Incorporation of Multiwalled Carbon Nanotubes into Electrospun Softwood Kraft Lignin-Based Fibers
Bibliographic record
Abstract
Abstract Intrinsic properties such as mechanical and electrical conductive properties make carbon nanotubes an ideal nanofiller for reinforcement of polymer materials. In this study, multi-walled carbon nanotubes (MWNTs) were suspended in various fractionated softwood kraft lignin (SKL) concentrations and electrospun into fibers. Solutions prepared from F1–3SKL suspensions contained aggregated MWNTs, which led to spraying and droplet accumulation during fiber formation. By contrast, MWNT suspensions prepared with F4SKL were well dispersed and readily electrospun into fibers. Increasing the F4SKL concentration in the MWNTs' suspension resulted in better electrospinnability. Adding 10 mg of F4SKL enabled as much as 18.6 mg of MWNTs (or 6 wt% based on fiber weight) to be dispersed and electrospun into fibers. The resulting MWNT-reinforced SKL fibers were then thermostabilized and carbonized and the resulting carbon fibers characterized. Unfortunately, the mechanical properties of the fibers did not improve with incorporation of MWNTs. However, the electrical conductivity increased from 2.3 to 3.0 S/cm when incorporating 6 wt% MWNTs into the fibers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".