It is desirable to control the mode states of a laser to stabilize it under the influence of environmental factors as well as to tailor the laser beam. Here, we demonstrate mixed-mode-state control in a photonic crystal surface-emitting laser at the chip level by leveraging the mechanism of mutual injection locking via dynamic control of the current injection distribution. We also demonstrate smart control, in which deep learning is applied to accurately predict the current injection distribution required to achieve a targeted beam pattern owing to manipulation of the mixed-mode state, and vice versa. These results could enable smart laser sources essential for advanced laser processing and additive manufacturing applications.
Mixed-mode-state control of lasers under continuous-wave (CW) operation, where multi-physics interactions among carriers, photons, and heat are involved, is important for realizing desired lasing characteristics, as well as for dynamic control of lasers. In this paper, we demonstrate mixed-mode-state control of a photonic-crystal surface-emitting laser (PCSEL) under CW operation by manipulating its current injection distribution. To control the current injection distribution, we introduce a multiple-electrode matrix into the p-side of the PCSEL, and we bond the PCSEL to a heatsink in the p-side-down-configuration to dissipate heat while also enabling current injection via each p-side electrode. Furthermore, we employ a convolutional neural network (CNN) to correlate the current distributions and the far-field patterns (FFPs) corresponding to the mode states, and to predict the current distributions necessary to obtain targeted FFPs. FFPs resembling the targeted ones with high fidelity (90%) are obtained by using the constructed CNN. These results lead to the realization of next-generation smart CW lasers capable of mixed-mode-state control even in a dynamic environment, which are essential for applications such as advanced material processing and even aerospace.
A novel lectin was purified to homogeneity from winter buds of Lysichiton camtschatcensis (L.) Schott of the Araceae family. It was a tetramer composed of two non-covalently associated polypeptides with small subunits (11 kDa) and large subunits (12 kDa). Sequencing of both subunits yielded unique N-terminal sequences. A cDNA encoding the lectin was cloned. The isolated cDNA contained an open reading frame that encoded 267 amino acids. It encoded both subunits, indicating that the lectin is synthesized as a single precursor protein that is post-translationally processed into two different subunits with 45% sequence identity. Each subunit contained a mannose-binding motif known to be conserved in monocot mannose-binding lectins, but its activity was not inhibited by monosaccharides, including methyl α-mannoside. Asialofetuin and yeast invertase were potent inhibitors. Lectin activity was detected in the buds formed during the winter season but not in the expanded leaves.
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