Selective area epitaxy at the nanoscale enables fabrication of high-quality nanostructures in regular arrays with predefined geometry. Here, we investigate the growth mechanisms of GaAs nanoridges on GaAs (100) substrates in selective area trenches by metal-organic vapor-phase epitaxy (MOVPE). It is found that pre-growth annealing results in the formation of valley-like structures of GaAs with atomic terraces inside the trenches. MOVPE growth of GaAs nanoridges consists of three distinct stages. Filling the trench in the first stage exhibits a step-flow growth behavior. Once the structure grows above the mask surface, it enters the second stage of growth by forming {101} side facets as the (100) flat top facet progressively shrinks. In the third stage, the fully formed nanoridge begins to overgrow onto the mask with a significantly reduced growth rate. We develop a kinetic model that accurately describes the width-dependent evolution of the nanoridge morphology through all three stages. MOVPE growth of fully formed nanoridges takes only about 1 min, which is 60 times faster than in our set of molecular beam epitaxy (MBE) experiments reported recently, and with a more regular, triangular cross-sectional geometry defined solely by the {101} facets. In contrast to MBE, no material loss due to Ga adatom diffusion onto the mask surface is observed in MOVPE until the third stage of growth. These results are useful for the fabrication of GaAs nanoridges of different dimensions on the same substrate for various applications and can be extended to other material systems.
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http://dx.doi.org/10.1021/acs.cgd.3c00316 | DOI Listing |
J Med Internet Res
January 2025
Department of Epidemiology, School of Public Health, Sun Yat-Sen University, Shenzhen, China.
Background: With the rapid expansion of social media platforms, the demand for health information has increased substantially, leading to innovative approaches and new opportunities in health education.
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Methods: A total of 5286 articles published on the "Dr Ding Xiang" WOA from January 2021 to December 2021 were collected and analyzed.
J Neuropathol Exp Neurol
January 2025
Neurotraumatology and Subarachnoid Hemorrhage Research Unit, Area 8: Neurosciences and Mental Health, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain.
Chitinase 3-like protein 1 (CHI3L1) is emerging as a promising biomarker for assessing intracranial lesion burden and predicting prognosis in traumatic brain injury (TBI) patients. Following experimental TBI, Chi3l1 transcripts were detected in reactive astrocytes located within the pericontusional cortex. However, the cellular sources of CHI3L1 in response to hemorrhagic contusions in human brain remain unidentified.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Department of Health Policy, Stanford School of Medicine, Stanford, CA 94305, United States.
Objectives: The inclusion of social drivers of health (SDOH) into predictive algorithms of health outcomes has potential for improving algorithm interpretation, performance, generalizability, and transportability. However, there are limitations in the availability, understanding, and quality of SDOH variables, as well as a lack of guidance on how to incorporate them into algorithms when appropriate to do so. As such, few published algorithms include SDOH, and there is substantial methodological variability among those that do.
View Article and Find Full Text PDFActa Orthop
January 2025
Department of Surgical Sciences, Section for Orthopaedics, Uppsala University, Uppsala, Sweden.
Background And Purpose: Evidence for long-term outcomes following acetabular fractures in older adults is limited. We aimed to evaluate mortality, complications, and need for subsequent surgical procedures in operatively and nonoperatively treated older patients with acetabular fractures.
Methods: Patients aged ≥ 70 years with acetabular fractures treated at Uppsala University Hospital between 2010 and 2020 were included.
JMIR Med Inform
January 2025
School of Software, Taiyuan University of Technology, Jingzhong, China.
Background: The prompt and accurate identification of mild cognitive impairment (MCI) is crucial for preventing its progression into more severe neurodegenerative diseases. However, current diagnostic solutions, such as biomarkers and cognitive screening tests, prove costly, time-consuming, and invasive, hindering patient compliance and the accessibility of these tests. Therefore, exploring a more cost-effective, efficient, and noninvasive method to aid clinicians in detecting MCI is necessary.
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