Viscoelastic and Deformation Characteristics of Structurally Different Commercial Topical Systems

Rheological characteristics and shear response have potential implication in defining the pharmaceutical equivalence, therapeutic equivalence, and perceptive equivalence of commercial topical products. Three creams (C1 and C3 as oil-in-water and C2 as water-in-oil emulsions), and two gels (G1 and G2 carbomer-based) were characterized using the dynamic range of controlled shear in steady-state flow and oscillatory modes. All products, other than C3, met the Critical Quality Attribute criteria for high zero-shear viscosity (η0) of 2.6 × 104 to 1.5 × 105 Pa∙s and yield stress (τ0) of 55 to 277 Pa. C3 exhibited a smaller linear viscoelastic region and lower η0 (2547 Pa∙s) and τ0 (2 Pa), consistent with lotion-like behavior. All dose forms showed viscoelastic solid behavior having a storage modulus (G′) higher than the loss modulus (G″) in the linear viscoelastic region. However, the transition of G′ > G″ to G″ > G′ during the continual strain increment was more rapid for the creams, elucidating a relatively brittle deformation, whereas these transitions in gels were more prolonged, consistent with a gradual disentanglement of the polymer network. In conclusion, these analyses not only ensure quality and stability, but also enable the microstructure to be characterized as being flexible (gels) or inelastic (creams).

Tailoring composite materials for nonlinear viscoelastic properties using artificial neural networks

Polymer matrix composites exhibit nonlinear viscoelastic behavior over a wide range of temperatures and loading frequencies, which requires an elaborate experimental characterization campaign. Methods are now available to accelerate the characterization process and recover elastic modulus from storage modulus ( E′). However, these methods are limited to the linear viscoelastic region and need to be expanded to nonlinear viscoelastic problems to enable materials design. The present work aims to build a general machine learning based architecture to accelerate the characterization and materials design process for nonlinear viscoelastic materials using the E′ results. To expand outside the linear viscoelastic region, general relations of viscoelasticity are first developed so the master relation of E′ considering nonlinear viscoelasticity can be transformed to time domain relaxation function. The transform starts with building the master relation by optimizing the artificial neural network (ANN) formulation using Kriging model and genetic algorithm. Then the master relation is transformed to a relaxation function, which can be used to predict the stress response with a given strain history and to further extract the elastic modulus. The transform is tested on high density polyethylene matrix syntactic foams and the accuracy is found by comparing the predicted materials properties with those obtained from tensile tests. The good agreements indicate the transform can predict the elastic modulus under a wide range of temperatures and strain rates for any composition of the composite and can be used for material design problems.

Viscoelastic analysis of oat grain within linear viscoelastic region by using dynamic mechanical analyzer

The stress relaxation, creep-recovery, temperature, and frequency sweep tests were performed within the linear viscoelastic region by using a dynamic mechanical analyzer to investigate the viscoelastic characteristic of oat grain. The result showed that 5-element Maxwell and Burgers model were able to describe viscoelastic behaviors better. The relaxation stress decreased with the increasing moisture content from 6.79 to 23.35%, while the creep strain increased as well as the final percentage recovery decreased from 58.61 to 32.50%. In frequency sweep, storage modulus increased with the increasing frequency. In temperature sweep, there was a clear turning point in storage modulus, loss modulus, and tan delta curves with increasing temperature. The turning value of 167.47, 147.44, 134.27, 132.41, 110.28, and 92.62 °C detected in the tan delta were regarded as the best glass transition temperatures. This temperature was found to be lower than gelatinization heating temperature and decrease with the increase of moisture content. The crystalline structure of oat exhibited a typical A-type pattern and corresponding crystallinity increased from 22.03 to 31.86% with increasing moisture content. The scanning electron microscopy (SEM) micrograph of oat section was found that the size and adhesive effect of starch granules increased due to hydration.

Impact of temperature on the linear viscoelastic region of wood

The impact of temperature on the linear viscoelastic region, which is characterized by critical strain, of Chinese fir ( Cunninghamia lanceolata (Lamb.) Hook.) was investigated at various temperatures between –100 and 220 °C for specimens with a moisture content of approximately 0.6%. The effect of oscillation frequency on the linear viscoelastic region under various constant temperatures was also examined. The results indicated that critical strain generally decreased with increasing temperature except at –80, –20, 40, 120, and 220 °C. These five exceptions were attributed to the occurrence of relaxation processes. With an increase in testing frequency from 1 to 20 Hz, the critical strain decreased slightly at all temperatures. It is suggested that the stored elastic energy and yield stress, which were obtained at critical strain, could be indicators to predict wood mechanical performance.