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Yebin Lee

Publications and source records attributed to Yebin Lee.

3 recordsLinked to original sources

Realizing record-high transverse thermoelectric figure of merit at room temperature in artificially tilted multilayers based on high power factor NiFe alloy

Transverse thermoelectric conversion using artificially tilted multilayers (ATMLs) offers a versatile device architecture that circumvents the structural limitations of conventional longitudinal thermoelectrics. However, achieving competitive room-temperature thermoelectric performance without an external magnetic field remains a critical challenge. Here, we report a record-high transverse thermoelectric figure of merit $z_{yx}T$ of 0.36 in Ni$_{50}$Fe$_{50}$/Bi$_{0.2}$Sb$_{1.8}$Te$_{3}$-based ATML at room temperature without an external magnetic field. Leveraging the longitudinal high power factor in a Ni$_{50}$Fe$_{50}$ alloy and the sharp contrast in electrical and thermal transport properties between $n$-type Ni$_{50}$Fe$_{50}$ and $p$-type Bi$_{0.2}$Sb$_{1.8}$Te$_{3}$, we engineer an anisotropic structure that simultaneously exploits high electrical conductivity, large transverse thermopower, and low thermal conductivity to maximize $z_{yx}T$ in ATML. Through the direct measurements of these thermoelectric transport parameters, we obtained $z_{yx}T$ of 0.36 in Ni$_{50}$Fe$_{50}$/Bi$_{0.2}$Sb$_{1.8}$Te$_{3}$-based ATML, which is in excellent agreement with the analytical prediction of 0.36 owing to the low interfacial electrical and thermal resistances at the Ni$_{50}$Fe$_{50}$/Bi$_{0.2}$Sb$_{1.8}$Te$_{3}$ junctions. These results pave the way for the practical implementation of transverse thermoelectric materials around room temperature.

cond-mat.mtrl-sci

Twist-Angle Engineering of Moir\'e Potentials for High-Performance Ionics in Bilayer Graphene

Controlling ion transport is a fundamental challenge for advanced energy storage. Bilayer graphene offers a unique platform for modulating ion diffusion via twist-angle-dependent moire superlattices, yet conventional stacking configurations face an inherent trade-off: AA stacking provides stable Li intercalation but high diffusion barriers, while AB stacking enables fast diffusion but poor intercalation stability. Twisted bilayer graphene (tBLG) offers potential to overcome this limitation, yet systematic understanding across different twist angles remains limited. Here, we investigate Li intercalation in tBLG using first-principles density functional theory, evaluating intercalation energies and diffusion barriers across multiple twist angles through potential energy surface (PES) mapping. The Sigma 37 structure (9.43 degrees) simultaneously achieves the most favorable intercalation energy (-2.39 eV) and the lowest diffusion barrier (0.14 eV) among all structures examined, resolving the conventional stacking trade-off. Furthermore, using the Smooth Overlap of Atomic Positions (SOAP) descriptor, we demonstrate that the PES is governed by local atomic environments and that a model trained on limited structures predicts the PES of untested configurations with high accuracy. This transferability enables efficient screening without exhaustive first-principles calculations, establishing a systematic framework for twist-angle engineering of ion transport in two-dimensional layered materials.

cond-mat.mtrl-sci

FLEUR: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal Model

Most existing image captioning evaluation metrics focus on assigning a single numerical score to a caption by comparing it with reference captions. However, these methods do not provide an explanation for the assigned score. Moreover, reference captions are expensive to acquire. In this paper, we propose FLEUR, an explainable reference-free metric to introduce explainability into image captioning evaluation metrics. By leveraging a large multimodal model, FLEUR can evaluate the caption against the image without the need for reference captions, and provide the explanation for the assigned score. We introduce score smoothing to align as closely as possible with human judgment and to be robust to user-defined grading criteria. FLEUR achieves high correlations with human judgment across various image captioning evaluation benchmarks and reaches state-of-the-art results on Flickr8k-CF, COMPOSITE, and Pascal-50S within the domain of reference-free evaluation metrics. Our source code and results are publicly available at: https://github.com/Yebin46/FLEUR.

cs.CV