Korea-Australia Rheology Journal最新文献

Recent experimental observation in thermal-induced gelation of silica-based shear thickening fluids (STFs) has necessitated the efforts to address the thermal instability of the STFs. The shear thickening behavior of hydrophilic fumed silica/PEG disappeared after thermal treatment. Hydrophobic surface is demonstrated to enhance thermal stability of the fumed silica/PEG system under the same thermal treatment. Dynamic temperature sweep experiments reveal a temperature-induced sol–gel transition of the hydrophilic silica/PEG. Meanwhile, hydrophobic silica/PEG does not undergo such gelation and still displays viscosity thickening after thermal treatment. This gelation process is found to be concentration-dependent and irreversible, with enhanced gelation at higher specific surface areas of hydrophilic silica particles. The small-angle X-ray scattering (SAXS) studies suggest that this sol–gel transition is likely attributed to the reduction of solvation layer which could be highly influenced by particle surface hydrophilicity. Comprehensive experiments, including Soxhlet extraction and rheology, confirm that the resulting stable three-dimensional structure is not formed by chemical crosslinks but behaves as a physical network. It is demonstrated how tailoring particle–medium interactions through controlling particle surface characteristics can be used to improve thermal stability of silica-based STFs.

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In the current study, cellulose nanocrystal (CNC) reinforced cyclic olefin copolymer (COC) composite film was fabricated via spin-coating. For the application to the transparent and flexible substrate, the physical characteristics of the composite film were investigated, such as transparency, coefficient of thermal expansion (CTE), thermal properties and scratch resistance. COC was used as a matrix due to its high optical transparency, and CNC was utilized as a filler to lower CTE and enhance the thermal and mechanical properties. As the CNC loading increased, the tensile strength and elastic modulus increased significantly. In addition, the CNC/COC film exhibited a high transparency of 85%.

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Carbon fiber-reinforced plastics (CFRPs) have attracted attention as lightweight materials with exceptional properties, making them suitable for applications in industries where enhanced fuel efficiency and vibration-damping effects are required. However, as CFRP applications expand beyond the mobility sector, the demand for additional functionalities, particularly electrical conductivity, has emerged. In this study, we employed a multi-drop filling process (MDF) to produce hybrid CFRP with imparted electrical conductivity. This approach enables precise resin drop and simultaneous curing, addressing the challenges associated with conventional CFRP production processes. Based on filtering effects during MDF, additives, such as carbon nanotubes (CNTs) and graphene, could concentrate on the surface while resin impregnated the fiber reinforcement, resulting in CFRP with surface-concentrated electrical conductivity (ScEC). We dispersed CNTs and graphene in the resin to induce a bridge effect between the additives. Resin dispersion was achieved using surfactants and solvents, preserving the enhanced electrical conductivity and mechanical properties of the CFRP. The structure, electrical conductivity, and mechanical properties of the fabricated CFRP were evaluated. The results show that the hybrid CFRPs possess the intended structure and exhibit ScEC. This study paves the way for the fabrication of CFRPs with different functionalities, thereby promoting their applications in various industries.

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The use of machine learning to predict rheological properties of polymers has great potential to facilitate the characterization of novel materials. Here, we have suggested the analogy between the double reptation (DR) and the deep neural network model. The double reptation model itself can be the special case of the deep learning method; linear activation function, and identical sets of weights for the two hidden layers are the characteristics of the double reptation model. The identical sets of weights in the double reptation model are related with the molecular weight distribution (MWD). We first generated ground truth data based on double reptation model. Then, we analyzed the dataset with reptation-guided deep neural network (RGDNN). We showed that the RGDNN model is available to determine entanglement molecular weight (plateau modulus), and monomeric friction factors from the simulated experimental rheological data (prepared using DR model) without any additional information. Overall, a noteworthy conceptual improvement in the determination of major factors that determine the rheological behavior of ultrahigh molecular weight polyethylene (UHMWPE) gels has been achieved.

The deformation capacity conditions several processes in cement-based materials, including workability and structural build-up. However, the origins of this deformation capacity present some ambiguities. This paper aims to contribute to improving the comprehension of the deformation capacity of fresh cement pastes. For this, the effect of water-to-cement ratio (w/c) and superplasticizer (SP) dosage on the viscoelastic properties of cement paste is examined using oscillatory rheology and yield stress measurements. It appears that water to cement ratio affects slightly the critical strain at the end of the linear viscoelastic domain (LVED) and strongly the storage modulus. The addition of superplasticizer seems to have a strong effect on the critical strain. In addition, it was shown that the critical strain at the end of the LVED is associated with strong physical forces (colloidal forces enhanced by early hydrates formation), while the transition strain at the flow onset is due to large structural reorganizations.

Fiber-based commodities represent a substantial fraction of plastic waste, leading to environmental harm. Discarded sanitary masks and fishing equipment undergo degradation, generating microfiber plastics, thereby presenting a notable hazard to both human health and the ecosystem. In this study, mechanically strong and environmentally friendly nanocomposite fibers were prepared by dry-jet wet spinning. The all-biomass-based fibers comprised agar and cellulose nanocrystals (CNC) as the matrix and nanofiller, respectively, and were highly miscible in deionized water as a cosolvent. Based on rheological characterization, the optimal spinning concentration and temperature were set to 13% (w/v) and 95 °C, respectively. The dry-jet wet-spun agar-based fibers exhibited remarkable mechanical performance compared with previously reported agar-based materials. In particular, the 1 wt% CNC (with respect to the agar amount) simultaneously improved the Young’s modulus, strength, and toughness by 8.3, 4.8, and 16.4% (2.6 GPa, 93.5 MPa, and 7.8 MJ m⁻³), respectively, compared to those of the control agar fibers (2.4 GPa, 89.2 MPa, and 6.7 MJ m⁻³), overcoming the trade-off of stiffness-toughness for conventional nanocomposite systems. In addition, the agar/CNC nanocomposite fibers rapidly adsorbed Methylene blue within 90 min, which is significantly faster than that of the film-type agar adsorbent. Therefore, all-biomass-based agar/CNC fibers are a promising remedy for alleviating water pollution.

The behavior of drops in the emulsion is significant in transport phenomena and the oil and the petrochemical industry. In this study, the behavior of drops that are close to each other was investigated. These drops were studied at two viscosity ratios (0.5 and 0.9, which are the viscosity ratio of drops to fluid) and six capillary numbers (0.05, 0.11, 0.17, 0.42, 0.28, and 0.36). The results demonstrate the effect of drops on each other at a range of volume fractions. Also, at capillary numbers of 0.42, 0.82, and 0.36, there were volume fractions at which drops stuck to each other and broke after combining. In contrast, the single drop at these new capillary numbers after merging was not broken. At the capillary number of 0.84, and volume fraction of 0.001495, the drops did not stick to each other, but they were broken under the influence of each other. For each capillary number, a specified volume fraction was achieved, at which the drops behave as a single drop. Therefore, in each capillary number, a volume fraction can be found so that in volume fractions less than that, drops behave individually and do not interact with each other.

Poly(2-cyano-p-phenylene terephthalamide) (CY-PPTA) has garnered significant interest as a promising precursor for super p-aramid fibers because of its organosolubility in N,N-dimethyl acetamide/lithium chloride (DMAc/LiCl) while conserving the superior properties of the resultant fibers. However, CY-PPTA has been reported to exhibit abnormal phase behavior owing to the strong dipole–dipole interactions induced by the cyano groups. Herein, we rheologically study the isotropic phases of CY-PPTA/DMAc solutions with respect to the concentration and temperature and compare them with those of CY-PPTA/sulfuric acid (H₂SO₄) solutions. In the isotropic region, the CY-PPTA solutions yield a higher power-law exponent of the dynamic viscosity (η') versus concentration of 6.0 (ηʹ ~ c^6.0) in the DMAc system than that in H₂SO₄ (ηʹ ~ c^3.2). Moreover, the CY-PPTA/DMAc solutions exhibit a lower critical solution temperature (LCST) behavior with increasing temperature, in contrast with the upper critical solution temperature in H₂SO₄. Consequently, the viscosity and exponent of the CY-PPTA/DMAc solutions increase at elevated temperatures. As shown by the Cole–Cole plot, the heterogeneity in the DMAc system becomes worse. The LCST of the CY-PPTA solution is ascribed to the intermolecular interactions between the highly polar cyano groups, which are negligible in H₂SO₄.

Pipeline transport at high Reynolds number can result in significant turbulent losses. One of the most effective methods for turbulent drag reduction is adding a very small amount of polymer drag-reducing agent to the pipeline. However, due to the complex interaction between polymers and turbulence, turbulence models incorporating polymer additives remain to be studied and developed. In the present work, we investigated the turbulence model using Reynolds averaged numerical simulation (RANS) to describe polyacrylamide drag reduction flow. A low-Reynolds-number k–ε model in turbulent flow has been developed by considering the concentration and type of polymers, which can be applied for polymer drag reduction prediction in the pipe. Mean velocity profile U_f, turbulent intensity, turbulent kinetic energy k, and turbulent dissipation rate ε in the regions of viscous sublayer, buffer layer and logarithmic layer have been predicted with various concentration θ, Reynolds number Re, degradation degrees, and changing laws of these factors have been revealed with wall distance. The developed turbulence model showed a good capability to qualitatively forecast mean velocity profile, turbulent intensity, turbulent kinetic energy, and turbulent dissipation rate, and the prediction error between the experimental and simulated values falls along the y = x curve, which can be used for the investigation and prediction of varies water-soluble, oil-soluble polymers in turbulent drag reduction flow in pipes with other parameters such as pipe diameter, pipe length, and the Reynolds number.

There have been developed a number of algorithms for the determination of relaxation spectrum from viscoelastic data. When viscoelastic data are given by stress relaxation test, the relation between relaxation spectrum and relaxation modulus can be transformed to that of Laplace transform. Hence, calculation of relaxation spectrum from relaxation modulus is a problem of inverting Laplace transform. Among various mathematical methods for inverse Laplace transform, the Post–Widder formula has been chosen by a number of researchers. However, they did not solve the problem by use of advance numerical technic but used low-order approximations. We suggest a new numerical algorithm which can calculate higher order solutions. In principle, the order of the Post–Widder formula is not limited in our algorithm.