Structure · dataset · 2024
Optimization of mechanical properties of multiscale hybrid polymer nanocomposites: A combination of experimental and machine learning techniques
Listed in e-cienciaDatos
Machine learning (ML) algorithms offer quick and accurate predictions of material properties at a low computational cost.
Description
In this project, the mechanical properties of multiscale poly(3-hydroxybutyrate) (P3HB)-based nanocomposites reinforced with different concentrations of multiwalled carbon nanotubes (MWCNTs), WS2 nanosheets and sepiolite (SEP) nanoclay have been predicted. The nanocomposites were prepared via solution casting.
SEM images revealed that the three nanofillers were homogenously and randomly dispersed into the matrix. A synergistic reinforcement effect was attained, resulting in an unprecedented stiffness improvement of 132% upon addition of 1:2:2 wt% SEP:MWCNTs:WS2. Conversely, the increments in strength were only moderates (up to 13.4%).
Read the rest (2 more)
A beneficial effect in the matrix ductility was also found due to the presence of both nanofillers. Four ML approaches, Recurrent Neural Network (RNN), RNN with Levenberg's algorithm (RNN-LV), decision tree (DT) and Random Forest (RF), were applied. The correlation coefficient (R2), mean absolute error (MAE) and mean square error (MSE) were used as statistical indicators to compare their performance.
The developed models represent a powerful tool for the optimization of the mechanical properties in multiscale hybrid polymer nanocomposites, saving time and resources in the experimental characterization process.
Links
Where it is published
- Dataverse dataset page edatos.consorciomadrono.es/dataset.xhtml?persistentId=doi%3A10.21950%2F2ZUDRR ↗
landing page · from edatos consorciomadrono es
- DOI doi.org/10.21950/2zudrr ↗
DOI / persistent id · from edatos consorciomadrono es
Catalogue records · 1
- Dataverse API edatos.consorciomadrono.es/api/datasets/:persistentId/?persistentId=doi%3A10.21950%2F2ZUD… ↗
metadata API · from edatos consorciomadrono es
Topics
- Stated by source
- Química
- From keywords
- Computer Science & AI · Engineering · Machine learning · Materials Science
- Inferred from text
- Image 65%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| e-cienciaDatos | doi:10.21950/2ZUDRR | 13 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:4611 | mapping · edatos consorciomadrono es | vocabulary-mapper@1.0.0 | keywords['Machine learning'] |
| concepts[field].dataverse_subject:qu-mica | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | /subjects |
| concepts[field].local:field:engineering | mapping · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | /subjects |
| concepts[field].local:field:materials-science | mapping · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | /subjects |
| concepts[modality].local:modality:image | enrichment · edatos consorciomadrono es | keyword-concept-rules@1.0.0 | title+description (65%) |
| created_date | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | |
| description | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | /description |
| publication_date | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | |
| title | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | /name |
| updated_date | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 | |
| version_label | source · edatos consorciomadrono es | connector:edatos_consorciomadrono_es@1.0.0 |