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Register a new userInhomogeneity of donor doping in SrTiO3 substrates studied by fluorescence-lifetime imaging microscopy
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Added by
Christian Rodenb\\\"ucher
Publication date
2013
fields
Physics
and research's language is
English
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Fluorescence-lifetime imaging microscopy (FLIM) was applied to investigate the donor distribution in SrTiO3 single crystals. On the surfaces of Nb- and La-doped SrTiO3, structures with different fluorescence intensities and lifetimes were found that could be related to different concentrations of Ti3+. Furthermore, the inhomogeneous distribution of donors caused a non-uniform conductivity of the surface, which complicates the production of potential electronic devices by the deposition of oxide thin films on top of doped single crystals. Hence, we propose FLIM as a convenient technique (length scale: 1 $mu$m) for characterizing the quality of doped oxide surfaces, which could help to identify appropriate substrate materials.
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We report the cell biological applications of a recently developed multiphoton fluorescence lifetime imaging microscopy system using a streak camera (StreakFLIM). The system was calibrated with standard fluorophore specimens and was shown to have high accuracy and reproducibility. We demonstrate the applicability of this instrument in living cells for measuring the effects of protein targeting and point mutations in the protein sequence which are not obtainable in conventional intensity based fluorescence microscopy methods. We discuss the relevance of such time resolved information in quantitative energy transfer microscopy and in measurement of the parameters characterizing intracellular physiology.
Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow processing speed, low signal-to-noise ratio (SNR), and expensive and challenging hardware setups. In this work, we demonstrate applying a denoising convolutional network to improve FLIM SNR. The network will be integrated with an instant FLIM system with fast data acquisition based on analog signal processing, high SNR using high-efficiency pulse-modulation, and cost-effective implementation utilizing off-the-shelf radio-frequency components. Our instant FLIM system simultaneously provides the intensity, lifetime, and phasor plots textit{in vivo} and textit{ex vivo}. By integrating image denoising using the trained deep learning model on the FLIM data, provide accurate FLIM phasor measurements are obtained. The enhanced phasor is then passed through the K-means clustering segmentation method, an unbiased and unsupervised machine learning technique to separate different fluorophores accurately. Our experimental textit{in vivo} mouse kidney results indicate that introducing the deep learning image denoising model before the segmentation effectively removes the noise in the phasor compared to existing methods and provides clearer segments. Hence, the proposed deep learning-based workflow provides fast and accurate automatic segmentation of fluorescence images using instant FLIM. The denoising operation is effective for the segmentation if the FLIM measurements are noisy. The clustering can effectively enhance the detection of biological structures of interest in biomedical imaging applications.
Fluorescence Lifetime Imaging Microscopy (FLIM) using multiphoton excitation techniques is now finding an important place in quantitative imaging of protein-protein interactions and intracellular physiology. We review here the recent developments in multiphoton FLIM methods and also present a description of a novel multiphoton FLIM system using a streak camera that was developed in our laboratory. We provide an example of a typical application of the system in which we measure the fluorescence resonance energy transfer between a donor/acceptor pair of fluorescent proteins within a cellular specimen.
Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in biomedical research that uses the fluorophore decay rate to provide additional contrast in fluorescence microscopy. However, at present, the calculation, analysis, and interpretation of FLIM is a complex, slow, and computationally expensive process. Machine learning (ML) techniques are well suited to extract and interpret measurements from multi-dimensional FLIM data sets with substantial improvement in speed over conventional methods. In this topical review, we first discuss the basics of FILM and ML. Second, we provide a summary of lifetime extraction strategies using ML and its applications in classifying and segmenting FILM images with higher accuracy compared to conventional methods. Finally, we discuss two potential directions to improve FLIM with ML with proof of concept demonstrations.
We show that a scanning capacitance microscope (SCM) can image buried delta-doped donor nanostructures fabricated in Si via a recently developed atomic-precision scanning tunneling microscopy (STM) lithography technique. A critical challenge in completing atomic-precision nanoelectronic devices is to accurately align mesoscopic metal contacts to the STM defined nanostructures. Utilizing the SCMs ability to image buried dopant nanostructures, we have developed a technique by which we are able to position metal electrodes on the surface to form contacts to underlying STM fabricated donor nanostructures with a measured accuracy of 300 nm. Low temperature (T=4K) transport measurements confirm successful placement of the contacts to the donor nanostructures.
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