arXiv subjects

Massimiliano Zamengo

Publications and source records attributed to Massimiliano Zamengo.

4 recordsLinked to original sources

arXiv2026

Heating of black-Si by microwaves at 2.45 GHz: effect of nano-needle orientation

Silicon, which is highly reflective and has negligible absorption at the microwave 2.45 GHz range, can be heated when it is turned into black-Si with a surface texture of nano-needles made by SF6/O2 plasma etching. Microwave heating of flat Si and of black-Si with vertical and with tilted (oriented) nano-needles was compared under identical conditions in a cylindrical TM010 resonator. Samples were placed horizontally on a 150-micrometer-thick cover glass, which standardized their height and position inside the cavity and allowed a common calibration of the radiation thermometer against a K-type thermocouple. Under a stepwise protocol of only 2-3-4 W of microwave power (60 s per step), black-Si with vertical needles reached ~ 260 C, systematically exceeding flat Si (~ 190 C), while black-Si with oriented needles showed a delayed but abrupt onset of heating at 3 W. In situ monitoring of the cavity resonance revealed a 2-3 MHz downshift of the resonance frequency f for all samples, with the drift consistently appearing once the sample temperature exceeded ~ 120 - 150 C, consistent with thermally activated carrier generation in Si. At a given temperature, each sample loads the cavity at a different f and quality factor Q, indicating an orientation-dependent effective permittivity of the nano-needle array, in line with the strong form birefringence of tilted black-Si. Application potential and challenges in the quantitative temperature and permittivity determination are discussed.

cond-mat.mes-hall↗

arXiv2026

Oxidation of Tantalum Nano-Film by Microwave Exposure

Oxidation and ablation of 200 nm tantalum films were carried out by three routes: (i) femtosecond (fs-)laser direct write, (ii) high-temperature annealing (HTA) in a tube furnace, and (iii) annealing in a 2.45 GHz microwave cavity. Complete conversion of the 200 nm Ta layer into 409 nm of Ta2O5 required one hour at 600 C in the furnace, but only minutes at ~ 50 W of microwave power. Fs-laser (515 nm/200 fs) oxidation of the Ta nano-film set in at an average single-pulse fluence of ~ 0.1 J/cm2 under strong pulse-to-pulse overlap (900 pulses per focal spot), i.e. within a narrow window bounded from above by the onset of ablation. Under microwave annealing, both the cavity resonance frequency and the quality factor Q changed markedly at the metal-to-oxide transition, reflecting the collapse of the real and imaginary parts of the permittivity at 2.45 GHz. This dielectric contrast turns the cavity into a sensor: the oxidation can be followed in real time from the shift of the cavity resonance, providing a non-invasive, in-situ diagnostic tool.

cond-mat.mtrl-sci↗

arXiv2025

Omics-scale polymer computational database transferable to real-world artificial intelligence applications

Developing large-scale foundational datasets is a critical milestone in advancing artificial intelligence (AI)-driven scientific innovation. However, unlike AI-mature fields such as natural language processing, materials science, particularly polymer research, has significantly lagged in developing extensive open datasets. This lag is primarily due to the high costs of polymer synthesis and property measurements, along with the vastness and complexity of the chemical space. This study presents PolyOmics, an omics-scale computational database generated through fully automated molecular dynamics simulation pipelines that provide diverse physical properties for over $10^5$ polymeric materials. The PolyOmics database is collaboratively developed by approximately 260 researchers from 48 institutions to bridge the gap between academia and industry. Machine learning models pretrained on PolyOmics can be efficiently fine-tuned for a wide range of real-world downstream tasks, even when only limited experimental data are available. Notably, the generalisation capability of these simulation-to-real transfer models improve significantly as the size of the PolyOmics database increases, exhibiting power-law scaling. The emergence of scaling laws supports the "more is better" principle, highlighting the significance of ultralarge-scale computational materials data for improving real-world prediction performance. This unprecedented omics-scale database reveals vast unexplored regions of polymer materials, providing a foundation for AI-driven polymer science.

physics.chem-ph↗

arXiv2020

Potentials and challenges of polymer informatics: exploiting machine learning for polymer design

There has been rapidly growing demand of polymeric materials coming from different aspects of modern life because of the highly diverse physical and chemical properties of polymers. Polymer informatics is an interdisciplinary research field of polymer science, computer science, information science and machine learning that serves as a platform to exploit existing polymer data for efficient design of functional polymers. Despite many potential benefits of employing a data-driven approach to polymer design, there has been notable challenges of the development of polymer informatics attributed to the complex hierarchical structures of polymers, such as the lack of open databases and unified structural representation. In this study, we review and discuss the applications of machine learning on different aspects of the polymer design process through four perspectives: polymer databases, representation (descriptor) of polymers, predictive models for polymer properties, and polymer design strategy. We hope that this paper can serve as an entry point for researchers interested in the field of polymer informatics.

cond-mat.soft↗