Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

DNA i-motif guided synthesis of a selective telomerase inhibitor.

Chemical communications (Cambridge, England)·2026
Same author

Enhanced Hypoglycemic Activity of Metformin with the Reduction of the Particle Size.

Biotechnology and applied biochemistry·2026
Same author

Exploring attachment, trauma, and cannabis use in psychotic disorders: a qualitative study of patient and family perspectives.

BMC psychiatry·2026
Same author

Lifetime Adversity Among Individuals With Early Phase Psychosis and Comorbid Substance Misuse.

Early intervention in psychiatry·2026
Same author

Thiazole peptidomimetics as chemical modulators of <i>KRAS</i> gene expression <i>via</i> G-quadruplex stabilization.

RSC chemical biology·2025
Same author

Dual Transcriptional Repression of Oncogenic <i>c-KIT</i> and <i>KRAS</i> by G4-Targeting Triazolyl-Indole Scaffolds Induces Synthetic Lethality in Leukemia Cells.

Journal of medicinal chemistry·2025
Same journal

Correction: Kang et al. Fluid Flow to Electricity: Capturing Flow-Induced Vibrations with Micro-Electromechanical-System-Based Piezoelectric Energy Harvester. <i>Micromachines</i> 2024, <i>15</i>, 581.

Micromachines·2026
Same journal

Femtosecond Laser Texturing of Wood Coatings with Bio-Based Epoxy and Wax Additives for Enhanced Hydrophobicity.

Micromachines·2026
Same journal

Engineering of Optoelectronic Devices for Renewable Energy Applications.

Micromachines·2026
Same journal

Phase Transformation and Electrochemical Behavior of Hexagonal TiO<sub>2</sub> Nanotubes Under Different Annealing Temperatures and Heating Rates.

Micromachines·2026
Same journal

Process Optimization and Predictive Modeling of Femtosecond Laser Precision Milling for Commercial PMMA Slices.

Micromachines·2026
Same journal

A Hybrid Preprocessing Multi-Objective Surrogate Model for Thermal MEMS Actuators.

Micromachines·2026
See all related articles

Related Experiment Video

Updated: Jul 15, 2025

Functional Surface-immobilization of Genes Using Multistep Strand Displacement Lithography
11:05

Functional Surface-immobilization of Genes Using Multistep Strand Displacement Lithography

Published on: October 25, 2018

7.5K

Deep-Learning-Based Digitization of Protein-Self-Assembly to Print Biodegradable Physically Unclonable Labels for

Sayantan Pradhan1, Abhi D Rajagopala2, Emma Meno3

  • 1Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA.

Micromachines
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel biological Physical Unclonable Function (PUF) using protein self-assembly for secure anti-counterfeiting. The method generates unique cryptographic keys from protein images, offering a scalable and low-cost solution.

Keywords:
biodegradable labeldeep learningdevice securitydiffusion-limited aggregationphysically unclonable functionsilk protein

More Related Videos

Combining QD-FRET and Microfluidics to Monitor DNA Nanocomplex Self-Assembly in Real-Time
14:36

Combining QD-FRET and Microfluidics to Monitor DNA Nanocomplex Self-Assembly in Real-Time

Published on: August 26, 2009

11.2K
TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks
07:02

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

Published on: May 17, 2020

24.3K

Related Experiment Videos

Last Updated: Jul 15, 2025

Functional Surface-immobilization of Genes Using Multistep Strand Displacement Lithography
11:05

Functional Surface-immobilization of Genes Using Multistep Strand Displacement Lithography

Published on: October 25, 2018

7.5K
Combining QD-FRET and Microfluidics to Monitor DNA Nanocomplex Self-Assembly in Real-Time
14:36

Combining QD-FRET and Microfluidics to Monitor DNA Nanocomplex Self-Assembly in Real-Time

Published on: August 26, 2009

11.2K
TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks
07:02

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

Published on: May 17, 2020

24.3K

Area of Science:

  • Biotechnology
  • Materials Science
  • Cryptography

Background:

  • Counterfeiting poses significant threats to public health and medical device authenticity.
  • Physical Unclonable Functions (PUFs) offer a robust solution for secure authentication.
  • Existing PUF technologies require further development for broad applicability.

Purpose of the Study:

  • To develop a novel biological PUF utilizing protein self-assembly.
  • To create a secure and scalable anti-counterfeiting technology.
  • To validate the randomness and applicability of the generated cryptographic keys.

Main Methods:

  • A facile protein self-assembly process was employed as an entropy source for a biological PUF.
  • A deep learning model digitized self-assembly images to extract feature vectors.
  • Generated keys were binarized, debiased, and tested for randomness using NIST SP 800-22.
  • Images were printed on silk-fibroin biodegradable labels using protein bioinks for physical deployment.

Main Results:

  • The protein self-assembly process successfully generated unique fingerprints.
  • Deep learning effectively extracted feature vectors, producing sufficiently stochastic cryptographic keys.
  • NIST SP 800-22 tests confirmed the high randomness of the generated keys.
  • Cellphone imaging of printed labels showed a low error rate compared to source images.

Conclusions:

  • The deep-learning-based biological PUF presents a promising low-cost, scalable, and highly randomized anti-counterfeiting strategy.
  • Protein self-assembly offers a unique and viable entropy source for biological PUFs.
  • Biodegradable, protein bioink-printed labels demonstrate practical deployment potential for authentication.