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Record W4416329296 · doi:10.1080/15397734.2025.2588312

Unified shear deformation modeling of FG nanobeam mass sensors

2025· article· en· W4416329296 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMechanics Based Design of Structures and Machines · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsConcordia University
Fundersnot available
KeywordsDeformation (meteorology)Shear (geology)StiffnessShear strength (soil)Structural health monitoring

Abstract

fetched live from OpenAlex

In the present study the vibration analysis of a functionally graded porous nanobeam mass sensor carrying multiple mass-spring-damper attachments has been performed using an analytical method. A nonlocal strain-gradient theory is utilized in conjunction with a unified Higher-Order Shear Deformation Beam Theory (HSDT) to give the governing equations of the motion of the nanosensor. Effect of various parameters and theories on the frequencies of the nanobeam have been studied comprehensively and illustrated graphically. It has been shown that if the attached particle has a degree of elasticity, modeling it as a lumped rigid particle, directly attached to the sensitive frequency based micro/nano sensors can result in considerable error. Therefore the attached particles should be modeled as mass-spring systems in vibration analysis of nano mass sensors.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.900
Threshold uncertainty score0.472

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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