The synthesis and mechanical characterization of novel, tough poly(N,N-dimethylacrylamide) (PDMA)-silica hydrogel hybrids are presented to understand the role played by strong physical interactions between silica nanoparticles and the PDMA polymer on the properties of chemically cross-linked highly swollen PDMA networks. A detailed comparison of the hybrids with unmodified PDMA gels indicates that the incorporation of silica nanoparticles in the hydrogel increases the compression strength and the fracture toughness of notched samples up to an order of magnitude while increasing its modulus by a factor of 6 with a volume fraction of particles of the order of only 7%. The hybrid gels present a strain-dependent hysteresis but no permanent damage or residual strain upon unloading even after repeated cycling, a very unique property for such tough hydrogels. The reason for this exceptional increase in toughness is attributed mainly to the combined effect of breakable silica/polymer bonds and of a wide distribution of elastic chain lengths.

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The electronic absorption and emission spectra of free UO2Cl2 and its Ar-coordinated complexes below 27 000 cm(-1) are investigated at the levels of ab initio complete active space second-order perturbation theory (CASPT2) and coupled-cluster singles and doubles and perturbative triples [CCSD(T)] using valence 3ζ-polarized basis sets. The influence of the argon matrix in the 12K experiment on the electronic spectra is explored by investigating the excited states of argon complexes ArnUO2Cl2. The calculated two most stable complexes with n = 2, 3 can explain the observed two matrix sites corresponding to the experimental two-component luminescence decay. In these uranyl complexes, Ar-coordination is found to have little influence on the (3)Φ (Ω = 2g) character of the luminescent state and on the electronic spectral shape. The calculations yield a coherent assignment of the experimental excitation spectra that improves on previous assignments. The simulated luminescence spectral curves based on the calculated spectral parameters of UO2Cl2 from both CASPT2 and CCSD(T) agree well with experiment.

BackgroundPredicting species’ potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs.MethodologyWe collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values.ResultsThe mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points).ConclusionsAccording to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.