Stochastic Orientational Encoding via Hydrogen Bonding Driven Assembly of Woven‐Like Molecular Physically Unclonable Functions

N Nilgun Kayaci (Department of Materials Science and Nanotechnology Engineering Abdullah Gül University Kayseri 38080 Turkiye) N Nuri Burak Kiremitler (ERNAM – Nanotechnology Research and Application Center Erciyes University Kayseri 38039 Turkiye) İbrahim Deneme (Department of Materials Science and Nanotechnology Engineering Abdullah Gül University Kayseri 38080 Turkiye) M Mustafa Kalay (ERNAM – Nanotechnology Research and Application Center Erciyes University Kayseri 38039 Turkiye) A Aleyna Ozbasaran (Department of Materials Science and Engineering Graduate School of Natural and Applied Sciences Erciyes University Kayseri 38039 Turkiye) Y Yunus Zorlu (Department of Chemistry Faculty of Science Gebze Technical University Gebze Kocaeli 41400 Turkiye) M Mustafa Serdar Onses (ERNAM – Nanotechnology Research and Application Center Erciyes University Kayseri 38039 Turkiye) H Hakan Usta (Department of Materials Science and Nanotechnology Engineering Abdullah Gül University Kayseri 38080 Turkiye)

Abstract

AbstractThe prevention of counterfeiting and the assurance of object authenticity require stochastic encoding schemes based on physically unclonable functions (PUFs). There is an urgent need for exceptionally large encoding capacities and multi‐level responses within a molecularly defined, single‐material system. Herein, a novel stochastic orientational encoding approach is demonstrated using a facile ambient‐atmosphere solution processing of a molecular thin film based on the rod‐shaped oligo(p‐phenyleneethynylene) (OPE) π‐architecture. The nanoscopic film, derived from the small molecule 2EHO‐CF3PyPE with donor, acceptor, and π‐spacer building units, is designed for energetically favorable uniaxial molecular assembly and crystal growth via directional multiple hydrogen‐bonding motifs at the molecular termini and short C─H···π contacts at the center. A facile solvent vapor annealing induces concurrent dewetting and microscopic 1D random crystallization, yielding a woven‐textured random features. Using convolutional neural networks, the rich variations in microcrystal domain properties and stochastic encoding of 1D crystal orientations generate artificial coloration, achieving an encoding capacity reaching (6.5 × 10⁴)(2752 × 2208). The results demonstrate an effective strategy for achieving ultrahigh encoding capacities in a thin film composed of a single‐material. This approach enables low‐cost, solution‐processed fabrication for mass production and broad adoption, while opening new opportunities to explore molecular‐PUFs through structural design and engineering noncovalent interactions.

Article Details

Volume / Issue Vol. 37, Issue 38
Published September 01, 2025
ISSN 0935-9648
Publisher Unknown Publisher

Journal Info

Advanced Materials

Unknown Publisher

ISSN: 0935-9648 Physical Sciences

Authors (8)

N

Nilgun Kayaci

Department of Materials Science and Nanotechnology Engineering Abdullah Gül University Kayseri 38080 Turkiye

N

Nuri Burak Kiremitler

ERNAM – Nanotechnology Research and Application Center Erciyes University Kayseri 38039 Turkiye

İbrahim Deneme

Department of Materials Science and Nanotechnology Engineering Abdullah Gül University Kayseri 38080 Turkiye

M

Mustafa Kalay

ERNAM – Nanotechnology Research and Application Center Erciyes University Kayseri 38039 Turkiye

A

Aleyna Ozbasaran

Department of Materials Science and Engineering Graduate School of Natural and Applied Sciences Erciyes University Kayseri 38039 Turkiye

Y

Yunus Zorlu

Department of Chemistry Faculty of Science Gebze Technical University Gebze Kocaeli 41400 Turkiye

M

Mustafa Serdar Onses

ERNAM – Nanotechnology Research and Application Center Erciyes University Kayseri 38039 Turkiye

H

Hakan Usta

Department of Materials Science and Nanotechnology Engineering Abdullah Gül University Kayseri 38080 Turkiye