arXiv subjects

Hiroki Ago

Publications and source records attributed to Hiroki Ago.

7 recordsLinked to original sources

arXiv2026

Ubiquitous Antiparallel Domains in 2D Hexagonal Boron Nitride Uncovered by Interferometric Nonlinear Optical Imaging

Hexagonal boron nitride (hBN) supports a wide range of two-dimensional (2D) technologies, yet assessing its crystalline quality over large areas remains a fundamental challenge. Both antiparallel domains, an intrinsic outcome of epitaxy on high-symmetry substrates, and associated structural defects have long evaded optical detection. Here, we show that interferometric second-harmonic generation (SHG) imaging provides a powerful, nondestructive probe of lattice orientation and structural integrity in chemical vapor deposition-grown hBN. This approach reveals the ubiquitous formation of antiparallel domains and quantifies their impact on crystalline order. SHG intensity also emerges as a direct optical metric of domain disorder, spanning three orders of magnitude across films produced by ten different growth routes. Correlation with Raman spectroscopy establishes a unified framework for evaluating crystalline quality. Beyond hBN, this method offers a high-throughput route to wide-area structural imaging in various non-centrosymmetric materials, advancing their deployment in electronics, photonics, and quantum technologies.

cond-mat.mtrl-sci↗

arXiv2025

Defect migration and phase transformations in 2D iron chloride inside bilayer graphene

The intercalation of metal chlorides, and particularly iron chlorides, into graphitic carbon structures has recently received lots of attention, as it can not only protect this two-dimensional (2D) magnetic system from the effects of the environment, but also substantially alter the magnetic, electronic, and optical properties of both intercalant and host material. At the same time, the intercalation can result in the formation of structural defects, or defects can appear under external stimuli, which can affect materials performance. These aspects have received so far little attention in the dedicated experiments. In this study, we investigate the behavior of atomic-scale defects in iron chlorides intercalated into bilayer graphene (BLG) by using scanning transmission electron microscopy (STEM) and first-principles calculations. We observe transformations between the FeCl2 and FeCl3 phases and elucidate the role of defects in the transformations. Specifically, three types of defects are identified: Fe vacancies in FeCl2 domains, Fe adatoms and interstitials in FeCl3 domains, each exhibiting distinct dynamic behaviors. We also observed a crystalline phase with an unusual stoichiometry of Fe5Cl18 which has not been reported before. Our findings not only advance the understanding of intercalation mechanism of 2D materials but also highlight the profound impact of atomic-scale defects on their properties and potential technological applications.

cond-mat.mtrl-sci↗

arXiv2025

Lattice-guided growth of dense arrays of aligned transition metal dichalcogenide nanoribbons with high catalytic reactivity

Transition metal dichalcogenides (TMDs) exhibit unique properties and potential applications when reduced to one-dimensional (1D) nanoribbons (NRs), owing to quantum confinement and high edge densities. However, effective growth methods for self-aligned TMD NRs are still lacking. We demonstrate a versatile approach for lattice-guided growth of dense, aligned MoS2 NR arrays via chemical vapor deposition (CVD) on anisotropic sapphire substrates, without tailored surface steps. This method enables the synthesis of NRs with widths below 10 nm and longitudinal axis parallel to the zigzag direction, being also extensible to the growth of WS2 NRs and MoS2-WS2 hetero-nanoribbons. Growth is influenced by both substrate and CVD temperature, indicating the role of anisotropic precursor diffusion and substrate interaction. The 1D nature of the NRs was asserted by the observation of Coulomb blockade at low temperature. Pronounced catalytic activity was observed at the edges of the NRs, indicating their promise for efficient catalysis.

cond-mat.mes-hall↗

arXiv2023

Coupling and decoupling of bilayer graphene monitored by electron energy loss spectroscopy

We studied the interlayer coupling and decoupling of bilayer graphene (BLG) by using spatially resolved electron energy loss spectroscopy (EELS) with a monochromated electron source. We correlated the twist-angle-dependent energy band hybridization with Moire superlattices and the corresponding optical absorption peaks. The optical absorption peak originates from the excitonic transition between the hybridized van Hove singularities (vHSs), which shifts systematically with the twist angle. We then proved that the BLG decouples when a monolayer of metal chloride is intercalated in its van der Waals (vdW) gap, and results in the elimination of the vHS peak.

cond-mat.mtrl-sci↗

arXiv2022

Determining the Twist Angle of Bilayer Graphene by Machine Learning Analysis of its Raman Spectrum

With the increasing interest in twisted bilayer graphene (tBLG) of the past years, fast, reliable, and non-destructive methods to precisely determine the twist angle are required. Raman spectroscopy potentially provides such method, given the large amount of information about the state of the graphene that is encoded in its Raman spectrum. However, changes in the Raman spectra induced by the stacking order can be very subtle, thus making the angle identification tedious. In this work, we propose the use of machine learning (ML) analysis techniques for the automated classification of the Raman spectrum of tBLG into a selected range of twist angles. The ML classification proposed here is low computationally demanding, providing fast and accurate results with ~99 % of agreement with the manual labelling of the spectra. The flexibility and non-invasive nature of the Raman measurements, paired with the predictive accuracy of the ML, is expected to facilitate the exploration of the nascent research of tBLG. Moreover, the present work showcases how the currently available open-source tools facilitate the study and integration of ML-based techniques.

cond-mat.mtrl-sci↗

arXiv2021

Data cluster analysis and machine learning for classification of twisted bilayer graphene

Twisted bilayer graphene (TBLG) has emerged as an exciting new material with tunable electronic properties ranging from superconductivity to correlated insulating phases. But current methods of fabrication and identification of TBLG are painstaking and laborious. In this work, we combine Raman spectroscopy with the Gaussian mixture model (GMM) data clustering algorithm to identify areas with particular twist angles, from a TBLG sample with a mixture of orientations. We present two approaches to this cluster analysis: training the GMM on Raman parameters returned by peak fits, and on full Raman spectra with dimensionality reduced by principal component analysis. In both cases we demonstrate that GMM can identify regions of distinct twist angle from within Raman datacubes. We also show that once a model has been trained, and the identified clusters labelled, the model can be reapplied to new Raman scans to assess the similarity between the materials in the new region and the testing region. This could enable high-throughput fabrication of TBLG, by allowing computerised detection of particular twist angles from automated large-area scans.

cond-mat.mtrl-sci↗

arXiv2014

Observation of spin-charge conversion in CVD-grown single-layer graphene

Conversion of pure spin current to charge current in single-layer graphene (SLG) is investigated by using spin pumping. Large-area SLG grown by chemical vapor deposition is used for the conversion. Efficient spin accumulation in SLG by spin pumping enables observing an electromotive force produced by the inverse spin Hall effect (ISHE) of SLG. The spin Hall angle of SLG is estimated to be 6.1*10-7. The observed ISHE in SLG is ascribed to its non-negligible spin-orbit interaction in SLG.

cond-mat.mtrl-sci↗