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Direct observations of nucleation and early-stage growth of Au-catalyzed GaAs nanowires on Si(111).
Nanotechnology
January 2025
Centre for Analysis and Synthesis, NanoLund, Lund University, Box 124, Lund, 221 00, SWEDEN.
Developing a reliable procedure for the growth of III-V nanowires (NW) on silicon (Si) substrates remains a significant challenge, as current methods rely on trial-and-error approaches with varying interpretations of critical process steps such as sample preparation, Au-Si alloy formation in the growth reactor, and nanowire alignment. Addressing these challenges is essential for enabling high-performance electronic and optoelectronic devices that combine the superior properties of III-V NW semiconductors with the well-established Si-based technology. Combining conventional scalable growth methods, such as Metalorganic Chemical Vapor Deposition (MOCVD) with in situ characterization using Environmental Transmission Electron Microscopy (ETEM-MOCVD) enables a deeper understanding of the growth dynamics, if that knowledge is transferable to the scalable processes.
View Article and Find Full Text PDFSystematic Study of the Synthesis of Monodisperse CsPbI Perovskite Nanoplatelets for Efficient Color-Pure Light Emitting Diodes.
Small
January 2025
Department of Materials Science and Engineering, and Center for Functional Photonics (CFP), City University of Hong Kong, Hong Kong SAR, 999077, P. R. China.
Metal halide perovskite nanoplatelets (NPls) possess ultra-narrow photoluminescence (PL) bands tunable over the entire visible spectral range, which makes them promising for utilization in light-emitting diodes (LEDs) with spectrally pure emission colors. This calls for development of synthetic methods toward perovskite NPls with a high degree of control over both their thickness and lateral dimensions. A general strategy is developed to obtain such monodisperse CsPbI NPls through the control over the halide-to-lead ratio during heating-up reaction.
View Article and Find Full Text PDFGeometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data.
J Imaging
December 2024
Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea.
The geometric feature characterization of fruit trees plays a role in effective management in orchards. LiDAR (light detection and ranging) technology for object detection enables the rapid and precise evaluation of geometric features. This study aimed to quantify the height, canopy volume, tree spacing, and row spacing in an apple orchard using a three-dimensional (3D) LiDAR sensor.
View Article and Find Full Text PDFLong-Term Oncological Outcomes Related to Lymphadenectomy in Clinical Stage I NSCLC: A Multicenter Retrospective Experience.
Curr Oncol
January 2025
Unit of Thoracic Surgery, Department of Pharmacy and Health and Nutrition Sciences, University of Calabria, 87036 Rende, Italy.
Background: Lymphadenectomy is considered a key part of the radical treatment of resectable lung cancer, although its appropriate extension in early stages is a debated topic due to the great heterogeneity of studies in the literature. This study aims to evaluate the impact of lymphadenectomy extent on survival and recurrence in the treatment of early-stage NSCLC patients undergoing lobectomy and lymph node dissection.
Methods: Data from clinical stage I NSCLC patients undergoing lobectomy and hilar-mediastinal lymphadenectomy at two thoracic surgery centers from 2016 to 2019 were retrospectively evaluated.
Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM.
Front Hum Neurosci
January 2025
Student Affairs Office, Guilin Normal College, Guilin, China.
Introduction: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.
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