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Record W4408389259 · doi:10.1016/j.powtec.2025.120867

Sorting particles in the air using direct and inverse Chladni patterns of a vibrating plate

2025· article· en· W4408389259 on OpenAlexafffund
Valentin Bourrud, Eloi Perez Compte, Maxime Lanoy, Olivier Robin

Bibliographic record

VenuePowder Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueCanada Foundation for Innovation
KeywordsSortingInverseMaterials scienceComposite materialMathematicsGeometry

Abstract

fetched live from OpenAlex

The combination of direct and inverse Chladni patterns to sort particles or control their motion was primarily studied in liquids through theoretical and numerical studies. The few proposed experimental demonstrations relied on micrometric or millimetric-scale vibrating systems. This work describes a proof of concept for sorting particles in the air using direct and inverse Chladni patterns of a vibrating plate at the decimetric scale. The plate has well-defined modal shapes and resonance frequencies thanks to controlled simply-supported boundary conditions. Sorting possibilities are evaluated using three materials of different densities and types (table salt, lycopodium powder, and iced tea dry mix). Our experimental results confirm numerical results from the literature and indicate that particles can be sorted according to their density or size using direct and inverse Chladni patterns. Finally, perspectives on applications and domains for this prototype aimed at vibrosorting particles are briefly discussed, and directions for future works are suggested. • Experimental demonstration of particle sorting using direct-inverse Chladni patterns. • Demonstration of feasibility on a decimetric scale. • Sorting possibilities evaluated using materials of different densities and types. • Validation of a sorting criterion initially proposed from numerical simulations.

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 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 score0.111
Threshold uncertainty score0.342

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 head0.013
GPT teacher head0.227
Teacher spread0.215 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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