Liquid-Phase Scanning Electron Microscopy for Imaging Hydrated Particle Structures
Bibliographic record
Abstract
Liquid-phase electron microscopy (LP-EM) is a novel characterization technique for in situ observation of samples in liquid environments. Electron microscopes traditionally operate under high vacuum, which requires specimens to be dried on a sample stage. However, liquid-phase imaging is possible with closed cells, in which the liquid is encapsulated behind an ultrathin electron-transparent membrane. While developments in liquid-phase transmission electron microscopy (LP-TEM) have accelerated over the past few decades [1, 2], liquid-phase scanning electron microscopy (LP-SEM) has seldom been explored. LP-SEM is advantageous for its fewer sample size restrictions, increased surface sensitivity, and reduced beam damage [3]. Here we explore LP-SEM as a means for expanding the fields of in situ and correlative microscopy. In this work, we characterize the resolution limit of the LP-SEM setup using Au nanoparticles (Au NPs). Au NPs are a high Z material with uniform particle size that have been commonly used as a standard in characterizing LP-SEM setups [4, 5]. The resolution of the liquid cell is limited by the broadening of the electron beam in liquid, and dependent on the particle depth in the liquid reservoir [6]. Au NPs minimize the Z-filtering effect in which electron signals from high Z specimens are less likely to be attenuated by the membrane before reaching the detector [3]. Furthermore, we employ LP-SEM for imaging hydrated bentonite clay particles that are not compatible with the specimen size requirements of LP-TEM (typically < 100 nm). “Bentonite” is the commercial term used to describe a type of clay that is comprised primarily of the montmorillonite mineral. “Montmorillonite”, an aluminosilicate, belongs to the phyllosilicate (sheet-like) family of clays known to expand upon integration of water. The clay swells when water molecules infiltrate spaces between the layered montmorillonite platelets. This volume expansion plays a crucial role in applications such as in nuclear waste containment, water treatment, and petroleum extraction. LP-SEM supports the visualization of hydrated bentonite whose features range from 20 nm to 1.3 µm [7]. Studies report a correlation between performance and an evolving microstructure during successive hydration-dehydration events, specifically, a change in water retention [8, 9]. Using LP-SEM, we study the bentonite microstructure during the transitional phase in situ. A custom closed liquid cell was designed for compatibility with the Hitachi SU7000 SEM. The cylindrical liquid reservoir (∼280 µL) is capped and hermetically sealed using a Si-based chip with five windows of 20 nm SiN deposited via low pressure chemical vapour deposition (LPCVD). The SiN windows range from 50 to 150 µm2 and allow for electron beam transmission into the liquid reservoir. For each experiment, both sides of the SiN chip are subjected to a UV-ozone treatment using the Hitachi ZoneSEM system to decrease the water contact angle and remove carbon contaminants [10]. Firstly, to test the effect of the SiN membrane on resolution, 50 nm Au NPs are drop-casted on either side of a membrane chip. Secondly, 0.005 wt% bentonite suspended in water is drop-casted on the bottom reservoir-facing side of a second chip. On the same chip, 20 nm Au NPs are drop-casted on the top vacuum-facing side, serving as a fiducial marker to aid electron beam alignment and verify resolution. Images are collected using the in-column secondary electron (SE) detector and in-column backscattered electron (BSE) detector and analyzed. Figure 1 shows 50 nm Au NPs on either side of the SiN membrane and the relative contrast intensity. Spatial resolution and signal-to-noise ratio are calculated for particles on top and below the SiN membrane using the SMARTJ ImageJ plugin based on the work of Joy [11] and are listed in Table 1. Relative intensity is calculated from the intensity profile in Figure 1d. Below the SiN membrane, the resolution of the Au NP decreases from 3.6 nm to 8.1 nm in the SE image and from 5.5 nm to 8.8 nm in the BSE image. The effect of the membrane is found to be minimal on the relative intensity and observed particle size of the Au NPs. In addition to resolution quantification, we investigate the strategic use of Au NPs on top of the membrane a tool for refocusing of the electron beam near areas of interest. A common challenge in electron microscopy is the deposition of electron beam-induced carbon contamination [12] [13]. Figure 1c demonstrates how the alignment of the electron beam on the top side Au NPs localizes carbon contamination (shown with blue arrow) away from the area of interest (enclosed in yellow region). Figure 2a shows a bentonite clay particle inside the liquid reservoir with water and several 20 nm Au NPs on the top membrane surface. Spatial resolution over the bentonite particle was calculated to be 30 nm. From comparing the relative intensity profile presented in Figure 2b with that in 1d, the effects of Z-filtering are clearly demonstrated. The particle size of the bentonite particle is estimated to be ∼250 nm from Figure 2b. Through these results, we demonstrate the technique of closed cell LP-SEM through resolution quantification, strategies in sample preparation, and high and low Z applications. The resolution decrease due to the SiN membrane is found to be minimal for Au NPs. Strategies for facile electron beam alignment and minimization of carbon contamination are presented using Au NPs on top of the SiN membrane. The effects of Z-filtering are shown through the relative intensity profiles of Au NPs and bentonite clay particles. Lastly, we show that LP-SEM presents a promising future for observation of the bentonite transitional phase. Through further exploration, LP-SEM will greatly contribute to the field of correlative microscopy by facilitating the visualization of hydrated structures and expanding our scope of in situ observation [14]. Simultaneously captured in-column SE (a) and in-column BSE (b) images of 50 nm Au NPs on top of and behind the 20 nm SiN membrane of the closed liquid cell; (c) SE image showing growth of carbon contamination (blue arrow) far from area of interest (region outlined in yellow); (d) relative contrast plot showing grey value intensity along the yellow dashed line in (b). SEM images were captured in the Hitachi SU7000 SEM at 10 kV (a, b) and 20 kV (c) with respective probe currents of 15 and 23 pA and working distance of 7 mm. Resolution and signal to noise ratio calculated from Figures 1a and 1b using the SMARTJ plugin for ImageJ. Relative intensity and full width half maxima (FWHM) are calculated from the intensity profile in Figure 1d. (a) BSE image of bentonite clay particle in water below SiN membrane and 20 nm Au NPs on top of membrane; (b) relative contrast plot showing grey value intensity along the yellow dashed line in (a). SEM image was captured in the Hitachi SU7000 SEM at 7 kV in high current mode with probe current of 190 pA and working distance of 7 mm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".