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Exploiting the potential of the material for the photovoltaic applications requires an extensive defect level analysis, mandatory. Photoluminescence (PL) technique was employed to probe the defect levels in p-SnS thin films deposited using chemical spray pyrolysis (CSP) technique. Three PL emissions were recorded at 1.09, 0.76, and 0.75 eV. Systematic investigations performed, focusing the 1.09 eV emission, revealed that the shoulder at 1.093 eV gets completely quenched beyond 110 K. From this study, we could identify a bound exciton associated to a shallow donor level whose activation energy was calculated to be 20 meV from Arrhenius plot. By studying the variation of PL intensity with excitation power, we could zeroin that the emission at 1.09 eV was a donor-acceptor pair (DAP) transition. Knowing the band gap to be 1.33 eV, we could identify a deep acceptor at 0.22 eV above valence band. The band structure deduced from the present analysis is depicted in the abstract figure.

Cerebellum measurements of routinely acquired ultrasound (US) images are commonly used to estimate gestational age and to assess structural abnormalities of the developing central nervous system. Investigating associations between the developing cerebellum and neurodevelopmental outcomes post partum requires standardized cerebellum measurements from large clinical datasets. Such investigations have the potential to identify structural changes that can be used as biomarkers to predict growth and neurodevelopmental outcomes. For this purpose, high throughput, accurate, and unbiased measurements are necessary to replace existing manual, semi-automatic, and automated approaches which are tedious and lack reproducibility and accuracy. In this study, we propose a new deep learning algorithm for automated segmentation of the fetal cerebellum from 2-dimensional (2D) US images. We propose ResU-Net-c a semantic segmentation model optimized for fetal cerebellum structure. We leverage U-Net as a base model with the integration of residual blocks (Res) and introduce dilation convolution in the last two layers to segment the cerebellum (c) from noisy US images. Our experiments used a 5-fold cross-validation with 588 images for training and 146 for testing. Our ResU-Net-c achieved a mean Dice Score Coefficient, Hausdorff Distance, Recall, and Precision of 87.00%, 28.15, 86.00%, and 90.00%, respectively. The superiority of the proposed method over the other U-Net based methods is statistically significant (p < 0.001). Our proposed method can be leveraged to enable high throughput image analysis in clinical research fetal US images and can be employed in the biometric assessment in fetal US images on a larger scale.INDEX TERMS Convolutional neural networks, fetal cerebellum, ResU-Net, segmentation, ultrasound images.

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