Conductive and auxetic composite membranes based on graphene nanoplatelets and polybutylene succinate produced via electrospinning
Bibliographic record
Abstract
Abstract This work presents a method to produce conductive and auxetic composite membranes from a biobased and biodegradable matrix: polybutylene succinate (PBS). The conductivity was improved by the addition of graphene nanoplatelets (GNP) and the samples were produced via solution electrospinning. The membrane properties were shown to increase with increasing GNP concentration and the rotational speed of the collector. In particular, a membrane having 0.2% w/v GNP and fabricated at the highest collector speed (9.96 m/s) showed the highest electrical conductivity (1.56 × 10−4 S/m) while having a negative Poisson's ratio (NPR) of −1.5 in tension. To complete the analysis, mechanical characterizations showed that the presence of GNP led to a substantial increase in Young's modulus (234%) and tensile strength (190%) compared to the neat PBS membrane produced under the same conditions. Differential scanning calorimetry (DSC) revealed a slight crystallinity increase since GNP are acting as heterogeneous nucleating agents, while thermogravimetric analysis (TGA) showed an improved thermal stability for the GNP/PBS membranes. This unique combination of auxetic and conductive properties can be useful for a wide range of innovative applications such as electronic devices, smart textiles, biomaterials, and biomedical devices. Highlights Electrospinning was successful to produce polybutylene succinate (PBS) nanofibers. A careful control of the processing conditions led to auxetic fiber mats. Electrically conductive mats were produced by adding graphene nanoplatelets (GNP) to PBS. The PBS mechanical properties were highly improved (200%) with low GNP content (0.2%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".