Infrared Spectral Analysis Research Articles

Information extraction of UV-NIR spectral data in waste water based on Large Language Model

In recent years, with the rise of various machine learning methods, the Ultraviolet and Near Infrared (UV-NIR) spectral analysis has been impressive in the determination of intricate systems. However, the UV-NIR spectral analysis based on traditional machine learning requires independent training with tedious parameter tuning for different samples or tasks. As a result, training a high-quality model is often complicated and time-consuming. Large language model (LLM) is one of the cutting-edge achievements in deep learning, with the parameter size of the order of billion. LLM can extract abstract information from input and use it effectively. Even without any additional training, using only simple natural language prompts, LLM can accomplish tasks that have never been seen before in completely new domains. We look forward to utilizing this capability in spectral analysis to reduce the time-consuming and operational difficulties. In this study, we used UV-NIR spectral analysis to predict the concentration of Chemical Oxygen Demand (COD) in three different water samples, including a complex wastewater. By extracting the characteristic bands in the spectrum, we input them into LLM for concentration prediction. We compared the COD prediction results of different models on water samples and discussed the effects of different experiments setting on LLM. The results show that even with brief prompts, the prediction of LLM in wastewater achieved the best performance, with R2 and RMSE equal to 0.931 and 10.966, which exceed the best results of traditional models, where R2 and RMSE correspond to 0.920 and 11.854. This result indicates that LLM, with simpler operation and less time-consuming, has ability to approach or even surpass traditional machine learning models in UV-NIR spectral analysis. In conclusion, our study proposed a new method for the UV-NIR spectral analysis based on LLM and preliminary demonstrated the potential of LLM for application.

Investigations on physical, structural, elastic, optical and radiation shielding properties of boro-tellurite glasses for radiation waste management applications

The boro-tellurite glasses with the chemical composition 29B2O3+20TeO2+50MO +1Dy2O3 containing different modifiers (where M = Ba, Li2, Sr, Ca, Mg and Zn) have been produced via conventional melt quenching method. The elastic, structural, physical, optical bandgap properties and the high energy photon shielding behavior of the produced glasses were extensively inspected through appropriate characterization techniques. The X-ray diffraction (XRD) pattern confirms amorphous character of the as-quenched samples. The Fourier-Transform Infrared (FTIR) spectral analysis reveals the presence of functional groups by the way of their stretching and bending vibrational modes. The estimation of parameters like Boron–Boron separation (dB−B), Bond density (n0), oxygen molar volume (V0), and Oxygen Packing Density (OPD) were facilitated to understand the detailed structural modifications and the moduli of elasticity (E, K, G, σ) parameters were determined for the fabricated the boro-tellurite glasses. The barium incorporated present boro-tellurite glass possess lower OPD and higher average molecular weight and it might make the glass network densely packed. To ensure the radiation protection ability, defending parameters such as equivalent atomic number, mass attenuation coefficient (MAC), Effective electron density, half-value layer (HVL), mean free path (MFP) and build-up factors of the titled glasses were determined employing the user friendly Phy-X software. Barium cations added boro-tellurite glass found to have the shortest MFP and HVL, as well as the lowest EBF in the low and intermediate energy zones indicating the potential for using the barium cations incorporated boro-tellurite glass as alternatives for conventional concretes in shielding and waste management applications.

Synthesis and characterization of cyclohexylammonium hydrogen maleate hemihydrate single crystal and its anti-bacterial activities

The single crystal of cyclohexylammonium hydrogen maleate hemihydrate (CYHMH) developed using slow solvent vaporization technique in distilled water solvent. The crystal structure of CYHMH was in monoclinic structure with space group C12/C. The CYHMH single crystal structure consists of an individual cyclohexylamine and an independent maleic acid molecule with all atoms placed in general positions. The prepared CYHMH single crystal was characterized by various analysis techniques such as, Fourier transform infrared (FT IR), Ultra Violet-Visible near infrared (UV–vis-NIR) spectral analysis, TG-DSC analyses, nonlinear optical studies and anti-bacterial activities. From the UV–Vis-NIR studies. The cut off wavelength of CYHMH single crystal is 328 nm with excellent translucence in complete visible region. Melting point of CYHMH was 112 °C determined from TG-DSC analysis. Nonlinear refractive index (-8.55 10 -8 cm2/W), nonlinear absorption coefficient (0.03 10-4 cm/W) nonlinear susceptibility (4.07 10-6 esu) were calculated by Z scan technique by diode pumped Continuous Wave Nd:YAG Laser. Gram negative bacteria such as Staphylococcus aureus and Escherichia coli were selected for anti-bacterial tests and it is found that CYHMH is most significant agent for bacterial strains.

Spectral mapping of zeolite bearing paleolake deposits at Lake Tecopa, California and its implications for mapping zeolites on Mars

• Zeolitic tuff at paleolake Tecopa investigated using in-situ and orbital methods. • Reasons for limited orbital detections of zeolites in paleolake. • Importance of ground investigation to interpreting orbital data. While non-analcime zeolites are plausible mineral constituents of the Martian surface, their lack of prominent spectral characteristics in the visible to shortwave infrared wavelength region makes them difficult to identify using orbital data. This lack of detection has led to the assumption that they are limited or absent. Here we assess the capability of mapping non-analcime zeolites using different mapping techniques and present the possible reasons that could lead to limited detection of zeolites using an analog study from Paleolake Tecopa, California. We used spaceborne hyperspectral Hyperion and multispectral ASTER data to identify zeolites following X-ray Diffraction (XRD) and visible-near infrared and shortwave infrared (VNIR-SWIR) spectral analysis of collected samples containing zeolites and other associated minerals. We used four of the most common spectral mapping methods: (1) band ratios, (2) minimum noise fraction (MNF), (3) spectral angle mapper (SAM), and (4) linear spectral unmixing (LSU). We were able to map zeolite-rich tuff beds using carefully selected band indices, MNF band combinations, and the pixel spectra corresponding to a zeolite-rich abandoned quarry area. Hyperion results show less overall accuracy than the ASTER image products mostly due to the low signal-to-noise ratio of the Hyperion data. The spectral characteristics of zeolites in zeolite-rich pixels are masked by phyllosilicates (e.g., smectite) present in the ground resolution cell (GRC), making it difficult to identify zeolites in phyllosilicate-bearing deposits solely based on zeolite spectra. Therefore, the SAM product derived using the pixel spectrum of the well-exposed zeolite-rich quarry shows the highest accuracy in Hyperion image products. ASTER band ratio Band 2/Band 1, representing the ferric ion, was able to identify the zeolite-rich beds with the highest accuracies. However, most of the zeolite-rich paleolake beds were difficult to identify since they are buried by other beds, mixed with other beds due to physical weathering, or the areas of exposed beds are smaller than the GRC of the satellite. The results imply that the paucity of detected zeolites on Mars, as we see at Lake Tecopa, does not preclude their wider presence, either beneath other materials, obscured by surface dust, or mixed with more spectrally dominant phases. :

Raman and Infrared Spectral Analysis, Normal Coordinate Analysis, DFT calculations of Novel Schiff Base Containing di-imine moieties.

Raman and Infrared Spectral Analysis, Normal Coordinate Analysis, DFT calculations of Novel Schiff Base Containing di-imine moieties.

Electrical conductivity and electrochemical studies of Cr-doped MoO3 nanoflakes for energy storage applications.

The growing demand for electricity has increased the interest of the researchers towards exploration of energy storing devices (ESDs). With the motif for developing electrochemical energy storage devices, this research work is focussed on the study of MoO3 nanoparticles and its doping with chromium as an efficient electrode material for energy storage applications. The nanoparticles were synthesized by hydrothermal method and were examined by powder X-ray diffraction, which determined the thermodynamically stable orthorhombic phase of MoO3, and their morphologies were examined using scanning electron microscopy displaying flake-like structures. The typical vibrational bands of Mo-O were identified from Infra-red and Raman spectral analysis. The ultra violet diffuse reflectance spectra revealed the decrease in optical band gap after doping with chromium. The temperature dependent AC and DC conductivities were enhanced on doping. Electrochemical behaviour of the nanoparticles was probed by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) measurements and galvanostatic charge-discharge (GCD) analysis for which specific capacitance (C sp) value of 334 Fg-1 was achieved for Cr-doped MoO3 nanoparticles. The electrochemical performance of the sample was found to be increased after doping with Cr.

Use of convolutional neural network (CNN) combined with FT-NIR spectroscopy to predict food adulteration: A case study on coffee

Food systems are negatively affected by food frauds with food recalls challenging the system's sustainability and consumer confidence in food safety. Coffee, an economically important commodity is frequently adulterated for economic gains, thereby requiring fast and reliable detection techniques. Of the various tracing strategies, spectroscopic techniques have seen considerable commercial success but rely heavily on human-engineered features. Thus, this study aims to evaluate feasibility of deep chemometrics (i.e., convolutional neural network, CNN) for coffee adulterant quantification in comparison to standard chemometrics approaches (i.e., partial least squares, PLS; and interval-PLS, iPLS). Commercial ‘espresso’ coffee was admixed with chicory, barley, and maize (0–25%, w/w) and subjected to Fourier Transformed-Near Infrared (FT-NIR) spectral analysis. The results confirmed the feasibility of CNN algorithm for adulterant quantification from FT-NIR spectra with excellent performances (R 2 > 0.98). Furthermore, CNN with Data augmentation (DA) with either autoscaling (AS) and/or standard normal variate (SNV) pre-treatment showed better prediction performances with RMSEP (0.76–0.82%) and BIASP (−1.00 × 10 −2 to −1.00 × 10 −1 %) that were better to comparable to those of PLS and/or iPLS models (0.72 < % RMSEP <3.045; −7.98 × 10 −2 < % BIASP <8.63 × 10 −2 ) for the adulterants tested. The study showed that deep learning algorithms can be potential alternatives to standard methods with little to no human interference for feature extraction during real-time applications of spectroscopic tools targeted to overcome food fraud crisis. • Deep chemometric algorithm tested for coffee adulteration prediction. • Convolutional neural network led to better performance than chemometrics. • Outlier removal and data augmentation benefitted the CNN algorithm. • CNN can be a feasible alternative for FT-NIR data analysis.

Silver nanoparticles synthesis and their effect on the SOPC lipid structure

We focus our attention on the influence of hydrophobic silver nanoparticles (Ag NPs) on the 1-Stearoyl-2-oleoylsn-glycero-3-phosphocholine (SOPC) model system. Results obtained by differential scanning calorimetry (DSC) and infrared (IR) spectroscopy were compared to their counterpart for lipid systems with incorporated hydrophobic gold nanoparticles (AuNPs) and pure SOPC lipid in a water environment. The results show a strong effect of hindering the gel-to-liquid crystalline phase transition for both types of noble metal hydrophobic NPs. The effect of the phase transitions shift to the lower temperatures is more pronounced for the silver particles for the concentrations studied. According to the IR spectral analyses, no negative effect is observed of the NPs on the lipid hydration for the concentration studied, and the plasmon effect of the Ag NPs appears to be more pronounced.

Synthesis, Characterization and Anti-Microbial Studies of Metal(II) complexes with 4-methoxybenzaldehyde with p-anisidine

Some divalent transition metal complexes were synthesized by the reaction of the chlorides of Co(II), Fe(II), Mn(II) and Ni(II) with Schiff bases derived from the reaction of 4-methoxybenzaldehyde with p-anisidine in methanolic solution. The Schiff base [L] was obtained as grey colored crystals with a melting point of 153ºC. The metal(II) complexes [ML] obtained were all colored and decomposed at the range of 171-177 ºC. Molar conductivity measurements for the complexes carried out using DMSO were found to be at the range of 1.19-16.07Ω–1mol–1cm2. The small values could be an indication for non-electrolytic nature of the complexes. From the values of the magnetic moment (4.40–5.85 B.M), all the complexes were found to be paramagnetic and soluble in most common solvents. The infra-red spectral analysis indicates complexation has taken place due to metal-nitrogen bands at the range, 632–645cm-1 for [ML]. The metal-ligands ratio in all the complexes was 1:2 as obtained from the Job’s method of continuous variation. The percentage composition of the metals and their corresponding ligands were found gravimetrically and the results obtained were in good agreement with the calculated values. The ligands and their corresponding complexes were screened in-vitro for their antimicrobial activity against Escherichia coli, Aspergillus fumigatus, Salmonella typhi, Staphylococcus aureus, Aspergillus flavus and Mucor species using the paper-discs diffusion method. Inhibition properties of the compounds showed that the complexes have higher antimicrobial activities than the ligands which could be attributed to chelation.

Forced Solid-State Oxidation Studies of Nifedipine-PVP Amorphous Solid Dispersion.

In the present study, the oxidative degradation behavior of nifedipine (NIF) in amorphous solid dispersions (ASDs) prepared with poly(vinyl pyrrolidone) (PVP) with a short (K30) and a long (K90) chain length was investigated. The ASDs were prepared via dry ball-milling and analyzed using Fourier transform infrared (IR) spectroscopy, X-ray scattering, and differential scanning calorimetry. The ASDs were exposed to accelerated thermal-oxidative conditions using a pressurized oxygen headspace (120 °C for 1 day) and high temperatures at atmospheric pressure (60-120 °C for a period of 42 days). Additionally, solution-state oxidative degradation studies showed that pure NIF degrades to a greater extent than in the presence of PVP. Electronic structure calculations were performed to understand the impact of drug-polymer intermolecular interactions on the autoxidation of drugs. While no drug degradation was observed in freshly prepared ASD samples, alkyl free radicals were detected via electron paramagnetic resonance (EPR) spectroscopy. The free radicals were found to be consumed to a greater extent by PVP K30- than PVP K90-based ASDs upon exposure to high oxygen pressures. This was consistent with the greater solid-state oxidative degradation of NIF observed in ASDs with PVP K30 than with PVP K90. As no drug recrystallization occurred during this study period, the lower glass-transition temperature and presumed greater molecular mobility of PVP K30 and its ASD as compared to the PVP K90 system appear to contribute to the greater drug degradation in PVP-K30-based ASDs. The extent and the rate of oxidative degradation were higher in the case of PVP-K30-based ASD as compared to that in PVP-K90-based ASD, and the overall degradation increased with an increase in temperature. IR spectral analysis of drug-polymer interactions supports the electronic calculations of the oxidation process. We infer that, apart from the initial free radical content, the difference in the extent of drug-polymer intermolecular interactions in ASDs and amorphous stabilization during the forced oxidation experiments contribute to the observed differences in the autoxidative reactivity of the drug in ASDs with different PVP chain lengths. Overall, the chemical degradation of NIF in ASDs with two PVP chain lengths obtained from accelerated solid-state oxidation studies was in qualitative agreement with that obtained from long-term (3 years) storage under ambient conditions. The study highlights the ability of accelerated processes to determine the oxidative degradation behavior of polymeric ASDs and suggests that the polymer chain length could factor into chemical as well as physical stability considerations.

TfNN15N: A γ-15N-Labeled Diazo-Transfer Reagent for the Synthesis of β-15N-Labeled Azides.

Azides are infrared (IR) probes that are important for structure and dynamics studies of proteins. However, they often display complex IR spectra owing to Fermi resonances and multiple conformers. Isotopic substitution of azides weakens the Fermi resonance, allowing more accurate IR spectral analysis. Site-specifically 15N-labeled aromatic azides, but not aliphatic azides, are synthesized through nitrosation. Both 15N-labeled aromatic and aliphatic azides are synthesized through nucleophilic substitution or diazo-transfer reaction but as an isotopomeric mixture. We present the synthesis of TfNN15N, a γ-15N-labeled diazo-transfer reagent, and its use to prepare β-15N-labeled aliphatic as well as aromatic azides.

Formulation and Standardization of Anti-acne Herbal Foaming Face Wash using Curcuma longa along with Aloe vera, Rosa centifolia, and Citrus sinensis

Objectives: Acne vulgaris is a very common skin disorder that peaks in teenagers, but many men and women between 20-40 years of age are also affected by the disorder. For the treatment of acne, herbal medications are considered safer than allopathic medicines as allopathic medicines are associated with side effects, such as contact allergy, local irritation, scaling, photosensitivity, itching, and redness of the skin, etc. The present research work was performed to check the effectiveness of foaming face wash formulation containing Curcuma longa along with herbals excipient Aloe vera, Rosa centifolia, and Citrus sinensis. Curcuma longa has been reported to contain active phytoconstituents having significant anti-microbial activity and being used locally for acne. Method: The plant materials Curcuma longa, Aloe vera, Rosa centifolia, and Citrus sinensis were authenticated, and their extracts have been prepared using Soxhlet Apparatus and the resulting essential oil was analyzed for its physical properties. The foaming face wash was then prepared by using the herbal extracts with excipients that were free from sulfates, parabens, silicone, and petroleum products. Two formulations, A1and A2, have been prepared and their physicochemical studies were performed. The presence and efficacy of Curcuma longa in the suppression of Propionibacterium acnes were assessed by analytical methods and anti-microbial techniques, respectively. Skin irritation studies were conducted using Wistar rats by the scoring method. Accelerated stability studies were also performed for 60 days. Result: The physicochemical properties were evaluated and found to be satisfactory. Analytical techniques like High-Performance Liquid Chromatography, High-Performance Thin Layer Chromatography, and Infra-Red spectral analysis confirmed the qualitative presence of Curcuminoid, which is a mixture of curcumin, desmethoxycurcumin (4-hydroxycinnamoyl-(4-hydroxy-3-methoxycinnamoyl) methane), and Bis- demethoxycurcumin (bis-(4-hydroxy cinnamoyl) methane) in the sample. The antibacterial activity of developed face wash assessed against Propionibacterium acnes was more than that of Clindamycin (10μg/ml). Also, the developed formulation showed very high activity against Staphylococcus epidermidis concerning the activity of the standard clindamycin. The prepared formulation showed no sign of localized reactions, which were confirmed by a skin irritation study, indicating the formulation was safe and compatible with the skin. Conclusion: Based on our study, it could be stated that the formulation has antimicrobial activity and could be used safely on human skin.

KINETICS OF QUATERNIZATION BETWEEN N, N-DIMETHYLANILINE AND BENZYL CHLORIDE IN POLAR SOLVENTS-A REVISIT TO MENSHUTKIN REACTION

This work reports an extension of Menshutkin reaction and involves the studies on kinetics of quaternization reaction between N,N-Dimethylaniline and benzyl chloride in acetone and in other various solvents at 300 K. The progress of the reaction was followed by measuring the conductance of the product formed at regular intervals of time. The effect of [benzyl chloride] and [N,N-Dimethylaniline] on reaction rate was studied under pseudo-first order condition and the rate showed first order dependence on both [benzyl chloride] and [N,N-Dimethylaniline]. The effect of dielectric constant of the medium on reaction rate was studied by employing various solvents such as methyl ethyl ketone, isopropyl alcohol, ethyl alcohol, methyl alcohol and acetonitrile and the rate is found to increase with increase in dielectric constant of the medium. The linear plot with a negative slope on plotting log k2 against 1/D where D is the dielectric constant of the medium indicates the existence of a highly charged or a polar transition state during the progress of the reaction. The quaternization reaction was also carried out at different temperatures so as to compute the various thermodynamic parameters like activation energy, enthalpy of activation, entropy of activation and Gibb’s free energy change. The higher value of activation energy is attributed for a slower reaction which is sterically controlled rather than thermodynamically controlled. The negative value of entropy of activation (ΔS*) indicates some sort of surrendering of disorder in the transition state on its formation which proves that it is highly ordered with sufficient charge separation and polar in character. The product after purification was analysed by melting point and infra-red spectral analysis and is proved to be N-Benzyl-N,N-dimethylphenylmethanaminium chloride. A plausible mechanism in accordance with the obtained experimental results is reported.

Use of artificial intelligence for optimizing biosorption of textile wastewater using agricultural waste

ABSTRACT Most of the dyes are toxic and non-biodegradable in textile industry wastewaters. Therefore, removal of textile dye using agriculture waste becomes crucial for the environment. This can be accomplished by the biosorption process which is the passive uptake of pollutants by agricultural waste. In this study, Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to obtain optimum conditions for Methylene Blue (MB) removal using sugarcane bagasse and peanut hulls as low-cost agricultural waste. The experimental design was carried out to study the effect of temperature, pH, biosorbent amount and dye concentration. The maximum MB dye removal considering the effect of total dissolved solids from aqueous solutions of 74.49% and 67.99% by sugarcane bagasse and peanut hulls, respectively. The models specify that they could predict biosorption with high accuracy having R 2-value above 0.9. Statistical studies for RSM, ANFIS and ANN models were compared. Further, the models were optimized for maximum dye removal was at 1.21 g of biosorbent, pH 5.24, 31.24 mg/L MB concentration, 22.29°C of dye solution using sugarcane bagasse and at 1.37 g of biosorbent, pH 5.77, 36.7 mg/L MB concentration, 26.8°C of dye solution using peanut hulls. Additionally, Fourier Transform Infra-Red (FTIR) spectral analysis was also carried out to confirm the biosorption.

Gas Chromatography-Mass spectrum and Infra-Red spectral analysis of Fixed Oil from Sudanese Adansonia digitata Seeds

Gas Chromatography-Mass spectrum and Infra-Red spectral analysis of Fixed Oil from Sudanese Adansonia digitata Seeds

The impact of naphthalimide derivative on the mitigation of mild steel corrosion in sulfamic acid medium: experimental and theoretical insights

The deterioration of iron-based alloys, especially mild steel (MS) is one amongst the most challenging problems faced in various chemical industries. The present work focuses on the potential activity of a naphthalimide derivative namely 2-(2-hydroxyethyl)benzo[de]isoquinoline-1,3-dione (HBIQ) as corrosion inhibitor for MS in sulfamic acid (SA) medium in the temperature range from 303 to 323 K. Potentiodynamic polarization (PP) and electrochemical impedance spectroscopy (EIS) were employed in the experimental measurement and HBIQ exhibited 89% inhibition at its optimum concentration. HBIQ demonstrated electrostatic interactions with MS surface and behaved as a mixed type of inhibitor by obeying Langmuir’s isotherm model. Surface characterization of uninhibited and inhibited MS specimens combined with elemental analysis data provided clear evidences for the formation of a protective adsorption layer of HBIQ on MS surface. Spectral analysis such as Ultraviolet visible and Fourier Transform Infra-red spectral analyses were carried out in order to confirm the adsorption of HBIQ on to the metal surface. The density functional theory calculations supported the experimental results and indicated the contribution of delocalized π-electrons in the naphthalimide unit and the lone-pair electrons of oxygen in the carbonyl and hydroxyl group for improved adsorption of HBIQ onto MS surface, thereby reducing the corrosion of the alloy in SA environment.Graphic abstract

A new ensemble modeling method for multivariate calibration of near infrared spectra.

Ensemble modeling has gained increasing attention for improving the performance of quantitative models in near infrared (NIR) spectral analysis. Based on Monte Carlo (MC) resampling, least absolute shrinkage and selection operator (LASSO) and partial least squares (PLS), a new ensemble strategy named MC-LASSO-PLS is proposed for NIR spectral multivariate calibration. In this method, the training subsets for building the sub-models are generated by sampling from both samples and variables to ensure the diversity of the models. In detail, a certain number of samples as sample subsets are randomly selected from training set. Then, LASSO is used to shrink the variables of the sample subset to form the training subset, which is used to build the PLS sub-model. This process is repeated N times and N sub-models are obtained. Finally, the predictions of these sub-models are used to produce the final prediction by simple average. The prediction ability of the proposed method was compared with those of LASSO-PLS, MC-PLS and PLS models on the NIR spectra of corn, blend oil and orange juice samples. The superiority of MC-LASSO-PLS in prediction ability is demonstrated.

Analysis of deep-ocean sediments from the TAG hydrothermal field (MAR, 26° N): application of short-wave infrared reflectance (SWIR) spectra for offshore geochemical exploration

PurposeThe cost-efficient methods of analysis, such as rapid short-wave infrared (SWIR) spectral analysis, have been applied for the efficient exploration of critical raw materials (CRM), including mineral components and rare earth elements (REE) from the deep-ocean sediments.MethodsGravity cored sediment samples were collected during an oceanographic mission to the Trans-Atlantic Geotraverse (TAG) hydrothermal field of the Mid-Atlantic Ridge (MAR, 26° N). SWIR reflectance spectra (dependent variable) of samples were mathematically tested against referent geochemical data (independent variable), obtained by conventional analysis (ICP/OES, ICP/MS), after applied full cross-validation multivariate partial least square regression (CVPLSR). Value of parameter-residual predictive deviation (RPD) was used for evaluation of CVPLSR modeling: RPD > 2.5 (satisfactory calibration model for the screening purposes) and RPD > 5.0 (model adequate for the quality control of the studied elements).ResultsThe CVPLSR modeling provided significant results for the determination of several mineral components: major elements (Fe and Si) had the values of RPD equal to 3.65 and 2.84, respectively, which indicated a viable potential for their routine analysis, whereas RPD for Ca was equal to 5.51, thus assuring its quality control by SWIR analysis, in sediment samples of the studied location. Among the REE, Ce (RPD = 2.55) and Er (RPD = 2.59) yielded the most satisfactory results.ConclusionsThe findings highlight the benefit of rapidly obtained empirical SWIR-reflectance data, which can be used for near-real-time exploration of geochemical deposits hosted in deep-ocean sediments.

Evaluation of the Physical Performance and Working Mechanism of Asphalt Containing a Surfactant Warm Mix Additive

This paper analyzes the influence of a surfactant warm mix additive on unmodified asphalt’s conventional performance, viscosity‐temperature characteristics, surface energy, and spreading performance on aggregate surfaces. The effect of the additive on asphalt’s microstructure was explored by infrared (IR) spectral analysis. The results show the additive has little influence on the penetration, softening point, ductility, and viscosity‐temperature characteristics of asphalt; this suggests that the additive does not work by lowering viscosity. The additive can reduce the zero‐shear viscosity of asphalt, and adding too much can reduce antirutting performance. The additive also increases the asphalt’s surface energy and the asphalt‐water contact angle, while the polar component of surface energy decreases. The additive improves the spreading performance of asphalt on aggregate surfaces and reduces the asphalt‐aggregate contact angle; the lower the temperature, the greater the reduction. IR spectral analysis shows that the additive does not react with asphalt—only physical blending occurs. The addition of a surfactant warm mix additive to asphalt allows asphalt mixtures to be more easily mixed and compacted at lower temperatures, thereby saving energy.

Quantitative Analysis of Gas Phase IR Spectra Based on Extreme Learning Machine Regression Model.

Advanced chemometric analysis is required for rapid and reliable determination of physical and/or chemical components in complex gas mixtures. Based on infrared (IR) spectroscopic/sensing techniques, we propose an advanced regression model based on the extreme learning machine (ELM) algorithm for quantitative chemometric analysis. The proposed model makes two contributions to the field of advanced chemometrics. First, an ELM-based autoencoder (AE) was developed for reducing the dimensionality of spectral signals and learning important features for regression. Second, the fast regression ability of ELM architecture was directly used for constructing the regression model. In this contribution, nitrogen oxide mixtures (i.e., N2O/NO2/NO) found in vehicle exhaust were selected as a relevant example of a real-world gas mixture. Both simulated data and experimental data acquired using Fourier transform infrared spectroscopy (FTIR) were analyzed by the proposed chemometrics model. By comparing the numerical results with those obtained using conventional principle components regression (PCR) and partial least square regression (PLSR) models, the proposed model was verified to offer superior robustness and performance in quantitative IR spectral analysis.