Refine
Document Type
Has Fulltext
- no (36)
Is part of the Bibliography
- no (36)
Keywords
- Fluorescence lifetime imaging (9)
- FD-FLIM (8)
- Fluorescence (8)
- Phase shift (8)
- fluorescence (8)
- Neural networks (7)
- Diode lasers (5)
- Fluoreszenz (4)
- Microscopy (4)
- Image processing algorithms (3)
Institute
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.
In this contribution the frequency domain fluorescence lifetime imaging microscopy (FD-FLIM) technique is evaluated for post-consumer wood sorting. The fluorescence characteristics of several wood samples were determined, whereby two excitation wavelengths (405 and 488 nm) were used. The measured data were processed using algorithmic methods to identify the wood species and post-consumer wood category. With the excitation wavelength of 405 nm, 16 out of 19 samples could be correctly assigned to the corresponding post-consumer wood category by means of the fluorescence lifetimes. Thus, the experimental results revealed the high potential of the FD-FLIM technique for automated post-consumer wood sorting.
For sustainable handling of wood and high material utilization, post-consumer wood has to be sorted by post-consumer wood categories in Germany. The non-destructive FD-FLIM method could improve the wood recycling process by automatic classification of waste wood into post-consumer wood classes. Thus, the phase-dependent fluorescence decay time of treated wood is analysed in this study to obtain the impact of the treatment on the fluorescence decay time. It is shown that treated and untreated wood can be differentiated using the fluorescence decay time, although the state-of-the-art theoretical fluorescence decay time differs from the experimentally determined fluorescence decay time.
Environmental pollution by plastics is an increasing problem. However, state-of-the-art methods have significant disadvantages in detecting and identifying plastics directly in an environmental matrix. In this study, we propose a blob detection algorithm in combination with a neural network for fast and automated identification of plastics and non-plastics in a single fluorescence lifetime image. Therefore an artificial environmental matrix is prepared that contains soil, grass, spruce and HDPE (high density polyethylene) particles. Several FD-FLIM (frequency domain fluorescence lifetime imaging microscopy) images are taken, and the detection algorithm and the neural network are applied. We successfully demonstrated the suitability of the thresholding algorithm and the binary classification of the HDPE particles directly in the environmental matrix.
Existing plastic analysis techniques such as Fourier transform infrared spectroscopy and Raman spectroscopy are problematic because samples must be anhydrous and identification can be hindered by additives. This article describes a new approach that has been successfully demonstrated in which plastics can be classified by neural networks that are trained, validated, and tested by frequency domain fluorescence lifetime imaging microscopy measurements.
Environmental pollution by plastics is a global issue of increasing concern. However, microplastic analysis in complex environmental matrices, such as soil samples, remains an analytical challenge. Destructive mass-based methods for microplastic analysis do not determine plastics’ shape and size, which are essential parameters for reliable ecological risk assessment. By contrast, nondestructive particle-based methods produce such data but require elaborate, time-consuming sample preparation. Thus, time-efficient and reliable methods for microplastic analysis are needed. The present study explored the potential of frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) for rapidly and reliably identifying as well as differentiating plastics and natural materials from terrestrial environments. We investigated the fluorescence spectra of ten natural materials from terrestrial environments, tire wear particles, and eleven different transparent plastic granulates <5 mm to determine the optimal excitation wavelength for identification and differentiation via FD-FLIM under laboratory conditions. Our comparison of different excitation wavelengths showed that 445 nm excitation exhibited the highest fluorescence intensities. 445 nm excitation was also superior for identifying plastic types and distinguishing them from natural materials from terrestrial environments with a high probability using FD-FLIM. We could demonstrate that FD-FLIM analysis has the potential to contribute to a streamlined and time-efficient direct analysis of microplastic contamination. However, further investigations on size-, shape-, color-, and material-type detection limitations are necessary to evaluate if the direct identification of terrestrial environmental samples of relatively low complexity, such as a surface inspection soil, is possible.
Microplastic pollution of the environment has been extensively studied, with recent studies focusing on the prevalence of microplastics in the environment and their effects on various organisms. Identification methods that simplify the extraction and analysis process to the point where the extraction can be omitted are being investigated, thus enabling the direct identification of microplastic particles. Currently, microplastic samples from environmental matrices can only be identified using time-consuming extraction, sample processing, and analytical methods. Various spectroscopic methods are currently employed, such as micro Fourier-transform infrared, attenuated total reflectance, and micro Raman spectroscopy. However, microplastics in environmental matrices cannot be directly identified using these spectroscopic methods. Investigations using frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) to identify and differentiate plastics from environmental materials have yielded promising results for directly identifying microplastics in an environmental matrix. Herein, two artificially prepared environmental matrices that included natural soil, grass, wood, and high-density polyethylene were investigated using FD-FLIM. Our first results showed that we successfully identified one plastic type in the two artificially prepared matrices using FD-FLIM. However, further research must be conducted to improve the FD-FLIM method and explore its limitations for directly identifying microplastics in environmental samples.
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.