Current polymeric transfer methods of 2D materials often bring about the presence of wrinkles, cracks, and polymer residue, limiting the quality of the transferred materials and performance of devices. Herein, we report a transfer approach combining pretreatment by liquid nitrogen and lithium ion intercalation with polymer composite of small molecules and polystyrene to achieve high-fidelity transfer of 2D transition metal dichalcogenides (TMDs) grown by chemical vapor deposition. In this method, the as-grown samples were pretreated by liquid nitrogen and lithium ion intercalation to weaken the bonding between the TMD and the substrate. A polymer composite incorporating small molecules, namely camphor or naphthalene, was used to increase the dissolution of the polymer film. These two processes work synergistically to enable nearly 100% transfer of monolayer TMDs virtually free of wrinkles, cracks, or organic residue with retained optical properties. Our technique can be generalized for the efficient and high quality transfer of other 2D materials.
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Sci Rep
January 2025
Division of Plastic, Craniofacial and Hand Surgery, Sidra Medicine, and Weill Cornell Medical College, C1-121, Al Gharrafa St, Ar Rayyan, Doha, Qatar.
Training a machine learning system to evaluate any type of facial deformity is impeded by the scarcity of large datasets of high-quality, ethics board-approved patient images. We have built a deep learning-based cleft lip generator called CleftGAN designed to produce an almost unlimited number of high-fidelity facsimiles of cleft lip facial images with wide variation. A transfer learning protocol testing different versions of StyleGAN as the base model was undertaken.
View Article and Find Full Text PDFNeural Netw
December 2024
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, 999077, Hong Kong, China. Electronic address:
Limited transferability hinders the performance of a well-trained deep learning model when applied to new application scenarios. Recently, Unsupervised Domain Adaptation (UDA) has achieved significant progress in addressing this issue via learning domain-invariant features. However, the performance of existing UDA methods is constrained by the possibly large domain shift and limited target domain data.
View Article and Find Full Text PDFSmall
January 2025
Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, No. 174 Shazheng Road, Chongqing, 400044, China.
Direct electrochemical detection of miRNA biomarkers in tumor tissue interstitial fluid (TIF) holds great promise for adjuvant therapy for tumors in the perioperative period, yet is limited by background interference and weak signal. Herein, a wash-free and separation-free miRNA biosensor based on photoexcited electro-driven reactive oxygen channeling analysis (LEOCA) is developed to solve the high-fidelity detection in physiological samples. In the presence of miRNA, nanoacceptors (ultrasmall-size polydopamine, uPDA) are responsively assembled on the surface of nanodonors (zirconium metal-organic framework, ZrMOF) to form core-satellite aggregates.
View Article and Find Full Text PDFSoft Robot
January 2025
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea.
Data-driven calibration methods have shown promising results for accurate proprioception in soft robotics. This process can be greatly benefited by adopting numerical simulation for computational efficiency. However, the gap between the simulated and real domains limits the accurate, generalized application of the approach.
View Article and Find Full Text PDFBMC Med Educ
January 2025
Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Introduction: Ultrasound is important in heart diagnostics, yet implementing effective cardiac ultrasound requires training. While current strategies incorporate digital learning and ultrasound simulators, the effectiveness of these simulators for learning remains uncertain. This study evaluates the effectiveness of simulator-based versus human-based training in Focused Assessed with Transthoracic Echocardiography (FATE).
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