Mechanical and thermal insulation properties of isocyanate crosslinked resorcinol formaldehyde aerogel: Effect of isocyanate structure
Bibliographic record
Abstract
ABSTRACT In this article, the mechanical and thermal properties of resorcinol formaldehyde (RF) aerogels were improved using two different crosslinkers including hexamethylene diisocyanate (HDI) and methylene diphenyl diisocyanate (MDI). The crosslinking was performed after the aerogel drying process, with two concentrations of crosslinking agent. The formation of urethane linkages was investigated by Fourier transform infrared spectroscopy. The effect of crosslinking process and crosslinking agent type on the mechanical properties was analyzed by compression, bending, and impact tests. The improvement of mechanical strength was attributed to the neck thickness, which was studied by atomic force microscopic test. The results revealed that a higher improvement was obtained by increasing the crosslinking agent value. HDI crosslinked aerogel represented the highest strain‐at‐break and MDI‐reinforced sample showed the highest strength. By crosslinking, the samples density was increased <59% and the reduced compressive strength was enhanced upto eight times. The effect of crosslinking on the thermal conductivity was explored. The increment of neck size and density enhanced the solid conductivity and, consequently, raised the thermal conductivity. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 48196.
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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".