2017
DOI: 10.1021/acs.analchem.7b04418
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Helium Ion Microscopy for Imaging and Quantifying Porosity at the Nanoscale

Abstract: Nanoporous materials are key components in a vast number of applications from energy to drug delivery and to agriculture. However, the number of ways to analytically quantify the salient features of these materials, for example: surface structure, pore shape, and size, remain limited. The most common approach is gas absorption, where volumetric gas absorption and desorption are measured. This technique has some fundamental drawbacks such as low sample throughput and a lack of direct surface visualization. In t… Show more

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Cited by 17 publications
(10 citation statements)
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“…[ 1 ] These properties make the technique a useful tool for nanoscale research. HIM has been used for imaging nanoporous SiO 2 , [ 2 ] cellulose nanofibrils, [ 3 ] mammalian cells, [ 4 ] and other biological samples. [ 5 ] To the best of our knowledge, we report on the first time that HIM has been used to image starch.…”
Section: Figurementioning
confidence: 99%
“…[ 1 ] These properties make the technique a useful tool for nanoscale research. HIM has been used for imaging nanoporous SiO 2 , [ 2 ] cellulose nanofibrils, [ 3 ] mammalian cells, [ 4 ] and other biological samples. [ 5 ] To the best of our knowledge, we report on the first time that HIM has been used to image starch.…”
Section: Figurementioning
confidence: 99%
“…Detailed surface analysis on the wear debris was performed next. Due to the powerful focusing capability of He ion beams with the probe size below nm [ 50 ], the technique enabled high-resolution imaging to identify the morphology and topography of the wear debris down to the nm scale ( Figure 4 B), as was recently shown on nanoporous materials [ 51 ]. A broad distribution of the debris size, shape and morphology was observed on a wider field of view (on the right), whereas distinct surface nanomorphologies were only revealed at the more close-up image (on the left).…”
Section: Resultsmentioning
confidence: 99%
“…Image analysis consisted of three main aspects: image pre-processing, segmentation, feature extraction, and quantification [60][61][62]. We used open-source Python 2.7 for all analysis steps.…”
Section: Discussionmentioning
confidence: 99%