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Record W4286214427 · doi:10.1002/advs.202201489

3D Generation of Multipurpose Atomic Force Microscopy Tips

2022· article· en· W4286214427 on OpenAlexfundno aff
Ayoub Glia, Muhammedin Deliorman, Mohammad A. Qasaimeh

Bibliographic record

VenueAdvanced Science · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsnot available
FundersTerry Fox FoundationNew York University Abu Dhabi
KeywordsAtomic force microscopyMaterials scienceNanotechnologyCarbon nanotubeFocused ion beamEtching (microfabrication)IonChemistryLayer (electronics)

Abstract

fetched live from OpenAlex

In this work, 3D polymeric atomic force microscopy (AFM) tips, referred to as 3DTIPs, are manufactured with great flexibility in design and function using two-photon polymerization. With the technology holding a great potential in developing next-generation AFM tips, 3DTIPs prove effective in obtaining high-resolution and high-speed AFM images in air and liquid environments, using common AFM modes. In particular, it is shown that the 3DTIPs provide high-resolution imaging due to their extremely low Hamaker constant, high speed scanning rates due to their low quality factor, and high durability due to their soft nature and minimal isotropic tip wear; the three important features for advancing AFM studies. It is also shown that refining the tip end of the 3DTIPs by focused ion beam etching and by carbon nanotube inclusion substantially extends their functionality in high-resolution AFM imaging, reaching angstrom scales. Altogether, the multifunctional capabilities of 3DTIPs can bring next-generation AFM tips to routine and advanced AFM applications, and expand the fields of high speed AFM imaging and biological force 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 score0.102
Threshold uncertainty score0.425

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 studies0.0010.000
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 head0.013
GPT teacher head0.304
Teacher spread0.292 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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