DaVinciQD: dating fingerprints using quantum dots

Bison code: L.28278

This 3S project will be part of a bigger project in which the Saxion research groups Advanced Forensic Technology and NanoBio will closely work together with the National Police, the University of Twente, and several private companies to develop a fingerprint aging technique.

Fingerprints are a useful source of information for a forensic researcher. However, at a crime scene lots of fingerprints may be present and it is difficult for the researcher to determine which fingerprint is relevant to investigate, because current methods only give information about the pattern of the fingerprint and not the time frame in which it was placed. In this project we want to establish a new method for analyzing fingerprints in which it will be possible to determine the age of the fingerprint as well as the pattern itself.

For this new method we want to use quantum dots. Quantum dots are small nanoparticles (diameter 2-5nm) which emit fluorescent light when light of certain wavelength is applied. Fingerprints consist of different components such as proteins, fatty acid, cholesterol and squalene. Some of these components are very stable, while other components degrade over time. In this project we want to functionalize the quantum dots in such a way that these can bind to the components in a fingerprint. By determining the ratio between a component that is stable and a component that degrades over time, it would be possible to get information about the age of the fingerprint.

Another important aspect of the project is investigating the demands of the end user so that the developed product is easy to use. For example, we want to investigate the possibility of using a smartphone for analyzing the fingerprints.

In the 3S project students will investigate, among others, biomarkers for aging present in the fingerprints, optimize and analyze production of quantum dots (carbon dots) and in a later phase functionalize these carbon dots, and investigate methods for visualization and analysis of the fingerprints.

Cluster: Security & Evidence 1