arXiv subjects

Ruben Millan-Solsona

Publications and source records attributed to Ruben Millan-Solsona.

5 recordsLinked to original sources

arXiv2026

Dynamics of Null and Electrostatic Blind Spots for Quantitative PFM

Piezoresponse force microscopy is a cornerstone technique for probing nanoscale electromechanical phenomena, yet quantitative and in some cases qualitative interpretation remains hindered by parasitic electrostatic forces coupled through cantilever dynamics. Recent approaches aim to suppress these artifacts by operating at resonance defined null spots or electrostatic blind spots, but whether these conditions are equivalent and how they evolve under realistic measurement conditions has remained unclear. Here, combining analytical beam models, geometrically faithful finite-element simulations, and automated interferometric measurements, we show that NS and ESBS are fundamentally different operating conditions. The NS is a modal zero at contact resonance where sensitivity to all excitations vanishes, whereas the ESBS is a quasistatic position where only the distributed electrostatic response is suppressed. Automated measurements reveal that both conditions evolve with the tip sample boundary condition yet remain spatially separated under realistic experimental conditions. While beam models capture the dominant cantilever mechanics, finite-element simulations and experiment show that quantitative prediction of near tip behavior requires realistic three dimensional probe electrostatics and mechanics. These findings establish NS and ESBS as dynamic operating conditions and provide a practical framework for advancing interferometric PFM toward truly quantitative electromechanical metrology.

cond-mat.mtrl-sci↗

arXiv2026

Local Multimodal Dynamics in Mixed Ionic-Electronic Conductors and Their Fingerprints in Organic Electrochemical Transistor Operation

Mixed ionic-electronic conductors host tightly coupled interactions among mobile ions, electronic charges, and the polymer matrix, giving rise to complex multimodal responses spanning electrical, mechanical, and morphological transformations. These materials underpin organic electrochemical transistors (OECTs), which translate such interactions into low-voltage signal amplification and sensing for applications in bioelectronics, neuromorphic computing, and memory. Despite their central role, OECT current-voltage transfer characteristics are often treated phenomenologically, as both the local multimodal dynamics and their connection to global device response remain unresolved. Here, we reveal that the transfer curve encodes a cascade of spatially localized electrochemical transitions, each associated with distinct changes in conductivity, stiffness, and morphology, fundamentally redefining it as a spatially resolved fingerprint of device's internal state. Using automated operando multimodal in-liquid scanning dielectric microscopy, we directly map these dynamics and identify region-specific electrochemical thresholds governing the interplay between source, channel, and drain. We found that the local tip-sample electrostatic force serves as a remarkable mechanistic observable of coupled multimodal dynamics in mixed conductors. A physically grounded model links it to general material, interfacial, and geometric parameters, enabling mechanistic interpretation and predictive insights. Our work provides a new framework for probing and understanding mixed conduction in ion-electron coupled systems.

cond-mat.mtrl-sci↗

arXiv2025

A Fourier-Based Global Denoising Model for Smart Artifacts Removing of Microscopy Images

Microscopy such as Scanning Tunneling Microscopy (STM), Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM) are essential tools in material imaging at micro- and nanoscale resolutions to extract physical knowledge and materials structure-property relationships. However, tuning microscopy controls (e.g. scanning speed, current setpoint, tip bias etc.) to obtain a high-quality of images is a non-trivial and time-consuming effort. On the other hand, with sub-standard images, the key features are not accurately discovered due to noise and artifacts, leading to erroneous analysis. Existing denoising models mostly build on generalizing the weak signals as noises while the strong signals are enhanced as key features, which is not always the case in microscopy images, thus can completely erase a significant amount of hidden physical information. To address these limitations, we propose a global denoising model (GDM) to smartly remove artifacts of microscopy images while preserving weaker but physically important features. The proposed model is developed based on 1) first designing a two-imaging input channel of non-pair and goal specific pre-processed images with user-defined trade-off information between two channels and 2) then integrating a loss function of pixel- and fast Fourier-transformed (FFT) based on training the U-net model. We compared the proposed GDM with the non-FFT denoising model over STM-generated images of Copper(Cu) and Silicon(Si) materials, AFM-generated Pantoea sp.YR343 bio-film images and SEM-generated plastic degradation images. We believe this proposed workflow can be extended to improve other microscopy image quality and will benefit the experimentalists with the proposed design flexibility to smartly tune via domain-experts preferences.

eess.IV↗

arXiv2025

Single-particle detection of a semiconductor-to-metal transition by scanning dielectric microscopy

Hybrid nanostructures that combine semiconducting and metallic components offer great potential for photothermal therapy, optoelectronics, and sensing, by integrating tunable optical properties with enhanced light absorption and charge transport. Boosting the integrated performance of these hybrid systems demands techniques capable of probing local variations of the physical properties inaccessible to bulk analysis. Here, we report the single-particle dielectric characterization of hybrid, semiconducting bismuth sulfide (Bi$_2$S$_3$) nanorods (NR) decorated with metallic Au nanoparticles (NP), employing scanning dielectric microscopy, which uses electrostatic force microscopy in combination with finite-element numerical simulations. We reveal a pronounced enhancement in the local dielectric response of Bi$_2$S$_3$ upon Au decoration, attributed to interfacial polarization and electron transfer from Au to the Bi$_2$S$_3$ matrix, thus suggesting a semiconductor-to-metal-like transition at the single-particle level. Numerical simulations show that the response is dominated by the vertical component of the permittivity and that the decorating metallic Au NP produce only moderate shielding of the semiconductor Bi$_2$S$_3$ NR core, indicating that the large increase in the dielectric response originates primarily from intrinsic modifications within the NR. Overall, these findings provide direct insight into structure--property relationships at the single-particle level, supporting the rational design of advanced hybrid nanostructures with tailored electronic functionalities.

physics.chem-ph↗

arXiv2025

A Bi-channel Aided Stitching of Atomic Force Microscopy Images

Microscopy is an essential tool in scientific research, enabling the visualization of structures at micro- and nanoscale resolutions. However, the field of microscopy often encounters limitations in field-of-view (FOV), restricting the amount of sample that can be imaged in a single capture. To overcome this limitation, image stitching techniques have been developed to seamlessly merge multiple overlapping images into a single, high-resolution composite. The images collected from microscope need to be optimally stitched before accurate physical information can be extracted from post analysis. However, the existing stitching tools either struggle to stitch images together when the microscopy images are feature sparse or cannot address all the transformations of images. To address these issues, we propose a bi-channel aided feature-based image stitching method and demonstrate its use on AFM generated biofilm images. The topographical channel image of AFM data captures the morphological details of the sample, and a stitched topographical image is desired for researchers. We utilize the amplitude channel of AFM data to maximize the matching features and to estimate the position of the original topographical images and show that the proposed bi-channel aided stitching method outperforms the traditional stitching approach. Furthermore, we found that the differentiation of the topographical images along the x-axis provides similar feature information to the amplitude channel image, which generalizes our approach when the amplitude images are not available. Here we demonstrated the application on AFM, but similar approaches could be employed of optical microscopy with brightfield and fluorescence channels. We believe this proposed workflow will benefit the experimentalist to avoid erroneous analysis and discovery due to incorrect stitching.

eess.IV↗