A High-Throughput Method for Bulgeless Liquid Cell Imaging in the Transmission Electron Microscope
Bibliographic record
Abstract
Liquid cell electron microscopy (LCEM) aims to image nanomaterials and biospecimens in their native liquid phase environment. To achieve this, liquid cells (LCs) in which the observed process is occurring must conform to strict requirements such as the establishment of hermetic sealing of liquid between two ultrathin windows and well-defined sample placement conditions. Precise nanofabrication of these LCs is therefore required, the details of which are outlined extensively in reviews [1]. The challenge of window membrane bulging has long remained unresolved [2, 3]. The bulging of these window membranes causes inhomogeneity across the viewing area (ViA). Since LC samples are prepared in the atmosphere of the laboratory environment and subsequently imaged in the high vacuum of the electron microscope, external bulging of the thin window membranes occurs. As a result, researchers have been led to collect their data in regions where this bulging is minimized (i.e., the window’s edge), and resolution is maximized. To address the challenge of window membrane bulging in LCEM, we have created a set of tools to prepare LCs for high-throughput imaging. In Figure 1, the primary components of this design are shown to include a LCEM holder, unique LCs, and a loading stage for the preparation of the LC assemblies. The process in brief consists of assembling two nanofluidic cell chips within the loading stage in the absence of air. Advanced details of both the design and methodology are available elsewhere [4]. Following this protocol, electron energy loss spectroscopy (EELS) results indicated that variation in the liquid layer thickness is on the order of 10’s of nanometres [4]. Gold nanorods (Figure 2a) were successfully imaged in the center of the ViA to readily obtain one nanometre resolution for mobile samples. This system has also demonstrated lattice resolution in the center of the ViA [4]. In addition, unstained dioleoyl-phosphatidylcholine (DOPC) liposomes (Figure 2b) were recorded with sufficient contrast. Note, that this specimen typically requires the use of contrast agents for imaging in standard transmission electron microscopy (TEM) [5, 6]. Therefore, soft materials are typically imaged through cryo-electron microscopy (cryo-EM) while the implementation of LCEM for this purpose is quickly growing. However, the preparation of these samples via many available LCEM approaches is often cumbersome, requiring a significant level of expertise, time, and the lack of a guarantee for uniform liquid layer thickness across the ViA of the LC. With our high-throughput approach, we can discern the thickness of an isolated DOPC lipid bilayer (Figure 2c) in a matter of minutes rather than hours, marking a vast improvement over both cryo-EM and conventional LCEM methods [7]. Further developments to this technology will aim at enhancing the maximum resolution capabilities by introducing features such as even thinner windows as well as other membrane materials [8]. The liquid cell electron microscopy (LCEM) imaging tool kit used to perform high-throughput sample preparation. (a) An image of the nanofluidic cell holder inside of the loading stage for sample preparation. (b) A nanofluidic cell chip designed to limit window bulging inside of the loading stage, in preparation for sample assembly. Transmission electron micrographs of gold nanorods and dioleoyl-phosphatidylcholine (DOPC) liposomes located at the centre of the viewing area (ViA). (a) Gold nanorods imaged at 200 kV in ultrapure water, a line profile (below (a)) indicates that the image yields approximately 1 nm of resolution. (b) A collection of DOPC liposomes imaged at 200 kV in a N-2-hydroxyethylpiperazine-N-2-ethane sulfonic acid (HEPES) / NaCl buffer mixture. (c) An isolated DOPC liposome from the same sample as (b) with an apparent double bilayer imaged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".