Development of Mechanically Stable Polymer-Based Silica Aerogel
Bibliographic record
Abstract
Silica aerogels have attracted attention for many applications due to their unique properties such as low density (0.003 g/cm), mesoporosity (pore size 2–50 nm), high thermal insulation and high surface area (500–1200 m 2 /g). However, their fragility and environmental sensitivity restricts the use of monolithic silica aerogel. In this paper, silica aerogel that is crosslinked with diisocyanate is introduced and the effects of polymer concentration on aerogel properties, especially mechanical strength are discussed. Fracture of silica aerogel mainly occurs at the interface of secondary particles that are formed during aging. It is believed that if the surface of silica aerogel is covalently bonded to nanocast polymer coating, the interparticle necks become wider and can reinforce the structure of the aerogel. In this study, several characterizations are performed to investigate the properties of the aerogel. First, bulk density is measured to visualize the change in density with increase in crosslinker concentration. Brunauer Emmett and Teller (BET) illustrated the pore size, bulk density and surface area. The critical temperature up to which polymer crosslinked aerogel decompose was investigated by Thermogravimetric Analysis (TGA). The phase change in the aerogel was studied with Differential Scanning Calorimetry (DSC) testing. Using Scanning Electron Microscope (SEM), the nanoscale pore size and pore distribution throughout the aerogel surface were investigated. Furthermore, compression tests were performed to study the effect of crosslinking polymer on mechanical strength over non-crosslinked framework.
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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 | 0.000 | 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".