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Record W4317931786 · doi:10.3390/nano13030477

Measuring the Diameter of Single-Wall Carbon Nanotubes Using AFM

2023· article· en· W4317931786 on OpenAlexaff
Dušan Vobornik, Maohui Chen, Shan Zou, Gregory P. Lopinski

Bibliographic record

VenueNanomaterials · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceReproducibilityNanotechnologySurface finishAtomic force microscopyPolymerNanotubeSubstrate (aquarium)Composite materialTube (container)Chemistry

Abstract

fetched live from OpenAlex

In this work, we identify two issues that can significantly affect the accuracy of AFM measurements of the diameter of single-wall carbon nanotubes (SWCNTs) and propose a protocol that reduces errors associated with these issues. Measurements of the nanotube height under different applied forces demonstrate that even moderate forces significantly compress several different types of SWCNTs, leading to errors in measured diameters that must be minimized and/or corrected. Substrate and nanotube roughness also make major contributions to the uncertainty associated with the extraction of diameters from measured images. An analysis method has been developed that reduces the uncertainties associated with this extraction to <0.1 nm. This method is then applied to measure the diameter distribution of individual highly semiconducting enriched nanotubes in networks prepared from polyfluorene/SWCNT dispersions. Good agreement is obtained between diameter distributions for the same sample measured with two different commercial AFM instruments, indicating the reproducibility of the method. The reduced uncertainty in diameter measurements based on this method facilitates: (1) determination of the thickness of the polymer layer wrapping the nanotubes and (2) measurement of nanotube compression at tube-tube junctions within the network.

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 score0.002
Threshold uncertainty score0.285

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 head0.047
GPT teacher head0.279
Teacher spread0.231 · 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

Citations17
Published2023
Admission routes1
Has abstractyes

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