Particle-based reconfigurable scattering masks for lensless imaging

Jennifer R. Miller, Cheng Yu Wang, Christine D. Keating, Zhiwen Liu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Light scattering is typically undesired in optical systems as it often introduces defects or otherwise negatively impacts device performance. However, rather than being a hindrance, scattering can also be exploited to achieve lensless imaging using a scattering mask instead of lenses to enable devices with low-cost, compact construction, and yet a large field of view. Lensless imaging can benefit greatly from the ability to dynamically tune the scattering pattern produced by the mask; however, this often results in increased complexity and cost. Herein, we propose and demonstrate particle-based reconfigurable scattering masks to dynamically tune light scattering for lensless imaging, enabling multishot image reconstruction. Disordered particle populations are tuned by rational application of electric fields without requiring bulky or expensive components. Several assembly motifs are explored and studied for optimal performance; in particular, gold nanowires chained between planar electrodes yield the best reconstruction quality and are the main focus in this study. The distinct gold nanowire based scattering masks achieve a complex wavelet structural similarity as low as 0.36. By leveraging the submicrometer thickness of particles and the resultant large optical memory effect, an angular field of view of ±45° is demonstrated. The reconfigurable nature of the particle arrays enables multishot reconstruction which results in enhanced image quality and improved signal-to-noise ratios by up to 10-fold. These results suggest that reconfigurable particle masks could be a broadly applicable means of achieving dynamically tunable light scattering with potential applications in lensless microscopy or high-resolution imaging.

Original languageEnglish (US)
Pages (from-to)13038-13046
Number of pages9
JournalACS nano
Issue number10
StatePublished - Oct 27 2020

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • General Engineering
  • General Physics and Astronomy


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