arXiv subjects

Umberto Celano

Publications and source records attributed to Umberto Celano.

4 recordsLinked to original sources

arXiv2025

Electrical Conductivity of Copper-Graphene (Cu-Gr) Composites: The Underlying Mechanisms of Ultrahigh Conductivity

Copper-graphene composite (CGC) conductors are widely considered as a potential alternative to pure copper (Cu). Yet, the effect of graphene (Gr) on the electrical conductivity of CGCs remains elusive, and their electrical performance is still controversial. This work addresses these unresolved questions by unambiguously quantifying how the electrical properties of CGCs depend on the characteristics of Gr and Cu. Gr is synthesized on Cu foils, foams, and wires by utilizing a wide range of chemical vapor deposition conditions to independently control their characteristics. Then the Gr-enhanced electrical conductivity (Δσ) is characterized for CGCs with different Cu geometries and Gr qualities. This study confirms that unprecedented electrical conductivity (Δσ = 17.1%) can be achieved only when both Gr and Cu are carefully optimized. Specifically, the study reveals three key factors: (1) Δσ is positively correlated with continuity of Gr; (2) CGCs with a continuous monolayer Gr exhibit a strong Δσ-A_s linear relation where A_s is the specific surface area of a CGC; and (3) Δσ becomes more pronounced when a Cu matrix has a curved cross-section. This work reveals the fundamental mechanisms of how Gr influences the overall electrical conductivity of CGCs and, therefore, is a crucial step toward designing and manufacturing high-performance CGC conductors for emerging applications.

cond-mat.mtrl-sci↗

arXiv2025

SparseC-AFM: a deep learning method for fast and accurate characterization of MoS$_2$ with C-AFM

The increasing use of two-dimensional (2D) materials in nanoelectronics demands robust metrology techniques for electrical characterization, especially for large-scale production. While atomic force microscopy (AFM) techniques like conductive AFM (C-AFM) offer high accuracy, they suffer from slow data acquisition speeds due to the raster scanning process. To address this, we introduce SparseC-AFM, a deep learning model that rapidly and accurately reconstructs conductivity maps of 2D materials like MoS$_2$ from sparse C-AFM scans. Our approach is robust across various scanning modes, substrates, and experimental conditions. We report a comparison between (a) classic flow implementation, where a high pixel density C-AFM image (e.g., 15 minutes to collect) is manually parsed to extract relevant material parameters, and (b) our SparseC-AFM method, which achieves the same operation using data that requires substantially less acquisition time (e.g., under 5 minutes). SparseC-AFM enables efficient extraction of critical material parameters in MoS$_2$, including film coverage, defect density, and identification of crystalline island boundaries, edges, and cracks. We achieve over 11x reduction in acquisition time compared to manual extraction from a full-resolution C-AFM image. Moreover, we demonstrate that our model-predicted samples exhibit remarkably similar electrical properties to full-resolution data gathered using classic-flow scanning. This work represents a significant step toward translating AI-assisted 2D material characterization from laboratory research to industrial fabrication. Code and model weights are available at github.com/UNITES-Lab/sparse-cafm.

cs.CV↗

arXiv2020

Electrical Tuning of Phase Change Antennas and Metasurfaces

The success of semiconductor electronics is built on the creation of compact, low-power switching elements that offer routing, logic, and memory functions. The availability of nanoscale optical switches could have a similarly transformative impact on the development of dynamic and programmable metasurfaces, optical neural networks, and quantum information processing. Phase change materials are uniquely suited to enable their creation as they offer high-speed electrical switching between amorphous and crystalline states with notably different optical properties. Their high refractive index has also been harnessed to fashion them into compact optical antennas. Here, we take the next important step by realizing electrically-switchable phase change antennas and metasurfaces that offer strong, reversible, non-volatile, multi-phase switching and spectral tuning of light scattering in the visible and near-infrared spectral ranges. Their successful implementation relies on a careful joint thermal and optical optimization of the antenna elements that comprise an Ag strip that simultaneously serves as a plasmonic resonator and a miniature heating stage.

physics.optics↗

arXiv2020

Exploring Nanoscale Ferroelectricity in Doped Hafnium Oxide by Interferometric Piezoresponse Force Microscopy

Hafnium oxide (HfO2)-based ferroelectrics offer remarkable promise for memory and logic devices in view of their compatibility with traditional silicon CMOS technology, high switchable polarization, good endurance and thickness scalability. These factors have led to steep rise in research on this class of materials over the past number of years. At the same time, only a few reports on the direct sensing of nanoscale ferroelectric properties exist, with many questions remaining regarding the emergence of ferroelectricity in these materials. While piezoresponse force microscopy (PFM) is ideally suited to probe piezo- and ferro-electricity on the nanoscale, it is known to suffer artifacts which complicate quantitative interpretation of results and can even lead to claims of ferroelectricity in materials which are not ferroelectric. In this paper we explore the possibility of using an improved PFM method based on interferometric displacement sensing (IDS) to study nanoscale ferroelectricity in bare Si doped HfO2. Our results indicate a clear difference in the local remnant state of various HfO2 crystallites with reported values for the piezoelectric coupling in range 0.6-1.5 pm/V. In addition, we report unusual ferroelectric polarization switching including possible contributions from electrostriction and Vegard effect, which may indicate oxygen vacancies or interfacial effects influence the emergence of nanoscale ferroelectricity in HfO2.

physics.app-ph↗