Cellulose-Reinforced Carbon Nanotube Buckypapers with Balanced Mechanical Strength and Conductivity
Bibliographic record
Abstract
Buckypaper (BP), a self-supporting conductive thin film made from carbon nanotubes, is known for its ease of manufacture, excellent repeatability, and promising potential in the electronics field. However, challenges remain in balancing its mechanical strength and conductivity. In this study, we present a simple and effective one-pot synthesis method to fabricate highly conductive and mechanically robust BPs composed of a conjugated polymer ( P1 )–single-walled carbon nanotube (SWNT) complex and microcrystalline cellulose (MCC). P1, which features a self-immolative linker (SIL) between the polymer backbone and side chain, is used to disperse SWNTs in organic solvents. Upon treatment with tetra- n -butylammonium fluoride (TBAF), the insulating side chains of the polymer are removed to yield P0, which significantly enhances the conductivity of the resulting BP. However, this side chain removal weakens the mechanical strength of the BP. To address this trade-off, MCC was introduced as a mechanical reinforcing agent. Leveraging the good solubility of MCC in a TBAF/DMSO mixture, we developed a one-pot synthesis method to fabricate composite BPs with P0–SWNT complexes and MCC, using TBAF for both solubilization and side chain removal. The composite BPs exhibited improved mechanical properties and conductivity. Notably, a BP containing 42% P0–SWNT by weight demonstrated an optimal balance of strength (tensile modulus of 161 ± 10 MPa, yield point of 11 ± 2%) and conductivity (107 ± 4 S/m). This straightforward and scalable preparation method allows production of BPs with balanced mechanical and electrical properties that may be valuable for downstream applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".