Preparation of hydroxyl and (3‐aminopropyl)triethoxysilane functionalized multiwall carbon nanotubes for use as conductive fillers in the polyurethane composite
Bibliographic record
Abstract
A new method has been developed to prepare hydroxyl‐functionalized multiwall carbon nanotubes (MWCNTs‐OH) and (3‐aminopropyl)triethoxysilane‐functionalized MWCNTs (MWCNTs‐APTES), which can be uniformly dispersed in solvent‐borne polyurethane (PU) to obtain the nanocomposites with enhanced mechanical, thermal, and electrical properties. Scanning electron microscope, X‐ray photoelectron spectroscopy, Fourier transform infrared spectrometer, and thermogravimetric analyzer were employed to characterize the changes in MWCNTs surface morphology and structure. The result showed that the oxidation of MWCNTs by H 2 O 2 in NaOH solution caused small damages to their structure, and oxygen‐containing functional groups were mainly present as hydroxyl groups, which acted as binding sites in the next silanization process. The functionalization provided MWCNTs with improved dispersibility and strong interfacial bonds in/with PU matrix, resulting in an increase in the wettability, tensile strength, hardness, storage modulus, glass transition temperature, thermal stability, and electronic conductivity of the PU composites. In comparison with the MWCNTs‐OH composites, MWCNTs‐APTES composites exhibited more enhanced above properties because hydroxyls or amines could increase the interfacial adhesion between MWCNTs and PU matrix, whereas alkyl groups of the silane are favor of increasing the filler's compatibility with polymer. At loading of 6 wt% MWCNTs, the tensile strength and electronic conductivity of MWCNTs‐OH/PU were 2.45 MPa and 1.72 S/cm, respectively, but increased to 3.45 MPa and 87 S/cm for the MWCNTs‐APETS/PU composite. POLYM. COMPOS., 39:1212–1222, 2018. © 2016 Society of Plastics Engineers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".