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Record W4413781372 · doi:10.1038/s41467-025-63413-2

Concept and demonstration of a low-cost compact electron microscope enabled by a photothermionic carbon nanotube cathode

2025· article· en· W4413781372 on OpenAlexafffund
Casimir Kuzyk, Alexander Dimitrakopoulos, Alireza Nojeh

Bibliographic record

VenueNature Communications · 2025
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsUniversity of British Columbia HospitalUniversity of British Columbia
FundersCanada First Research Excellence FundCanada Foundation for InnovationUniversity of British ColumbiaGovernment of CanadaBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsCarbon nanotubeCathodeElectron microscopeMaterials scienceElectronNanotechnologyNanotubeMicroscopeCarbon fibersOpticsChemistryPhysicsComposite materialNuclear physicsPhysical chemistry

Abstract

fetched live from OpenAlex

The scanning electron microscope (SEM) delivers high resolution, high depth of field, and an image quality as if microscopic objects are seen by the naked eye. This makes it not only a powerful scientific instrument, but a tool inherently applicable to nearly all fields of study and curiosity involving the small scale. However, SEMs have remained complex, expensive, and beyond the reach of many. To broaden access, we demonstrate an SEM using simple, low-cost, off-the-shelf components, and hobby-level electromechanics; this has been enabled by a thermionic electron source based on a carbon nanotube array excited by low optical power. The instrument offers sub-micrometer resolution, a depth of field of the order of a hundred micrometers, and an image quality comparable to commercial SEMs; it also tolerates poor vacuum and moist specimens, making it broadly applicable. It has a flexible design that lends itself to customization for different use scenarios. We describe the conceptual approach and high-level design in this paper; the detailed blueprints of our specific implementation are provided separately online. We hope that specialists and non-specialists alike will build variations that fit their own needs and interests, helping electron microscopy expand further into industry and society.

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 score0.081
Threshold uncertainty score0.423

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 head0.006
GPT teacher head0.257
Teacher spread0.252 · 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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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