Functionalization of multiwalled carbon nanotubes by amidation and Michael addition reactions and the effect of the functional chains on the properties of waterborne polyurethane composites
Bibliographic record
Abstract
ABSTRACT A new class of hydrophilic multiwalled carbon nanotubes (MWCNTs) was prepared by amidation of carboxylated MWCNTs and then undergoing the Michael addition reactions with hydroxyethyl acrylate (HEA), acryloyl morpholine (ACMO), and acrylamide (AM), respectively. The functionalized MWCNTs were dispersible well in water showing zeta potential value of higher than −33.8 mV. After used as reinforcing filler to fabricate the waterborne polyurethane (WPU) composites, the functionalized MWCNTs showed more homogenous dispersion and stronger interfacial adhesion in/with WPU matrix in comparison with pristine MWCNTs, leading to an effective improvement in the mechanical properties, hydrophilicity and electrical conductivity of the MWCNTs/WPU composites. As fabrication of 1.5 wt % MWCNTs composite, the tensile stress was greatly enhanced by about 20.3, 22.5, and 30.1% from 13.3 MPa of the pristine MWCNTs composites to 16.0, 16.3, and 17.3 MPa for the MWCNTs‐HEA, ‐ACMO, and ‐AM composites, respectively. Varying hydrophilic moieties offered different mechanical properties and electrical conductivities to the MWCNTs/WPU composites, which is suggested to be caused by the improved compatibility and hydrogen bonds between the functionalized MWCNTs and WPU macromolecules. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 46757.
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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".