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dc.contributor.authorBurg, SL
dc.contributor.authorWashington, AL
dc.contributor.authorVillanova, J
dc.contributor.authorDennison, AJC
dc.contributor.authorMcLoughlin, D
dc.contributor.authorMykhaylyk, OO
dc.contributor.authorVukusic, P
dc.contributor.authorFurnass, W
dc.contributor.authorJones, RAL
dc.contributor.authorParnell, AJ
dc.contributor.authorFairclough, JPA
dc.date.accessioned2021-07-14T14:18:57Z
dc.date.issued2020-05-29
dc.description.abstractHigh resolution X-ray nano-tomography experiments are often limited to a few tens of micrometer size volumes due to detector size. It is possible, through the use of multiple overlapping tomography scans, to produce a large area scan which can encompass a sample in its entirety. Mounting and positioning regions to be scanned is highly challenging and normally requires focused ion beam approaches. In this work we have imaged intact beetle scale cells mounted on the tip of a needle using a micromanipulator stage. Here we show X-ray holotomography data for single ultra-white scales from the beetles Lepidiota stigma (L. stigma) and Cyphochilus which exhibit the most effective scattering of white light in the literature. The final thresholded matrices represent a scan area of 25 × 70 × 362.5 µm and 25 × 67.5 × 235µm while maintaining a pixel resolution of 25 nm. This tomographic approach allowed the internal structure of the scales to be captured completely intact and undistorted by the sectioning required for traditional microscopy techniques.en_GB
dc.description.sponsorshipInnovate UKen_GB
dc.description.sponsorshipAkzoNobelen_GB
dc.description.sponsorshipUniversity of Sheffielden_GB
dc.identifier.citationVol. 7, article 163en_GB
dc.identifier.doi10.1038/s41597-020-0502-y
dc.identifier.grantnumber33692-239251en_GB
dc.identifier.urihttp://hdl.handle.net/10871/126410
dc.language.isoenen_GB
dc.publisherNature Researchen_GB
dc.rights© The Author(s) 2020. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.en_GB
dc.subjecthttps://software.pan-data.eu/software/74/pyhst2en_GB
dc.titleX-ray nano-tomography of complete scales from the ultra-white beetles Lepidiota stigma and Cyphochilusen_GB
dc.typeArticleen_GB
dc.date.available2021-07-14T14:18:57Z
dc.descriptionThis is the final version. Available on open access from Nature Research via the DOI in this recorden_GB
dc.descriptionCode availability: The ESRF High Speed Tomography in Python (PyHST2) software which was used to reconstruct the phase images is open source and can be found at: https://software.pan-data.eu/software/74/pyhst2. The current pipeline for processing the raw data prior to its use in the PyHST2 algorithm is a large collection of scripts in MATLAB, Python and GNU Octave which makes it difficult to bundle into a single tomography pipeline. However, the ESRF is currently working to convert all of the scripts to Python to create a completely open source pipeline, though additional computing power, such a high performance computing cluster will likely be necessary. All additional image processing was done using open source Python libraries; these have been noted at the appropriate stages in the text.en_GB
dc.identifier.eissn2052-4463
dc.identifier.journalScientific Dataen_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
dcterms.dateAccepted2021-04-21
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2020-05-29
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2021-07-14T14:16:10Z
refterms.versionFCDVoR
refterms.dateFOA2021-07-14T14:19:09Z
refterms.panelBen_GB


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