Characterization of gasoline/ethanol blends by infrared and excess infrared spectroscopy

This work was supported by the Northern Research Partnership (NRP) in Scotland and the Scottish Sensor Systems Centre (SSSC) funded by the Scottish Funding Council (SFC).Peer reviewedPostprin

Optical manipulation : advances for biophotonics in the 21st century

We thank the UK Engineering and Physical Sciences Research Council for funding (Grant Nos. EP/P030017/1 and EP/R004854/1).Significance: Optical trapping is a technique capable of applying minute forces that has been applied to studies spanning single molecules up to microorganisms. AIM: The goal of this perspective is to highlight some of the main advances in the last decade in this field that are pertinent for a biomedical audience. Approach: First, the direct determination of forces in optical tweezers and the combination of optical and acoustic traps, which allows studies across different length scales, are discussed. Then, a review of the progress made in the direct trapping of both single-molecules, and even single-viruses, and single cells with optical forces is outlined. Lastly, future directions for this methodology in biophotonics are discussed. Results: In the 21st century, optical manipulation has expanded its unique capabilities, enabling not only a more detailed study of single molecules and single cells but also of more complex living systems, giving us further insights into important biological activities. Conclusions: Optical forces have played a large role in the biomedical landscape leading to exceptional new biological breakthroughs. The continuous advances in the world of optical trapping will certainly lead to further exploitation, including exciting in-vivo experiments.Publisher PDFPeer reviewe

Probing the Evaporation Dynamics of Ethanol/Gasoline Biofuel Blends Using Single Droplet Manipulation Techniques

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Light sheet fluorescence microscopy for neuroscience

We thank the UK Engineering and Physical Sciences Research Council for funding through grants EP/R004854/1 and EP/P030017/1.Background:  The functions of the central nervous system (CNS) rely on the interaction between large populations of neurons across different areas. Therefore, to comprehend CNS functions there is a need for imaging techniques providing access to the neuronal activity of large networks of neurons with very high spatiotemporal resolution. New method:  Light sheet fluorescence microscopy (LSFM) is a very promising optical sectioning technique that allows volumetric imaging over many length scales while retaining high spatial resolution and minimizing photobleaching and phototoxicity. Results:  The application of LSFM in neuroscience opened up the possibility of imaging in-vivo the development of the CNS and acquiring morphological images of whole cleared mammalian brains with sub-cellular resolution. The use of propagation invariant Bessel and Airy beams has shown potential for increasing the penetration depth in turbid neural tissues. Comparison with existing methods:  The lack of temporal and/or spatial resolution of traditional neuroscience imaging techniques call attention to a need for a technique capable of providing high spatio temporal resolution. LSFM, which is capable of acquiring high resolution volumetric images is increasingly becoming an interesting imaging technique for neuroscience. Conclusions:  The use of different LSFM geometries has shown the potential of this technique in acquiring in-vivo functional images of the CNS and morphological images of entire cleared mammalian brains. Further development of single objective LSFM implementations and fibre based LSFM combined with the use of propagation invariant beams could allow this technique to be used for in depth in-vivo imaging.PostprintPeer reviewe

Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams

This project was funded by the UK Engineering and Physical Sciences Research Council (grants EP/P030017/1 and EP/R004854/1), and has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement (EC-GA 871212) and H2020 FETOPEN project "Dynamic” (EC-GA 863203). P.W. was supported by the 1851 Research Fellowship from the Royal Commission. KRD was supported by a Mid-Career Fellowship from the Hospital Research Foundation (C-MCF-58-2019). K.D. acknowledges support from the Australian Research Council through a Laureate Fellowship. S.S. was funded by BBSRC (BB/M00905X/1).Deconvolution is a challenging inverse problem, particularly in techniques that employ complex engineered point-spread functions, such as microscopy with propagation-invariant beams. Here, we present a deep-learning method for deconvolution that, in lieu of end-to-end training with ground truths, is trained using known physics of the imaging system. Specifically, we train a generative adversarial network with images generated with the known point-spread function of the system, and combine this with unpaired experimental data that preserve perceptual content. Our method rapidly and robustly deconvolves and super-resolves microscopy images, demonstrating a two-fold improvement in image contrast to conventional deconvolution methods. In contrast to common end-to-end networks that often require 1000–10,000s paired images, our method is experimentally unsupervised and can be trained solely on a few hundred regions of interest. We demonstrate its performance on light-sheet microscopy with propagation-invariant Airy beams in oocytes, preimplantation embryos and excised brain tissue, as well as illustrate its utility for Bessel-beam LSM. This method aims to democratise learned methods for deconvolution, as it does not require data acquisition outwith the conventional imaging protocol.Publisher PDFPeer reviewe

Widefield light sheet microscopy using an Airy beam combined with deep-learning super-resolution

Imaging across length scales and in depth has been an important pursuit of widefield optical imaging. This promises to reveal fine cellular detail within a widefield snapshot of a tissue sample. Current advances often sacrifice resolution through selective sub-sampling to provide a wide field of view in a reasonable time scale. We demonstrate a new avenue for recovering high-resolution images from sub-sampled data in light sheet microscopy using deep-learning super-resolution. We combine this with the use of a widefield Airy beam to achieve high-resolution imaging over extended fields of view and depths. We characterise our method on fluorescent beads as test targets. We then demonstrate improvements in imaging amyloid plaques in a cleared brain from a mouse model of Alzheimer’s disease, and in excised healthy and cancerous colon and breast tissues. This development can be widely applied in all forms of light sheet microscopy to provide a two-fold increase in the dynamic range of the imaged length scale. It has the potential to provide further insight into neuroscience, developmental biology, and histopathology

Intermediate phases during solid to liquid transitions in long-chain n-alkanes

© the Owner Societies 2017. The solid to liquid phase transition of n-alkanes with more than ten carbon atoms is an interesting phenomenon relevant to many fields, from cosmetics to automotive. Here we report Raman spectroscopy of tetradecane, pentadecane and hexadecane as a function of temperature. In order to gain information on the structural changes that the hydrocarbons undergo during melting, and to determine the temperature and the speed at which the phase change occurs, their temperature-dependent Raman spectra are acquired. The spectra are analysed not only with respect to frequency shifts, band widths, and intensity ratio of certain bands, but also using a principal component analysis. The spectroscopic data suggest that the solid to liquid phase transition in hexadecane, differently from tetradecane and pentadecane, is almost instantaneous. Tetradecane shows a slightly faster transition than pentadecane. In addition, a rotator phase as an intermediate state between the liquid and crystalline solid phases is identified in pentadecane. Different characteristic features in the solid spectra of the hydrocarbons relate tetradecane and hexadecane to a tryclinic crystalline structure, and pentadecane to an orthorhombic structure