Raw data for the dissertation "Reader-friendly Edible Binarycodes and Sensors Based on Smart Hydrogel"

doi: 10.4121/b9f35ef9-28d6-43de-b5d1-a2969aa4f965.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/b9f35ef9-28d6-43de-b5d1-a2969aa4f965
Datacite citation style:
Zhang, Mengmeng (2023): Raw data for the dissertation "Reader-friendly Edible Binarycodes and Sensors Based on Smart Hydrogel". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/b9f35ef9-28d6-43de-b5d1-a2969aa4f965.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

This is the raw data for the dissertation titled "Reader-friendly Edible Binarycodes and Sensors Based on Smart Hydrogel". This dissertation addresses the critical challenges of counterfeiting and product deterioration in the global food and medicine industries. It introduces four innovative prototypes to tackle these issues. The first prototype establishes a system for creating magnetic microparticles embedded with superparamagnetic colloids, forming binary code matrices for product authentication. The second prototype employs a Physical Unclonable Functions (PUF) algorithm to enhance the safety of On-Dose-Authentication (ODA) binary codes, making them readable even in basic laboratory conditions. The third prototype introduces a humidity indicator that reacts to high humidity by bending and rolling, providing a safe means to detect moisture exposure in food packages. The fourth prototype utilizes an alginate TTI bead encapsulating a natural colorant, betacyanin, to monitor and indicate temperature abuse in perishable products through irreversible thermochromic changes. These innovations hold promise for enhancing product quality control, tracking, and anti-counterfeiting measures in the food and medicine industries. The raw data in this dataset is utilized for generating the visual representations presented in this dissertation.

  • 2023-11-01 first online, published, posted
xlsx, py, mp4, jpg, txt
TU Delft, Faculty of Mechanical, Maritime and Materials Engineering (3ME), Department of Process and Energy


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