XLEAP-Enabled Opportunities for Micron-Scale Confocal X-ray Fluorescence Imaging

Aug 7, 2026, 10:00 AM
15m
Bradfield 101 (Cornell University)

Bradfield 101

Cornell University

306 Tower Road, Ithaca, NY 14853, USA Cornell University College of Agriculture and Life Sciences
Oral presentation Looking ahead Looking forward

Speaker

Arthur Woll (CLASSE)

Description

A nearly ubiquitous challenge in conventional x-ray fluorescence (XRF) microscopy, especially for biological applications, is preparing sufficiently thin samples to obtain the best-possible spatial resolution. To make full use of the sub-micron beamsize available at XLEAP in this mode, sample thicknesses will be limited to a few microns or less. Apart from the time and skill required to prepare thin sections, this degree of thinning can damage or otherwise disrupt the morphology and/or elemental distribution that is the target of the investigation. XRF Computed Tomography (XRF-CT) provides one way around this challenge by enabling 3D elemental mapping within intact samples, but imposes a constraint on sample width.

Confocal X-ray Fluorescence (CXRF) is a well-established but less common approach to XRF microscopy that employs a secondary optic, placed between the sample and detector, to enable 3D elemental localization without constraining sample thickness or width. Moreover, a unique implementation of CXRF, developed at CHESS, enables it to be performed with a spatial resolution of close to 1 micron in largest linear dimension, which is 10-20 times better than previous implementations of this technique.

The combination of sub-micron beamsize and high intensity available at XLEAP represent an unprecedented opportunity to implement micron-scale CXRF in biology and other domains. In plant sciences in particular, this technique has already helped elucidate aspects of Copper signaling and homeostasis. Here, after reviewing the approach in more detail, we explore new scientific possibilities we believe will be enabled by the combination of this methodology and the unique capabilities of XLEAP.

Author

Arthur Woll (CLASSE)

Co-authors

Ju-Chen Chia (Cornell University) Louisa Smieska Olena K Vatamaniuk (Cornell University)

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