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Record W4384346970 · doi:10.1039/d3ra03766e

Analytical expressions for spreading resistance in lossy media and their application to the calibration of scanning microwave microscopy

2023· article· en· W4384346970 on OpenAlexaff
Marco Farina, C. H. Joseph, S. N. Afifa Azman, A. Morini, Luca Pierantoni, Davide Mencarelli, Andrea Di Donato, Tiziana Pietrangelo, Richard Al Hadi

Bibliographic record

VenueRSC Advances · 2023
Typearticle
Languageen
FieldEngineering
TopicNear-Field Optical Microscopy
Canadian institutionsÉcole de Technologie Supérieure
FundersHorizon 2020 Framework Programme
KeywordsLossy compressionCalibrationMicrowaveMaterials scienceDistilled waterMicroscopeElectrical impedanceScanning electron microscopeOpticsChemistryComputer scienceComposite materialMathematicsPhysicsTelecommunicationsChromatographyEngineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents the analytical derivation of spreading resistance expressions for diverse geometries of a conducting probe submerged in a lossy medium. Resulting equations can be used to calibrate scanning impedance/scanning microwave microscopes operating in liquid. The expressions are systematically validated through numerical and experimental methods for the calibration of an inverted Scanning Microwave Microscope (iSMM) when operating in a lossy saline medium, such as Dulbecco's Modified Eagle Medium (DMEM), a widely used medium for supporting the growth of biological cells. The calibration process within DMEM plays an important role in the quantitative local evaluation of electromagnetic properties of biological samples under physiological conditions. Additionally, measurements are performed in distilled water for comparative analysis.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.012
GPT teacher head0.281
Teacher spread0.269 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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