Near-Infrared Luminescent Imaging-Based 3D QR Cube Platform for Spatial Information Storage and Security

Abstract

The growing significance of information technology requires advanced information storage and security solutions. While extending traditional 2D codes with additional parameters has led to promising 3D codes, increasing information capacity and security remains challenging. Herein, a 3D quick response (QR) cube platform that utilizes near-infrared (NIR)-to-NIR upconversion nanoparticles as light-emitting probes, benefiting from their photostability and low scattering properties. These features enable precise reconstruction of the 3D QR cube. The platform employs volumetric space for information encoding by leveraging spatial information in a 3D environment, demonstrating potential to significantly increase information capacity and facilitate access from all three spatial dimensions (x, y, z), while enhancing security. This study develops a platform for analyzing and reconstructing 3D QR cubes using NIR imaging and employs a convolutional neural network model to determine the 3D structure from image intensity variations, achieving 99.9% accuracy in predicting cube configurations. By leveraging 3D spatial information and logical circuits, the encryption method has the potential to significantly surpass the encryption strength of traditional 2D codes. The findings demonstrate high prediction accuracy and introduce new possibilities for multi-level encryption with spatial security keys in 3D space, offering a robust solution for advanced information storage and security.