2nd Generation Layered Silicates Nanocomposites with improved Mechanical and Electrical Properties
Bibliographic record
Abstract
In this study a novel kind of reinforcing polymethylmethacrylate (PMMA) aas well as improving the electrical properties of polyetherimide (PEI) with a 2 nd generation of layered silicates is shown.Layered silicates are used as nanofillers in polymers due to their ability to increase the mechanical strength as well as to improve flame retardancy [1] and barrier properties [2], respectively.The first studies about polymeric nanocomposites with layered silicates were published in the mid eighties of the last century and lead to the development of a nylon-6-montmorillonite nanocomposite which has been the first layered silicate nanocomposite to be commercialized [3].Since these first studies about polymer/clay nanocomposites [4], a rapid development has brought further improvement of the overall properties of these materials.However, the potential of commercially available natural layered silicates seems to have reached its limitations due to small lateral dimensions and a high heterogeneity of surface charge.Processing via melt compounding results mostly in incomplete delamination of the tactoids, which further reduces the maximal possible aspect ratio and therefore the desired properties.Also the incorporation of commercially available natural organo-clay in PMMA by melt-compounding leads to an increase of the stiffness, but an unsatisfactory dispersion quality of the nanoclay in the PMMA matrix.This leads to an decrease in toughness.Therefore we developed a new kind of synthetic layered silicate and used them in an innovative transfer batch moulding process to create a PMMA-nanocomposite.With these synthetic layered silicates which have aspect ratios of up to 600, it was possible to significantly increase the young's modulus of about 55% and the fracture toughness of about 70 %, without any decrease in tensile strength.Furthermore analysis of the corresponding fracture surfaces by scanning electron microscopy show in case of the novel filler additional energy dissipating mechanisms like crack deflection, crack bridging as well as debonding effects with platelets pull-out leading to enhanced fracture toughness.In addition to the improvement of the mechanical behavior, the layered silicates possess the ability to decrease the coefficient of thermal expansion (CTE) of the matrix material [5].Therefore layered silicates provide the possibility of utilizing thermoplastic materials for applications which require a lower CTE.In electric devices e.g.substrate material has to have a CTE in the range of the copper foil (around 17 ppm/K) to avoid thermal stresses between materials.Therefore current studies are evaluating the effect of layered silicates on thermal and electrical properties of PEI.
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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".