Search Results (9)

The modification of cementitious binders using active mineral additives and nano-components represents a crucial pathway for developing high-performance, sustainable concrete composites. Nevertheless, unilateral modification of the matrix with highly reactive siliceous materials often leads to an undesirable increase in composite brittleness. This study investigates the synergistic effect of the concurrent application of nano-silica (NS), silica fume (SF), and Class F fly ash (FA) in ternary and quaternary binders, aimed at optimizing both load-bearing capacity and fracture toughness. The experimental program was conducted on seven concrete series, evaluating their mechanical parameters and non-linear fracture properties using the two-parameter fracture model (TPFM) on notched beams subjected to three-point bending. Additionally, a high-resolution energy partitioning framework was applied, decomposing the total fracture energy into four distinct components—fracture initiation energy in the elastic range (G_ini), pre-peak microcracking energy (G_pre), main material softening energy (G_soft), and residual tail energy dissipated at large crack openings (G_tail)—along with the determination of the characteristic length (l_ch). The results demonstrated that while purely siliceous systems (modified with NS and SF) generate high strength increments, they simultaneously trigger a “brittleness trap,” manifested by a 13.65% decrease in the l_ch parameter. The introduction of FA effectively mitigates this hazard, transforming the failure mode into a quasi-ductile behavior. The concrete series modified with the NS+FA hybrid (Mix-5) exhibited a spectacular 107% increase in G_f and an increase in l_ch of nearly 50%, while maintaining high fracture toughness. Energy decomposition analysis in quaternary concretes confirmed a desirable reduction in the initiation energy share in favor of the softening and tail phases (G_tail reaching a record 13.1% for Mix-7), suggesting the probable activation of macroscopic crack-bridging mechanisms driven by the delayed hydration of FA particles. The research indicates that precise design of multi-component binders allows for achieving an optimal technological equilibrium point—the “sweet spot”—combining high structural capacity with safe material ductility. Full article

An integrated statistical–graphical framework is introduced for designing sustainable cement grout mixes that incorporate polyethylene terephthalate (PET) waste and supplementary cementitious materials (SCMs) for semi-flexible pavement applications. A correlated multivariate linear mixed-effects model employs additive log-ratio transformations of PET and SCM proportions (fly ash or silica fume relative to cement) to predict 1-day, 7-day, and 28-day compressive strengths and 28-day flexural strength within a single unified framework. This approach quantifies both the systematic strength penalty of PET substitution and the benefits of SCM additions. The model results demonstrate high random-intercept correlations, substantial reductions in the Akaike information criterion (AIC) and root mean squared error (RMSE) compared to a null model, and marginal and conditional coefficient of determination (R²) values of 0.96 and 0.99, respectively, confirming major capture of the variance in the mechanical response. Complementary ternary plots visualize predicted 28-day performance across the cement–PET–SCM compositional space. These plots reveal that zero-PET formulations along the cement–binder edge achieve maximum strengths, with both fly ash and silica fume maximizing compressive and flexural strengths and any PET addition uniformly degrading performance. By combining rigorous compositional modeling with intuitive visualization, the proposed framework offers quantitative rigor, practical mix design guidelines, and a scalable protocol for optimizing sustainable grout formulations and informing future exploration of alternative fillers, flow regimes, and durability assessments. Full article

Non-photosynthetic vegetation fractional cover (f_NPV) is a key indicator of vegetation decline and ecological health. Traditional inversion models assume identical spectral signatures for the same vegetation cover class across entire study areas. Spectral variations occur among regions due to divergent soil properties and vegetation types. To address this limitation, extensive ground sampling was conducted; ground observation data from multiple regions were utilized to establish localized spectral libraries, thereby enhancing spectral variability representation within the study area while concurrently optimizing vegetation indices across different sensor systems. The results reveal that, within the optimized spectral mixture analysis model, the coefficient of determination (R²) for f_NPV using the NPV soil separation index (NSSI) for Sentinel sensor is 0.6258, and that of f_PV using the modified soil adjusted vegetation index (MSAVI) is 0.8055. The MSAVI-NSSI achieved an R² of 0.7825 for f_NPV and 0.8725 for photosynthetic vegetation fractional cover (f_PV). Optimized vegetation indices also yielded favorable validation results. Landsat’s theoretical predictions improved by 0.1725, with validated results up by 0.1635. MODIS showed improvements of 0.1365 and 0.1923, respectively. This enhancement significantly improves the accuracy of NPV fractional cover identification, providing critical insights for vegetation ecological health assessment in arid and semi-arid regions under global warming. Furthermore, by optimizing the spectral constraint weights in remote sensing images, a solution is provided for the long-term monitoring of vegetation health status. Full article

Non-photosynthetic vegetation (NPV) significantly impacts ecosystem degradation, drought, and wildfire risk due to its flammable and persistent litter. Yet, the accurate estimation of NPV in heterogeneous landscapes, such as dry–hot valleys, has been limited. This study utilized multi-source time-series remote sensing data from Sentinel-2 and GF-2, along with field surveys, to develop an NDVI-DFI ternary linear mixed model for quantifying NPV coverage (f_NPV) in a typical dry–hot valley region in 2023. The results indicated the following: (1) The NDVI-DFI ternary linear mixed model effectively estimates photosynthetic vegetation coverage (f_PV) and f_NPV, aligning well with the conceptual framework and meeting key assumptions, demonstrating its applicability and reliability. (2) The RGB color composite image derived using the minimum inclusion endmember feature method (MVE) exhibited darker tones, suggesting that MVE tends to overestimate the vegetation fraction when distinguishing vegetation types from bare soil. On the other hand, the pure pixel index (PPI) method showed higher accuracy in estimation due to its higher spectral purity and better recognition of endmembers, making it more suitable for studying dry–hot valley areas. (3) Estimates based on the NDVI-DFI ternary linear mixed model revealed significant seasonal shifts between PV and NPV, especially in valleys and lowlands. From the rainy to the dry season, the proportion of NPV increased from 23.37% to 35.52%, covering an additional 502.96 km². In summary, these findings underscore the substantial seasonal variations in f_PV and f_NPV, particularly in low-altitude regions along the valley, highlighting the dynamic nature of vegetation in dry–hot environments. Full article

The arsenic adsorption performance of silicon (Si), iron (Fe), and magnesium (Mg) mixed hydrous oxide containing a Si: Fe: Mg metal composition ratio of 0.05:0.9:0.05 (SFM05905) was investigated. SFM05905 was synthesized by the co-precipitation method. Batch experiments on arsenic adsorption were conducted at various temperatures and concentrations. Adsorption isotherms models were represented by a linearized equations and were insensitive to temperature change. The anion selectivity of SFM05905 at single component was high for arsenite (III), arsenate (V), and phosphate (PO₄), indicating that PO₄ inhibits arsenic adsorption. The adsorption amount of As (III), As (V), and PO₄ were compared using a column packed with granular SFM05905, and an aqueous solution was passed by a combination of several anions that are single, binary, and ternary adsorbate systems. As (III) had the highest adsorption amount; however, As (III) and PO₄ were affected by each other under the ternary mixing condition. Although the adsorption amount of As (V) was smaller than that of As (III), it was not affected by other adsorbates in the column experiments. Finally, although the adsorption of both arsenic continued, the adsorbed PO₄ gradually desorbed. Full article

The development of an antisolvent crystallization process requires the construction of an accurate phase diagram for this ternary system of compound, solvent and antisolvent, preferably as a function of temperature. This study gives an efficient methodology to systematically determine such antisolvent phase diagrams, exemplified with four model compounds: Sodium bromate, DL-Asparagine Monohydrate, Mefenamic acid and Lovastatin. Using clear point temperature measurements, single solvent and mixed solvent-antisolvent solubilities are obtained, showing strongly non-linear solubility dependencies as well as more complex solubility behaviour as a function of antisolvent fraction. A semi-empirical model equation is used to describe the phase diagram of the antisolvent crystallization system as a function of both temperature and antisolvent fraction. The phase diagram model then allows for the identification of condition ranges for optimal productivity, yield, and suspension density in continuous antisolvent crystallization processes. Full article

Alkali-activated products composed of industrial waste materials have shown promising environmentally friendly features with appropriate strength and durability. This study explores the mechanical properties and structural morphology of ternary blended alkali-activated mortars composed of industrial waste materials, including fly ash (FA), palm oil fly ash (POFA), waste ceramic powder (WCP), and granulated blast-furnace slag (GBFS). The effect on the mechanical properties of the Al₂O₃, SiO₂, and CaO content of each binder is investigated in 42 engineered alkali-activated mixes (AAMs). The AAMs structural morphology is first explored with the aid of X-ray diffraction, scanning electron microscopy, and Fourier-transform infrared spectroscopy measurements. Furthermore, three different algorithms are used to predict the AAMs mechanical properties. Both an optimized artificial neural network (ANN) combined with a metaheuristic Krill Herd algorithm (KHA-ANN) and an ANN-combined genetic algorithm (GA-ANN) are developed and compared with a multiple linear regression (MLR) model. The structural morphology tests confirm that the high GBFS volume in AAMs results in a high volume of hydration products and significantly improves the final mechanical properties. However, increasing POFA and WCP percentage in AAMs manifests in the rise of unreacted silicate and reduces C-S-H products that negatively affect the observed mechanical properties. Meanwhile, the mechanical features in AAMs with high-volume FA are significantly dependent on the GBFS percentage in the binder mass. It is also shown that the proposed KHA-ANN model offers satisfactory results of mechanical property predictions for AAMs, with higher accuracy than the GA-ANN or MLR methods. The final weight and bias values given by the model suggest that the KHA-ANN method can be efficiently used to design AAMs with targeted mechanical features and desired amounts of waste consumption. Full article

This work aims to detect volatile organic compounds (VOC), i.e., acetone, ethanol and isopropyl alcohol (IPA) and their binary and ternary mixtures in a simulated indoor ventilation system. Four metal-oxide-semiconductor (MOS) gas sensors were chosen to form an electronic nose and it was used in a flow-through system. To speed up the detection process, transient signals were used to extracted features, as opposed to commonly used steady-state signals, which would require long time stabilization of testing parameters. Five parameters were extracted including three in phase space and two in time space. Classifier and regression models based on backpropagation neural network (BPNN) were used for the qualitative and quantitative detection of VOC mixtures. The VOCs were mixed at different ratios; ethanol and isopropyl alcohol had similar physical and chemical properties, both being challenging in terms of obtaining quantitative results. To estimate the amounts of VOC in the mixtures, the Levenberg–Marquardt algorithm was chosen in network training. When compared with the multivariate linear regression method, the BPNN-based model offered better performance on differentiating ethanol and IPA. The test accuracy of the classification was 82.6%. The concept used in this work could be readily translated for detecting closely related chemicals. Full article

Clay minerals play an important role in shrinking–swelling of soils and off–road vehicle mobility mainly due to the presence of smectites including montmorillonites. Since soils are composed of different minerals intimately mixed, an accurate estimation of its abundance is challenging. Imaging spectroscopy in the short wave infrared spectral region (SWIR) combined with unmixing methods is a good candidate to estimate clay mineral abundance. However, the performance of unmixing methods is mineral-dependent and may be enhanced by using appropriate spectral preprocessings. The objective of this paper is to carry out a comparative study in order to determine the best couple spectral preprocessing/unmixing method to quantify montmorillonite in intimate mixtures with clays, such as montmorillonite, kaolinite and illite, and no-clay minerals, such as calcite and quartz. To this end, a spectral database is built with laboratory hyperspectral imagery from 51 dry pure mineral samples and intimate mineral mixtures of controlled abundances. Six spectral preprocessings, standard normal variate (SNV), continuum removal (CR), continuous wavelet transform (CWT), Hapke model, first derivative (1st SGD) and pseudo–absorbance (Log(1/R)), are applied and compared with reflectance spectra. Two linear unmixing methods, fully constrained least square method (FCLS) and multiple endmember spectral mixture analysis (MESMA), and two non-linear unmixing methods, generalized bilinear method (GBM) and multi-linear model (MLM), are compared. Global results showed that the benefit of spectral preprocessings occurs when spectral absorption features of minerals overlap for SNV, CR, CWT and 1st SGD, whereas the use of reflectance spectra performs the best when no overlap is present. With one mineral having no spectral feature (quartz), montmorillonite abundance estimation is difficult and gives RMSE higher than 50%. For the other mixtures, performances of linear and non-linear unmixing methods are similar. Consequently, the recommended couple spectral preprocessing/unmixing method based on the trade-off between its simplicity and performance is 1st SGD/FCLS for clay binary and ternary mixtures (RMSE of 9.2% for montmorillonite–illite mixtures, 13.9% for montmorillonite–kaolinite mixtures and 10.8% for montmorillonite–illite–kaolinite mixtures) and reflectance/FCLS for binary mixtures with calcite (RMSE of 8.8% for montmorillonite–calcite mixtures). These performances open the way to improve the classification of expansive soils. Full article