One step densification of SDC—Na2CO3 nano-composite electrolytes for SOFC applications by cold sintering process

Sm_0.2Ce_0.8O_1.9- 30% Na₂CO₃ (Sm doped ceria (SDC)-30N) nano-composite electrolytes were densified in a single step via cold sintering process (CSP). At 200°C and 450 MPa of uniaxial pressure, samples up to 97% of their theoretical density could be obtained. The effect of processing parameters, such as temperature, uniaxial pressure, processing duration, and moisture content, on the densification of the nano-composite electrolytes was investigated. The thermal, microstructural, and electrical properties of nano-composites were investigated by differential scanning calorimetry, X-ray diffractometer, scanning electron microscope, and EIS analysis. SDC crystallite sizes were found to be around 25 nm, barely coarsened after CSP by which the true nano nature of the nano-composite could be preserved. Because, by conventional processing high density values could not be attained and high processing temperatures in excess of 600°C had to be used, promoting particle coarsening. The highest total electrical conductivity was found to be 2.2 × 10⁻² S cm⁻¹ at 600°C, with an activation energy of 0.83 eV for SDC-30N nano-composites. The present investigation revealed that the implementation of cold sintering technique resulted in significant enhancements in the densification of nano-composite electrolytes, thereby rendering them suitable for efficient utilization in SOFC applications, as compared to the conventional production methods.     

Modified kaolinites‐polyalkyl methacrylate nanocomposites: Exploring relations between solubility parameters and thermal properties for in situ solution polymerization

Poly(methyl methacrylate) (PMMA), poly(ethyl methacrylate) (PEMA), and their nanocomposites (Kao‐PMMAs and Kao‐PEMAs) with various kaolinite intercalation compounds were prepared in several solution polymerization media in order to examine relations between solubility parameters of polymerization medium including monomer and solvent/solvents and of intercalating agents and thermal properties of the resultant materials. The measurements of X‐ray diffraction, scanning electron microscopy, transmission electron microscopy, thermogravimetry/derivative thermogravimetry, and differential scanning calorimetry were used to characterize these materials. The increase in solubility parameter of polymerization medium improved thermal decomposition temperatures of PEMA and Kao‐PEMAs when it usually induced a decrease in those of PMMA and Kao‐PMMAs and in glass transition temperatures of all materials. These results were also examined in respect of the effects of hydrogen bonding and dispersion components of solubility parameters of intercalating agents on the thermal properties of these materials. POLYM. COMPOS., 37:2333–2341, 2016. © 2015 Society of Plastics Engineers     

LSS-net: 3-dimensional segmentation of the spinal canal for the diagnosis of lumbar spinal stenosis

Lumbar Spinal Stenosis (LSS) is one of the main causes of chronic low back pain. Chronic low back pain not only reduces the quality of life of people but also can be an important expense item in the country's economy due to the inability of the person to participate in working life and treatment costs. As in other diseases, rapid diagnosis and early treatment of LSS significantly affect the quality of life of the person. Magnetic Resonance (MR) imaging is one of the methods used to diagnose LSS. Diagnosis by interpreting MR images requires serious expertise, and it has been frequently studied by academics in recent years because it is a system that assists the doctor with an objective approach. This field of study is machine learning, which we can call the sub-branch of Artificial Intelligence. Deep learning-based machine learning is very successful in processing biomedical images such as MR. In this study, a model that performs 3-dimensional automatic segmentation on T2 sequence Lumbar MR Images is proposed for the diagnosis of LSS. This 3D LSS segmentation study, according to our knowledge, has the feature of being the first in its field and will be an important resource for those who work in this field. In addition, with the proposed model, parts that cannot be fully opened in LSS surgical operations, especially in the nerve roots, can be fully determined beforehand which will ensure that the patient's complaints are completely eliminated after the operation. In MR images, a total of 6 classes were created and segmentation was carried out, including the spinal disc, canal, thecal sac, posterior element, and other regions and background in the image, which are important for LSS. To measure the success of segmentation, the Intersection over Union (IoU) metric was calculated for each class. 3D segmentation success for the validation set in the dataset; Background (IoU = 0.83), Canal (IoU = 0.61), Disc (IoU = 0.91), Other (IoU = 0.97), Posterior element (IoU = 0.82), and Thecal Sac (IoU = 0.81). The 3D automatic segmentation success rates obtained are quite high and show that a Computer Aided Diagnosis system can be created in LSS diagnosis.