Drop-on-demand printed negative dielectric anisotropy liquid crystal droplets for adaptive complex beam manipulation and assessment
Jinge Guo, Xuke Qiu, Runchen Zhang, Qihao Han, Liangyu Deng, Yishun Lu, Zimo Zhao, Mengmeng Li, Waqas Kama, Junseok Ma, Yongge Ma, Steve J Elston, Alfonso A. Castrej\'on-Pita, Stephen M Morris, and Chao He

TL;DR
This paper introduces an inkjet-printed liquid crystal droplet platform that enables adaptive complex beam generation and full vectorial optical field sensing within a single, scalable architecture, advancing optical manipulation and measurement.
Contribution
It presents a novel, integrated liquid crystal droplet system for adaptive beam control and optical field sensing, combining multiple functionalities in a printed soft-matter photonic platform.
Findings
Voltage-driven director reconfiguration produces tunable birefringence.
The system can generate skyrmionic-like optical fields.
It enables spectral and polarization retrieval and phase reconstruction.
Abstract
Adaptive manipulation of vectorial optical fields are important for optical metrology, imaging, and structured light related applications, yet existing approaches often rely on bulky or sequentially operated systems. Here we demonstrate an inkjet-printed negative dielectric anisotropy nematic liquid crystal droplet platform that unifies adaptive complex beam generation and full vectorial optical field sensing within a single printed architecture. For complex beam generation, voltage-driven director reconfiguration in the droplets produces tunable birefringence and wavelength-dependent polarization textures, including skyrmionic like optical fields. For adaptive full vectorial optical field sensing, the same droplet array enables spectral and polarization retrieval through wavelength-dependent intensity patterns and division-of-wavefront polarimetry, while also functioning as a microlens…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
