Electronic devices have been ever-shrinking toward atomic dimensions and have reached operation frequencies in the GHz range, thereby outperforming most conventional test equipment, such as vector network analyzers (VNA). Here the capabilities of a VNA on the atomic scale in a scanning tunneling microscope are implemented. Nonlinearities present in the voltage-current characteristic of atoms and nanostructures for phase-resolved microwave spectroscopy with unprecedented spatial resolution at GHz frequencies are exploited. The amplitude and phase response up to 9.3 GHz is determined, which permits accurate de-embedding of the transmission line and application of distortion-corrected waveforms in the tunnel junction itself. This enables quantitative characterization of the complex-valued admittance of individual magnetic iron atoms which show a lowpass response with a magnetic-field-tunable cutoff frequency.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/smtd.202301526 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Graph convolutional network with probabilistic spatial regression: application to craniofacial landmark detection from 3D photogrammetry.
Med Image Comput Comput Assist Interv
September 2022
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus.
Quantitative evaluation of pediatric craniofacial anomalies relies on the accurate identification of anatomical landmarks and structures. While segmentation and landmark detection methods in standard clinical images are available in the literature, image-based methods are not directly applicable to 3D photogrammetry because of its unstructured nature consisting in variable numbers of vertices and polygons. In this work, we propose a graph-based convolutional neural network based on Chebyshev polynomials that exploits vertex coordinates, polygonal connectivity, and surface normal vectors to extract multi-resolution spatial features from the 3D photographs.
View Article and Find Full Text PDFInterpreting Lung Cancer Health Disparity between African American Males and European American Males.
Proceedings (IEEE Int Conf Bioinformatics Biomed)
December 2024
Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, USA.
Lung cancer remains a predominant cause of cancer-related deaths, with notable disparities in incidence and outcomes across racial and gender groups. This study addresses these disparities by developing a computational framework leveraging explainable artificial intelligence (XAI) to identify both patient- and cohort-specific biomarker genes in lung cancer. Specifically, we focus on two lung cancer subtypes, Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC), examining distinct racial and sex-specific cohorts: African American males (AAMs) and European American males (EAMs).
View Article and Find Full Text PDFPhosphoric acid based geopolymer foam-activated carbon composite for methylene blue adsorption: isotherm, kinetics, thermodynamics, and machine learning studies.
RSC Adv
January 2025
National Centre of Excellence in Physical Chemistry, University of Peshawar Peshawar Pakistan.
In this study, a binary composite adsorbent based on activated carbon and phosphoric acid geopolymer foam (ACP) was prepared by combining phosphoric acid geopolymer (PAGP) with activated carbon (AC) and applied for the removal of methylene blue (MB). Activated carbon was thoroughly mixed with a mixture of fly ash and metakaolin in varying ratios, followed by phosphoric acid activation and thermal curing. The ACP adsorbent was characterized using scanning electron microscope (SEM), Fourier transform infrared (FTIR) spectrophotometer, X-ray diffractometer (XRD), surface area analyser (SAP), and thermogravimetric analyser (TGA).
View Article and Find Full Text PDFA systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities.
Front Artif Intell
January 2025
CONAHCYT-Instituto Potosino de Investigación Científica y Tecnológica, A.C. División de Geociencias Aplicadas, San Luis Potosí, Mexico.
This systematic review provides a state-of-art of Artificial Intelligence (AI) models such as Machine Learning (ML) and Deep Learning (DL) development and its applications in Mexico in diverse fields. These models are recognized as powerful tools in many fields due to their capability to carry out several tasks such as forecasting, image classification, recognition, natural language processing, machine translation, etc. This review article aimed to provide comprehensive information on the Machine Learning and Deep Learning algorithms applied in Mexico.
View Article and Find Full Text PDFUnmasking the Dark Triad: A Data Fusion Machine Learning Approach to Characterize the Neural Bases of Narcissistic, Machiavellian and Psychopathic Traits.
Eur J Neurosci
January 2025
Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Trento, Italy.
The Dark Triad (DT), encompassing narcissism, Machiavellianism and psychopathy traits, poses significant societal challenges. Understanding the neural underpinnings of these traits is crucial for developing effective interventions and preventive strategies. Our study aimed to unveil the neural substrates of the DT by examining brain scans from 201 individuals (mean age: 32.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!