Tag: fingerprints

New nanomaterial could transform how we visualise fingerprints

New nanomaterial could transform how we visualise fingerprints

Innovative nanomaterials have the potential to revolutionise forensic science, particularly in the detection of latent (non-visible) fingermarks, following research conducted at Diamond’s labSAXS instrument

Researchers created a fluorescent nanoparticle using a combination of materials (MCM-41, chitosan and dansylglycine) to examine latent fingermarks. These nanoparticles have special properties that make them adhere well to fingerprint residues, even old ones. The nanoparticles work on various surfaces, including metal, plastic, glass and complex objects such as polymer banknotes. They have the potential to be used directly at crime scenes without lab facilities, which is a significant advantage over some previous reagents. They produce high-quality fingerprint images, with the vast majority of those tested meeting the UK Home Office standards for a successful identification. This new method captures the finer details of a fingermark, making it easier to identify individuals and is expected greatly to aid in forensic investigations. 

he research was published in a Royal Society of Chemistry paper, highlighting that the new nanomaterial has proven to be a versatile and effective tool for visualising fingermark evidence. Small angle X-ray scattering (SAXS) techniques at Diamond provided useful data to validate these results. 

The research team includes scientists from the Technical and Scientific Section of Alagoas, Federal Police, Brazil; the National Institute of Criminalistics of the Federal Police, Brazil; the University of Leicester’s School of Chemistry; the Federal University of Alagoas, Brazil; and the UK’s national synchrotron, Diamond Light Source.

Ridge patterns on fingertips remain unchanged during and beyond a person’s life. They provide the primary method of personal identification in criminal investigations. When an object’s surface is touched by a finger, sweat and oily substances are transferred and deposited onto the surface, resulting in the formation of a mark. Most fingermarks are invisible to the naked eye and are referred to as latent fingermarks.  

The international collaboration of researchers developed the new nanostructured hybrid material, MCM-41@chitosan@dansylglycine, to visualise latent fingermarks. This material combines mesoporous silica nanoparticles with a fluorescent dye (dansylglycine) and chitosan, a polysaccharide derived from the exoskeletons of shrimps, crabs and lobsters. 

Latent fingermarks require physicochemical development techniques to enhance their visibility and make them interpretable for forensic purposes. Traditional methods for developing fingerprints include optical, physical, and chemical processes that involve interaction between the developing agent (often a coloured or fluorescent reagent) and the fingermark residue. These methods have limitations in recovering high-quality results in certain conditions.  

Recently, new methods using mass spectrometry, spectroscopy, electrochemistry, and nanoparticles have improved the development of latent fingermarks. These techniques offer better contrast, sensitivity, and selectivity, with low toxicity. The ability to adjust nanomaterial properties further enhances the detection of both fresh and aged fingermarks. 

Read more on Diamond website

Image: Images for a fingermark deposited on glass, enhanced with MCM-41@Ch@DnsGly NPs, illuminated at 365 nm and viewed with different filters.

New artificial intelligence method to create material ​‘fingerprints’

New artificial intelligence method to create material ​‘fingerprints’

Like people, materials evolve over time. They also behave differently when they are stressed and relaxed. Scientists looking to measure the dynamics of how materials change have developed a new technique that leverages X-ray photon correlation spectroscopy (XPCS), artificial intelligence (AI) and machine learning.

This technique creates ​“fingerprints” of different materials that can be read and analyzed by a neural network to yield new information that scientists previously could not access. A neural network is a computer model that makes decisions in a manner similar to the human brain.

In a new study by researchers in the Advanced Photon Source (APS) and Center for Nanoscale Materials (CNM) at the U.S. Department of Energy’s (DOE) Argonne National Laboratory, scientists have paired XPCS with an unsupervised machine learning algorithm, a form of neural network that requires no expert training. The algorithm teaches itself to recognize patterns hidden within arrangements of X-rays scattered by a colloid — a group of particles suspended in solution. The APS and CNM are DOE Office of Science user facilities.

“The goal of the AI is just to treat the scattering patterns as regular images or pictures and digest them to figure out what are the repeating patterns. The AI is a pattern recognition expert.” — James (Jay) Horwath, Argonne National Laboratory

“The way we understand how materials move and change over time is by collecting X-ray scattering data,” said Argonne postdoctoral researcher James (Jay) Horwath, the first author of the study.

These patterns are too complicated for scientists to detect without the aid of AI. ​“As we’re shining the X-ray beam, the patterns are so diverse and so complicated that it becomes difficult even for experts to understand what any of them mean,” Horwath said.

For researchers to better understand what they are studying, they have to condense all the data into fingerprints that carry only the most essential information about the sample. ​“You can think of it like having the material’s genome, it has all the information necessary to reconstruct the entire picture,” Horwath said.

The project is called Artificial Intelligence for Non-Equilibrium Relaxation Dynamics, or AI-NERD. The fingerprints are created by using a technique called an autoencoder. An autoencoder is a type of neural network that transforms the original image data into the fingerprint — called a latent representation by scientists — and that also includes a decoder algorithm used to go from the latent representation back to the full image.

The goal of the researchers was to try to create a map of the material’s fingerprints, clustering together fingerprints with similar characteristics into neighborhoods. By looking holistically at the features of the various fingerprint neighborhoods on the map, the researchers were able to better understand how the materials were structured and how they evolved over time as they were stressed and relaxed.

AI, simply put, has good general pattern recognition capabilities, making it able to efficiently categorize the different X-ray images and sort them into the map. ​“The goal of the AI is just to treat the scattering patterns as regular images or pictures and digest them to figure out what are the repeating patterns,” Horwath said. ​“The AI is a pattern recognition expert.”

Using AI to understand scattering data will be especially important as the upgraded APS comes online. The improved facility will generate 500 times brighter X-ray beams than the original APS. ​“The data we get from the upgraded APS will need the power of AI to sort through it,” Horwath said.

Read more on Argonne website

Image: The AI-NERD model learns to produce a unique fingerprint for each sample of XPCS data. Mapping fingerprints from a large experimental dataset enables the identification of trends and repeating patterns which aids our understanding of how materials evolve.

Credit: Argonne National Laboratory.