Performance investigation of hydrothermally stressed polyamide nanocomposites for insulation applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper investigates the performance of novel nano ZnO filled polyamide nanocomposites under hydrothermal conditions for cable insulation applications. Neat polyamide (PA0) and its nanocomposite with 0 wt% (PA0), 1 wt% (PA1), 3 wt% (PA3), 5 wt% (PA5), and 7 wt% (PA7) of nano ZnO were prepared and subjected to accelerated hydrothermal aging conditions in a programmable chamber at 85 °C and 85% relative humidity for 300 h. The samples were analyzed with visual inspection, hydrophobicity evaluation, optical microscopy, Fourier Transform Infrared (FTIR) spectroscopy, leakage current and UV–vis spectroscopy after every 100 h of aging. Scanning Electron Microscopy (SEM) and x-ray Diffraction (XRD) were employed for analyzing filler dispersion. Maximum filler dispersion was achieved in the case of 3 wt% of nanofiller. All the samples expressed surface degradation and increase in leakage current after aging. Maximum surface roughness and highest leakage current of 7 μ A were noticed for PA0, however PA3 expressed lowest leakage current and surface degradation. PA0 expressed the lowest hydrophobicity class of HC-3 and lowest contact angle of 75° after aging. Among the nanocomposites, PA3 expressed the highest hydrophobicity class (HC-1) and contact angle (112°) after aging. FTIR results expressed that all the samples suffered from oxidation and the C=O peaks at ∼1728 cm −1 increased by 120%, 100% and 120% for PA1, PA3 and PA7 respectively. The peaks of –OH group at ∼3500 cm −1 increased for all the sample indicating moister absorption. However, it is observed that the addition of nanofiller enhanced the overall performance of composites and among the composites PA3 performed better.
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.
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 | 0.001 | 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 it