Helium Ion Microscopy for Imaging and Quantifying Porosity at the

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Helium Ion Microscopy for Imaging and Quantifying Porosity at the Nanoscale Matthew J. Burch, Anton V. Ievlev, Kyle Mahady, Holland Hysmith, Philip D. Rack, Alex Belianinov, and Olga S. Ovchinnikova Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04418 • Publication Date (Web): 11 Dec 2017 Downloaded from http://pubs.acs.org on December 12, 2017

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Analytical Chemistry

Helium Ion Microscopy for Imaging and Quantifying Porosity at the Nanoscale

Matthew J. Burch1, Anton V. Ievlev1, Kyle Mahady2, Holland Hysmith1, Philip D. Rack1,2, Alex Belianinov1, and Olga S. Ovchinnikova1*.

1

2

The Center for Nanophase Materials Sciences and the Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.

Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA.

*

Corresponding authors: Olga S. Ovchinnikova Nanofabrication Research Laboratory Group Center for Nanophase Materials Sciences Oak Ridge National Laboratory, Oak Ridge, TN 37831 E-mail: [email protected] Phone: 865-574-4871

Notice: This manuscript has been authored by UT-Battelle, LLC, under Contract No. DEAC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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Abstract Nano-porous 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 in these materials, e.g. 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 this work, we demonstrate Helium Ion Microscopy (HIM) as a tool for imaging and quantification of pores in industrially relevant SiO2 catalyst supports. We start with the fundamental principles for ionsample interaction, and build on this knowledge to experimentally observe and quantify surface pores by using the HIM and image data analytics. We contrast our experimental results to gas absorption and demonstrate full statistical agreement between two techniques. The principles behind the theoretical, experimental, and analytical framework presented herein offer an automated framework for visualization and quantification of pore structures in a wide variety of materials.

Keywords: helium ion microscopy, image analytics, simulations, catalysis



Introduction Nanoporous materials play a significant role in many engineering and production efforts. Their ubiquitous use covers a vastly diverse area ranging from biomaterials and drug delivery, to solar cells, and catalysts.1-6 In catalysis, porous materials act as a support for the active ingredient to enhance the interaction surface area and minimize cost.1,2,4,7-10 Therefore ways to rapidly screen and visualize nanopores directly , without sample modification, are highly desired. Currently, transmission electron microscopy (TEM) is the most commonly used technique, due in part to its sub-nanometer resolution and the ability to observe even the smallest pore structure directly. However, sample preparation methods for TEM can fundamentally change the underlying pore structure and size.1 Scanning electron microscopy (SEM), and its drastic improvements in resolution over the past decade, have been explored as an alternative to observe pores.11,12 The SEM, however, lacks the necessary spatial resolution even at low acceleration voltages,13 especially when imaging highly insulating materials.10

Helium ion microscopy (HIM) utilizes He+ ions for imaging, and offers a viable alternative to both TEM and SEM. Helium ions, with their shorter de Broglie wavelengths and smaller interaction volumes when compared to electrons, offer higher sample surface sensitivity and higher spatial resolution. Ions generate fewer parasitic secondary-secondary electrons, (SE2) a byproduct of elextronic excitation by primary secondary electrons (SE) – a fundamental shortcoming of the electron beam. As a result, the ion- generated SE images offer higher spatial resolution and a better depth-of-field.13-15 In addition, since the helium ions are positively charged, an electron flood gun offers easy and robust charge compensation for insulating materials. Thus, the enhanced surface resolution, combined with the robust charge compensation, make the HIM an attractive prospect for direct imaging of surface pore structures in non-conductive materials.16-18 Naturally, there are drawbacks to HIM as well; ions sputter surface material. Therefore to avoid image misinterpretation due to surface 4 ACS Paragon Plus Environment

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modification know to occur with ion sources,

19,20, 21-23

a model simulating the extent of ion

damage has to be implemented. This is especially important for any tasks seeking to extract quantitative information from imaging data.

We have utilized a mixture of theoretical simulations, experimental methods, and image data analytics approaches to extract salient information related to substrate modification during the HIM imaging of nano-porous SiO2 particles.

This HIM imaging study is aimed at

augmenting the standard gas absorption approach to quantify porosity in catalytically relevant materials.14,24-30

In gas absorption, changes in the partial pressure of a gas (generally

nitrogen) are recorded before and after its exposure to porous materials. The results are then fitted to a model of choice, where the pore size is an extractable parameter.2,31-34 There are known issues with this methodology, as materials with large variance in pore sizes may cause large errors in the analysis.31 Furthermore, the calculated pore size varies significantly based on a model of choice.6 To alleviate these issues, some researchers have attempted to use simulation to predict pore structure and correlate results to the absorption data with some success.35,36

In this work, we demonstrate HIM as a tool for imaging and quantification of pores in industry relevant SiO2 catalyst supports. We utilize advanced ion beam matter simulations and experimentally observe changes in pore structures under ion beam irradiation.37-39 We then use image analysis methods to quantify pore structure and to track their evolution as a function of ion beam dose. Once the HIM imaging modality was verified, we demonstrated automatic pore identification and quantification to contrast the results of industry standard gas absorption methodology.

We demonstrate 0.05 nm (0.3%) differences between gas

absorption and our direct quantification results for a well characterized test sample, as well as



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yield information to supplement gas absorption data for experimentaly observed catalyst performance.

Results and Discussion To elucidate porous material interaction with the helium ion irradiation, we simulated various pore structures in a SiO2 matrix using EnvizION simulations, Figure 1.37-43 The simulation parameters approximately replicate imaging conditions used in following results, please see Experimental Section for further details. In Figure 1 (a-f), we plot the evolution of 5 nm diameter pores as a function of dose. At 8 frames (roughly equivalent to the experimental dose), the pore structure is largely intact, while at 40 frames, some degradation of the structure is apparent. Figure 1 (g-l) is the evolution of pores with 7 nm diameter.

At

comparable doses, the 7 nm pores degrade less than the 5 nm pores. We believe the degradation of the pore is largely due to sputtered material filling the pore, and quick pore edge degradation.

Since the 7 nm pores are larger this infilling mechanism is less

pronounced. Simulations thus suggest two conclusions: (i) at low doses the HIM has a small effect on the structure of the porous SiO2 material, (ii) this effect is inversely proportional to the local vacancy fraction. Another interesting feature of the simulations is the formation of nano-sized “pits” at the surface of the pore SiO2 particle in both the 5 nm and 7 nm pore systems, shown in Fig. 1(f, l).


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Figure 1: To understand the effect of helium ion bombardment on nanoporous SiO2, ionbeam/matter simulations were performed to understand how a simple pore structure evolves under helium ion irradiation. (a) A cross-sectional view of 5 nm simulated pores before ion irradiation. (b) The cross-sectional view of the surface and pore structure evolution of 5 nm simulated pores after 8 frames of ion irradiation, where each frame represents 62,500 ions of irradiation. (c) The cross-sectional view of the surface and pore structure evolution of 5 nm simulated pores after 40 frames of ion irradiation. (d) The surface view of 5 nm simulated pores before ion irradiation. (e) The surface view of 5 nm simulated pores after 8 frames pf ion irradiation. (f)

The surface view of 5 nm simulated pores after 40 frames pf ion

irradiation. (g) A cross-sectional view of 7 nm simulated pores before ion irradiation. (h) The cross-sectional view of the surface and pore structure evolution of 7 nm simulated pores 7 ACS Paragon Plus Environment


after 8 frames of ion irradiation. (i) The cross-sectional view of the surface and pore structure evolution of 7 nm simulated pores after 40 frames of ion irradiation. (j) The surface view of 7 nm simulated pores before ion irradiation. (k) The surface view of 7 nm simulated pores after 8 frames pf ion irradiation. (l) The surface view of 7 nm simulated pores after 40 frames pf ion irradiation.

Colors represent the following identity in the all the panels, Red:

Empty, Green: Oxygen, Blue: Silicon.rs represent the following identity in the all the panels, Red: Empty, Green: Oxygen, Blue: Silicon.

To experimentally confirm the results of the simulation, we continuously imaged three separate areas under three different ion currents, Figure 2. We then utilized an image analysis workflow (see Experimental Details) to track whether the pore size changed as a function of ion dose. Pore evolution movies consisted of 22-25 frames acquired at 10 µs dwell time, 25 keV accelerating voltage, image resolution of 1024×1024 pixels, and three different beam currents of 1.6, 3.9 and 12.6 pA, with the 1.6 pA current corresponding to simulated ion doses shown in Figure 1. Figure 2, shows pore changes as a function of dose at three representative frames during the movie (All frames are included in Supplemental Material). The plotted data, in Fig. 3(a, b, c) corresponds to a dose of 4.18×1015 ions/cm2, 1.02×1016 ions/cm2, and 3.3×1016 ions/cm2, respectively. At the lowest current of 1.6 pA (Figure 2(a)), matching imaging condition of 1.6-1.8 pA, the pore distribution changes little as a function of time: an average pore size of 6.50 nm at frame 1 to 6.42 nm at frame 22. At the medium current of 3.9 pA (Figure 2(e)), moderate increase in pore size is observed after 12 frames and continues to increase to the maximum value of 18.10 nm at by frame 22, Figure 2(f). At high imaging current of 12.6 pA (Figure 2(g)), the surface changes after the initial frames, such that at frame two the average is already at 15.49 nm. At high current, the pore size distributions fluctuate as the ion beam mills the edges of the existing pores and opens up new ones. This 8 ACS Paragon Plus Environment

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causes the average to increase dramatically to 23.47 nm at frame 11 (Fig. 2(h)) and then decrease to 15.17 nm at frame 22, Fig. 2(i), due to formation of small pores from the ion beam damage. This result is consistent with the simulations of ion-beam/SiO2 interactions, where at high doses large amount of surface rearrangement is observed. This pitting is detected by our algorithm and skews averages toward smaller pore sizes, as observed in the experimental data at high currents.

Figure 2: This figure illustrates surface change as a function of ion dose, imaged at three different currents as a movie, with 1st, 11th and 22nd movie frames shown as representative imaging effects. The detected pores are overlaid in red for all images, with size histograms shown in Figure 3. The scale bar is 200 nm for all images. (a) The 1st image frame for a 9 ACS Paragon Plus Environment


movie collected at 1.6 pA. (b) The 11th image frame for a movie collected at 1.6 pA. (c) The 22nd frame of a movie collected at 1.6 pA. (d) The 1st image frame for a movie collected at 3.9 pA. (e) The 11th image frame for a movie collected at 3.9 pA. (f) The 22nd frame of a movie collected at 3.9 pA. (g) The 1st image frame for a movie collected at 12.6 pA. (h) The 11th image frame for a movie collected at 12.6 pA. (i) The 22nd frame of a movie collected at 12.6 pA.

In the histograms of Figure 3(a, b, c), the overall number of detected pores varies from area to area, however the overall pore distribution in all early frames deviate within a few percent. For example in the 1.6 pA study, 51% of the pores are below 10 nm compared to 44% for the 3.9 pA study.

Figure 3. This figure quantifies surface structure changes as a function of ion currents at three different frames during imaging (Figure 2). (a) The histogram of pore sizes of frames (a), 11 (b), and 22 (c) shown in Figure 2 from movie of 1.6 pA. (b) The histogram of pore sizes of frames 1 (d), 11 (e), and 22 (f), Figure 2 from movie of 3.9 pA current. (c) The histogram of pore sizes of frames 1 (g), 11 (h), and 22 (i) Figure 2 from movie of 12.6 pA current.


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The authors surmise that the pore density varies from location to location on the particles surface. This observation, however, did raise questions regarding the robustness of our image analysis methods, and we wanted to confirm that our routine consistently found the same pores in subsequent frames. To this end, three subsequent frames were co-registered, and we used our image analysis methods to verify the pore structure (Supplemental figure S.5). We show that the pore locations detected from one frame to the other are very similar, with high correlation of shape. Since we are mostly interested in the ensemble of pores, to augment gas absorption data and less interested in individual pores, we conclude that the robustness of our routine is acceptable; and we are able to find the same pore structures from frame to frame with very little identified variation. The pore distribution values as a function of dose, and their matched behavior to theoretically predicted results, demonstrate the relatively insignificant ion damage effect at imaging dosages; demonstrating the viability of HIM imaging as a robust technique to determine pore size in SiO2. The authors note, however, that with high currents and prolonged imaging at high resolution sputtering effects are real, and pore sizes do increase.



Figure 4: Demonstration of the pore finding routine and algorithm on the SiO2 nanporous

particles. (a) The histogram comparing our pore finding routine (blue bins) and the gas absorption (red line) for sample 1. (b) HIM micrograph of sample 1 with the pores detected and overlaid in red outlines for sample 1 in (c). A colormap of the pores detected for sample 1, which shows the location and detected pore radius in nm. (e) The histogram comparing our pore finding routine (blue bins) and the gas absorption (red line) for sample 2. (b) HIM micrograph of sample 2 with the pores detected and overlaid in red outlines for sample 2 in (c). A colormap of the pores detected for sample 2, which shows the location and detected pore radius in nm. (a) The histogram comparing our pore finding routine (blue bins) and the gas absorption (red line) for sample 3. (b) HIM micrograph of sample 3 with the pores detected and overlaid in red outlines for sample 3 in (c). A colormap of the pores detected for sample 3, which shows the location and detected pore radius in nm.


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Utilizing experimentally and theoretically derived optimized imaging parameters, and verifying the extent of ion beam damage, we imaged three industry catalyst precursor particles. We imaged these samples to demonstrate the techniques ability to add salient information to the gas absorption data and provide additional information on surface morphology to help predict and screen catalyst support structures beyond the current ability of gas absorption. Figure 4(a, e, i) are histograms of pore sizes found using our imaging technique for samples 1, 2 and 3, respectively. The data obtained from the HIM images, diameter as a function of counts (blue bar), is overlaid with the gas absorption data (red line) and plotted to scale for each sample. The minimum pore width depends on the spatial resolution of the image, affects the error in the calculated pore average slightly, but does not change the overall distribution of the pore sizes, which matches the gas absorption distributions quite well. Sample 1 matches the gas absorption data ideally. Samples 2 and 3, Figures 3h and 3k, show differences between the HIM analysis and gas absorption data. Our results, however, match the expected results for catalyst performance observed by ExxonMobil Chemical Company in an experimental setting, which presume a larger pore size for sample 3 over sample 2.

HIM images of the three distinct types of commercial SiO2

particles with varying porosity and surface structure are shown in Figure 4(b, f, j). Figure 4(c, g, k) highlights surface pores with a red outline after background correction, Otsu’s thresholding, and particle tracking, see Experimental Procedure for additional details. Figure 4(d, h, l) illustrates pores size distributions as the colors of pore represent the pore width in nanometers. The high-resolution HIM images allow direct visualization of the pores, in all three samples, with the image processing workflow automating the extraction of pore statistics.



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Table 1. Demonstration of the differences in average pore size found through the HIM rapid screening technique and nitrogen gas absorption using BJH44 model fitting for samples 1, 2 and 3. The error in HIM Rapid Screening is defined as a standard deviation in pore size distribution. HIM Rapid Screening

Nitrogen Gas Absorption

Sample 1

6.62 nm (+/- 7.98 nm)

6.59 nm

Sample 2

5.45 nm (+/- 7.55 nm)

4.02 nm

Sample 3

6.10 nm (+/- 8.68 nm)

4.35 nm

Table 1 tabulates the values of the pore diameter obtained with image processing and gas absorption. The deviation between the HIM rapid screening technique and the gas absorption for sample 1 is 0.45%, while for samples 2 and 3 it is 35.5% and 43.5%, respectively. One reason for this discrepancy in the gas absorption data for Samples 2 and 3, is believed to be artificially lower due to the roughness of samples 2 and 3 (Supplemental Figure S.1) compared to our test sample, Sample 1. Since the pores are imaged directly with the HIM, the image analysis approach is more robust, and resistant to outliers related to sample preparation, cracking, destruction, and other sample handling inhomogeneities that are impossible to quantify with the gas absorption. On the other hand, to make the methodology even more robust, multiple images and analyses for a single sample should be compiled when generating a pore size average and building a size distribution. Another advantageous aspect of HIM based analysis is the ability to quantify surface coverage. For example, the total percent area covered by the pores for each sample is 8.2% for Sample 1, 8.6% for Sample 2 and 11.22% for Sample 3, shown in Figure 2. 14 ACS Paragon Plus Environment

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Conclusion In this work, we demonstrate a method to directly observe and quantify the porosity of nonconductive porous surfaces. Our results show that this method leads to comparable results to gas absorption methods within 1% for test samples and, in certain cases, can give superior insights to the gas absorption data, including direct high-resolution pore visualization, pore size distribution, and pore coverage. Through Monte Carlo simulations and irradiation studies as a function of dose, we verified the imaging parameters in HIM experiments to not influence the surface.

Our imaging workflow then automatically extracted pore size,

distribution, surface coverage, and a plethora of other geometric parameters related to quantification of pore objects. We believe this approach may be utilized across multiple disciplines by those interested in quantifying feature sizes in a variety of materials, and aid in material processing and development.

Experimental Section

Sample Preparation and Imaging The nanoporous SiO2 particles were mounted onto carbon tape and were left uncoated for imaging. The helium ion microscope images were obtained on the Zeiss Orion Nanofab with imaging parameters of 25 keV and between 1.2-1.8 pA beam current and the use of an electron flood gun for charge compensation.26,28

Data analytics: Pore finding algorithm The pore finding rapid screening algorithm was written in Python 2.7, and consisted of three main subroutines. First subroutine is a background subtraction that uses a morphological operator to dilate features in the image and remove image gradients. In the second subroutine we identify the connected components in the binary image after Otsu’s thresholding method. Once the features of interest are identified in the image, the last subroutine converts pixel 15 ACS Paragon Plus Environment


counts to areas, produces histograms, and plots the final results. Further details are provided in Supplemental Material.

Gas Absorption The gas absorption data was obtained on the ASAP 2420 gas absorption instrument by micrometrics. The data was then processed using the BJH desorption model and the average pore size extracted.

The bath temperature of the gas absorption was 77 K, with an

equilibration interval of 10 seconds. The mass of the samples were weighed before and after degas and heating to remove additional moisture and trapped air and moisture from the samples.

Ion-beam Matter Simulations The simulations were performed in the Envision software, which simulations ion beam/solid interactions. 37-40 In the software, a created model that accurately simulates the SiO2 substrate, Si and O atoms are populated at random, with seven 100 nm deep pores arranged in a hexagonal pattern with a 10 nm spacing; see Figure 1a. We then simulate a 50 nm by 50 nm raster scan of a 25keV He+ ion beam, with a 2 nm pixel pitch, 1.6pA current, and 10μs dwell time, corresponding to 62,500 ions per raster frame; parameters approximately match actual imaging conditions. For illustrative purposes we simulate both a standard imaging dose that is represented in the 8 frame image collection and a very high applied dose of 40 frames.

Acknowledgment The HIM imaging, image analytics and simulations portion of this research was conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility. This research was funded by the Center for Nanophase Materials Sciences, which is a U. S. Department of Energy Office of Science User Facility (HH, AVI, PR, OSO), part of the data analytics work was supported by the Laboratory Directed Research and Development 16 ACS Paragon Plus Environment

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Program (AB), and the ExxonMobil Chemical Company (MJB).

The authors acknowledge

Robert Colby, David Abmayr, Sergey Yakovlev, Lubin Luo and Bill Lamberti from the Exxon Mobil Corporation for much appreciated input and helpful discussions.



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Figure Captions

Figure 1: To understand the effect of helium ion bombardment on nanoporous SiO2, ionbeam/matter simulations were performed to understand how a simple pore structure evolves under helium ion irradiation. (a) A cross-sectional view of 5 nm simulated pores before ion irradiation. (b) The cross-sectional view of the surface and pore structure evolution of 5 nm simulated pores after 8 frames of ion irradiation, where each frame represents 62,500 ions of irradiation. (c) The cross-sectional view of the surface and pore structure evolution of 5 nm simulated pores after 40 frames of ion irradiation. (d) The surface view of 5 nm simulated pores before ion irradiation. (e) The surface view of 5 nm simulated pores after 8 frames pf ion irradiation. (f)

The surface view of 5 nm simulated pores after 40 frames pf ion

irradiation. (g) A cross-sectional view of 7 nm simulated pores before ion irradiation. (h) The cross-sectional view of the surface and pore structure evolution of 7 nm simulated pores after 8 frames of ion irradiation. (i) The cross-sectional view of the surface and pore structure evolution of 7 nm simulated pores after 40 frames of ion irradiation. (j) The surface view of 7 nm simulated pores before ion irradiation. (k) The surface view of 7 nm simulated pores after 8 frames pf ion irradiation. (l) The surface view of 7 nm simulated pores after 40 frames pf ion irradiation.

Colors represent the following identity in the all the panels, Red:

Empty, Green: Oxygen, Blue: Silicon.rs represent the following identity in the all the panels, Red: Empty, Green: Oxygen, Blue: Silicon.

Figure 2: This figure illustrates surface change as a function of ion dose, imaged at three different currents as a movie, with 1st, 11th and 22nd movie frames shown as representative imaging effects. The detected pores are overlaid in red for all images, with size histograms shown in Figure 3. The scale bar is 200 nm for all images. (a) The 1st image frame for a movie collected at 1.6 pA. (b) The 11th image frame for a movie collected at 1.6 pA. (c) The 20 ACS Paragon Plus Environment

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22nd frame of a movie collected at 1.6 pA. (d) The 1st image frame for a movie collected at 3.9 pA. (e) The 11th image frame for a movie collected at 3.9 pA. (f) The 22nd frame of a movie collected at 3.9 pA. (g) The 1st image frame for a movie collected at 12.6 pA. (h) The 11th image frame for a movie collected at 12.6 pA. (i) The 22nd frame of a movie collected at 12.6 pA.

Figure 3. This figure quantifies surface structure changes as a function of ion currents at three different frames during imaging (Figure 2). (a) The histogram of pore sizes of frames (a), 11 (b), and 22 (c) shown in Figure 2 from movie of 1.6 pA. (b) The histogram of pore sizes of frames 1 (d), 11 (e), and 22 (f), Figure 2 from movie of 3.9 pA current. (c) The histogram of pore sizes of frames 1 (g), 11 (h), and 22 (i) Figure 2 from movie of 12.6 pA current.

Figure 4. Demonstration of the pore finding routine and algorithm on the SiO2 nanporous

particles. (a) The histogram comparing our pore finding routine (blue bins) and the gas absorbtion (red line) for sample 1. (b) HIM micrograph of sample 1 with the pores detected and overlaid in red outlines for sample 1 in (c). A colormap of the pores detected for sample 1, which shows the location and detected pore raduis in nm. (e) The histogram comparing our pore finding routine (blue bins) and the gas absorbtion (red line) for sample 2. (b) HIM micrograph of sample 2 with the pores detected and overlaid in red outlines for sample 2 in (c). A colormap of the pores detected for sample 2, which shows the location and detected pore raduis in nm. (a) The histogram comparing our pore finding routine (blue bins) and the gas absorbtion (red line) for sample 3. (b) HIM micrograph of sample 3 with the pores detected and overlaid in red outlines for sample 3 in (c). A colormap of the pores detected for sample 3, which shows the location and detected pore raduis in nm.



TOC 247x80mm (96 x 96 DPI)

ACS Paragon Plus Environment

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Figure 1: To understand the effect of helium ion bombardment on nanoporous SiO2, ionbeam/ matter simulations were performed to understand how a simple pore structure evolves under helium ion irradiation. (a) A cross-sectional view of 5 nm simulated pores before ion irradiation. (b) The cross-sectional view of the surface and pore structure evolution of 5 nm simulated pores after 8 frames of ion irradiation, where each frame represents 62,500 ions of irradiation. (c) The cross-sectional view of the surface and pore structure evolution of 5 nm simulated pores after 40 frames of ion irradiation. (d) The surface view of 5 nm simulated pores before ion irradiation. (e) The surface view of 5 nm simulated pores after 8 frames pf ion irradiation. (f) The surface view of 5 nm simulated pores after 40 frames pf ion irradiation. (g) A crosssectional view of 7 nm simulated pores before ion irradiation. (h) The cross-sectional view of the surface and pore structure evolution of 7 nm simulated pores after 8 frames of ion irradiation. (i) The cross-sectional view of the surface and pore structure evolution of 7 nm simulated pores after 40 frames of ion irradiation. (j) The surface view of 7 nm simulated pores before ion irradiation. (k) The surface view of 7 nm simulated pores after 8 frames pf ion irradiation. (l) The surface view of 7 nm simulated pores after 40 frames pf ion



irradiation. Colors represent the following identity in the all the panels, Red: Empty, Green: Oxygen, Blue: Silicon.rs represent the following identity in the all the panels, Red: Empty, Green: Oxygen, Blue: Silicon. 194x233mm (96 x 96 DPI)


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Figure 2: This figure illustrates surface change as a function of ion dose, imaged at three different currents as a movie, with 1st, 11th and 22nd movie frames shown as representative imaging effects. The detected pores are overlaid in red for all images, with size histograms shown in Figure 3. The scale bar is 200 nm for all images. (a) The 1st image frame for a movie collected at 1.6 pA. (b) The 11th image frame for a movie collected at 1.6 pA. (c) The 22nd frame of a movie collected at 1.6 pA. (d) The 1st image frame for a movie collected at 3.9 pA. (e) The 11th image frame for a movie collected at 3.9 pA. (f) The 22nd frame of a movie collected at 3.9 pA. (g) The 1st image frame for a movie collected at 12.6 pA. (h) The 11th image frame for a movie collected at 12.6 pA. (i) The 22nd frame of a movie collected at 12.6 pA. 254x190mm (96 x 96 DPI)



Figure 3. This figure quantifies surface structure changes as a function of ion currents at three different frames during imaging (Figure 2). (a) The histogram of pore sizes of frames (a), 11 (b), and 22 (c) shown in Figure 2 from movie of 1.6 pA. (b) The histogram of pore sizes of frames 1 (d), 11 (e), and 22 (f), Figure 2 from movie of 3.9 pA current. (c) The histogram of pore sizes of frames 1 (g), 11 (h), and 22 (i) Figure 2 from movie of 12.6 pA current. 254x190mm (96 x 96 DPI)


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Figure 4: Demonstration of the pore finding routine and algorithm on the SiO2 nanporous particles. (a) The histogram comparing our pore finding routine (blue bins) and the gas absorption (red line) for sample 1. (b) HIM micrograph of sample 1 with the pores detected and overlaid in red outlines for sample 1 in (c). A colormap of the pores detected for sample 1, which shows the location and detected pore radius in nm. (e) The histogram comparing our pore finding routine (blue bins) and the gas absorption (red line) for sample 2. (b) HIM micrograph of sample 2 with the pores detected and overlaid in red outlines for sample 2 in (c). A colormap of the pores detected for sample 2, which shows the location and detected pore radius in nm. (a) The histogram comparing our pore finding routine (blue bins) and the gas absorption (red line) for sample 3. (b) HIM micrograph of sample 3 with the pores detected and overlaid in red outlines for sample 3 in (c). A colormap of the pores detected for sample 3, which shows the location and detected pore radius in nm. 338x190mm (96 x 96 DPI)


Helium Ion Microscopy for Imaging and Quantifying Porosity at the

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