Fatigue Life Data Fusion Method of Different Stress Ratios Based on Strain Energy Density.

To accurately evaluate the probabilistic characteristics of the fatigue properties of materials with small sample data under different stress ratios, a data fusion method for torsional fatigue life under different stress ratios is proposed based on the energy method. A finite element numerical modeling method is used to calculate the fatigue strain energy density during fatigue damage. Torsional fatigue tests under different stresses and stress ratios are carried out to obtain a database for research. Based on the test data, the Wt-Nf curves under a single stress ratio and different stress ratios are calculated. The reliability of the models is illustrated by the scatter band diagram. More than 85% of points are within ±2 scatter bands, indicating that the fatigue life under different stress ratios can be represented by the same Wt-Nf curve. Furthermore, P-Wt-Nf prediction models are established to consider the probability characteristics. According to the homogeneity of the Wt-Nf model under different stress ratios, we can fuse the fatigue life data under different stress ratios and different strain energy densities. This data fusion method can expand the small sample test data and reduce the dispersion of the test data between different stress ratios. Compared with the pre-fusion data, the standard deviations of the post-fusion data are reduced by a maximum of 21.5% for the smooth specimens and 38.5% for the notched specimens. And more accurate P-Wt-Nf curves can be obtained to respond to the probabilistic properties of the data.

A Method for Predicting the Creep Rupture Life of Small-Sample Materials Based on Parametric Models and Machine Learning Models.

In view of the differences in the applicability and prediction ability of different creep rupture life prediction models, we propose a creep rupture life prediction method in this paper. Various time-temperature parametric models, machine learning models, and a new method combining time-temperature parametric models with machine learning models are used to predict the creep rupture life of a small-sample material. The prediction accuracy of each model is quantitatively compared using model evaluation indicators (RMSE, MAPE, R2), and the output values of the most accurate model are used as the output values of the prediction method. The prediction method not only improves the applicability and accuracy of creep rupture life predictions but also quantifies the influence of each input variable on creep rupture life through the machine learning model. A new method is proposed in order to effectively take advantage of both advanced machine learning models and classical time-temperature parametric models. Parametric equations of creep rupture life, stress, and temperature are obtained using different time-temperature parametric models; then, creep rupture life data, obtained via equations under other temperature and stress conditions, are used to expand the training set data of different machine learning models. By expanding the data of different intervals, the problem of the low accuracy of the machine learning model for the small-sample material is solved.

Synthesis and properties of poly(1,3-dioxolane) in situ quasi-solid-state electrolytes via a rare-earth triflate catalyst.

We report a rare-earth triflate catalyst Sc(OTf)3 for the ring-opening polymerization of 1,3-dioxolane and the in situ production of a quasi-solid-state poly(1,3-dioxolane) electrolyte, which not only demonstrates a superior ionic conductivity of 1.07 mS cm-1 at room temperature, but achieves dendrite-free lithium deposition and a high Coulombic efficiency of 92.3% over 200 Li plating/striping cycles.

A facile method for the synthesis of a sintering dense nano-grained Na3Zr2Si2PO12 Na+-ion solid-state electrolyte.

We report dense Na3Zr2Si2PO12 with an average grain size of 546 ± 58 nm and prepared by a facile method. The nano-grained Na3Zr2Si2PO12 exhibits an extremely high conductivity of 1.02 × 10-3 S cm-1 and low interfacial resistance of 35 Ω cm2 at 25 °C. Such processing facilitates the exploration of nanocrystalline conductors.

Spider-Inspired Ultrasensitive Flexible Vibration Sensor for Multifunctional Sensing.

Flexible vibration sensors can not only capture broad classes of physiologically relevant information, including mechano-vibration signatures of body processes and precision kinematics of core-body motions, but also detect environmental seismic waves, providing early warning to wearers in time. Spider is one of the most vibration-sensitive creatures because of its hairlike sensilla and lyriform slit structure. Here, a spider-inspired ultrasensitive flexible vibration sensor is designed and fabricated for multifunctional sensing. The vibration sensitivity of the flexible sensor is increased over 2 orders of magnitude from 0.006 to 0.5 mV/g, and the strain sensitivity is hugely enhanced from 0.08 to 150 compared to a plain sensor counterpart. It is shown that the synergistic effect of cilium arrays and cracks is the key for achieving the greatly enhanced vibration and strain sensitivity. The dynamic sensitivity of 0.5 mV/g outperforms the corresponding commercial vibration sensors. The flexible sensor is demonstrated to be generally feasible for detecting vibration signals caused by walk, tumble, and explosion as well as capturing human body motions, indicating its great potential for applications in human health-monitoring devices, posture control in robotics, early earthquake warning, and so forth.

Preparation of Nanocomposite Polymer Electrolyte via In Situ Synthesis of SiO2 Nanoparticles in PEO.

Composite polymer electrolytes provide an emerging solution for new battery development by replacing liquid electrolytes, which are commonly complexes of polyethylene oxide (PEO) with ceramic fillers. However, the agglomeration of fillers and weak interaction restrict their conductivities. By contrast with the prevailing methods of blending preformed ceramic fillers within the polymer matrix, here we proposed an in situ synthesis method of SiO2 nanoparticles in the PEO matrix. In this case, robust chemical interactions between SiO2 nanoparticles, lithium salt and PEO chains were induced by the in situ non-hydrolytic sol gel process. The in situ synthesized nanocomposite polymer electrolyte delivered an impressive ionic conductivity of ~1.1 × 10-4 S cm-1 at 30 °C, which is two orders of magnitude higher than that of the preformed synthesized composite polymer electrolyte. In addition, an extended electrochemical window of up to 5 V vs. Li/Li+ was achieved. The Li/nanocomposite polymer electrolyte/Li symmetric cell demonstrated a stable long-term cycling performance of over 700 h at 0.01-0.1 mA cm-2 without short circuiting. The all-solid-state battery consisting of the nanocomposite polymer electrolyte, Li metal and LiFePO4 provides a discharge capacity of 123.5 mAh g-1, a Coulombic efficiency above 99% and a good capacity retention of 70% after 100 cycles.

Dual Substitution and Spark Plasma Sintering to Improve Ionic Conductivity of Garnet Li7La3Zr2O12.

Garnet Li7La3Zr2O12 is one of the most promising solid electrolytes used for solid-state lithium batteries. However, low ionic conductivity impedes its application. Herein, we report Ta-doping garnets with compositions of Li7-xLa3Zr2-xTaxO12 (0.1 ≤ x ≤ 0.75) obtained by solid-state reaction and free sintering, which was facilitated by graphene oxide (GO). Furthermore, to optimize Li6.6La3Zr1.6Ta0.4O12, Mg2+ was select as a second dopant. The dual substitution of Ta5+ for Zr4+ and Mg2+ for Li+ with a composition of Li6.5Mg0.05La3Zr1.6Ta0.4O12 showed an enhanced total ionic conductivity of 6.1 × 10-4 S cm-1 at room temperature. Additionally, spark plasma sintering (SPS) was applied to further densify the garnets and enhance their ionic conductivities. Both SPS specimens present higher conductivities than those produced by the conventional free sintering. At room temperature, the highest ionic conductivity of Li6.5Mg0.05La3Zr1.6Ta0.4O12 sintered at 1000 °C is 8.8 × 10-4 S cm-1, and that of Li6.6La3Zr1.6Ta0.4O12 sintered at 1050 °C is 1.18 × 10-3 S cm-1.

A mechanical method of cerebral cortical folding development based on thermal expansion.

Cortical folding malformations are associated with several severe neurological disorders, including epilepsy, schizophrenia and autism. However, the mechanism behind cerebral cortical folding development is not yet clear. In this paper, we propose a mechanical method based on thermal expansion to simulate the development of human cerebral cortical folding. The influences of stiffness ratio, growth rate ratio, and initial cortical plate thickness on cortical folding are discussed. The results of our thermal expansion model are consistent with previous studies, indicating that abnormal values of the aforementioned three factors could directly lead to cortical folding malformation in a generally fixed pattern.