Atomic layer deposition of nanolaminate structures of alternating PbTe and PbSe thermoelectric films

For this study PbTe and PbSe thin film nanolaminates have been prepared on silicon substrates with native oxide by Atomic Layer Deposition (ALD) using lead(II)bis(2,2,6,6-tetramethyl-3,5-heptanedionato) (Pb(C11H19O2)2), (trimethylsilyl) telluride ((Me3Si)2Te) and bis-(triethyl silyl) selane ((Et3Si)2Se) as ALD precursors for lead, tellurium and selenium. The experimental evidence revealed the ALD growth of lead telluride and lead selenide followed the Vollmer-Weber island growth mode. We found a strong dependence of the nucleation process on the temperature. In this paper, we present the optimized conditions for growing PbTe and PbSe thin film nanolaminates within the ALD process window range of 170°C to 210°C and discuss an early nano-scale PbTe/PbSe bilayer structure. Results of various physical characterizations techniques and analysis are reported

Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders

Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This study was conducted to validate the accuracy of Convolutional-Neural-Network (CNN) enabled m(mobile)-Health device deployed with FHWs for delineation of suspicious oral lesions (malignant/potentially-malignant disorders). The effectiveness of the device was tested in tertiary-care hospitals and low-resource settings in India. The subjects were screened independently, either by FHWs alone or along with specialists. All the subjects were also remotely evaluated by oral cancer specialist/s. The program screened 5025 subjects (Images: 32,128) with 95% (n = 4728) having telediagnosis. Among the 16% (n = 752) assessed by onsite specialists, 20% (n = 102) underwent biopsy. Simple and complex CNN were integrated into the mobile phone and cloud respectively. The onsite specialist diagnosis showed a high sensitivity (94%), when compared to histology, while telediagnosis showed high accuracy in comparison with onsite specialists (sensitivity: 95%; specificity: 84%). FHWs, however, when compared with telediagnosis, identified suspicious lesions with less sensitivity (60%). Phone integrated, CNN (MobileNet) accurately delineated lesions (n = 1416; sensitivity: 82%) and Cloud-based CNN (VGG19) had higher accuracy (sensitivity: 87%) with tele-diagnosis as reference standard. The results of the study suggest that an automated mHealth-enabled, dual-image system is a useful triaging tool and empowers FHWs for oral cancer screening in low-resource settings. © 2022, The Author(s).Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]