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Carbon Fiber - Crossref

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Last Updated: 10 August 2022

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Nonlinearity managed passively harmonic mode-locked Er-doped fiber laser based on carbon nanotube film

We explore the implications of intracavity nonlinearity for harmonic mode locking in a carbon nanotube film mode-locked Er-doped fiber laser using a highly nonlinear fiber. Under the pump power of 280 mW, Via fine nonlinearity control, almost 4 GHz repetition rate pulses at the 91st harmonic with 936 fs pulse duration are produced. In addition, the laser has a steep pumping efficiency slope of > 19 M H z / m W, which is also a record among all of the passively HML fiber lasers.

Source link: https://doi.org/10.1364/ol.425898


Micromachining of glassy carbon using a Yb-based master oscillator power amplifier nanosecond fiber laser

A glassy carbon machining experiment was experimentally performed using a 1065 nm wavelength Yb-based master oscillator power amplifier fiber laser source. Ablation characteristics were determined for SIGRADUR G glassy carbon samples using laser fluence of less than 3 J/c m 2. With the right laser characteristics, high-quality glassy carbon microstructures were produced.

Source link: https://doi.org/10.1364/ao.430689


Passively mode-locked thulium-doped fiber laser based on saturable absorption of carbon nanofibers

Carbon nanofibers, a new type of carbon-based material, have attracted a lot of attention due to their unique physical shape and optical properties. CNF SA was able to prepare, the nonlinearity of which was determined as follows: the modulation depth was 1. 3 percent, and the saturation intensity was 18 M W/ c m 2. Mode-locked laser pulses of a central wavelength of 1954. 47 nm and a 3 dB bandwidth of 5. 93 nm were obtained by inserting the CNF SA into the TDFL ring cavity. This is the first time CNFs have been identified as SAs for mode-locked lasers in the 2 u00b5m wavelength region, to our knowledge.

Source link: https://doi.org/10.1364/ao.442979


Artificial neural network for predicting the mechanical performance of additive manufacturing thermoset carbon fiber composite materials

Although additive manufacturing has attracted significant interest in recent years to produce reinforced composite structures as in reinforced carbon fiber composites, it is still difficult to determine the fiber content within composites to obtain tailored material properties, particularly at high loads of fibers. Thus, a modular neural network was developed in this research to determine the mechanical characteristics of 3D printing thermoset carbon fiber composites at any carbon fiber concentration. Even when dealing with small experimental datasets, the developed ANN system consisted of three model methods for predicting the bending strain and the thermoset carbon fiber composite composites' flexural modulus. Nevertheless, the 1-4-8-12-1 model has produced a very high number of forecasts for the mechanical performance of the AM epoxy/carbon fiber composites, but it has not met a single prediction for the AM epoxy/carbon fiber composites' mechanical results.

Source link: https://doi.org/10.1515/jmbm-2022-0054

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions