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Erschienen in: Optical Memory and Neural Networks 3/2023

01.12.2023

Photovoltaic Tweezers Based on Optical Holography: Application to 2D Trapping of DNA Molecules on a Lithium Niobate Crystal

verfasst von: Lusine Tsarukyan, Anahit Badalyan, Lusine Aloyan, Yeva Dalyan, Rafael Drampyan

Erschienen in: Optical Memory and Neural Networks | Sonderheft 3/2023

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Abstract

The nonuniform 2D photovoltaic fields generated near the surface of a photorefractive Fe-doped lithium niobate (LN:Fe) crystal by a nondiffracting optical Bessel beam with concentric ring structures and 532 nm wavelength are used for the trapping of DNA molecules in NaCl buffer on the crystal surface. The simultaneous observation of the long-living Bessel-like refractive lattice recorded in the LN:Fe crystal and the trapped DNA molecules on the crystal surface was performed by an optical phase microscope operating in the transmission mode. With this approach, the DNA molecules are registered as refractive index nonuniformities on the Bessel lattice refractive index pattern. Observations show that DNA molecules are immobilized and trapped at the borderlines of the concentric rings of the refractive lattice recorded by the Bessel beam. The formation of neutral molecular clusters of DNA by Na+ counterions with a nearly globular shape and cluster average size of ~4 μm is revealed. A physical model is developed for the analysis of the electric forces map and explanation of the experimental results. The photovoltaic strategy of trapping and manipulation of micro- and nanoparticles on the crystal surface is promising for the elaboration of the lab-on-a-chip devices operating in an autonomous regime with applications in photonics, micro/nano-electronics and biotechnology.

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Metadaten
Titel
Photovoltaic Tweezers Based on Optical Holography: Application to 2D Trapping of DNA Molecules on a Lithium Niobate Crystal
verfasst von
Lusine Tsarukyan
Anahit Badalyan
Lusine Aloyan
Yeva Dalyan
Rafael Drampyan
Publikationsdatum
01.12.2023
Verlag
Pleiades Publishing
Erschienen in
Optical Memory and Neural Networks / Ausgabe Sonderheft 3/2023
Print ISSN: 1060-992X
Elektronische ISSN: 1934-7898
DOI
https://doi.org/10.3103/S1060992X23070214

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