIR data together with principal component analysis provide evidence that administration of vitamin E following the treatment with AMIO can ameliorate these retinal changes and, these biophysical changes are too early to be detected by Fundus examination.

Fourier-Transform Infrared Spectroscopy and Gas Chromatography-Mass Spectrometry Characterization of Arachis Oil for Production of Laundry Soap

The study aimed to extract oil from Arachis seeds (Arachis hypogaea), characterize the oil with FTIR and GC-MS, and use it to produce laundry soap. Arachis oil was extracted via the Soxhlet method in nhexane. Standard methods were adopted to examine the properties of the extracted oil, and the produced laundry soap. Fourier-Transform Infrared Spectroscopy (FTIR) and Gas Chromatography-Mass Spectrometry (GC-MS) were used to analyze the chemical composition of the oil. The results showed that 70.0 g of the Arachis seed gave 49.56±1.24 % oil yield. The physicochemical characterization of the oil showed that the relative density, saponification value, acid value, and iodine value were 0.65±0.15 g/mL, 190.74±2.17 mgKOH/g, 2.08±0.15 mgKOH/g, and 91.88±0.25 gI2/100g, respectively. Functional groups analysis from FTIR data revealed unique absorption peaks of methyl group at 1379 cm–1, methylene group at 723, 1461, 2847, 2922 cm–1, carbonyl group at 1744 cm–1, and olefin group at 1654 cm–1, which are attributed to the fatty acids present in the Arachis oil. The GC-MS analysis confirmed the following fatty acids: Oleic acid, Elaidic acid, and Vaccenic acid in the oil. The soap’s quality parameters determination offered 99.75±0.26 cm, 4.63±0.37 cm, 7.10±0.78 % and 59.25±2.65 % for pH, foam height, moisture content and total fatty matter, respectively. Besides, the soap was slightly pinkish and moderately hard. The findings of the study proved that Arachis seeds oil is a richsource of fatty acids with the required functional groups for efficient saponification reaction, and has a huge potential for large-scale production of quality laundry soap.

Advancements in medical research: Exploring Fourier Transform Infrared (FTIR) spectroscopy for tissue, cell, and hair sample analysis.

Fourier Transform Infrared (FTIR) spectroscopy has emerged as a powerful analytical tool in medical research, offering non-invasive and precise examination of the molecular composition of biological samples. The primary objective of this review is to underscore the benefits of FTIR spectroscopy in medicinal research, emphasizing its ability to delineate molecular fingerprints and assist in the identification of biochemical structures and key peaks in biological samples. This review comprehensively explores the diverse applications of FTIR spectroscopy in medical investigations, with a specific focus on its utility in analyzing tissue, cells, and hair samples. Various sources, including Google Scholar, PubMed, WorledCat and Scopus, were utilized to conduct this comprehensive literature review. Recent advancements showcase the versatility of FTIR spectroscopy in elucidating cellular and molecular processes, facilitating disease diagnostics, and enabling treatment monitoring. Notably, FTIR spectroscopy has found significant utility in clinical assessment, particularly in screening counterfeit medicines, owing to its user-friendly operation and minimal sample preparation requirements. Furthermore, customs officials can leverage this technique for preliminary analysis of suspicious samples. This review aims to bridge a gap in the literature and serve as a valuable resource for future research endeavors in FTIR spectroscopy within the medical domain. Additionally, it presents fundamental concepts of FTIR spectroscopy and spectral data interpretation, highlighting its utility as a tool for molecular analysis using Mid-Infrared (MIR) radiation.

Improved photocatalytic activity of (Ni, Mn) co-doped ZnO nanoparticles prepared via green synthesis route using orange peel extract

(Ni–Mn) co-doped ZnO nanoparticles were synthesized for different doping concentrations (1 %, 2 %, and 3 %) and compared with the purely synthesized ZnO nanoparticles. Green synthesis route was followed to fabricate both pure and co-doped ZnO nanoparticles and the orange peel extract was used as the reducing agent. The main objectives of this research were to reduce the use of toxic chemicals and develop an eco-friendly green route for both pure and doped ZnO nanoparticle synthesis. Additionally, the co-doping effect on the photocatalytic activity was also observed. The synthesized particles were extensively characterized by X-ray diffraction (XRD), Scanning Electron Microscopy (SEM) analysis, Fourier Transform Infrared spectroscopy (FTIR), and Ultraviolet–visible (UV–vis) spectroscopy. XRD analysis confirmed the exitance of ZnO nanoparticles with wurtzite structure. No extra peaks were found for the co-doped sample confirming the successful doping. The crystallite size decreased after doping and the range varied from 36 nm to 35 nm with increasing doping concentration. SEM analysis revealed the average particle size ranging from 66.42 nm to 37.52 nm with increasing doping concentration, the particle was irregular in shape, and agglomeration was observed. The FTIR data unveiled the existence of Ni–O, Mn–O, and Zn–O bands in the synthesized materials. The band gap of the NPs was determined from the UV–Visible spectroscopy analysis which varied from 3.30 eV to 2.71 eV with increasing doping concentration. The photocatalytic activity significantly improved with the increment of the doping concentration. The 2 % and 3 % doped samples degraded almost 97 % of the methylene blue dye at 30 min.

Мultivariate analysis for rapid screening and prediction of solid-state compatibility in pharmaceutical preformulation studies-paving the road for machine learning

Multivariate analysis models were developed to evaluate the results obtained from a compatibility study designed for ibuprofen with a large group of different types of excipients, as a possible approach for rapid screening of the incompatibility between the active pharmaceutical ingredient (API) and excipients. The solid-state characterization of the binary mixtures was performed by Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) using SIMCA® software were applied for evaluation of the experimentally obtained results. The optimal PCA model for the FTIR spectra explains 96.2 % of the variations in the dataset with good statistical indicators (R2X = 0.960, Q2 = 0.900), which was also the case for the PCA model for the DSC curves (R2X = 0.981, Q2 = 0.866). The applied PLS-DA models have shown similar behaviour to the PCA. Moreover, the main spectral variations in the FTIR spectra and the thermal events in the DSC data were attributed the highest variable importance for the projection (VIP) scores in the corresponding VIP plots, confirming the model capability for predicting ibuprofen interactions. Furthermore, the prediction power of the optimal models for FTIR and DSC experimental data was evaluated by the root mean square error of prediction (RMSEP) of 0.10 and 0.16, respectively. The obtained results demonstrated the potential of multivariate statistical analysis as a machine learning-based technique for screening and prediction of ibuprofen-excipients solid-state compatibility in the preformulation phase of the pharmaceutical development of dosage forms.

Influence of Biogenic Material Content on the Biodegradability of Styrene-Butadiene Composites with Incorporated Chlorella vulgaris Biomass.

Bio-fillers are intensively studied for advanced polymer composite circular design and production. In this context, the algal biomass may be considered an important and relatively low-cost resource, when harvested as a by-product from wastewater treatment plants. The biomass of the algal species Chlorella vulgaris is frequently used in this type of environmental process, and its macro constituents' composition ranges from around 15-25% carbohydrates, 10-20% lipids, and 50-60% proteins. Poly (styrene-butadiene-styrene) (SBS) copolymers have a matrix composed of glassy polystyrene domains connected by flexible polybutadiene segments. Although the physical-mechanical properties of SBS copolymers recommend them for many industrial applications, they have the drawback of low biodegradability. This study aimed to assess the aerobic biodegradability of polymer composites by integrating biomass from Chlorella vulgaris at varying mass percentages of 5, 10, and 20% into SBS copolymer composites. Biodegradation tests were conducted under industrial composting conditions (58 °C and 50% relative humidity) for 180 days. The biodegradability of materials was evaluated by measuring the CO2 produced in each vessel during the study period. Potential correlations between the amount of carbon dioxide released and the percentage of biomass added to the polymer matrix were examined. Structural and morphological changes were assessed using Fourier Transform infrared spectroscopy (FTIR), thermal analysis (DSC), and scanning electron microscopy (SEM). Physical and chemical testing revealed a decrease in sample density after the industrial composting test, along with noticeable changes in melt flow index (MFI). The observed physical and chemical changes, coupled with FTIR, SEM, and DSC data, indicate increased cross-linking and higher porosity in biodegraded polymer structures with higher biomass content. This behavior is likely due to the formation of cross-linked connections between polymer chains and polypeptide chains resulting from protein degradation, enhancing connections between polystyrene units facilitated by peptide bonds with the benzene units of the styrene blocks within the polymer matrix.

Binary Mixtures of Meloxicam and L-Tartaric Acid for Oral Bioavailability Modulation of Pharmaceutical Dosage Forms.

Binary mixtures of active pharmaceutical ingredients (API) are researched to improve the oral bioavailability of pharmaceutical dosage forms. The purpose of this study was to obtain mixtures of meloxicam and L-tartaric acid because tartaric acid improves intestinal absorption and meloxicam is more soluble in a weakly basic environment. The mixtures in the 0-1 molar fraction range, obtained from solvent-assisted mechanosynthesis, were investigated by differential scanning calorimetry (DSC), Fourier Transform Infrared (FTIR) spectroscopy, Fourier Transform Raman spectroscopy (FT-Raman), X-ray powder diffraction (XRD) and solubility tests. The physicochemical characteristics of the compounds obtained from DSC data reveal, for the first time, the formation of a co-crystal at meloxicam molar fraction of 0.5. FTIR spectroscopy data show the existence of hydrogen bonds between the co-crystal components meloxicam and L-tartaric acid. FT-Raman spectroscopy was used complementary with FT-IR spectroscopy to analyze the pure APIs and their mixtures, to emphasize the appearance/disappearance and the shifts of the position/intensity of vibrational bands, following the formation of hydrogen-bonded structures or van der Waals interactions, and to especially monitor the crystal lattice vibrations below 400 cm-1. The experimental results obtained by X-ray powder diffraction confirmed the formation of the co-crystal by the loss and, respectively, the apparition of peaks from the single components in the co-crystal diffractogram. The solubility tests showed that the co-crystal product has a lower aqueous solubility due to the acidic character of the other component, tartaric acid. However, when the solubility tests were performed in buffer solution of pH 7.4, the solubility of meloxicam from the co-crystal mixture was increased by 57% compared to that of pure meloxicam. In conclusion, the studied API mixtures may be considered potential biomaterials for improved drug release molecular solids.

Investigation of the affinity and interaction of fibrinogen with trehalose as a protein stabilizer

Trehalose, a remarkable substance, holds great importance in biomedicine for its anti-inflammatory properties and its capacity to hinder scar formation at wounds. This study aims to explore how trehalose interacts with fibrinogen, a crucial protein in wound healing, paying particular attention to their structural characteristics. An array of analytical methods, such as steady-state fluorescence, UV–Vis analysis, circular dichroism (CD), Zeta potential measurements, Dynamic light scattering (DLS), and Fourier-transform infrared spectroscopy (FT-IR), are employed to examine the influence of trehalose on fibrinogen. The findings demonstrate that trehalose induces structural alterations on the surface of fibrinogen, resulting in its compaction. Moreover, computational modeling approaches like docking and molecular dynamics simulations are utilized to enhance the understanding of the interactions between trehalose and fibrinogen. The findings indicate the establishment of a stable complex between the two entities, accompanied by slight modifications in the protein's structure due to their interaction. The main forces facilitating the bonding of trehalose (the ligand) and the protein are hydrogen bonds and van der Waals forces, resulting in an entropy-driven spontaneous bonding process. FT-IR data reveals the emergence of fresh bonds between fibrinogen and trehalose post their interaction, whereas Zeta potential investigations suggest that the engagement with trehalose enhances the stability of the protein structure.Tests assessing cytotoxicity on a typical fibroblast cell line demonstrated that the fibrinogen-trehalose compound displays lower toxicity levels compared to trehalose alone. This suggests a milder impact on cells from the compound. Moreover, molecular docking analysis supports these experimental results, indicating that trehalose molecules primarily bind to the C-terminal end of the coiled-coil segment and the globular area of the Bβ chain within the Dβ fragment. These outcomes highlight the promise of trehalose as a valuable material for wound dressing purposes.

Quantifying Ageing of 35-50 and 70-100 Asphalts Using Fourier Transform Infrared Spectroscopy and Dynamic Shear Rheometer Measurements

In this study, Dynamic Shear Rheometer (DSR) and Fourier Transform Infrared (FTIR) spectroscopy tests were performed on 35-50 and 70-100 penetration-graded asphalts. Both tests were performed on specimens at three different ageing states: unaged, short-term aged using the standard rolling thin film oven (RTFO) device, and long term aged by placing RTFO residue in the pressure aging vessel (PAV) apparatus. The tests were accomplished to quantify how ageing affects the results of both tests, namely the complex shear modulus for the DSR test and the carbonyl, sulfoxide, and ageing indices for the FTIR spectroscopy test. Both sets of measurements confirm that the harder asphalt stays harder than the softer asphalt after undergoing ageing, but the rate of hardness is higher for the softer asphalt. For instance, on average, PAV ageing was found to increase the amplitude of the shear complex modulus, from the unaged state, by 4.3 times and by 6.2 times for the 35-50 and 70-100 asphalts, respectively. It was also found that ageing decreases the phase angle for asphalt and the decrease is more pronounced for the softer asphalt (70-100) than the harder one (35-50). This was attributed to more maltenes content in the softer asphalt that transform into asphaltenes during ageing. Correlation coefficient between FTIR signals from the same samples revealed that FTIR spectroscopy as used in this research has an excellent repeatability. The repeatability was also confirmed using measures of dispersion such as the coefficient of variation, which was found not to exceed the 1.2%. The 35-50 asphalt was found to have more C=O and S=O links than the 70-100 asphalt since it showed more absorbance in the carbonyl and sulfoxide bands. These links are thought to make the asphalt physically harder. An analysis of variance study revealed that ageing is a statistically significant factor as measured using ageing indices calculated from FTIR spectroscopy data with the index AIFTIR being the most sensitive to ageing among all considered indices. Empirical power equations with high coefficient of determination values were established to predict DSR results from FTIR index values, which was possible since both types of measurements show the same trend with ageing.

Pore scale investigation of chelating agents flooding for enhanced oil recovery

Recently, laboratory tests have shown a positive response to chelating agent brines in sandstone and carbonate reservoirs as a new enhanced oil recovery technique. There is little research on direct and visual evidence of underlying mechanisms behind the chelating agent effects. In this study, a microfluidic method was established using glass micromodel to investigate the micro-mechanisms of displacement under ethylenediaminetetraacetic acid (EDTA) and diethylenetriaminepentaacetic acid (DTPA) chelating agent at the pore-scale level. Furthermore, for the study of fluid/fluid interactions, EDTA brine and DTPA brine were brought into contact with two different crude oils to examine the possible compositional changes. For this purpose, sample tests, Fourier Transform Infrared Spectroscopy (FTIR), microscopic image analysis, and dynamic interfacial tension (IFT) measurements were conducted. The FTIR data showed EDTA brine and DTPA brine induced changes in the oil sample composition compared to seawater, which can be related to the formation of water-in-oil micro-dispersions. Microscopic image analysis on samples confirmed the formation of these micro-dispersions. The visual study by the micromodel demonstrated the micro-dispersion formation in the tertiary injection of EDTA and DTPA brine. Based on the observation made, the pore-scale mechanisms of chelating agent flooding involve the chelating agent brine penetrating the crude oil, resulting in micro-dispersion formation. These micro-dispersions tend to alter wettability to a more water-wet state, facilitate oil detachment from the surface, redistribution of oil saturation, and thus leading to improved oil recovery. The observed IFT reduction aids the penetration of EDTA and DTPA brine into the crude oil to form micro-dispersions.

Synthesis and Characterization of Ni/Al Layered Double Hydroxides Composite Based-Material with Chitosan, Cellulose, and Graphene Oxide

In this study, the synthesis of composite materials produced from Ni/Al with three types of carbon-based materials namely chitosan (Ch), cellulose (Ce), and graphene oxide (GO) was successfully carried out supported by X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FT-IR), and Brunauer-Emmett-Teller (BET) analysis. The combined characteristics of the two starting materials are present in the composite material based on XRD and FT-IR data. The surface area of Ni/Al-LDH increased after compositing with Ch, Ce, and GO, from 3.3 m2/g to 9.5 m2/g in Ni/Al-Ch, 5.1 m2/g in Ni/Al-Ce, and 78.4 m2/g in Ni/Al-GO. These findings indicate that the materials are suitable for various applications such as photocatalytic and adsorption as effective materials in further research.

Discrimination of Cheese Products Regarding Milk Species’ Origin Using FTIR, 1H-NMR, and Chemometrics

The present study deals with the discrimination of various European cheese products based on spectroscopic data and chemometric analysis. It is the first study that includes cheese products from Cyprus along with cheese samples from abroad and several different cheese types. Therefore, forty-nine samples were collected, freeze-dried, and measured by using spectroscopic techniques, such as FTIR (Fourier-Transform Infrared Spectroscopy) and 1H-NMR (proton nuclear magnetic resonance). Discriminant analysis was applied, particularly OPLS-DA. All data obtained from 1H-NMR were included, whereas, regarding the FTIR data, only the spectral subregion between 1900 and 400 cm−1 was used in the extracted model. The cheese samples were classified according to the milk species’ origin. In the future, the samples of this study will be enriched for further testing with spectroscopic techniques and chemometrics.

Physical characterization, magnetic interactions and DC electrical resistivity properties of La3+ substituted NiZnCd nano ferrites

In this article, we substituted La3+ ions to explore the structural, magnetic, and DC electrical resistivity properties of Ni0.5Zn0.4Cd0.1Fe2-xLaxO4 (x = 0.0, 0.02, 0.04, 0.06, and 0.08) nano-structured spinel ferrites. We employed X-ray diffraction (XRD), Field-emission scanning electron microscopy (FESEM) with Energy dispersive X-ray (EDX), Fourier transform infrared spectroscopy (FTIR), Vibrating sample magnetometry (VSM), and electrical measurements to characterize the studied samples, which were synthesized using the sol–gel auto-combustion process. XRD analysis confirmed that the prepared materials formed a single-phase nanostructure. Using Debye-Scherer's formula, we calculated the average crystallite size, which ranged from 29.01 to 21.52 nm. FESEM images demonstrated that each sample exhibited spherical nanocrystalline behavior. The variations of the prepared samples were confirmed constitutionally by EDX. The FTIR data revealed two absorption bands for all synthesized materials at υ1 = 562.18 to 590.51 cm−1 and υ2 = 410.18 to 430.51 cm−1, corresponding to the tetrahedral and octahedral sites of the spinel structure, respectively. Vibrant sample magnetometry spectra were utilized to investigate how La3+-doping affects the magnetic characteristics of NiZnCd ferrite. Due to enhanced La3+-doping, saturation magnetization, and coercivity values decreased. The La3+ doping of NiZnCd nano ferrites resulted in changes in saturation magnetization (from 83.66 to 69.57 emu/g), coercivity (from 33.75 to 60.51 Oe), and remanence magnetization (from 29.62 to 33.12 emu/g). These variations suggest the potential utility of the samples for magnetic recording and memory applications. The semiconducting behavior has been confirmed by the DC electrical resistivity measured values using a two-probe method. Data on DC electrical resistivity were also utilized to determine carrier concentration and activation energy, establishing a connection with La3+ replacement.

Ante- and Post-Mortem Fracture Identification Protocol Based on Low- and High-Level Fusion Using Fourier Transform Infrared Spectroscopy and Raman Spectroscopy Association.

In this study, the application of low-level fusion (LLF) and high-level fusion (HLF) strategies using a combination of Fourier transform infrared spectroscopy (FT-IR) and Raman spectroscopy in the identification of antemortem and postmortem fracture at different postmortem intervals (PMIs) was investigated. On a technical level, the same hard tissue sample can be detected using a mix of FT-IR and Raman techniques. At the method level, two cutting-edge chemometrics approaches (LLF and HLF) combining FT-IR and Raman spectroscopic data are explored. The models were ranked in accordance with their parametric quality as follows: HLF and LLF + HLF models > LLF single model > Raman single model > FT-IR single model. The LLF model performed marginally better than the Raman model, however, when compared to other models, the HLF model performed considerably better. The HLF model achieved the best performance, with both cross-validation accuracy and test data set accuracy of 0.88. The importance of the feature wavelengths in the model construction process was subsequently evaluated by intersection fusion, and it was found that the absorbance bands of amide I, PO43- ν1 ν3, and CH2 in FT-IR and phenylalanine, CO32- ν1- PO43- ν3, and amide III in Raman have outstanding contributions to the construction of antemortem and postmortem fractures identification models. Overall, the combination of FT-IR and Raman with the HLF strategy is a novel and promising approach for developing antemortem and postmortem fracture identification models at different PMIs.