Narrow Results

This work develops a chain scission-induced anisotropic damage constitutive model for double network (DN) hydrogels while considering the damage cross effect. A free energy of a single polymer chain comprising the configuration entropy of the polymer chain and the internal energy of persistent segments is proposed to avoid the force singularity caused by chain stretch limit. Then, a distribution function of the initial stretch ratio of polymer chains in DN hydrogels is derived to consider the randomness of chain length. Based on the micro-sphere full-network model and an energy-form fracture criterion of polymer chains, the damage evolution model of polymer chains in a certain orientation is formulated by the critical initial stretch ratio of the polymer chain, showing that only polymer chains with the initial stretch ratio lower than the critical initial stretch ratio survive in a certain chain stretch. The critical initial stretch ratio is determined by the maximum chain stretch and the maximum first invariant of the deformation state by considering the damage cross effect. Using the developed damage constitutive model, we describe the stress softening, strain hardening behavior and the nonmonotonic stress–strain curve of DN hydrogels. It also reveals that the residual strain of DN hydrogels in the loading cycles is caused by the anisotropic damage of polymer chains in DN hydrogels.

Watercore and sugar content are internal qualities which are impossible for exterior determination. Therefore the aims of this study were to develop models for nondestructive detection of watercore and predicting sugar content in pear using Near Infrared Spectroscopy (NIR) technique. A total of 93 samples of Asian pear variety "SH-078" were used. For sugar content, spectrum of each fruit was measured in the short wavelength region (700–1100 nm) in the reflection mode and the first derivative of spectra were then correlated with the sugar content in juice determined by digital refractometer. Prediction equation was performed by multiple linear regression. The result showed Standard Error of Prediction (SEP) = 0.58°Bx, and Bias=0.11. The result from t-test showed that sugar content predicted by NIR was not significantly different from the value analyzed by refractometer at 95% confidence. For watercore disorder, NIR measurement was performed over the short wavelength range (700–850 nm) in the transmission mode. The first derivative spectra were correlated with internal qualities. Then principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to perform discrimination models. The accuracy of the PCA model was greater than the PLSDA one. The scores from PC1 were separated into two boundaries, one predicted rejected pears with 100% classification accuracy, and the other was accepted pears with 92% accuracy. The high accuracy of sugar content determining and watercore detecting by NIR reveal the high efficiency of NIR technique for detecting other internal qualities of fruit in the future.