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A kilowatt-level Raman fiber laser is demonstrated with an integrated Ytterbium-Raman fiber amplifier architecture. A high power Ytterbium-doped fiber master oscillator power amplifier at 1080 nm is seeded with a 1120 nm fiber laser at the same time. By this way, a kilowatt-level Raman pump laser at 1080 nm and signal laser at 1120 nm is combined in the fiber core. The subsequent power conversion from 1080 nm to 1120 nm is accomplished in a 70 m long passive fiber. A 1.28 kW all-fiber Raman amplifier at 1120 nm with an optical efficiency of 70% is demonstrated, limited only by the available pump power. To the best of our knowledge, this is the first report of Raman fiber laser with over one kilowatt output.

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Nanofluidic channels of $40·60 nm (width · depth) were fabricated with focused ion beam (FIB) milling instrument on a silicon nitride (Si 3 N 4 ) film. Stained k-phage DNA molecules were driven into these open channels by capillary force and observed with fluorescence microscopy. The movements of DNA molecule in these channels were discussed. These sub-100 nm scale channels may be useful in studying single biomacromolecules.

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Neural Radiance Fields (NeRF) have been proposed for photorealistic novel view rendering. However, it requires many different views of one scene for training. Moreover, it has poor generalizations to new scenes and requires retraining or finetuning on each scene. In this paper, we develop a new NeRF model for novel view synthesis using only a single image as input. We propose to combine the (coarse) planar rendering and the (fine) volume rendering to achieve higher rendering quality and better generalizations. We also design a depth teacher net that predicts dense pseudo depth maps to supervise the joint rendering mechanism and boost the learning of consistent 3D geometry. We evaluate our method on three challenging datasets. It outperforms state-of-the-art single-view NeRFs by achieving 5∼20% improvements in PSNR and reducing 20∼50% of the errors in the depth rendering. It also shows excellent generalization abilities to unseen data without the need to finetune on each new scene.