Sorting particles in the air using direct and inverse Chladni patterns of a vibrating plate
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".