Oluwaseyi Balogun | Searcharxiv

arXiv2024

Excitation laser energy dependence of the gap-mode TERS spectra of WS$_2$ and MoS$_2$ on silver

We present a systematic study of the dependence of gap mode tip-enhanced Raman scattering (TERS) of mono- and bi-layer WS$_2$ and MoS$_2$ as a function of excitation laser energy. We collected consecutive TERS maps of mono-and bi-layer regions with 6 different excitation lasers. To decrease the acquisition time, we used for the first time concurrent excitation and collection with two lasers simultaneously. We found that the E$_{2g}$/A$_{1g}$ peak intensity ratio for bilayer WS$_2$@Ag and the A'/A$_{1g}$ peak intensity ratio of the out-of-plane modes for mono- and bilayer change in a significantly non-monotonous way with excitation laser energies from 1.58 to 2.62 eV. The former ratio increases at energies corresponding to A and B excitons in bilayer WS$_2$. The intensity of the A peak in the monolayer, and hence the A/A$_{1g}$ ratio, is surprisingly high at low excitation energies, dips dramatically at energy corresponding to the A exciton, and is restored partially in between A and B excitons, though still showing a descending trend with increasing energy. A similar picture was observed in mono- and bi-layer MoS$_2$, though the existing set of lasers did not match its excitonic profile as nicely as for WS$_2$. We attribute the observed behavior to intermediate (Fano resonance) or strong (Rabi splitting) coupling between the excitons in transition metal dichalcogenides (TMDs) and the plasmons in the tip-substrate nanocavity. This is akin to the so-called Fano (Rabi) transparency experimentally observed in far field scattering from TMDs between two plasmonic metals. The possibility of intermediate/strong coupling between excitonic resonances in TMDs and the nanocavity re-evaluates the role of resonances in gap-mode TERS and should become an important factor to be considered by TERS practitioners when planning experiments. Finally, we propose the ideal substrate for efficient TERS and tip enhanced photoluminescence measurements.

cond-mat.mtrl-sci↗

arXiv2022

GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. Past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the "real-world", "free-form" uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of 1)~building a universal uncertainty quantification model compatible with both shape and topological designs, 2)~modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and 3)~allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performances after fabrication.

cs.CE↗