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Record W4385093901 · doi:10.1093/micmic/ozad067.810

Quantification of Gas-Based Charge Compensation by Off-Axis Electron Holography in Open-Cell Environmental TEM

2023· article· en· W4385093901 on OpenAlexaff
Makoto Schreiber, Cathal Cassidy

Bibliographic record

VenueMicroscopy and Microanalysis · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectron holographyElectronCharge (physics)Materials scienceHolographyCompensation (psychology)OpticsPhysicsQuantum mechanicsPsychology

Abstract

fetched live from OpenAlex

Samples with low electrical conductivity when imaged in transmission electron microscopy (TEM) can undergo complex charging processes which cause various distortions to measurements. These distortions can include apparent movement of the sample, changes to the effective optical parameters [1], distortions of objects at different heights (Fig. 1a), and additional phase ramps (Fig. 1b). Several methods exist to compensate for or reduce charge buildup on samples. These include coating samples with thin films of high electrical conductivity or imaging a sample in close proximity to a material with high electrical conductivity [2]. The coating method is often irreversible and is not possible for all samples. The proximity method can require specific apertures or sample preparations and is not always consistent. Another method that has been established in scanning electron microscopy (SEM) but has not been widely applied or studied in TEM is the use of gas flow over a sample [3]. This technique is applicable to almost any sample and is dynamically reversible. Off-axis electron holography is often used to quantitatively measure long-range fields. The presence of uncontrolled charging can mask the fields of interest. Being able to modulate the charging process in situ may also allow for deeper studies of how beam-induced charges are distributed on different samples and interfaces. While the effects of gasses on TEM imaging [4] and off-axis holography [5] have been studied before, the effects on the charging process have not. In the present study, we quantify the gas-mediated charge reduction process on thin dielectric films through off-axis electron holography in an open-cell type environmental TEM. First, we show that the large phase ramp present in a low-conductivity silicon nitride film (Fig. 1b) can be greatly reduced by the introduction of a gas (Fig. 2a). This demonstrates the charge compensation effect. The degree of charging is a complicated interplay of many parameters and we study the effects of gas pressure and dose rate as well as specimen material, thickness, and gas species. We show that although the additional scattering of the electron beam by the gas reduces the beam’s coherence and intensity at the sample plane, the charge compensation effects still improve the fringe contrast on the film (Fig. 2b). Finally, we discuss how this in situ reversible charge-modulation method may be utilized to investigate other processes such as beam-induced specimen vibrations. Lorentz TEM image of an unbiased electron biprism below the interface of a 20nm thick SiN film and vacuum. b) Reconstructed Lorentz-mode electron hologram of the interface of a vacuum region and 20nm thick SiN film. Both images are obtained in standard high-vacuum conditions with no gas flow. a) Reconstructed electron hologram of the interface of the same area shown in Fig. 1b but with 1950 Pa N2 gas pressure around the sample area. b) Trends in the hologram fringe contrast with N2 gas pressure when both arms of the hologram pass through a 20nm thick SiN film or through an empty gas filled region far from the membrane. Average and standard deviation over 10 measurements.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.292
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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