A compact method for efficient evaluation of acoustic absorbers with submicron materials
Bibliographic record
Abstract
Materials with submicron or nano-scale features possess unique physical properties and are well-suited for application in noise reduction. To date, experimental characterization of the sound absorbing ability of the submicron/nano materials is still a challenging task, because the measuring of sound absorptivity usually requires bulky samples, while the preparation of large quantities of submicron/nano materials or structures is generally costly and laborious. In this work, an acoustic testing method is proposed to evaluate the acoustic absorptivity of submicron/nano materials using small samples. Based on the transfer-matrix algorithm, the method establishes correlations among acoustic-related parameters of a large sensor fixture and a small sample holder. A proof-of-principle experimental setup was developed to test absorbers with well-known acoustic behavior to verify accuracy of the method. Finally, the sound absorption properties of two submicron materials are characterized, with one comprising dispersed silver submicron fibers and the other comprising electrospinning submicron fibers. The results indicate that acoustic absorption coefficients can be effectively retrieved using only 1/200 of the amount of materials that are typically required in the standard test.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".