Anisotropic materials are characterized by a unique optical response, which is highly polarization-dependent. Of particular interest are layered materials formed by the stacking of two-dimensional (2D) crystals that are naturally anisotropic in the direction perpendicular to the 2D planes. Black phosphorus (BP) is a stack of 2D phosphorene crystals and a highly anisotropic semiconductor with a direct band gap. We show that the angular dependence of polarized Raman spectra of BP is rather unusual and can be explained only by considering complex values for the Raman tensor elements. This result can be traced back to the electron-photon and electron-phonon interactions in this material.

Black phosphorus has recently emerged as a new layered crystal that, due to its peculiar and anisotropic crystalline and electronic band structures, may have important applications in electronics, optoelectronics and photonics. Despite the fact that the edges of layered crystals host a range of singular properties whose characterization and exploitation are of utmost importance for device development, the edges of black phosphorus remain poorly characterized. In this work, the atomic structure and behaviour of phonons near different black phosphorus edges are experimentally and theoretically studied using Raman spectroscopy and density functional theory calculations. Polarized Raman results show the appearance of new modes at the edges of the sample, and their spectra depend on the atomic structure of the edges (zigzag or armchair). Theoretical simulations confirm that the new modes are due to edge phonon states that are forbidden in the bulk, and originated from the lattice termination rearrangements.

We demonstrated a method to construct high efficiency saturable absorbers based on the evanescent light field interaction of CVD monolayer graphene deposited on side-polished D-shaped optical fiber. A set of samples was fabricated with two different core-graphene distances (0 and 1 μm), covered with graphene ranging between 10 and 25 mm length. The mode-locking was achieved and the best pulse duration was 256 fs, the shortest pulse reported in the literature with CVD monolayer graphene in EDFL. As result, we find a criterion between the polarization relative extinction ratio in the samples and the pulse duration, which relates the better mode-locking performance with the higher polarization extinction ratio of the samples. This criterion also provides a better understanding of the graphene distributed saturable absorbers and their reproducible performance as optoelectronic devices for optical applications.

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For the first time, we demonstrated the fabrication of mechanically exfoliated molybdenum disulfide (MoS₂) samples deposited onto a D-shaped optical fiber. The MoS₂ exfoliated flakes were deposited onto a stacked of 1.2 µm PVA (polyvinyl alcohol) and 300 nm PMMA (polymethyl methacrylate) layers and then transferred directly onto a side polished surface of D-shaped optical fiber with polishing length of 17 mm and no distance from the fiber core. The sample exhibited a high polarization performance as a polarizer with relative polarization extinction ratio of 97.5%. By incorporating the sample as a saturable absorber in the Erbium-doped fiber laser (EDFL), bandwidth of 20.5 nm and pulse duration of 200 fs were generated, which corresponded to the best mode-locking results obtained for all-fiber MoS₂ saturable absorber at 1.5 µm wavelength.

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The valley transport properties of a superlattice of out-of-plane Gaussians deformations are calculated using a Green's function and a Machine Learning approach. Our results show that periodicity significantly improves the valley filter capabilities of a single Gaussian deformation, these manifest themselves in the conductance as a sequence by valley filter plateaus. We establish that the physical effect behind the observed valley notch filter is the coupling between counter-propagating transverse modes; the complex relationship between the design parameters of the superlattice and the valley filter effect make difficult to estimate in advance the valley filter potentialities of a given superlattice. With this in mind, we show that a Deep Neural Network can be trained to predict valley transmission with a precision similar to the Green's function but with much less computational effort.

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