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Go to Editorial ManagerThis comprehensive study undertakes a two-tiered comparative analysis to systematically evaluate the fatigue and cracking performance of a 40-50 penetration grade asphalt binder and its corresponding asphalt concrete mixtures, modified with varying dosages (2%, 4%, and 6% by binder weight) of Nano-Alumina (NA) and Nano-Silica (NS). The experimental methodology involved extensive binder-level testing, including the evaluation of physical properties (penetration, softening point, ductility), rheological behavior (Rotational Viscosity (RV)), and fatigue characteristics using the Superpave parameter G* sin δ and the advanced Linear Amplitude Sweep (LAS) test. Furthermore, compatibility was assessed via storage stability and Scanning Electron Microscopy (SEM). The research culminated in mixture-level performance evaluation using the Indirect Tensile Cracking Test (IDEAL-CT) to derive the Cracking Tolerance Index (CT-Index), Flexibility Index (FI), and Crack Resistance Index (CRI). The results confirmed that both nanomaterials significantly enhance binder stiffness and thermal stability. Nano-Alumina (NA) consistently induced the most profound stiffening effect, reflected by a major reduction in penetration. Rheological and LAS testing indicated that NA provides a stable and progressive, dose-proportional enhancement in fatigue life from 2% to 6%, attributed to the formation of a sustained nanoscale reinforcement network. Conversely, Nano-Silica (NS) exhibited a potent viscosity-building effect due to its high surface area, achieving superior initial cracking tolerance and fatigue life at low concentrations (2% to 4%). Crucially, the study identified a narrow optimal range for NS; concentrations at 6% led to an adverse reduction in fatigue resistance (G* sin δ increase) and diminished flexibility, suggesting a constraint imposed by excessive stiffening and potential particle agglomeration. Mixture-level IDEAL-CT results further validated these trends: NA offered a balanced overall contribution, maximizing the CT-Index at 6% and CRI at 4%, while NS yielded an exceptionally high CT- Index value at 2% but showed a decline in performance at higher contents. The overall findings recommend an optimal practical dosage of 2-4% for NS and 4-6% for NA, underscoring the necessity of material-specific optimization for achieving enhanced durability and fatigue life under repeated loading.
Polyurethane (PU) products enjoy remarkable versatility due to their tunable chemistry, segmented structure, and a wide range of mechanical properties, making them useful in flexible foam products, structural systems, and biomedical applications. However, the complex multiphase morphology and the strong interaction between reaction and processing processes make experimental characterization incomprehensible on its own. In turn, computational studies have become essential to study and design PU systems at a range of spatial and temporal scales. The current review provides an overview of simulation methodologies that are relevant to polyurethane, including atomistic molecular dynamics (MD), coarse-grained (CG), and mesoscale simulations, including dissipative particle dynamics (DPD), finite element method (FEM) modeling, and computational fluid dynamics (CFD) simulations. Atomistic models provide data on molecular interactions, hydrogen bonding, and thermomechanical behavior, and CG and mesoscale methods on phase separation and morphological evolution. At the bigger length scale, nonlinear mechanical response can be predicted using FEM, whereas foaming and mold-filling processes can be predicted using CFD that is coupled with reaction kinetics and population balance equations. Its focus is on multiscale modeling strategies, which combine these apparently different approaches, hence allowing the explanation of structure-property-process links. New trends and modern issues, including the integration of machine learning and tool models of digital twins, are also mentioned, highlighting new opportunities in predictive design, based on simulations, of polyurethane materials.
The objective of the current study is to determine the accuracy of a computational model that has been developed to simulate polyurethane foaming reactions by comparing its results with experimental findings on the system using both physical and chemical blowing agents. There was high concordance between the model outputs and the laboratory results in regard to the temporal development of reaction temperature as well as the resulting foam density, both of which were highly faithful recreations. The discussion provided further information about the optimization of the performance of cyclohexane, particularly when used in synergy with chemically active blowing agents, which speed up foaming. Besides, the polymerization dynamics were contained in the simulation, thus providing rich information on the structural changes that occur during the foaming process. Taken together, the results present a strong basis for the process performance optimization, as well as the predictive modeling of the blowing agent behavior. In the future, it will involve expanding the simulation model to include a wider range of agents, reaction mechanisms, and kinetics.
Nanoparticle additives emerge as a modern solution to eliminate the performance gap between conventional water-based drilling fluids (WBDFs), and more superior but environmentally challenging oil-based drilling fluids (OBDFs). This study focuses on the enhancement of KCl polymer mud using nano-additives. While nano-additives like copper oxide (CuO NPs) were studied and showed promising results, another form of copper (elemental copper nanoparticles, Cu NPs) with a potential as a multifunction mud additive remains largely unexplored. This research systematically investigates the impact of Cu NPs (0.04–0.8 wt%) on the lubricity, rheology, and filtration properties of KCl polymer mud. All the measurements were done in the lab at room temperature, using lubricity tester, viscometer, and low-pressure filter press. Most additives tend to enhance one property of the mud, but the Cu NPs acted as a more superior properties enhancer, as it didn't enhance only one aspect of KCL polymer mud, but acted as multifunctional additive. For the lubricity, the effect of Cu NPs was significant on the coefficient of friction (CoF), with maximum reduction of 41.68% observed at 0.8% concentration, however at the 0.2% concentration, a relatively similar result of CoF reduction was observed with 39.78% making it the optimal concentration for the lubricity aspect. For the rheological properties, the addition of Cu NPs to the KCL polymer mud enhanced the overall rheological properties, increasing the plastic viscosity (PV), yield point (YP), apparent viscosity (AV), and gel strength, the highest values [PV (44.5 cP), YP (69.4 lb/100ft²), AV (77.35 cP)] were observed at 0.2% concentration. Unlike its beneficial effects on lubricity and rheology, the addition of Cu NPs to KCl polymer mud resulted in increased fluid loss and thicker filter cakes. The study concludes that a concentration of 0.2 %wt of Cu NPs is optimal for the simultaneous enhancement of lubricating and rheological properties in KCl polymer mud. This study highlights the potential of Cu NPs as a multifunctional additive that can be used in advanced water–based drilling fluids systems.