Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have b...Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training images.Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset.DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%.A semi-supervisory technique for labeling images is introduced to reduce manual efforts.The sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle.This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.展开更多
采用响应面法优化瞿麦中大黄素的提取工艺.利用单因素法进行考察,以大黄素提取量为指标,考察液固比、超声时间、温度、盐酸质量分数及用量对大黄素提取量的影响,并结合Design-Expert软件中的Box-Behnken实验设计和响应面分析方法对超声...采用响应面法优化瞿麦中大黄素的提取工艺.利用单因素法进行考察,以大黄素提取量为指标,考察液固比、超声时间、温度、盐酸质量分数及用量对大黄素提取量的影响,并结合Design-Expert软件中的Box-Behnken实验设计和响应面分析方法对超声提取瞿麦中大黄素的工艺进行优化,再与药典中虎杖提取大黄素方法进行比较,确定大黄素超声提取最佳工艺:液固比为61倍,温度为40℃,超声30 min,加质量分数为8%的盐酸1 m L.通过优化得到的大黄素超声提取工艺省时,稳定,且与回流法相比提取量高.展开更多
基金Project supported by the National Key Research and Development Program of China(Grant No.2022YFB2803900)the National Natural Science Foundation of China(Grant Nos.61974075 and 61704121)+2 种基金the Natural Science Foundation of Tianjin Municipality(Grant Nos.22JCZDJC00460 and 19JCQNJC00700)Tianjin Municipal Education Commission(Grant No.2019KJ028)Fundamental Research Funds for the Central Universities(Grant No.22JCZDJC00460).
文摘Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training images.Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset.DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%.A semi-supervisory technique for labeling images is introduced to reduce manual efforts.The sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle.This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.
文摘采用响应面法优化瞿麦中大黄素的提取工艺.利用单因素法进行考察,以大黄素提取量为指标,考察液固比、超声时间、温度、盐酸质量分数及用量对大黄素提取量的影响,并结合Design-Expert软件中的Box-Behnken实验设计和响应面分析方法对超声提取瞿麦中大黄素的工艺进行优化,再与药典中虎杖提取大黄素方法进行比较,确定大黄素超声提取最佳工艺:液固比为61倍,温度为40℃,超声30 min,加质量分数为8%的盐酸1 m L.通过优化得到的大黄素超声提取工艺省时,稳定,且与回流法相比提取量高.
