“…However, the peaks obtained in PER06 (2θ=28.1°) and PER08 (2θ=66.9°) correspond to the anorthite structure. 35 Figure 1.X-ray diffraction patterns of the pure and perlite added EPs.…”
Section: Resultsmentioning
confidence: 99%
“…However, the peaks obtained in PER06 (2θ=28.1°) and PER08 (2θ=66.9°) correspond to the anorthite structure. 35 FTIR spectra of the pure and perlite added EPs are given in Figure 2. In the spectrum of pure EP, characteristic EP peaks due to cross-links appeared.…”
In this study, the thermal conductivity, thermal degradation and mechanical properties of the perlite added epoxy materials were investigated. Perlite was added into the epoxy resin (EP) at the rate of 2%, 4%, 6%, 8% and 10% compared to the total mass. For the analysis of these epoxy materials, the thermal conductivity, density, hardness, X-ray diffraction (XRD), FTIR spectra, thermogravimetric analysis (TGA), tensile strength, flexural strength, limit oxygen index (LOI) and vertical burning tests were carried out. With the added perlite (PER10), a decrease of 24.03% occurred in the thermal conductivity coefficient compared to the pure EP material. A maximum decrease of 6.86% was detected in its density. It gained fire retardant feature as it increased from 6.15% to 28.71% in V-0 and carbon residue as inflammability class. Despite the decrease of tensile stress, tensile modulus increased. The maximum increase PER02 was 36.9%. It did not provide any improvement in terms of other mechanical properties.
“…However, the peaks obtained in PER06 (2θ=28.1°) and PER08 (2θ=66.9°) correspond to the anorthite structure. 35 Figure 1.X-ray diffraction patterns of the pure and perlite added EPs.…”
Section: Resultsmentioning
confidence: 99%
“…However, the peaks obtained in PER06 (2θ=28.1°) and PER08 (2θ=66.9°) correspond to the anorthite structure. 35 FTIR spectra of the pure and perlite added EPs are given in Figure 2. In the spectrum of pure EP, characteristic EP peaks due to cross-links appeared.…”
In this study, the thermal conductivity, thermal degradation and mechanical properties of the perlite added epoxy materials were investigated. Perlite was added into the epoxy resin (EP) at the rate of 2%, 4%, 6%, 8% and 10% compared to the total mass. For the analysis of these epoxy materials, the thermal conductivity, density, hardness, X-ray diffraction (XRD), FTIR spectra, thermogravimetric analysis (TGA), tensile strength, flexural strength, limit oxygen index (LOI) and vertical burning tests were carried out. With the added perlite (PER10), a decrease of 24.03% occurred in the thermal conductivity coefficient compared to the pure EP material. A maximum decrease of 6.86% was detected in its density. It gained fire retardant feature as it increased from 6.15% to 28.71% in V-0 and carbon residue as inflammability class. Despite the decrease of tensile stress, tensile modulus increased. The maximum increase PER02 was 36.9%. It did not provide any improvement in terms of other mechanical properties.
“…The medium strong vibrations of the bonded OH appearing at (3415, 3622 and 3690 cm −1 ) in natural kaolin clay in the IR spectra are shifted to (3437.69, 3626.41 and 3695.17 cm −1 ) and (3434.41, (Caponi et al, 2017). The large halo peak appears in the region of 28 < 2θ > 34 as a result of the formation of an amorphous aluminosilicate phase of the geopolymer materials (Aziz et al, 2021). Also, the characteristic peak in case of natural clay at 5.66 0…”
Natural, acid and base modified kaolin clays were studied for the sake of phenol and 4-chlorophenol removal from aqueous environments and their application to real ground and industrial wastewater samples. Scanning electron microscope (SEM), infrared spectroscopy (IR), X-ray diffraction (XRD), Thermo Gravimetric Analysis (TGA), Differential Thermal Analysis (DTA), and Surface area analysis were employed for characterization of the adsorbents microstructure. Operating factors such as adsorbent dose, solution pH, initial phenol concentration, and contact time were studied. The experimental data displayed that the increase of the adsorbent dose, contact time, and pH value from 2 to 7 increases the efficiency of the removal process. Optimal conditions for phenolic removal were; contact time of 300 min, primary phenol solution of 25 mg/L, pH 7 and 2.5 g/L as an appropriate adsorbent dose using crude (natural), acid modified and base modified kaolin clays. The higher phenolic removal efficiencies were obtained at 5 mg/L as 90, 97, 96.2%, respectively, for the adsorbents in the previously mentioned order. The adsorption capacity in the removal of phenol and 4-chlorophenol were 7. 481 and 4.195, 8.2942 and 3.211, and 8.05185 and 18.565 mg/g, respectively, for the adsorbents in the same mentioned order. The adsorption equilibrium data were fitted and analyzed with four isotherm models, namely, Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich isotherm equations.The adsorption process of phenol on studied adsorbents was exothermic, spontaneous and thermodynamically favorable proved by the negative values of their thermodynamic parameters ΔH • and ΔG • . The correlation coefficient (R 2 ) for all concentrations was higher than 0.94, which indicates that in the studied system, the data suitably fit the first-order kinetics. The % desorption capacity was amounted to 96%, 91.11%, and 87.06% of adsorbed phenol, respectively, for the adsorbents in the previous order using 0.1N NaOH and 10% V/V ethanol solutions as eluents at 25 • C, indicating the reusability of the adsorbents. Kaolin and its modified forms can be introduced as eco-friendly and low-cost adsorbents in water remediation implementation.
“…Agro-based environmentally friendly pozzolanic materials, such as rice husk ash, palm oil fuel ash, bagasse ash, coconut shell ash, coconut husk ash, corn cob ash, almond shell ash, etc., have gained considerable attention in soil stabilisation given the ever-growing costs of their disposal [7][8][9][10][11][12][13][14][15][16][17][18]. The major chemical composition of these plant-based pozzolans are alumino-silicates [19][20][21].…”
The unconfined compressive strength (UCS) of a stabilised soil is a major mechanical parameter in understanding and developing geomechanical models, and it can be estimated directly by either lab testing of retrieved core samples or remoulded samples. However, due to the effort, high cost and time associated with these methods, there is a need to develop a new technique for predicting UCS values in real time. An artificial intelligence paradigm of machine learning (ML) using the gradient boosting (GB) technique is applied in this study to model the unconfined compressive strength of soils stabilised by cementitious additive-enriched agro-based pozzolans. Both ML regression and multinomial classification of the UCS of the stabilised mix are investigated. Rigorous sensitivity-driven diagnostic testing is also performed to validate and provide an understanding of the intricacies of the decisions made by the algorithm. Results indicate that the well-tuned and optimised GB algorithm has a very high capacity to distinguish between positive and negative UCS categories (‘firm’, ‘very stiff’ and ‘hard’). An overall accuracy of 0.920, weighted recall rates and precision scores of 0.920 and 0.938, respectively, were produced by the GB model. Multiclass prediction in this regard shows that only 12.5% of misclassified instances was achieved. When applied to a regression problem, a coefficient of determination of approximately 0.900 and a mean error of about 0.335 were obtained, thus lending further credence to the high performance of the GB algorithm used. Finally, among the eight input features utilised as independent variables, the additives seemed to exhibit the strongest influence on the ML predictive modelling.
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