Search for: All records

Warm pressing of organosilicon polymers is often challenging due to the formation of cracks due to release of volatile compounds during the warm pressing process. The focus of the present study is to warm press crosslinked SMP‐10 powders into crack‐free compacts and to pyrolyze them to get bulk SiC monoliths. Crack formation during warm pressing is addressed by optimizing the crosslinking temperature, and the loss of formability of the powders crosslinked at higher temperatures is overcome with the use of uncured polymer as a binder. The crosslinking temperature of the preceramic polymer plays a crucial role in developing crack‐free green bodies. The amount of binder used is varied to study its effect on the bulk density of the pyrolyzed product. The warm pressed green bodies pyrolyzed at 1400 °C result in the formation of bulk silicon carbide ceramics and are characterized using X‐ray diffractometer and FTIR spectroscopy. Warm pressing is performed at a lower temperature than reported in the literature, and this limits the incorporation of oxygen during the warm pressing. 

Predicting materials’ microstructure from the desired properties is critical for exploring new materials. Herein, a novel regression‐based prediction of scanning electron microscopy (SEM) images for the target hardness using generative adversarial networks (GANs) is demonstrated. This article aims at generating realistic SEM micrographs, which contain rich features (e.g., grain and neck shapes, tortuosity, spatial configurations of grain/pores). Together, these features affect material properties but are difficult to predict. A high‐performance GAN, named ‘Microstructure‐GAN’ (or M‐GAN), with residual blocks to significantly improve the details of synthesized micrographs is established . This algorithm was trained with experimentally obtained SEM micrographs of laser‐sintered alumina. After training, the high‐fidelity, feature‐rich micrographs can be predicted for an arbitrary target hardness. Microstructure details such as small pores and grain boundaries can be observed even at the nanometer scale (∼50 nm) in the predicted 1000× micrographs. A pretrained convolutional neural network (CNN) was used to evaluate the accuracy of the predicted micrographs with rich features for specific hardness. The relative bias of the CNN‐evaluated value of the generated micrographs was within 2.1%–2.7% from the values for experimental micrographs. This approach can potentially be applied to other microscopy data, such as atomic force, optical, and transmission electron microscopy. 

Abstract In this study, we demonstrate a novel environmental barrier coating processed from polymer‐derived ceramics (PDCs) with homogeneously distributed sub‐micrometer Y₂O₃as the filler. Under suitable conditions, dense and crack‐free coatings can be achieved for all the designed compositions with the volumetric content of Y₂O₃varied from 45 to 93 vol%. To process the PDC SiC–Y₂O₃composite coatings, Y₂O₃particles and SiC liquid precursor were uniformly dispersed in hexane and then dip‐coated on SiC substrates. After cross‐linking at 250°C and heat‐treated at 900°C in argon, dense and crack‐free PDC SiC–Y₂O₃composite coatings were formed. The effect of coating thickness and heat‐treatment temperature on the formation of cracks due to constrained pyrolysis was studied. The critical thickness for realizing crack‐free coatings of three compositions (i.e., 93, 77, and 45 vol% Y₂O₃) was studied for heat treatment from 1000 to 1300°C using atomic force microscope and scanning electron microscopy. As heat‐treatment temperature increases, the critical coating thickness decreases for the same coating compositions due to enhanced shrinkage at higher temperature. With higher Y₂O₃content, the critical thickness of the coating increased. The inert Y₂O₃particles reduce the amount of polymer leading to reduction in the overall constrained shrinkage of the coating during heat treatment.