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Record W4319444945 · doi:10.26434/chemrxiv-2023-834fn

High-Resolution Bulgeless Liquid-Cell Electron Microscopy

2023· preprint· en· W4319444945 on OpenAlexafffund
Tyler S. Lott, Ariel A. Petruk, Nicolette A. Shaw, Natalie Hamada, Carmen M. Andrei, Yibo Liu, Juewen Liu, Germán Sciaini

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsMcMaster UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of Energy
KeywordsElectron microscopeUSableMaterials scienceMicrofluidicsResolution (logic)NanotechnologyMicroscopyOpticsComputer scienceArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Liquid cell electron microscopy (LCEM) has long suffered from irreproducibility and its inability to confer high-quality images over a wide field of view. LCEM demands the encapsulation of the in-liquid sample between two ultrathin membranes (windows). In the vacuum environment of the electron microscope, the windows bulge, drastically reducing the achievable resolution and the usable viewing region. Herein, we introduce a shape-engineered nanofluidic cell architecture and an air-free drop-casting sample loading technique, which combined, provide robust bulgeless imaging conditions. We demonstrate the capabilities of our approach through the study of in-liquid model samples and quantitative measurements of the liquid layer thickness. The presented LCEM method confers high throughput, lattice resolution across the complete viewing window, and sufficient contrast for the observation of unstained liposomes, paving the way to high-resolution movies of biospecimens in their near native environment.

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 categoriesMeta-epidemiology (narrow)
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 score0.066
Threshold uncertainty score1.000

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 science0.0010.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.318
Teacher spread0.307 · 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.

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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