Engineering topics

Khatib, Omar

Publications and source records attributed to Khatib, Omar.

4 records

DOE OSTI2024

Fundamental absorption bandwidth to thickness limit for transparent homogeneous layers

Abstract Past work has considered the analytic properties of the reflection coefficient for a metal-backed slab. The primary result established a fundamental relationship for the minimal layer thickness to bandwidth ratio achievable for an absorber. There has yet to be establishment of a similar relationship for non-metal-backed layers, and here we present the universal result based on the Kramers–Kronig relations. Our theory is validated with transfer matrix calculations of homogeneous materials, and full-wave numerical simulations of electromagnetic metamaterials. Our results place more general fundamental limits on absorbers and thus will be important for both fundamental and applied studies.

Padilla, Willie J.↗

DOE OSTI2023

Multimodal Characterization of Crystal Structure and Formation in Rubrene Thin Films Reveals Erasure of Orientational Discontinuities

Abstract Multimodal multiscale characterization provide opportunities to study organic semiconducting thin films with multiple length scales, across multiple platforms, to elucidate crystallization mechanisms of the various microstructures that impact functionality. With polarized scanning transmission X‐ray and 4D‐scanning transmission electron microscopy, hybrid crystalline structures in rubrene thin films in which large crystalline domains surround a common nucleus and transition to a spherulite morphology at larger radii is observed. These high‐resolution techniques reveal how azimuthal orientational discontinuities at smaller radii are erased as spherulite morphology takes hold. In situ crystallization in the films with optical microscopy is also captured, discovering the importance of considering the initial temperature increase of a film during thermal annealing over the crystallization timescale. This kinetic information of the radial crystallization rate and of corresponding film heating kinetics is used to estimate the temperature at which the larger crystalline regions transition into a spherulite. By combining the results obtained from the different characterization modes, it is learned that thermal conditions can sensitively affect the crystallization of rubrene and other organic thin films. The observations suggest opportunities for more complex temperature‐dependent processing to maximize hybrid structures’ functionality in organic thin films and demonstrate that multimodal studies deepen the understanding of structure‐function dynamics.

Tan, Jenna A.↗

DOE OSTI2022

Ultrafast infrared nano-imaging of far-from-equilibrium carrier and vibrational dynamics

Ultrafast infrared nano-imaging has demonstrated access to ultrafast carrier dynamics on the nanoscale in semiconductor, correlated-electron, or polaritonic materials. However, mostly limited to short-lived transient states, the contrast obtained has remained insufficient to probe important long-lived excitations, which arise from many-body interactions induced by strong perturbation among carriers, lattice phonons, or molecular vibrations. Here, we demonstrate ultrafast infrared nano-imaging based on excitation modulation and sideband detection to characterize electron and vibration dynamics with nano- to micro-second lifetimes. As an exemplary application to quantum materials, in phase-resolved ultrafast nano-imaging of the photoinduced insulator-to-metal transition in vanadium dioxide, a distinct transient nano-domain behavior is quantified. In another application to lead halide perovskites, transient vibrational nano-FTIR spatially resolves the excited-state polaron-cation coupling underlying the photovoltaic response. These examples show how heterodyne pump-probe nano-spectroscopy with low-repetition excitation extends ultrafast infrared nano-imaging to probe elementary processes in quantum and molecular materials in space and time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Inverse deep learning methods and benchmarks for artificial electromagnetic material design

In this work we investigate the use of deep inverse models (DIMs) for designing artificial electromagnetic materials (AEMs) – such as metamaterials, photonic crystals, and plasmonics – to achieve some desired scattering properties (e.g., transmission or reflection spectrum). DIMs are deep neural networks (i.e., deep learning models) that are specially-designed to solve ill-posed inverse problems. There has recently been tremendous growth in the use of DIMs for solving AEM design problems however there has been little comparison of these approaches to examine their absolute and relative performance capabilities. In this work we compare eight state-of-the-art DIMs on three unique AEM design problems, including two models that are novel to the AEM community. Our results indicate that DIMs can rapidly produce accurate designs to achieve a custom desired scattering on all three problems. Although no single model always performs best, the Neural-Adjoint approach achieves the best overall performance across all problem settings. As a final contribution we show that not all AEM design problems are ill-posed, and in such cases a conventional deep neural network can perform better than DIMs. We recommend that a deep neural network is always employed as a simple baseline approach when addressing AEM design problems. Furthermore, we publish python code for our AEM simulators and our DIMs to enable easy replication of our results, and benchmarking of new DIMs by the AEM community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗