We investigated the mechanical properties of graphyne monolayers using first-principles calculations based on the Density Functional Theory. Graphyne has a relatively low in-plane Young's modulus (162 N m⁻¹) and a large Poisson ratio (0.429) compared to graphene. It can sustain large nonlinear elastic deformations up to an ultimate strain of 0.2 followed by strain softening until failure. The single bond is more vulnerable to rupture than the triple bond and aromatic bond, although it has a shorter bond length (0.19 Å shorter) than the aromatic bond. A rigorous continuum description of the elastic response is formulated by expanding the elastic strain energy density in a Taylor series in strain truncated after the fifth-order term. We obtained a total of fourteen nonzero independent elastic constants which are components of tensors up to the tenth order. Pressure effects on the second-order elastic constants, in-plane Young's modulus, and Poisson ratio are predicted. This study implies that graphyne-based surface acoustic wave sensors and waveguides may be synthesized by introducing precisely controlled local strains on graphyne monolayers.

We investigated the effect of the hydrogenation of graphene to graphane on its mechanical properties using first-principles calculations based on density-functional theory. The hydrogenation reduces the ultimate strengths in all three tested deformation modes – armchair, zigzag, and biaxial – and the in-plane stiffness by 1/3. The Poisson ratio was reduced from 0.178 to 0.078, a 56% decrease. However, the ultimate strain in zigzag deformation was increased by 8.7%. The shear mode elastic constants are more sensitive than longitudinal ones to hydrogenation. The fourth and fifth order longitudinal mode elastic constants are inert to the hydrogenation, in contrast to a large decrease of those in second and third order. The hydrogenation does not change the monotonic decrement of the Poisson ratio with increasing pressure, but the rate is tripled. Our results indicate that graphene–graphane systems could be used for hydrogen storage with high speed of charge–discharge of hydrogen.

Radiation therapy of thoracic and abdominal tumors requires incorporating the respiratory motion into treatments. To precisely account for the patient’s respiratory motions and predict the respiratory signals, a generalized model for predictions of different types of patients’ respiratory motions is desired. The aim of this study is to explore the feasibility of developing a long short-term memory (LSTM)-based generalized model for the respiratory signal prediction. To achieve that, 1703 sets of real-time position management (RPM) data were collected from retrospective studies across three clinical institutions. These datasets were separated as the training, internal validity and external validity groups. Among all the datasets, 1187 datasets were used for model development and the remaining 516 datasets were used to test the model’s generality power. Furthermore, an exhaustive grid search was implemented to find the optimal hyper-parameters of the LSTM model. The hyper-parameters are the number of LSTM layers, the number of hidden units, the optimizer, the learning rate, the number of epochs, and the length of time lags. The obtained model achieved superior accuracy over conventional artificial neural network (ANN) models: with the prediction window equaling to 500 ms, the LSTM model achieved an average relative mean absolute error (MAE) of 0.037, an average root mean square error (RMSE) of 0.048, and a maximum error (ME) of 1.687 in the internal validity data, and an average relative MAE of 0.112, an average RMSE of 0.139 and an ME of 1.811 in the external validity data. Compared to the LSTM model trained with default hyper-parameters, the MAE of the optimized model results decreased by 20%, indicating the importance of tuning the hyper-parameters of LSTM models to obtain superior accuracies. This study demonstrates the potential of deep LSTM models for the respiratory signal prediction and illustrates the impacts of major hyper-parameters in LSTM models.