The effect of graphene-nanoplatelets on gelation and structural integrity of a polyvinyltrimethoxysilane-based aerogel
Bibliographic record
Abstract
Aerogels suffer greatly from poor mechanical properties resulting from their particulate structure. They also experience noticeable pore shrinkage during drying due to their low structural integrity. These shortfalls limit their broad application. To enhance the mechanical properties and improve the structural integrity of silica-based aerogels, graphene nanoplatelets (GnPs), as a nanofiller, were embedded into the solution of polymerized vinyltrimethoxysilane (VTMS) to prepare P-VTMS-based silica/GnP (PE-b-Si/GnP) hybrid aerogel monoliths based on sol-gel synthesis and supercritical drying. The inclusion of GnPs in our polymer-based silica aerogel processes reinforced the nanostructure and suppressed PE-b-Si nanopore shrinkage during supercritical drying, thus acting as an effective anti-shrinkage nanofiller. Accordingly, the GnPs significantly contributed to the PE-b-Si solution's uniform gelation and to the change of the hydrophilic nature to a hydrophobic one even with 1 wt% addition. In this study, the influence of the GnP content on the sol-gel process, structure, and physical properties of PE-based silica aerogels is studied.
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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".