ATR-FTIR spectroscopy and chemometric techniques for determination of polymer solution viscosity in the presence of SiO2 nanoparticle and salinity

Relationship Between Elastic, Chemical, and Thermal Properties of SiO2 Flint Aggregate

Understanding the relationship between elastic, chemical, and thermal properties is essential for the prevention of the behavior of SiO2 flint aggregates during their application. In fact, the elastic properties of silica depend on chemical and heat treatment. In order to identify the crystallite sizes for natural SiO2 before and after chemical treatment samples, Williamson-Hall plots and Scherer's formulas are used. The silica nanofibers obtained and their microstructure changes under thermal and chemical treatment are characterized using different techniques (XRD, VP-SEM, TEM, FTIR, TDA, and TGA). Both the strains (ε) and the crystallite sizes (DW-H) are obtained from the slope and from the βcosθ-intercept of a graph, respectively. The crystalline quality is improved upon heating, as shown by the decrease in the FWHM of the SiO2(101) peaks, which is confirmed by Fourier transform infrared spectroscopy (FTIR). The microstrain estimated at 1.50 × 10-4 units for natural SiO2 is smaller than that for SiO2 after chemical attack which is estimated at 2.01 × 10-4 units. Based on the obtained results, SiO2 characterized with controlled micromechanical, thermal, and chemical properties may be used as a filler to improve the performance properties of the strength, microstructure, and durability of some composites.

Copper recovery from low-grade copper sulfides using bioleaching and its community structure succession in the presence of Sargassum

Bioleaching characteristics and bacterial community structure were studied during low-grade copper sulfide ores bioleaching in the presence of pretreated Sargassum (PSM). Results indicated that proportion of attached bacteria and copper recovery were improved by using appropriate-dosage PSM. High copper recovery (82.99%) and low Fe3+ concentration were obtained when 150 mg L-1 PSM was used. Precipitation, such as KFe3(SO4)2(OH)6 and (H3O)Fe3(SO4)2(OH)6, was not found in samples used PSM according to XRD, FTIR and TG analyses, which may result from less passivation layer formed by Fe3+ hydrolysis. I- contained in PSM can act as the reductant to convert Fe3+ into Fe2+, which can reduce Fe3+ hydrolysis and adjust Eh value. Bacterial community structure was influenced significantly by PSM according to the 16 S rDNA analysis. Acidithiobacillus ferrooxidans dominated proportion of bacterial community throughout bioleaching process, whose proportion reached 89.1091% after 14 days in sample added 150 mg L-1 PSM.

Nitrogen-doped metal-organic framework derived porous carbon/polymer membrane for the simultaneous extraction of four benzotriazole ultraviolet stabilizers in environmental water

Benzotriazole ultraviolet stabilizers (BUVSs) that are added to pharmaceutical and personal care products (PPCPs) have raised global concerns because of their high toxicity. An efficient method to monitor its pollution level is urgently imperative. Here, a nitrogen-doped metal-organic framework (MOF) derived porous carbon (UiO-66-NH₂/DC) was prepared and integrated into polyvinylidene fluoride mixed matrix membrane (PVDF MMM) as an adsorbent for the first time. The hydrophobic UiO-66-NH₂/DC with a pore size of 162 Å exhibited outstanding extraction performance for BUVSs, which solves the problem of difficult enrichment of large-size and hydrophobic targets. Notably, the density functional theory simulation was employed to reveal the structure of the derived carbon material and explored the recognition and enrichment mechanism (synergy of π-π conjugation, hydrogen bond, coordination, hydrophobic interaction and mesoporous channel) of BUVSs by UiO-66-NH₂/DC-PVDF MMM. And then, an influential method based on dispersive membrane extraction (DME) coupled with ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was developed for the simultaneous analysis of four BUVSs in environmental water samples. The validated method benefited from the high sensitivity (the limits of detection within 0.25-1.40 ng/L), accuracy (recoveries of 71.9-102.8% for wastewater) and rapidity (50 min to enrich 9 samples). This study expands the application prospects of porous carbon derived from MOF for sample pretreatment of pollutants in water.

Immobilization of lead (Pb) using ladle furnace slag and carbon dioxide

Global sustainable development faces challenges in greenhouse gas emissions, consumption of energy and non-renewable resources, environmental pollution, and waste landfilling. Current technologies for immobilization of heavy metals face similar challenges; for example, the use of cement, magnesia, lime, and other binders for immobilization of heavy metals is associated with carbon dioxide emission and consumption of limestone/magnesite and energy. In these contexts, this study introduced a novel and sustainable method for immobilization of lead (Pb) by using an industrial solid waste (ladle furnace slag, LFS) and a greenhouse gas (carbon dioxide). In this laboratory investigation, LFS was first mixed with the lead nitrate and then treated by conventional curing (without carbon dioxide) and carbonation curing (with carbon dioxide) for different periods. The treated LFS were then analyzed by various chemical analyses and microanalysis. The results showed that LFS with conventional curing is not effective in immobilization of lead, while LFS with carbonation curing can effectively immobilize lead. The leaching concentrations of Pb from carbonated LFS were four orders of magnitude lower than those with conventional curing. LFS can achieve carbon dioxide uptake of up to 8% of LFS mass. During the carbonation process, carbonates were produced and wrapped LFS particles to prevent the release of lead, lead nitrate was also carbonated into lead carbonate, and the pH of LFS was reduced to 9.36-9.58, close to the minimum solubility of lead carbonate; these are the main reasons for lead immobilization. In summary, the use of LFS with carbon dioxide for immobilization of lead can not only sequester carbon dioxide, but also reduce the cost of binders, non-renewable resource consumption, energy use, and LFS landfilling.

The interfacial and assembly properties of in situ producing silica nanoparticle at oil-water interface

In multiphase materials, structured fluid-fluid interfaces can provide mechanical resistance against destabilization, applicable for conformance control, Pickering emulsion, liquid 3D printing and molding, etc. Currently all research prepare the particle-ladened fluid-fluid interfaces by dispersing ex situ acquired particles to the immiscible interface, which limits their application in the harsh environment, such as oil reservoir which can impair particle stability and transport ability. Here, we investigated the interfacial and assembly properties of the interface where SiO2 nanoparticles (NPs) were in situ produced. The experimental results show that ammonia as catalyst could accelerate the processes of silica NPs formation as well as the interfacial tension (IFT) evolution. High temperature could not accelerate the reaction processes to achieve the lowest equilibrium IFT, but it induced the sine-wave IFT evolution curves regardless of the presence of ammonia. The equilibrium IFTs corresponded to the saturation states of interfaces trapping with SiO2 NPs, while the sine-wave fluctuating patterns of IFT were attributed to the alternating transition between interfacial jammed and unjammed states changing along with the reaction process. Silica NPs diffusing into aqueous phase with high salinity also showed good stability, due to the abundant surface decoration with in situ anchored organic species.

Toward More Universal Prediction of Polymer Solution Viscosity for Solvent-Based Recycling

The viscosity of polymer solutions is important for both polymer synthesis and recycling. Polymerization reactions can become hampered by diffusional limitations once a viscosity threshold is reached, and viscous solutions complicate the cleaning steps during the dissolution–precipitation technique. Available experimental data is limited, which is more severe for green solvents, justifying dedicated viscosity data recording and interpretation. In this work, a systematic study is therefore performed on the viscosity of polystyrene solutions, considering different concentrations, temperatures, and conventional and green solvents. The results show that for the shear rate range of 1–1000 s^–1, the solutions with concentrations between 5 and 39 wt % display mainly Newtonian behavior, which is further confirmed by the applicability of the segment-based Eyring-NRTL and Eyring-mNRF models. Moreover, multivariate data analysis successfully predicts the viscosity of polystyrene solutions under different conditions. This approach will facilitate future data recording for other polymer–solvent combinations while minimizing experimental effort.

Improving the mechanical properties and thermal stability of sodium alginate/hydrolyzed collagen films through the incorporation of SiO2

Biopolymer-based films have become leading alternatives to traditional fossil-based packaging plastics. Among the countless types of biopolymers with potential for such applications, films containing hydrolyzed collagen in their composition were scarcely explored. This study determined the effect of different loads of nano-SiO₂ (0, 2, 6, 8 and 10% w/w of sodium alginate) in the sodium alginate (SA) and hydrolyzed collagen (HC) blend films in terms of structure, thickness, mechanical properties, and thermal stability. The results indicated an improvement in the general mechanical and thermal behavior. Tensile strength increased from 18.2 MPa (control sample) to 25.4 MPa for the SA/HC film incorporated with 10% nano-SiO₂. In the same condition, the film's elongation at break improved impressively (from 19.5 to 35.8%). Thermal stability improved slightly for all proportions of nano-SiO₂. Therefore, the addition of nano-SiO₂ can be an easy and simple strategy to improve crucial properties of SA/HC blend films, increasing its performance for future application as sustainable packaging.

Effect of mixed bacteria on cemented tailings backfill: Economic potential to reduce binder consumption

Tailings used as backfilling material in the presence of mixed bacteria are discussed, and the relationship between mixed bacteria and compressive strength, size variation, water-holding capacity is analyzed in this study. The results illustrate a strong improving response of mixed bacteria with enhanced compressive strength, small size variation and low water-holding capacity of cemented tailings backfill (CTB) specimens. The binder dosage and mixed bacteria proportion have great influence on CTB specimens, which indicate that with the increase of mixed bacteria proportion and binder dosage, compressive strength increased obviously. The maximum compressive strength (4.01 MPa) is obtained in the presence of 100.00% mixed bacteria in contrast to only 2.79 MPa in its absence. Samples added high mixed bacteria proportion yield low water-holding capacity and small size variation. 16S rDNA analysis illustrates that bacteria community is influenced significantly during experiment. Further, possible reaction mechanism is proposed suggesting the possible role of mixed bacteria as promoter to form precipitation (KFe₃(SO₄)₂(OH)₆, (NH₄)Fe₃(SO₄)₂(OH)₆ and (KH₃O)₄Fe₃(SO₄)₂(OH)₆), which reduces tiny cracks in CTB specimens. The technique of using mixed bacteria to reduce binder consumption in this study shows economic benefits to some extent.

Photocatalytic kinetics of 2,4-dichloroaniline degradation by NiO-clinoptilolite nanoparticles

The ball-mill clinoptilolite nanoparticles (CNP) was ion-exchanged in Ni(II) solutions and calcined to obtain NiO-CNP catalysts with various NiO loadings. The resultant CNP was ion-exchanged in 0.1, 0.2, 0.3, and 0.4 M Ni(II) solutions and then calcined at 450 °C. The resultant NiO-CNPs contained 1.9, 2.3, 3.0, and 3.2% NiO, respectively. The XRD, FTIR, and DRS characterization techniques were applied. By applying the Scherrer equation on the XRD results, the average crystallite size for the NiO-CNP samples was estimated in the range of 42-65 nm. The pHpzc of the NiO-CNP species was slightly changed from 6.8 to 7.6 by an increase in the loaded NiO. The band gap energy of the samples was calculated by applying the Kubelka-Munk equation on the DRS results. The band gap energies of 3.81, 4.05, and 3.63 eV were estimated for the direct electronic transitions of the CN2, CN2.3, and CN3.2 samples, respectively. The boosted photoactivity was obtained in 2,4-dichloroanilyne (DCA) degradation when NiO supported onto both micronized clinoptilolite and its nanoparticles. The effects of the most important experimental variables on DCA photodegradation rate were kinetically studied by applying the Hinshelwood model on the results. The faster rate for the DCA photodegradation was achieved at the optimal conditions, including the catalyst dose: 0.5 g/L, C_DCA: 5 ppm, and the initial pH: 3. Some new peaks were observed in the HPLC chromatograms for the photodegraded DCA solutions after 180 min and 300 min, which showed 84% and 95% DCA photodegradation.

A hybrid optimization method for sample partitioning in near-infrared analysis

The division of calibration and validation is one of the essential procedures that affect the prediction result of the calibration model in quantitative analysis of near-infrared (NIR) spectroscopy. The conventional methods are Kennard-Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). These algorithms use Euclidean distance to cover as many representative samples as possible. This paper proposes an Adaptive Hybrid Cuckoo-Tabu Search (AHCTS) algorithm for partitioning samples based on optimization. The algorithm combines the characteristics of cuckoo search (CS) and tabu search (TS) and fuses with an adaptive function. For comparison, using fishmeal samples as spectral analysis data, KS, SPXY, and AHCTS algorithms were used to divide the modeling samples to establish partial least squares regression (PLSR) models. The experimental results showed that the model established by the proposed algorithm performs better than KS and SPXY. It reveals that the AHCTS method may be an advantageous alternative for quantitative analysis of NIR spectroscopy.

Genetic algorithm based support vector machine regression for prediction of SARA analysis in crude oil samples using ATR-FTIR spectroscopy

In the current research, an analytical method was proposed for rapid quantitative determination of saturates, aromatics, resins and asphaltenes (SARA) fractions of crude oil samples. Rapid assessments of SARA analysis of crude oil samples are of substantial value in the oil industry. The conventional SARA analysis procedures were determined with the standards established by the American Society for Testing and Materials (ASTM). However, the standard test methods are time consuming, environmental nonfriendly, expensive, and require large amounts of the crude oil samples to be analyzed. Thus, it be would useful to approve some supportive approaches for rapid evaluation of the crude oils. The attenuated total reflection Fourier-transform infrared spectroscopy ATR-FTIR coupled with chemometric methods could be used as analytical method for crude oil analysis. A hybrid of genetic algorithm (GA) and support vector machine regression (SVM-R) model was applied to predict SARA analysis of crude oil samples from different Iranian oil field using ATR-FTIR spectroscopy. The result of GA-SVM-R model were compared with genetic algorithm-partial least square regression (GA-PLS-R) model. Correlation coefficient (R²) and root mean square error (RMSE) for calibration and prediction of samples were also calculated, in order to evaluate the calibration models for each component of SARA analysis in crude oil samples. The performance of GA-SVM-R is found to be reliably superior, so that it can be successfully applied as an alternative approach for the quantitative determination of the SARA analysis of crude oil samples.

Quantification of schizophyllan directly from the fermented broth by ATR-FTIR and PLS regression

Non-destructive methods that allow the quantification of bioproducts in a simple and quick manner during fermentation are extremely desirable from a practical point of view. Therefore, a 9 day fermentation experiment with Schizophyllum commune was carried out to investigate the possibility of using ATR-FTIR to quantify the schizophyllan biopolymer (SPG) directly from the culture medium. On each day, aliquots of the fermentation were taken, and the cell-free supernatant was analyzed by ATR-FTIR. The main objective of this step was to evaluate whether FTIR would be able to detect the appearance of specific peaks related to the production of SPG. The results of the PCA analysis showed that there was a reasonable separation of the days through the FTIR spectra. Then PCA-LDA was applied to the same dataset, which confirmed the formation of groups for each day of fermentation, after which, a calibration and test set was developed. Through a matrix generated by an experimental design with 2 factors and 5 levels, 25 samples were created with variations in the concentration of the culture medium and SPG. The ATR-FTIR spectra of this data set were modeled using PLS regression with backward selection of predictors. The results revealed that the amount of SPG produced can be quantified directly in the culture medium with excellent precision with R²CV = 0.951, R²P = 0.970, RMECV = 0.205 g, RMSEP = 0.170 g, RPDcv = 4.53 and RPDp = 5.88. The traditional method to quantify SPG is time consuming, requires several steps and uses solvents. In contrast, the method proposed in this work is a viable, faster, and a simpler alternative, which does not use reagents and does not require extensive pre-treatment of the samples.

Experimental Investigation of the Effect of Adding Nanoparticles to Polymer Flooding in Water-Wet Micromodels

Recently, the combination of conventional chemical methods for enhanced oil recovery (EOR) and nanotechnology has received lots of attention. This experimental study explores the dynamic changes in the oil configuration due to the addition of nanoparticles (NPs) to biopolymer flooding. The tests were performed in water-wet micromodels using Xanthan Gum and Scleroglucan, and silica-based NPs in a secondary mode. The microfluidic setup was integrated with a microscope to capture the micro-scale fluid configurations. The change in saturation, connectivity, and cluster size distributions of the non-wetting phase was evaluated by means of image analysis. The biopolymer content did not affect the ability of the NPs to reduce the interfacial tension. The experiments showed that the reference nanofluid (NF) flood led to the highest ultimate oil recovery, compared to the Xanthan Gum, Scleroglucan and brine flooding at the same capillary number. In the cases of adding NPs to the biopolymer solutions, NPs-assisted Xanthan flooding achieved the highest ultimate oil recovery. This behavior was also evident at a higher capillary number. The overall finding suggests a more homogenous dispersion of the NPs in the solution and a reduction in the polymer adsorption in the Xanthan Gum/NPs solution, which explains the improvement in the sweep efficiency and recovery factor.

Rapid determination and classification of crude oils by ATR-FTIR spectroscopy and chemometric methods

Classification based on °API gravity is very important to estimate the parameters related to the extraction, purification, toxicity, and pricing of crude oils. Spectroscopy methods show some advantages over ASTM and API methods for crude oil analysis. The attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with chemometric methods has been applied as a quick and non-destructive method for crude oil analysis. In this work, a new analytical method using ATR-FTIR spectroscopy associated with chemometric methods were proposed for adressing regression and classification tasks for crude oils analysis based on °API gravity values. The designed methods are rapid, economic, and nondestructive ways in production process of oil industry. The spectral data were used for estimation of °API gravity using two approaches according to PLS-R and SVM-R algorithm, separately. The ATR-FTIR spectral data were also analyzed by classification method using the partial least squares-discriminant analysis (PLS-DA) for crude oil classification. The samples were classified into three classes based on their °API gravity values. The SVM-R model showed better results than PLS-R for °API gravity values using the F-test at 95% of confidence. The result of classification, showed about 100% accuracy and a zero classification error for calibration and prediction samples in PLS-DA algorithm.