Case studies and installation data on the acoustical properties of a new class of translucent, lightweight insulation material from NASA called aerogel.
Bibliographic record
Abstract
Aerogels are well-known as a class of thermal insulation with “green” benefits including translucence, hydrophobicity, and light weight. However, before 2008, the acoustical properties of aerogels had not been characterized. Used by NASA in the 1970s, aerogels became available in the 1990s as insulation for the construction industry in skylights, underwater pipelining, and roofing fabric. In 2008, laboratory testing and field research began on the acoustical properties of thin profile (2–8 mm) architectural “tensile membrane” fabrics incorporating silica aerogel granules. Data from a tension structure in Vancouver—where aerogel-enhanced fabric was used to block aircraft noise—exhibited excellent acoustic absorption and acoustic impedance matching properties compared to insulators of comparable thickness. In this installation the material increased transmission loss of exterior to interior noise, and reduced indoor reverberation. Also in 2008, U.S. field tests demonstrated an aerogel blanket material as a surface treatment in offices to reduce broadband reverberation, resulting in increased speech intelligibility and enhanced acoustical comfort (an important factor in the 2009 LEED rating system). The acoustical attributes—combined with aerogel’s thermal value, thin form factor, translucence, hydrophobicity, light weight, and absence of VOCs has led to growing interest in applications ranging from aircraft interiors to hospitals.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".