Search NASA⌕ Search

DOE OSTI · 1975188

Electrically controllable chirality in a nanophotonic interface with a two-dimensional semiconductor

Abstract

Chiral nanophotonic interfaces enable propagation direction-dependent interactions between guided optical modes and circularly dichroic materials. Electrical tuning of interface chirality would aid active, switchable non-reciprocity in on-chip optoelectronic and photonic circuitry, but remains an outstanding challenge. Here, in this paper, we report electrically controllable chirality in a nanophotonic interface with atomically thin monolayer tungsten diselenide (WSe 2 ). Titanium dioxide waveguides are directly fabricated on the surface of low-disorder, boron nitride-encapsulated WSe 2 . Following integration, photoluminescence from excitonic states into the waveguide can be electrically switched between balanced and directionally biased emission. The operational principle leverages the doping-dependent valley polarization of excitonic states in WSe 2 . Furthermore, the nanophotonic waveguide can function as a near-field source for diffusive exciton fluxes, which display valley and spin polarizations that are inherited from the interface chirality. Our versatile fabrication approach enables the deterministic integration of photonics with van der Waals heterostructures and could provide optical control over their excitonic and charge-carrier behaviour.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shreiner, Robert, Hao, Kai, Butcher, Amy, High, Alexander A.. 2022-03-17. Electrically controllable chirality in a nanophotonic interface with a two-dimensional semiconductor. https://doi.org/10.1038/s41566-022-00971-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗