π-conjugated polymers (PCPs) are suitable gain materials for laser applications due to their high photoluminescence (PL) quantum yield. However, the effect of molecular weight on amplified spontaneous emission (ASE) of PCPs is unknown. In this work, we investigated ASE in poly [2-methoxy-5-(2'ethylhexyloxy)-1, 4-phenylethylene] (MEH-PPV) with different molecular weight in 0.5 mg/mL, 1 mg/mL and 2 mg/mL solutions by using a pulsed laser with pulse width of 10 ns. The molecular weights are 40000-70000, 70000-100000, and 100000-150000, respectively. We found that the threshold decreases as the molecular weight increases, which is due to higher emission intensity at larger molecular weight. In addition, the threshold decreases with concentration increasing, this is because slightly aggregation is helpful for ASE. Our results have shown that molecular weight as well as concentration play an important role in the ASE threshold in MEH-PPV solutions, as well as PCP lasers.
The shallow water areas, such as near shore, near reef, shallow sea and wetland, are blind areas for detection, and are
also important research fields of LiDAR bathymetry technology. In this paper, we used the self-developed LiDAR
bathymetry experimental system to obtain echo waveforms in the air and water tank in the laboratory. A deep learning
method of one-dimensional convolutional neural network was proposed to directly invert the depth of water based on
these original experimental echo data. The results denote that the deep learning method is feasible for the processing of
large amount of LiDAR bathymetry echo waveforms. This method can be further used for echo waveforms processing
outdoor.
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