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Vanadium doping can effectively modify the physical properties of transition metal dichalcogenides (TMDCs), broadening their application prospect in electronic, spintronic, and valleytronic devices. However, vanadium dopants always suppress the growth of TMDCs and lower the growth controllability. Here, we report the morphology-controlled growth of vanadium-doped MoS2 (V-MoS2). Both the layer-controlled growth and fractal growth of monolayer V-MoS2 were realized, and the mechanism was analyzed, including contributions from the localized concentration of Mo atoms, the etching effect, and formation energies of different terminations. It will shed light on the morphology-controlled growth of other vanadium-doped TMDCs, promoting the construction of novel devices. Controll vanadium dopants enables layer-controlled and fractal growth of V-MoS2 in this work with contributions from the localized concentration of Mo atoms, the etching effect, and formation energies of different terminations. It will help to realize the morphology-controlled growth of other vanadium-doped TMDCs and thus promotes the construction of novel devices. [ABSTRACT FROM AUTHOR]

Nonlinear inverse problems arise in various fields ranging from scientific computation to engineering technology. Inverse problems are intrinsically ill-posed, and effective regularization techniques are necessary. The core of a suitable regularization method is to introduce the prior information of the model via an explicit or implicit regularization function. Plug-and-play regularization is a flexible framework that integrates the most effective denoising priors into an iterative algorithm, and it has recently shown great potential in the solution of linear ill-posed problems. Unlike traditional regularization methods, plug-and-play regularization does not require an explicit regularization function to represent the prior information of the model. In this work, by using total variation, block-matching and three-dimensional filtering, and fast and flexible denoising convolutional neural network denoisers, we propose a novel iterative regularization algorithm based on the alternating direction method of multipliers method. The combination of total variation and block-matching three-dimensional filtering regularizers can take advantage of the sparsity and nonlocal similarity in the solution of inverse problems. When combined with traditional and novel regularizers, deep neural networks have been shown to be an effective regularization approach, which can achieve state-of-the-art performance. Finally, we apply the proposed algorithm to the full waveform inversion problem to show the effectiveness of our method. Numerical results demonstrate that the proposed algorithm outperforms existing inversion methods in terms of quantitative measures and visual perceptual quality. [ABSTRACT FROM AUTHOR]