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Nowadays, fluorescence lifetime measurements and the method of fluorescence lifetime imaging are already state of the art in biomedical research. Also first investigations show that polymers could be identified due to their endogenous fluorescent lifetime. Thus an approach of identifying polymers with fluorescence lifetime imaging is done in this contribution. Therefore, four different polymers are examined and evaluated with statistical methods in order to determine their specific fluorescence lifetimes.
A novel approach to optically distinguish plastics based on fluorescence lifetime measurements
(2020)
In medical and biological research, fluorescence lifetime measurements and fluorescence lifetime imaging is already a part of the standardized analysis procedures. As first investigations have shown, polymers can be identified using fluorescence lifetime imaging and an evaluation algorithm. Thus, this contribution pursues a novel approach for the direct differentiation of four polymers with fluorescence lifetime imaging. Therefore, the evaluation algorithm is extended to compare several fluorescence lifetime images to prove that a distinction is possible.
The fluorescence decay time allows to identify and distinguish polymers from each other. Three differently treated biaxially-oriented polyethylene terephthalate films are examined with two excitation wavelengths of 445 and 488nm. The fluorescence decay time is dependent of the treatment method of the films and is a means for identification.
Holz ist ein vielseitig einsetzbarer nachwachsender Rohstoff. Neben seinem wirtschaftlichen Nutzen ist er für den Erhalt des Klimas unersetzlich. Eine sortenreine Sortierung für die Weiterverarbeitung von Altholz spielt für einen ressourcenschonenden Umgang eine wichtige Rolle. Um das Potenzial eines neuronalen Netzwerks basierend auf Messdaten der bildgebenden Fluoreszenzabklingzeitmessung für die Altholzsortierung aufzuzeigen, wurden zwei unterschiedliche Klassifikationsansätze auf Basis der Programmiersprache Python gewählt. Die Ergebnisse zeigen, dass die bildbasierte Klassifizierung der Holzart mit einer Genauigkeit von 47,36 % noch ausbaufähig ist. Eine datenbasierte Klassifizierung der Holzart mit einer Identifikationsgenauigkeit von 98,28 % ist dagegen vielversprechend.
A novel approach to optically distinguish plastics based on fluorescence lifetime measurements
(2020)
Today, the automatic separation of polymers from each other in an industrial scale is an unsolved problem. In laboratory environments, two methods are known whereby plastic is sorted either by color or by fluorescence decay time measurements that require fast synchronization and thus expensive equipment. A simple and pragmatic process is proposed to separate plastics from each other: all fluorescent photons are counted in relation to the absorbed photons. A theoretical model and an experimental setup are built in order to determine an apparatus specific quantum efficiency.
Development of a neural network to identify plastics using Fluorescence Lifetime Imaging Microscopy
(2022)
Plastics have become a major part of human’s daily life. An uncontrolled usage of plastic leads to an accumulation in the environment posing a threat to flora and fauna, if not recycled correctly. The correct sorting and recycling of the most commonly available plastic types and an identification of plastic in the environment are important. Fluorescence lifetime imaging microscopy shows a high potential in sorting and identifying plastic types. A data-based and an image-based classification are investigated using python programming language to demonstrate the potential of a neural network based on fluorescence lifetime images to identify plastic types. The results indicate that the data-based classification has a higher identification accuracy compared to the image-based classification.