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Record W4409798103 · doi:10.1093/mictod/qaaf013

Streamlined Liquid-Phase Electron Microscopy: Engineered Nanocontainers and Air-Free Loading for High-Resolution Imaging of Biospecimens

2025· article· en· W4409798103 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMicroscopy Today · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsUniversity of Waterloo
FundersOntario Ministry of Research and InnovationUniversity of WaterlooCanada First Research Excellence FundInnovation, Science and Economic Development Canada
KeywordsMaterials scienceElectron microscopeResolution (logic)Phase (matter)MicroscopyHigh resolutionPhase imagingNanotechnologyOpticsChemistryPhysicsComputer scienceRemote sensingGeology

Abstract

fetched live from OpenAlex

Abstract Liquid-phase electron microscopy has emerged as an advanced technique for observing dynamic phenomena in liquids at near-atomic spatial resolution. A newly developed technique offers an easier, faster, and more reproducible way to prepare liquid samples for transmission electron microscopy. Herein, we introduce proprietary nanocontainers and an air-free sample loading method that have significantly simplified the preparation of in-liquid specimens through straightforward drop-casting. By demonstrating this technique with ultrathin windows, we successfully imaged low-contrast vesicles and plasmid DNA. Our approach simplifies usage and enhances throughput and reproducibility, thereby lowering the entry barrier to this rapidly growing field.

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.

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 score0.238
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 science0.0000.000
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 head0.004
GPT teacher head0.315
Teacher spread0.311 · 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