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Record W4397005033 · doi:10.3390/nano14100874

Magnetic-Dielectric Cantilevers for Atomic Force Microscopy

2024· article· en· W4397005033 on OpenAlexaff
Gala Sanchez-Seguame, Hugo Avalos-Sanchez, J. E. Lugo, Eduardo Antonio Murillo-Bracamontes, M. Palomino‐Ovando, Orlando Hernández‐Cristóbal, J. J. Gervacio-Arciniega, M. Toledo-Solano

Bibliographic record

VenueNanomaterials · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCantileverMaterials scienceCharacterization (materials science)NanotechnologyNanoscopic scaleDielectricAtomic force microscopyMagnetic force microscopeSuperparamagnetismElectrostatic force microscopeAtomic force acoustic microscopyOptoelectronicsComposite materialMagnetic fieldMagnetization

Abstract

fetched live from OpenAlex

Atomic force microscopy (AFM) is a technique that relies on detecting forces at the nanonewton scale. It involves using a cantilever with a tiny tip at one end. This tip interacts with the short- and long-range forces of material surfaces. These cantilevers are typically manufactured with Si or Si3N4 and synthesized using a lithography technique, which implies a high cost. On the other hand, through simple chemical methods, it is possible to synthesize a magneto-dielectric composite made up of artificial SiO2 opals infiltrated with superparamagnetic nanoparticles of Fe3O4. From these materials, it is possible to obtain tipless cantilevers that can be used in AFM analysis. Tipless cantilevers are an alternative tool in nanoscale exploration, offering a versatile approach to surface analysis. Unlike traditional AFM probes, tipless versions eliminate the challenges associated with tip wear, ensuring prolonged stability during measurements. This makes tipless AFM particularly valuable for imaging delicate or soft samples, as it prevents sample damage and provides precise measurements of topography and mechanical and electromechanical properties. This study presents the results of the characterization of known surfaces using magneto-dielectric cantilevers and commercial cantilevers based on Si. The characterization will be carried out through contact and non-contact topography 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 categoriesInsufficient payload (model declined to judge)
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 score0.142
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)0.0010.000

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 head0.007
GPT teacher head0.286
Teacher spread0.279 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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