Nitrogen-Doped Carbon Nanotube/Polypropylene Composites with Negative Seebeck Coefficient
Bibliographic record
Abstract
This study describes the application of multi-walled carbon nanotubes that were nitrogen-doped during their synthesis (N-MWCNTs) in melt-mixed polypropylene (PP) composites. Different types of N-MWCNTs, synthesized using different methods, were used and compared. Four of the five MWCNT grades showed negative Seebeck coefficients (S), indicating n-type charge carrier behavior. All prepared composites (with a concentration between 2 and 7.5 wt% N-MWCNTs) also showed negative S values, which in most cases had a higher negative value than the corresponding nanotubes. The S values achieved were between 1.0 μV/K and −13.8 μV/K for the N-MWCNT buckypapers or powders and between −4.7 μV/K and −22.8 μV/K for the corresponding composites. With a higher content of N-MWCNTs, the increase in electrical conductivity led to increasing values of the power factor (PF) despite the unstable behavior of the Seebeck coefficient. The highest power factor was achieved with 4 wt% N-MWCNT, where a suitable combination of high electrical conductivity and acceptable Seebeck coefficient led to a PF value of 6.1 × 10−3 µW/(m·K2). First experiments have shown that transient absorption spectroscopy (TAS) is a useful tool to study the carrier transfer process in CNTs in composites and to correlate it with the Seebeck coefficient.
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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) | 0.000 | 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".