arXiv subjects

Masayuki Abe

Publications and source records attributed to Masayuki Abe.

8 recordsLinked to original sources

arXiv2026

Reconstruction-Dependent Imaging, Reactivity and Local Reduction of the CeO$_2$(100) surface

The possibility of mapping the local reactivity and reduction state to the atomic structure of chemically active oxide surfaces opens new avenues for further understanding of catalysis. Here, we combine scanning tunnelling (STM) and atomic force microscopy (AFM) with first-principles modelling to explore this possibility on the CeO2(100) surface. While STM reveals the periodicity of cerium-terminated and oxygen-terminated CeO$_2$(100) reconstructions coexisting on the same surface, AFM imaging and force spectroscopy provide direct identification of the exposed atomic species and their reactivity as the chemical interaction with the probe. Density functional theory based STM and AFM simulations reproduce the main experimental observations and show that STM contrast cannot be in general assigned to the atomic positions of certain chemical species, as traditionally assumed from previous studies. Simulated STM contrast of the two reconstructions across different reduction states associated with the removal of oxygen atoms in deeper layers, evidence that STM alone does not offer a robust fingerprint of the local reduction state for the cerium-terminated reconstruction, but it is sensitive to the reduced state in the case of the oxygen-terminated one, being able to provide information on a mixed distribution of Ce$^{3+}$ and Ce$^{4+}$ ions on the first sub-surface Ce layer.

cond-mat.mtrl-sci↗

arXiv2026

Integrating Domain-Specialized Language Models with AI Measurement Tools for Deterministic Atomic-Resolution Experimentation

Self-driving laboratories based on large language models promise to transform scientific discovery through general experimental automation. However, realizing this vision on precision platforms remains challenging, requiring deterministic execution and effective domain adaptation under strict physical constraints. We address these requirements through a framework that specializes in small language models for autonomous control of scanning probe microscopy, coordinating task-specific models with AI-driven measurement tools. We demonstrate real-time, atomic-resolution SPM experiments at room temperature, achieving instruction-level control and multi-step experimental planning. Fine-tuning reduces perplexity from 1.44 to 1.20 and improves reliability, with the adapted model reaching 99.3% and 95.2% command accuracy, outperforming OpenAI o4-mini on domain-specific tasks. This architecture achieves lower computational cost while maintaining deterministic execution and enabling deployment on consumer-grade hardware. This work bridges probabilistic language models with deterministic experimental control through a modular, domain-specialized architecture, providing a generalizable pathway toward scalable and trustworthy self-driving laboratories across diverse scientific platforms.

physics.app-ph↗

arXiv2025

Near-surface Defects Break Symmetry in Water Adsorption on CeO$_{2-x}$(111)

Water interactions with oxygen-deficient cerium dioxide (CeO$_2$) surfaces are central to hydrogen production and catalytic redox reactions, but the atomic-scale details of how defects influence adsorption and reactivity remain elusive. Here, we unveil how water adsorbs on partially reduced CeO$_{2-x}$(111) using atomic force microscopy (AFM) with chemically sensitive, oxygen-terminated probes, combined with first-principles calculations. Our AFM imaging reveals water molecules as sharp, asymmetric boomerang-like features radically departing from the symmetric triangular motifs previously attributed to molecular water. Strikingly, these features localize near subsurface defects. While the experiments are carried out at cryogenic temperature, water was dosed at room temperature, capturing configurations relevant to initial adsorption events in catalytic processes. Density functional theory identifies Ce$^{3+}$ sites adjacent to subsurface vacancies as the thermodynamically favored adsorption sites, where defect-induced symmetry breaking governs water orientation. Force spectroscopy and simulations further distinguish Ce$^{3+}$ from Ce$^{4+}$ centers through their unique interaction signatures. By resolving how subsurface defects control water adsorption at the atomic scale, this work demonstrates the power of chemically selective AFM for probing site-specific reactivity in oxide catalysts, laying the groundwork for direct investigations of complex systems such as single-atom catalysts, metal-support interfaces, and defect-engineered oxides.

cond-mat.mtrl-sci↗

arXiv2024

Leveraging Large Language Models and Social Media for Automation in Scanning Probe Microscopy

We present the development of an automated scanning probe microscopy (SPM) measurement system using an advanced large-scale language model (LLM). This SPM system can receive instructions via social networking services (SNS), and the integration of SNS and LLMs enables real-time, language-agnostic control of SPM operations, thereby improving accessibility and efficiency. The integration of LLMs with AI systems with specialized functions brings the realization of self-driving labs closer.

physics.app-ph↗

arXiv2024

AI-equipped scanning probe microscopy for autonomous site-specific atomic-level characterization at room temperature

We present an advanced scanning probe microscopy system enhanced with artificial intelligence (AI-SPM) designed for self-driving atomic-scale measurements. This system expertly identifies and manipulates atomic positions with high precision, autonomously performing tasks such as spectroscopic data acquisition and atomic adjustment. An outstanding feature of AI-SPM is its ability to detect and adapt to surface defects, targeting or avoiding them as necessary. It's also engineered to address typical challenges such as positional drift and tip apex atomic variations due to the thermal effect, ensuring accurate, site-specific surface analyses. Our tests under the demanding conditions of room temperature have demonstrated the robustness of the system, successfully navigating thermal drift and tip fluctuations. During these tests on the Si(111)-(7x7) surface, AI-SPM autonomously identified defect-free regions and performed a large number of current-voltage spectroscopy measurements at different adatom sites, while autonomously compensating for thermal drift and monitoring probe health. These experiments produce extensive data sets that are critical for reliable materials characterization and demonstrate the potential of AI-SPM to significantly improve data acquisition. The integration of AI into SPM technologies represents a step toward more effective, precise and reliable atomic-level surface analysis, revolutionizing materials characterization methods.

physics.comp-ph↗

arXiv2020

Machine-learning Based Extraction of the Short-Range Part of the Interaction in Non-contact Atomic Force Microscopy

A machine-learning method for extracting the short-range part of the probe-surface interaction from force spectroscopy curves is presented. Our machine-learning algorithm consists of two stages: the first stage determines a boundary that separates the region where the short-range interaction is dominantly acting on the probe, and a second stage that finds the parameters to fit the interaction over the long-range region. We successfully applied this method to force spectroscopy maps acquired over the Si(111)-(7x7) surface and found, as a result, a faint structure on the short-range interaction for one of the probes used in the experiments that would have probably been obviated using human-supervised fitting strategies.

cond-mat.mes-hall↗

arXiv2018

Maskless laser processing of graphene

Graphene on a SiO$_2$/Si substrate was removed by ultraviolet pulsed laser irradiation. Threshold laser power density to remove graphene depended on the graphene thickness. The mechanism is discussed using kinetic energy of thermal expansion of the substrate surface. Utilizing the thickness dependence, thickness (or layer-number) selective process for graphene is demonstrated. Maskless patterning of graphene using laser irradiation in the air is also demonstrated.

physics.app-ph↗

arXiv2018

Characterization of X-ray charge neutralizer using carbon-nanotube field emitter

An X-ray charge neutralizer using a screen-printed carbon-nanotube field emitter is demonstrated to show the possibility of a large-area flat-panel charge neutralizer, although the device dimensions in the present work are not very large. The X-ray yields and spectra are characterized to estimate the ion generation rate as one of the figures of merit of neutralizers. Charge neutralization characteristics are measured and show good performance.

physics.app-ph↗