Aggregation Concentration Research Articles

Modulating the assembly of starch-lecithin inclusion complexes and its underlying mechanism for controlling starch digestibility under the dense system

Starch-lipid inclusion complexes were used to regulate starch digestibility by changing the binding affinity of digestive enzymes such as α-amylase and α-glucosidase. However, when above a critical aggregation concentration (CAC) of starch and lipid molecules, they tended to self-assemble into double helices or normal micelles respectively, rather than interacting with each other to form starch-lipid complexes. Thus, how to enhance the complexation efficiency under a dense system is the key approach for the improvement of starch bioaccessibility and nutritional function. Herein, based on the dimethyl sulfoxide (DMSO) reverse solvent method, the effect of water/DMSO ratios and starch chain structures on the interaction and assembly behaviors between starch and lecithin molecules in high concentrations were systematically investigated. The results revealed that water/DMSO in a ratio of 1.5:1 promoted the interactions between cavities of starch helices and long alkyl chains of lecithin molecules, with higher level of complexed lecithin content, relative crystallinity (RC), enthalpy (ΔH) (Tp＞100°C), ordered-aggregation structures and resistant starch (RS) content. In this kind of solvent, starch samples with higher content of amylose and longer chains (degree of polymerization (DP) of B2 chains and B3+ chains) is advantageous to the formation of VII-type inclusion starch-lecithin complexes, with a rise in RS from 14.17% to 43.64%. It provided an effective method to modulate starch digestibility under the dense system of starch and lecithin via modulating the water/DMSO ratios and starch fine structures and lay the foundation for the large-scale preparation and application of starch-lecithin complexes.

Influence of Compressive Strength and Steel-Tube Thickness on Axial Compression Performance of Ultra-High-Performance Concrete-Filled Stainless-Steel Tube Columns Containing Coarse Aggregates

With the increasing use of concrete-filled steel tubular (CFST) structures, exposed steel tubes are highly susceptible to corrosion, posing potential safety hazards. This study innovatively proposes the use of stainless-steel tubes instead of traditional carbon-steel ones and introduces coarse aggregates into ultra-high-performance concrete (UHPC), forming a coarse aggregate-incorporated ultra-high-performance concrete-filled stainless-steel tube (CA-UFSST). The inclusion of coarse aggregates not only compensates for the shortcomings of UHPC but also enhances the overall mechanical performance of the composite structure. Twenty sets of specimens were designed to analyze the influence of four parameters, including the coarse aggregate content, compressive strength, stainless-steel-tube thickness, and stainless-steel type on the axial compression performance of UHPC. The experimental results indicate that the failure mode of UHPC is related to the confinement ratio. As the confinement ratio increases, the failure mode transitions from shear failure to bulging failure. The addition of coarse aggregates enhances the stiffness of the specimens. Furthermore, this paper discusses the applicability of six current codes in predicting the bearing capacity of CA-UFSST and finds that the European code exhibits the best prediction performance. However, as the confinement ratio increases, the prediction accuracy becomes notably insufficient. Therefore, it is necessary to establish a more accurate calculation model for the axial compression bearing capacity.

Dual Responsive Emulsions Based on Amphiphilic Elastin-like Polypeptide Bioconjugates.

To achieve the desired therapeutic response, drug delivery systems must ensure the controlled release of the loaded content at the targeted site. One possible strategy relies on the improvement of conventional drug delivery systems. To do so, smart polymers, able to change their behavior upon chemical, physical, or biological stimuli, can be used. In this context, this study aims to evaluate the potential of natural amphiphilic smart elastin-like polypeptides grafted with alkyl chains (ELP-g-Bu) to stabilize conventional oil-in-water emulsions and trigger the release of loaded molecules upon dual stimuli. With butyl pendant chains and methionine residues, the macromolecular surfactant ELP-g-Bu demonstrated a modification of physicochemical properties, looking at critical aggregation concentration, upon both temperature and oxidation stimuli. The macromolecular surfactant was then able to stabilize a paraffin-oil-in-water emulsion. The ELP-g-Bu emulsion presented a droplet size of 9 ± 1 μm and stability for at least a month at 4 and 25 °C. After successful loading of a fluorescent lipophilic molecule used as a drug model, a complete destabilization of the ELP-g-Bu emulsion and burst release of the content was achieved with thermal triggering at 42 °C. In oxidative conditions, a partial release was measured, which can be improved by increasing the number of oxidable thioether groups. Overall, these dually responsive amphiphilic ELP-g-Bu demonstrated their potential for smart-polymer-based drug delivery systems that can be promising for inflammatory disease treatment as increased temperature and radical oxygen species are present in such cases.

Rare and Abundant phoD‐Harboring Bacteria Mediate the Mineralization of Organic Phosphorus Fractions in Soil Aggregates During Cut Slope Restoration

ABSTRACTPhosphorus (P) is the key nutritional element in the soil of cut road slopes undergoing ecological restoration, and the transformation of organic P (Po) is a crucial part of the P cycle. However, the role of phoD‐harboring bacteria in driving the mineralization of Po fractions in road slope soil aggregates is unclear. This study analyzed road slope soils that had undergone 7, 11, and 14 years of restoration in the western Sichuan Plateau of China. We examined the differences and associations between the Po fraction content, alkaline phosphatase (ALP) activity, and community composition of rare and abundant phoD‐harboring bacteria in soil aggregates of four particle sizes (0.053–0.25, 0.25–2, 0.053–0.25, and, > 2 mm). The results showed that NaHCO3‐extracted Po (NaHCO3‐Po) content in soil aggregates increased with restoration years, while NaOH‐extracted Po (NaOH‐Po) content decreased. ALP activity in soil aggregates increased with restoration years, but there was no significant relationship between ALP activity with the phoD‐harboring bacterial community. There were significant differences in the composition of rare and abundant phoD‐harboring bacterial communities during slope restoration. Soil moisture, pH, organic carbon, and the C:P ratio in soil aggregates were the primary factors affecting the distribution of the Po fractions and the phoD‐harboring bacterial community. NaHCO3‐Po and NaOH‐Po in soil aggregates were likely the main substrates for ALP‐mediated Po mineralization. More genera were involved in Po mineralization in slope soils restored for 14 years than in soils restored for 7 and 11 years, and rare phoD‐harboring genera were more actively involved in Po mineralization than abundant phoD‐harboring genera. This study provides some theoretical basis for P effectiveness enhancement and P management during slope soil restoration.

Hierarchical Structures of Amino Acid Derived Polyhydroxyurethanes: Promising Candidates as Drug Carriers and Cell Adhesive Scaffolds.

In this study, we examined the self-assembly of a series of biodegradable and biocompatible amino acid-based polyhydroxyurethanes (PHUs), investigating the structural influence of these polymers on their self-assembly and the resulting morphological features. The presence of hydrophilic and hydrophobic segments, along with carbonyl urethane, ester, and hydroxyl groups in the PHU backbone, facilitates intermolecular hydrogen bonding, enabling the formation of self-assemblies with hierarchical nanodimensional morphologies. We determined the critical aggregation concentration (CAC) and found that it largely depends on the PHU's structure. In-depth morphological studies demonstrated that the evolution of morphology proceeds in four steps: (1) the initial formation of micelles, which act as seeds at very low concentrations, (2) the elongation of these micelles into nanorod or nanopalette shapes below the CAC range, (3) the epitaxial growth of nanofibers at the CAC, and (4) the complete formation of fibrous mats above the CAC. Additionally, these hierarchical structures were utilized for the encapsulation and release of the drug doxorubicin (DOX). We observed that 75% of the encapsulated DOX was readily released in a mildly acidic environment, similar to the physiological conditions of cancer cells. Cellular uptake studies confirmed the effective uptake of the drug-loaded nanoassemblies into the cytoplasm of cells. Our studies also confirmed that these self-assembled structures can serve as effective cell adhesive scaffolds for tissue engineering applications.

Disulfide-Rich Self-Assembling Peptides Based on Aromatic Amino Acid.

Aromatic residues in assembling peptides play a crucial role in driving peptide self-assembly through π-π stacking and hydrophobic interactions. Although various aromatic capping groups have been extensively studied, systematic investigations into the effects of single aromatic amino acids in assembling peptides remain limited. In this study, the influence of aromatic-aromatic interactions on disulfide-rich assembling peptides is systematically investigated by incorporating three different aromatic amino acids. Their folding propensity, self-assembling properties, and rheological behaviors are evaluated. These results indicate that different aromatic-aromatic interactions have a significant effect on self-assembly abilities, as determined by critical aggregation concentration (CAC) measurements. Furthermore, the biocompatibility of these hydrogels is assessed, and their potential for 3D cell culture is explored. The injectable F1-ox hydrogel demonstrate excellent biocompatibility for SHED and NIH3T3 cells and exhibit a porous structure that facilitates nutrient and cellular metabolic waste exchange. This work provides new insights into the molecular design of peptide-based biomaterials, with a focus on the impact of aromatic residues on disulfide-rich assembling peptides.

Durability and Compressive Strength of Composite Polyolefin Fiber-Reinforced Recycled Aggregate Concrete: An Experimental Study

This study investigates of using recycled concrete aggregates along with the reinforcement of polyolefin fibers to augment both the compressive strength and durability of concrete, in alignment with the principles of sustainable development. This study experimentally investigated the compressive strengths and durability of composite polyolefin fiber-reinforced recycled aggregate concrete (PFRRAC) exposed to chloride and acidic environments. For this purpose, 150 cubic samples of 100 × 100 × 100 concrete with various combinations of recycled aggregates and polyolefin fibers were made and subjected to axial compressive loading. The results show that the addition of fibers significantly enhances the compressive strength of concrete, with an increase of up to 34.36% at 5% fiber content. However, increasing the proportion of recycled aggregates reduces the compressive strength, with reductions ranging from 21.12% to 43.85% as the recycled aggregate content rises to 70%. Moreover, the combination of fibers and recycled aggregates demonstrates potential for improving the sustainability and durability of concrete under challenging environmental conditions, particularly in chloride and acidic environments. In acidic environments, the inclusion of fibers significantly enhances the resistance to strength reduction. Furthermore, the study uncovers that a higher concentration of recycled aggregates exacerbates the reduction in strength in chloride-rich settings, emphasizing the imperative nature of meticulous mix design and material selection. The findings for the integration of even minor quantities of polyolefin fibers to amplify the performance and sustainability of concrete mixtures, especially when utilizing recycled aggregates, thus promoting eco-friendly construction practices.

Early age time-dependent mechanical properties of 3D-printed concrete with coarse aggregates

Incorporating coarse aggregate into 3D-printed concrete is essential for promoting its practical application, yet the impact on the early age time-dependent mechanical properties, crucial for the multilayer structure stability and quality, is underexplored. In this study, 3D-printed concrete with coarse aggregate (3DPCA) with varying mortar-to-coarse aggregate ratios (M/A) and coarse aggregate gradations were prepared, of which uniaxial unconfined compression and direct shear performance were experimentally investigated. The fundamental formulations between the early age mechanical parameters and the resting time of 3DPCA were characterized and then utilized in a digital model simulating actual printing. Results indicated that the 3DPCA early age mechanical properties, including compressive strength, Young's modulus, cohesion, and friction angle, increased with longer resting time and decreasing M/A, and higher proportions of aggregates above 9.5 mm. High coarse aggregate content caused three distinct compressive strength-displacement curve stages and complicated strength eigenvalue extraction. Coarse aggregate gradation affected shear properties more than content, with larger aggregates improving cohesion and friction angles. The failure mode prediction model based on ABAQUS could accurately predict the maximum collapse layer of 3DPCA that occurred in overall unstable collapse and over-expansion failure but was incapable of localized deformation.

Prediction of fracture toughness of concrete using the machine learning approach

In the process of structural design, it is useful to estimate the fracture toughness of concrete samples. This research showcases the effectiveness of utilizing machine learning methods to determine the fracture toughness of concrete. Taking into account variables such as mix design, machine learning techniques can accurately predict the mode I fracture toughness of concrete. Dimensionless stress intensity factor of concrete prediction using twelve different machine learning techniques namely, Linear regression (LR), Extreme Gradient-Boosting (XGboost), K-Nearest Neighbors (KNN), Random Forest (RF), Category Boosting (CB), Decision Tree (DT), Extra Trees (ET), Light Gradient-Boosting (LightGB), Adaptive boosting (AdaBoost), Bagging (BA), Gaussian Process (GP), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The result of utilizing the training adaptive moment estimation algorithm has been developed to create an outstanding machine learning-based system. After carefully analyzing comparisons between the predictions produced by different models and experimental findings, it has been discovered that the models demonstrate an impressive accuracy rate of about 90 percent when it comes to forecasting concrete fracture toughness. The research findings emphasize that the ANN model exhibited superior accuracy in its predictions (R2 value of 0.90, RMSE of 0.1517, and MAE of 0.1238). Upon conducting a thorough examination of the ANN method’s sensitivity, the cement parameter holds utmost significance in accurately estimating concrete’s fracture toughness using the available dataset. So, the ANN model can be used as a valuable method to provide practical assistance in predicting the fracture toughness of concrete. When evaluating the effective parameters, the cement and metakaolin dosage and the notch height to specimen height ratio have the greatest effect on the fracture resistance, while the coarse aggregate content is minimal. This result is consistent with the experimental data.

Influence of fiber shapes on the compressive behaviors of ultra-high performance concrete containing coarse aggregate

The addition of coarse aggregate in ultra-high performance concrete (UHPC-CA) has the potential to reduce autogenous shrinkage and improve mechanical properties. However, it also affects the fiber distribution and crack propagation, thereby compressive relationship of UHPC. This study investigated the effects of different fiber shapes on the compressive behaviors of UHPC-CA. Three fiber shapes (straight fiber, hooked fiber, and hybrid fiber) and six coarse aggregate content (0 - 1200 kg/m3) are the main variables. The test results have indicated that the addition of coarse aggregate in UHPC rendered wider main cracks. Hybrid fiber was preferred over the hooked-end and straight fibers to optimize ductility in UHPC-CA. An increase in compressive strength of up to 17.7% and elastic modulus of UHPC of up to 23.7% after moderate addition of coarse aggregate contents, The fiber shape affected peak strain and ultimate strain values for UHPC-CA. Their peak and ultimate strains were improved by 12.8% - 15.3% and 38.2%-127.4% with the inclusion of 2% hooked and hybrid fibers. Based on the experimental data and relevant equations, a proper rational equation from the existing literature is used to generate the complete compressive stress-strain curves of UHPC-CA with different fiber shapes. In summary, this study establishes that hybrid fiber shapes significantly enhance the compressive properties of UHPC-CA. The high elastic modulus and ductility of the UHPC-CA with hybrid fiber are particularly promising for blast and impact applications.

Mechanical and Microstructural Properties of Environmentally Friendly Concrete Partially Replacing Aggregates with Recycled Rubber and Recycled PET

Today, waste generation is a worldwide problem, where the major source of waste is generated by worn-out tires and Polyethylene Terephthalate (PET) bottles. Therefore, as a way to provide an ecological disposal and reduce the environmental impact, the feasibility of producing ecological concrete (EC) with recycled rubber (RB) and PET as substitutes for natural aggregates is explored, determining their physical, mechanical and microstructural properties. This experimental study considered the replacement of fine aggregate with RB at 1%, 4%, 7%, 10%, 20% and 30%, and the optimum content of coarse aggregate plus the replacement of coarse aggregate with PET at 1%, 5%, 10% and 15%. The results showed that the optimum percentage of RB was 1%, which, when combined with PET, resulted that the design with 1% RB+ 1% PET was the optimum replacement combination, increasing 10% in bending, 1% in compression and for tension it decreased by 11%, in turn with the XRD test quartz and different aluminosilicates were found, such as portlandite and by SEM-EDS it was possible to visualize polymer flakes embedded in the analyzed fragment. It is concluded that RB and PET can be viable for the production of EC only if low percentages of replacement by natural aggregates are used.

Research on the random generation model of 2D irregular aggregates in concrete based on fast grid region division method and its ground penetrating radar characteristics

Concrete can be regarded as a composite material with aggregates embedded in hardened slurries. The size, shape and distribution of aggregates directly affect multiple properties of concrete, including mechanical properties, ultrasonic field characteristics of concrete and ground penetrating radar (GPR) wave field characteristics. Therefore, conducting in-depth research on the random generation model of two-dimensional irregular aggregates is particularly important. This article introduces a fast grid region division method (FGRDM), which is an efficient reconstruction algorithm designed for two-dimensional concrete aggregate models. The algorithm takes into consideration the random micro-aggregate structure of concrete. A two-dimensional random distribution model of irregular aggregates in concrete was successfully established by generating concrete models with different particle size distributions and aggregate contents. Furthermore, the finite difference time domain method was used to analyze the GPR wave field of the random model. The significant influence of aggregate shape and distribution on the GPR wave field was discussed. Compared with existing two-dimensional irregular aggregate random generation models, the FGRDM proposed in this paper avoids computational redundancy and generates irregular aggregates at an increasingly fast speed. In the analysis of GPR data, electromagnetic waves are greatly affected by the shape and distribution of aggregates, highlighting the critical role of two-dimensional random distribution models in GPR data analysis. The paper uses FGRDM to generate random irregular aggregates to simulate the distribution of aggregates in actual concrete. Different diameter glass fiber reinforced polymer (GFRP) bars are inserted into the actual concrete and the numerical model to investigate the effect of aggregate size on the recognition of different diameter GFRP bars. The simulation results are generally consistent with the experimental results.

Mechanical and Microstructural Properties of Environmentally Friendly Concrete Partially Replacing Aggregates with Recycled Rubber and Recycled PET

Today, waste generation is a worldwide problem, where the major source of waste is generated by worn-out tires and Polyethylene Terephthalate (PET) bottles. Therefore, as a way to provide an ecological disposal and reduce the environmental impact, the feasibility of producing ecological concrete (EC) with recycled rubber (RB) and PET as substitutes for natural aggregates is explored, determining their physical, mechanical and microstructural properties. This experimental study considered the replacement of fine aggregate with RB at 1%, 4%, 7%, 10%, 20% and 30%, and the optimum content of coarse aggregate plus the replacement of coarse aggregate with PET at 1%, 5%, 10% and 15%. The results showed that the optimum percentage of RB was 1%, which, when combined with PET, resulted that the design with 1% RB+ 1% PET was the optimum replacement combination, increasing 10% in bending, 1% in compression and for tension it decreased by 11%, in turn with the XRD test quartz and different aluminosilicates were found, such as portlandite and by SEM-EDS it was possible to visualize polymer flakes embedded in the analyzed fragment. It is concluded that RB and PET can be viable for the production of EC only if low percentages of replacement by natural aggregates are used.

A Hybrid Strategy Two‐Dimensional Concrete Aggregate Filling Algorithm

ABSTRACTAccurate and highly efficient construction of numerical concrete models with different gradations is important to analyze the acoustic properties of ultrasonic waves in concrete. The random aggregates‐based method is a classic approach to meso‐structural modeling of concrete and is particularly suitable for generating models with low‐density aggregates. However, when facing the simulation requirements of concrete models with large volumes, small particle sizes, and high aggregate content, traditional random aggregates‐based methods encounter challenges in satisfying aggregate packing density and modeling efficiency. In order to improve the efficiency of aggregate placement in modeling concrete meso‐models, we proposed a hybrid‐strategy point cloud placement algorithm that combines the re‐placement and sedimentation strategies. Then, the algorithm's effectiveness in improving the modeling efficiency is verified by reconstruction experiments on random aggregate models with different aggregate grades. Finally, the model's reliability and validity is further confirmed by simulation analysis of the ultrasonic attenuation characteristics of the model. The results show that the algorithm can rapidly generate more than 75% aggregate packing density concrete specimens and significantly outperforms similar algorithms regarding aggregate placement efficiency and model generation time. In addition, the model generated by the proposed algorithm can accurately simulate the attenuation characteristics of ultrasonic waves in concrete.

Low-dose cement stabilized large particle size graded crushed stone: Laboratory and field investigations

Compared with traditional graded crushed stone (GCS), the maximum aggregate size of large particle size GCS (LPS-GCS) is larger and the content of large-size aggregates is more, which can form an interlocking skeletal structure with a stronger bearing capacity. Nevertheless, LPS-GCS occasionally exhibits unstable performance, and one effective way to enhance LPS-GCS's mechanical and physical properties is to stabilize it with low-dose cement. In this study, four cement contents (1.5 %, 2.0 %, 2.5 %, and 3.0 %, by mass) were selected to form low-dose cement stabilized LPS-GCS (LCS-LPS-GCS) mixtures with a maximum aggregate particle size of 53 mm. Unconfined compressive strength (UCS) and compressive resilient modulus (CRM) tests were performed on LCS-LPS-GCS samples for different cement contents and curing times. Moreover, prediction models and correlations for the UCS and CRM at different curing times were established. Furthermore, a test road was used to evaluate the LCS-LPS-GCS's field compaction level and deflection. Laboratory test results indicated that while the California bearing ratio was significantly higher than that of conventional GCS, the maximum dry density of LPS-GCS designed in this study was close to that of conventional GCS. With the same cement content, LCS-LPS-GCS outperformed conventional GCS in terms of UCS and CRM. Based on the variation law of the mechanical properties, the ideal cement content should be around 2.5 %. There was a strong positive correlation between UCS and CRM scores from LCS-LPS-GCS. The UCS and CRM were appropriate to be predicted by exponential and growth functions models, respectively. The field investigations indicated that asphalt pavements reconstructed with LCS-LPS-GCS had high compaction and low deflection, resulting in better bearing capacity and durability. The results of this investigation can serve as a reference for the design and application of LCS-LPS-GCS base for long-lasting and resilient pavements.

Predictive modeling of compressive strength in silica fume‐modified self‐compacted concrete: A soft computing approach

AbstractSelf‐compacting concrete (SCC) is a specialized type of concrete that features excellent fresh properties, enabling it to flow uniformly and compact under its weight without vibration. SCC has been one of the most significant advancements in concrete technology over the past two decades. In efforts to reduce the environmental impact of cement production, a major source of CO2 emissions, silica fume (SF) is often used as a partial replacement for cement. SF‐modified SCC has become a common choice in construction. This study explores the effectiveness of soft computing models in predicting the compressive strength (CS) of SCC modified with varying amounts of silica fume. To achieve this, a comprehensive database was compiled from previous experimental studies, containing 240 data points related to CS. The compressive strength values in the database range from 21.1 to 106.6 MPa. The database includes seven independent variables: cement content (359.0–600.0 kg/m3), water‐to‐binder ratio (0.22–0.51), silica fume content (0.0–150.0 kg/m3), fine aggregate content (680.0–1166.0 kg/m3), coarse aggregate content (595.0–1000.0 kg/m3), superplasticizer content (1.5–15.0 kg/m3), and curing time (1–180 days). Four predictive models were developed based on this database: linear regression (LR), multi‐linear regression (MLR), full‐quadratic (FQ), and M5P‐tree models. The data were split, with two‐thirds used for training (160 data points) and one‐third for testing (80 data points). The performance of each model was evaluated using various statistical metrics, including the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), objective value (OBJ), scatter index (SI), and a‐20 index. The results revealed that the M5P‐tree model was the most accurate and reliable in predicting the compressive strength of SF‐based SCC across a wide range of strength values. Additionally, sensitivity analysis indicated that curing time had the most significant impact on the mixture's properties.

Mulching Improves the Soil Hydrothermal Environment, Soil Aggregate Content, and Potato Yield in Dry Farmland

Mulching could effectively improve the soil hydrothermal environment, improve changes in the soil structure, increase entropy, and conserve soil moisture to solve the problem of grain reduction caused by perennial drought in Northwest China. Thus, a two-growing-season field experiment (2020–2021) with five treatments (PM1, biodegradable plastic film mulching; PM2, plastic film mulching; SM1, straw strip mulching; SM2, crushed corn straw full mulching; and CK, no mulching as the control) was conducted to investigate the effects of different mulching materials on the soil hydrothermal environment, soil aggregate distribution, stability, and tuber yield of rainfed potato farmland in Northwest China. Over two growing seasons, mulching planting, on average, increased (p < 0.05) the soil moisture at the 0–200 cm depth by 9.0% relative to CK (SM2 (11.6%) > SM1 (10.3%) > PM2 (8.6%) > PM1 (7.0%)). The mulching treatments significantly regulated the soil temperature during the whole growth period, in which plastic mulching significantly increased the soil temperature of the 0–25 cm soil depth during the whole growth period by 2.1 °C (PM2 (2.1 °C) > PM1 (2.0 °C)); meanwhile, straw mulching significantly reduced the soil temperature by 1.4 °C (SM2 (0.9 °C) > SM1 (0.6 °C)). All mulching treatments improved the soil macroaggregate content and soil aggregate stability in all soil depths from 0 to 40 cm, with increases of 31.4% and 27.1% in the mean weight diameter (MWD) and 22.6% and 21.2% in the geometric mean diameter (GWD) compared with CK, respectively. Straw and plastic mulching significantly increased the fresh tuber yield by 12.5% and 12.6% compared with CK, respectively. The increases were greatest in SM2 and PM2. Crushed corn straw full mulching is difficult to sow and harvest; therefore, straw strip mulching could improve the soil hydrothermal environment, increase production, and provide an environmentally friendly technology for dryland potato production.

Improving thermal insulation and thermal resistance properties of laterite‐based geopolymer foams by direct foaming

AbstractThis work aims to synthesize new foaming laterite geopolymer foam using laterite, sodium silicate solution, sand, and aluminium powder. The porosity rapidly increased with the addition of the foaming agent. The foam matrix had thermal conductivity values of 0.10 W/m K with 0.7% of Al powder and 0.64 W/m K with 0% of Al powder. For fire resistance, samples exposed to high temperatures (200°C and 500°C) showed increased flexural strength, linear shrinkage at 500°C, and a decrease at 900°C due to structural weakening under high thermal pressure and the appearance of new phases such as nepheline and akermanite in X‐ray diffraction analysis. The results also showed that a 30% increase in fine aggregate content increased the strength of the foam matrix, with flexural strength ranging from 5 to 9.1 MPa after 28 days of ambient curing. These laterite geopolymer foams have shown promising thermal insulation and mechanical qualities that are appropriate for building applications.

An experimental investigation to predict the compressive strength of lightweight Ceramsite aggregate UHPC using boosting and bagging techniques

Ceramsite aggregate concrete is renowned for its lightweight properties but often exhibits low compressive strength (CS), limiting its structural applications. This study introduces ultra-high-performance powder into Ceramsite aggregate concrete to enhance its CS while maintaining low density. Achieving optimal CS for this composite material requires a carefully designed mix. In this research, advanced machine learning (ML) techniques, including random forest, extreme gradient boosting, categorical boosting, light gradient boosting machine (LightGBM), natural gradient boosting (NGBoost), and adaptive boosting, were employed to predict the CS of lightweight Ceramsite aggregate UHPC (LWCA-UHPC). The study utilized 189 datasets from the authors' own experimental results for training and testing the models. The results demonstrated that NGBoost and LightGBM outperformed other models, with R² values of 0.9614 and 0.9645, respectively, in the testing phase. The models also showed low errors, with NGBoost having a mean absolute error (MAE) of 2.0624 and a root mean squared error (RMSE) of 2.9140, while LightGBM recorded an MAE of 2.0161 and an RMSE of 2.7778. Moreover, feature importance and Shapley additive explanations (SHAP) analyses revealed that the most influential factors affecting the CS of LWCA-UHPC were cement content, density, sand, and Ceramsite aggregate content, consistent with experimental findings. Additionally, a graphical user interface was developed to integrate the hybrid ML model into a user-friendly desktop tool, achieving an R² value of 0.9671 with a low mean squared error of 3.5204 when tested against experimental datasets. This tool not only enables practical applications in engineering but also helps reduce the need for extensive experimental work by providing accurate predictions of CS.

Effect of coarse aggregates on contact explosion resistance of concrete—A mesoscopic investigation

The intricate relationship between the mechanical properties of concrete and its internal microstructure underscores the importance of comprehending explosion performance and damage mechanisms at a mesoscopic level to effectively enhance blast resistance. This study employed three-dimensional (3D) mesoscale models to numerically investigate the dynamic behavior of concrete mixed with coarse aggregates under contact explosion. Rigorous validation of numerical models and simulation techniques was untaken through the contact explosion tests. The study explored mesoscopic damage mechanisms in heterogeneous concrete targets with randomly distributed coarse aggregates, drawing comparisons with a homogeneous concrete target. Critical mesoscopic parameters influencing the contact explosion resistance of concrete were thoroughly examined. Structural effects of coarse aggregates emerge as pivotal, shifting the damage mode from overall failure with spalling-dominated damage in homogeneous concrete to localized failure in mesoscopic concrete. The mesoscopic concrete experienced a distinct four-stage damage evolution—cratering, crack initiation, perforation, and dynamic fragmentation—diverging from homogeneous concrete with multi-layer spalling originating from the boundaries. The exponential attenuation of shock waves observed in homogeneous concrete was locally disrupted by coarse aggregates in mesoscopic concrete, attributed to wave impedance mismatch and aggregate extrusion effects. Mortar strength primarily contributed to concrete cracking, with minimal impact on damage modes. Failure modes were predominantly influenced by the content and particle size of coarse aggregates. Higher volumetric fractions significantly reduced concrete spalling, while increased coarse aggregate size exacerbated perforation failure. This comprehensive study advances our understanding of blast-resistant concrete design at a mesoscopic level, providing valuable insights for strategies aimed at enhancing structural resilience.