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Record W2037057907 · doi:10.1109/memsys.2014.6765759

Single-chip atomic force microscope with integrated Q-enhancement and isothermal scanning

2014· article· en· W2037057907 on OpenAlexaff
Neil Sarkar, Raafat R. Mansour

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsScannerMaterials scienceCantileverChipMicroscopeAtomic force microscopyDynamic rangeOptoelectronicsIsothermal processScanning probe microscopyNon-contact atomic force microscopyCrosstalkNanosensorOpticsConductive atomic force microscopyNanotechnologyElectrical engineeringPhysicsComposite materialEngineering

Abstract

fetched live from OpenAlex

We present the highest resolution imaging performance attained to date with a single-chip Atomic Force Microscope (AFM) that does not require off-chip scanning or sensing hardware. The marked improvement in sensitivity of the instrument stems in part from an internal quality (Q) factor enhancement mechanism that relies on the interplay between effects in the electrical, thermal and mechanical domains. In addition, careful matching of the strain sensor in an electrothermally actuated, piezoresistively detected resonant cantilever improves the dynamic range of the instrument. Furthermore, an integrated isothermal electrothermal scanner has been developed to scan a surface area of ∼50μm × ∼15μm while maintaining a constant temperature at the tip and sensor locations, thereby suppressing the deleterious thermal crosstalk effects that have plagued previously reported electrothermal scanner designs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.231
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2014
Admission routes1
Has abstractyes

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