Near-Infrared Absorbing/Emitting Nanoparticles-Based 3D Data Platform

The increasing importance of information technology has led to an increasing need for improved data storage and security solutions. While traditional two-dimensional (2D) codes have been extended by incorporating additional parameters to create three-dimensional (3D) codes, further enhancing information density and security remains a challenge. In this study, we introduce a 3D Quick Response (QR) cube platform that employs near-infrared (NIR)-to-NIR upconversion nanoparticles (UCNPs) as light-emitting probes. These nanoparticles offer high photostability and low scattering properties, facilitating precise reconstruction of the 3D QR cube. By encoding data within volumetric space, 3D QR cube offers the potential to substantially increase information density and allow access from all three spatial dimensions (x, y, z axis), while also improving security protocols. A platform using NIR imaging was developed to analyze and reconstruct 3D QR cubes, utilizing a convolutional neural network (CNN) model that can accurately predict the 3D structure based on variations in image intensity. This method achieved a 99.9% accuracy in determining cube configurations. The results show that this platform not only ensures high prediction accuracy but also introduces new possibilities for multi-level encryption using spatial security keys within 3D space. This approach provides a powerful solution for enhanced information security and storage. By integrating 3D spatial data with logical circuits, the proposed encryption mechanism offers significantly greater encryption potential compared to conventional 2D QR codes, marking a major advancement in data protection.