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The yellow luminescence (YL) in as-grown high-resistance (HR) and unintentional-doped (UID) GaN films grown by metal organic chemical vapor deposition has been investigated by means of photoluminescence and monoenergetic positron annihilation spectroscopy. It is found there is stronger YL in UID-GaN with higher concentration of gallium vacancy (VGa), suggesting that VGa-involved defects are the origin responsible for the YL in UID-GaN. Contrastly, there is much stronger YL in HR-GaN that is nearly free from VGa, suggesting that there is another origin for the YL in HR-GaN, which is thought as the carbon-involved defects. Furthermore, it is found that the HR-GaN film with shorter positron diffusion length Ld exhibits stronger YL. It is suggested that the increased wave function overlap of electrons and holes induced by the extremely strong space localization effect of holes deduced from the short Ld is the vital factor to enhance the YL efficiency in HR-GaN.

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Due to the complexity of, and low accuracy in, iron ore classification, a method of Laser-Induced Breakdown Spectroscopy (LIBS) combined with machine learning is proposed. In the research, we collected LIBS spectra of 10 iron ore samples. At the beginning, principal component analysis algorithm was employed to reduce the dimensionality of spectral data, then we applied k-nearest neighbor model, neural network model, and support vector machine model to the classification. The results showed that the accuracy of three models were 82.96%, 93.33%, and 94.07% respectively. The results also demonstrated that LIBS with machine learning model exhibits an excellent classification performance. Therefore, LIBS technique combined with machine learning can achieve a rapid, precise classification of iron ores, and can provide a completely new method for iron ores’ selection in the metallurgical industry.

As a new material, graphene shows excellent properties in mechanics, electricity, optics, and so on, which makes it widely concerned by people. At present, it is difficult for graphene pressure sensor to meet both high sensitivity and large pressure detection range at the same time. Therefore, it is highly desirable to produce flexible pressure sensors with sufficient sensitivity in a wide working range and with simple process. Herein, a relatively high flexible pressure sensor based on piezoresistivity is presented by combining the conical microstructure polydimethylsiloxane (PDMS) with bilayer graphene together. The piezoresistive material (bilayer graphene) attached to the flexible substrate can convert the local deformation caused by the vertical force into the change of resistance. Results show that the pressure sensor based on conical microstructure PDMS-bilayer graphene can operate at a pressure range of 20 kPa while maintaining a sensitivity of 0.122 ± 0.002 kPa−1 (0–5 kPa) and 0.077 ± 0.002 kPa−1 (5–20 kPa), respectively. The response time of the sensor is about 70 ms. In addition to the high sensitivity of the pressure sensor, it also has excellent reproducibility at different pressure and temperature. The pressure sensor based on conical microstructure PDMS-bilayer graphene can sense the motion of joint well when the index finger is bent, which makes it possible to be applied in electronic skin, flexible electronic devices, and other fields.