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Record W4406274631 · doi:10.1177/10812865241302745

Uniqueness of solutions in the boundary value problems of bending of thin micropolar plates with surface effects

2025· article· en· W4406274631 on OpenAlexaff
Alireza Gharahi

Bibliographic record

VenueMathematics and Mechanics of Solids · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNonlocal and gradient elasticity in micro/nano structures
Canadian institutionsMcGill University
Fundersnot available
KeywordsUniquenessBoundary value problemElasticity (physics)StiffnessBending of platesSurface (topology)Plate theoryMathematicsBendingBending stiffnessMathematical analysisMaterials scienceMechanicsGeometryPhysicsComposite material

Abstract

fetched live from OpenAlex

We propose a micropolar thin plate theory that incorporates surface elasticity effects to take into account simultaneously the contribution of high surface-to-volume ratios and the influence of microstructural mechanics at micro/nano scales. Using a direct averaging approach across the plate’s thickness, we derive governing equations that consider the elastic properties of the upper and lower surfaces, treated as two-dimensional micropolar bodies. The boundary value problems are analyzed, with particular attention to solution uniqueness, as a first step toward establishing the well-posedness of the model. A simplified example demonstrates the mathematical formulation and compares the results with classical plate theory. A numerical example highlights the combined effects of surface elasticity and micropolar characteristics as the plate thickness varies, illustrating the influence of these factors on effective bending stiffness. The results reveal that surface and micropolar effects contribute to deformations by varying magnitudes, depending on material properties and plate size.

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)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.001
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 head0.008
GPT teacher head0.229
Teacher spread0.220 · 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
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

Citations0
Published2025
Admission routes1
Has abstractyes

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