Publications by authors named "J Munuera"

Thin films fabricated from solution-processed graphene nanosheets are of considerable technological interest for a wide variety of applications, such as transparent conductors, supercapacitors, and memristors. However, very thin printed films tend to have low conductivity compared to thicker ones. In this work, we demonstrate a simple layer-by-layer deposition method which yields thin films of highly-aligned, electrochemically-exfoliated graphene which have low roughness and nanometer-scale thickness control.

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Background: 3D technologies [Virtual and Augmented 3D planning, 3D printing (3DP), Additive Manufacturing (AM)] are rapidly being adopted in the healthcare sector, demonstrating their relevance in personalized medicine and the rapid development of medical devices. The study's purpose was to understand the state and evolution of 3DP/AM technologies at the Point-of-Care (PoC), its adoption, organization and process in Spanish hospitals and to understand and compare the evolution of the models, clinical applications, and challenges in utilizing the technology during the COVID-19 pandemic and beyond.

Methods: This was a questionnaire-based qualitative and longitudinal study.

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Solution-processable 2D materials are promising candidates for a range of printed electronics applications. Yet maximizing their potential requires solution-phase processing of nanosheets into high-quality networks with carrier mobility (μ) as close as possible to that of individual nanosheets (μ). In practice, the presence of internanosheet junctions generally limits electronic conduction, such that the ratio of junction resistance () to nanosheet resistance (), determines the network mobility via μ/μ ≈ / + 1.

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Article Synopsis
  • * Current imaging methods like CT and MRI are crucial for analyzing these lesions, with artificial intelligence (AI) and deep learning (DL) improving the identification and classification process.
  • * A review of 45 studies shows that AI algorithms, particularly those using convolutional neural networks (CNNs), are highly effective in detecting and distinguishing between benign and malignant FLLs, suggesting AI could reduce invasive procedures and enhance diagnostic accuracy.
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