Polythiophenes (PTs) are promising electron donors in organic solar cells (OSCs) due to their simple structures and excellent synthetic scalability. Benefiting from the rational molecular design, the power conversion efficiency (PCE) of PT solar cells has been greatly improved. Herein, five batches of the champion PT (P5TCN-F25) with molecular weights ranging from 30 to 87 kg mol-1 were prepared, and the effect of the molecular weight on the blend film morphology and photovoltaic performance of PT solar cells was systematically investigated. The results showed that the PCEs of the devices improved first and then maintained a high value with the increase of molecular weight, and the highest PCE of 16.7% in binary PT solar cells was obtained. Further characterizations revealed that the promotion in photovoltaic performance mainly comes from finer phase separation structures and more compact molecular packing in the blend film. The best device stabilities were also achieved by polymers with high molecular weights. Overall, this study highlights the importance of optimizing the molecular weight for PTs and offers directions to further improve the PCE of PT solar cells.
The low microalloying content and high yield strength of 365 MPa have been achieved simultaneously in extruded Mg–Ce–Al alloy which is mainly ascribed to the unexpected grain refinement (mean size of ∼420 nm). It is found that adding tiny Al (0.05 wt.%) into Mg–0.2 wt.% Ce alloy can promote the formation of Ce–Al-enriched segregation along dislocations and grain boundaries which can effectively counterbalance the thermally activated dislocation recovery and thus guarantee grain refinement. The ultra-fined grains are mainly related to both linear and planar cosegregation of solutes, rather than the traditional planar segregations and/or nanoprecipitations at grain boundaries.
Liquid inclusions in the steel may play an important role in the wear of the stopper tip refractories in tundish operations. In the present study, the attack and wear of the tundish stopper refractories by inclusion slags in OneSteel Whyalla billet product have been investigated.Three stopper tip refractories, Al2O3-C, ZrO2-C and MgO-C, have been tested with inclusion slags (SiO2-Al2O3-CaO-MnO-FeO) under argon at 1570-1610°C for 1-4h using an in-situ gravimetric technique. The in-situ gravimetric data, combined with autopsy of the samples after the tests, provide valuable insights into the dynamic processes of the slag-refractory interactions and the mechanisms for refractory wear.It was found that the performance of the three refractories differed considerably and was determined predominantly by the resistance to the chemical attack by the liquid inclusions and the extent of internal carbon-oxide reactions. The FeO in the inclusion had a detrimental effect on the oxidation of carbon in the refractory, causing vigorous reaction and severe wear. The MnO in the slag also reacted with carbon, but to a much lesser extent, while severe wear only occurred when slag attacked the refractory grains. The weight loss, due to internal carbon-oxide reactions, appeared to be an important issue.Of the refractories tested, the MgO-C performed the worst, suffering from severe inclusion attack on both the carbon and the periclase grains, and considerable weight loss due to internal carbon reaction. The ZrO2-C showed a reasonable resistance to the chemical attack from the inclusion, with MnO up to 33 wt%; but showed extensive weight loss due to internal carbon reaction. The Al2O3-C was the best performer in terms of resistance to both the inclusion attack and internal carbon reaction. The results were found to be in broad agreement with plant experience and observations.
Twinning is a common deformation mechanism in metals, and twin boundary (TB) segregation of impurities/solutes plays an important role in the performances of alloys such as thermostability, mobility, and even strengthening. The occurrence of such segregation phenomena is generally believed as a one-layer coverage of solutes alternately distributed at extension/compression sites, in an orderly, continuous manner. However, in the Mn-free and Mn-containing Mg-Nd model systems, we reported unexpected three- and five-layered discontinuous segregation patterns of the coherent {101̅1} TBs, and not all the extension sites occupied by solutes larger in size than Mg, and even some larger sized solutes taking the compression sites. Nd/Mn solutes selectively segregate at substitutional sites and thus to generate two new types of ordered two-dimensional TB superstructures or complexions. These findings refresh the understanding of solute segregation in the perfect coherent TBs and provide a meaningful theoretical guidance for designing materials via targeted TB segregation.
ABSTRACT Surgical robotics application in the field of minimally invasive surgery has developed rapidly and has been attracting increasingly more research attention in recent years. A common consensus has been reached that surgical procedures are to become less traumatic and with the implementation of more intelligence and higher autonomy, which is a serious challenge faced by the environmental sensing capabilities of robotic systems. One of the main sources of environmental information for robots are images, which are the basis of robot vision. In this review article, we divide clinical image into direct and indirect based on the object of information acquisition, and into continuous, intermittent continuous, and discontinuous according to the target-tracking frequency. The characteristics and applications of the existing surgical robots in each category are introduced based on these two dimensions. Our purpose in conducting this review was to analyze, summarize, and discuss the current evidence on the general rules on the application of image technologies for medical purposes. Our analysis gives insight and provides guidance conducive to the development of more advanced surgical robotics systems in the future.
Abstract Background Endoscopic instrument segmentation is essential for ensuring the safety of robotic‐assisted spinal endoscopic surgeries. However, due to the narrow operative region, intricate surrounding tissues, and limited visibility, achieving instrument segmentation within the endoscopic view remains challenging. Purpose This work aims to devise a method to segment surgical instruments in endoscopic video. By designing an endoscopic image classification model, features of frames before and after the video are extracted to achieve continuous and precise segmentation of instruments in endoscopic videos. Methods Deep learning techniques serve as the algorithmic core for constructing the convolutional neural network proposed in this study. The method comprises dual stages: image classification and instrument segmentation. MobileViT is employed for image classification, enabling the extraction of key features of different instruments and generating classification results. DeepLabv3+ is utilized for instrument segmentation. By training on distinct instruments separately, corresponding model parameters are obtained. Lastly, a flag caching mechanism along with a blur detection module is designed to effectively utilize the image features in consecutive frames. By incorporating specific parameters into the segmentation model, better segmentation of surgical instruments can be achieved in endoscopic videos. Results The classification and segmentation models are evaluated on an endoscopic image dataset. In the dataset used for instrument segmentation, the training set consists of 7456 images, the validation set consists of 829 images, and the test set consists of 921 images. In the dataset used for image classification, the training set consists of 2400 images and the validation set consists of 600 images. The image classification model achieves an accuracy of 70% on the validation set. For the segmentation model, experiments are conducted on two common surgical instruments, and the mean Intersection over Union (mIoU) exceeds 98%. Furthermore, the proposed video segmentation method is tested using videos collected during surgeries, validating the effectiveness of the flag caching mechanism and blur detection module. Conclusions Experimental results on the dataset demonstrate that the dual‐stage video processing method excels in performing instrument segmentation tasks under endoscopic conditions. This advancement is significant for enhancing the intelligence level of robotic‐assisted spinal endoscopic surgeries.
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