Fabrication of Bacterial Cellulose/Polyaniline Nanocomposite Paper with Excellent Conductivity, Strength, and Flexibility
Bibliographic record
Abstract
Bacterial cellulose/polyaniline (BC/PANI) nanocomposites display many potential applications in various fields. However, the conductivity and mechanical properties remain a challenge. Here, we developed a novel method to prepare BC/PANI nanocomposites via the chemical grafting of PANI onto epoxy modified BC (EBC), followed by the grafting of polyacrylamide (PAM). For comparison, an in situ BC/PANI sample was also prepared. The grafting reaction between PANI and EBC and the retention of PANI on EBC were confirmed by FTIR, X-ray photoelectron spectroscopy, and elemental analysis. The cross-section morphology of BC transformed into a three-dimensional and continuous network structure with the incorporation of PANI. The effects of epoxy and PAM contents on the morphology, conductivity, and mechanical properties of PANI-g-EBC and PANI-g-EBC3/PAM nanocomposites were investigated. Compared with those of the in situ BC/PANI sample, the conductivity of PANI-g-EBC increased from 0.12 to 1.08 S/cm, while the stress increased from 8.18 to 18.47 MPa. With the addition of PAM, the conductivity of PANI-g-EBC/PAM nanocomposite paper further increased to 1.43 S/cm, and the stress increased to 47.94 MPa. The conductivity of PANI-g-EBC3/PAM nanocomposites only decreased from 1.43 to 1.36 S/cm after refolding 160 times. PANI-g-EBC and PANI-g-EBC3/PAM nanofibers could be blended with conventional plant cellulose fiber to prepare flexible and high strength conductive composite paper.
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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) | 0.001 | 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".