Improved Machine Learning Approach for Wavefront Sensing.

In the adaptive optics (AO) system, to improve the effectiveness and accuracy of wavefront sensing-less technology, a phase-based sensing approach using machine learning is proposed. In contrast to the traditional gradient-based optimization methods, the model we designed is based on an improved convolutional neural network. Specifically, the deconvolution layer, which reconstructs unknown input by measuring output, is introduced to represent the phase maps of the point spread functions at the in focus and defocus planes. The improved convolutional neural network is utilized to establish the nonlinear mapping between the input point spread functions and the corresponding phase maps of the optical system. Once well trained, the model can directly output the aberration map of the optical system with good precision. Adequate simulations and experiments are introduced to demonstrate the accuracy and real-time performance of the proposed method. The simulations show that even when atmospheric conditions D/r0 = 20, the detection root-mean-square of wavefront error of the proposed method is 0.1307 λ, which has a better accuracy than existing neural networks. When D/r0 = 15 and 10, the root-mean-square error is respectively 0.0909 λ and 0.0718 λ. It has certain applicative value in the case of medium and weak turbulence. The root-mean-square error of experiment results with D/r0 = 20 is 0.1304 λ, proving the correctness of simulations. Moreover, this method only needs 12 ms to accomplish the calculation and it has broad prospects for real-time wavefront sensing.

Highly selective adsorption of methanol in carbon nanotubes immersed in methanol-water solution.

The systems of open-ended carbon nanotubes (CNTs) immersed in methanol-water solution are studied by molecular dynamics simulations. For the (6,6) CNT, nearly pure methanol is found to preferentially occupy interior space of the CNT. Even when the mass fraction (MF) of methanol in bulk solution is as low as 1%, the methanol MF within the CNT is still more than 90%. For CNTs with larger diameters, the methanol concentrations within CNTs are also much higher than those outside CNTs. The methanol selectivity decreases with increasing CNT diameter, but not monotonically. From microscopic structural analyses, we find that the primary reason for the high selectivity of methanol by CNTs lies on high preference of methanol in the first solvation shell near the inner wall of CNT, which stems from a synergy effect of the van der Waals interaction between CNT and the methyl groups of methanol, together with the hydrogen bonding interaction among the liquid molecules. This synergy effect may be of general significance and extended to other systems, such as ethanol aqueous solution and methanol/ethanol mixture. The selective adsorption of methanol over water in CNTs may find applications in separation of water and methanol, detection of methanol, and preservation of methanol purity in fuel cells.