Indium-tin-zinc oxide (ITZO) thin-film transistor (TFT) technology holds promise for achieving high mobility and offers significant opportunities for commercialization. This paper provides a review of progress made in improving the mobility of ITZO TFTs. This paper begins by describing the development and current status of metal-oxide TFTs, and then goes on to explain the advantages of selecting ITZO as the TFT channel layer. The evaluation criteria for TFTs are subsequently introduced, and the reasons and significance of enhancing mobility are clarified. This paper then explores the development of high-mobility ITZO TFTs from five perspectives: active layer optimization, gate dielectric optimization, electrode optimization, interface optimization, and device structure optimization. Finally, a summary and outlook of the research field are presented.

Ultrashort pulse lasers have vital significance in the field of ultrafast photonics. A saturable absorber (SA) as the core device to generate ultrashort pulses has innovative design strategies; the most interesting of which is the integration strategy based on 2D materials. This review presents recent advances in the optoelectronic properties of 2D materials and in the way the materials are prepared, characterized, and integrated into devices. We have done a comprehensive review of the optical properties of materials and material-based devices and their current development in the field of fiber lasers and solid-state lasers. Finally, we offer a look at future applications for 2D materials in ultrafast lasers and their prospects.

This paper investigates the combination of laser-induced breakdown spectroscopy (LIBS) and deep convolutional neural networks (CNNs) to classify copper concentrate samples using pretrained CNN models through transfer learning. Four pretrained CNN models were compared. The LIBS profiles were augmented into 2D matrices. Three transfer learning methods were tried. All the models got a high classification accuracy of >92%, with the highest at 96.2% for VGG16. These results suggested that the knowledge learned from machine vision by the CNN models can accelerate the training process and reduce the risk of overfitting. The results showed that deep CNN and transfer learning have great potential for the classification of copper concentrates by portable LIBS.

导模法是一种超高熔点晶体的有效生长方法，可以生长高质量、大尺寸、高掺杂的Er∶Lu₂O₃晶体。本文测试了导模法生长的Er∶Lu₂O₃晶体的荧光发射谱及连续激光特性，在室温下获得了斜效率为23.1% 的2.85 μm连续激光输出，最高输出功率5.24 W。80 min功率RMS（Root mean square）稳定性优于1.4%，不同输出功率水平的激光光束质量M²因子优于2.17。实验结果表明，导模法生长的Er∶Lu₂O₃激光晶体具备输出高功率、高效率中红外激光的能力。