Space Charge Modulation in Low-Density Polyethylene via Electrode Interface Engineering: A PEA-Based Experimental Study
Bibliographic record
Abstract
We investigate how the chemical symmetry of adjacent semiconductive screens modulate space-charge formation and transport in Low-Density PolyEthylene (LDPE). Using the Pulsed Electro-Acoustic (PEA) method on$\mathbf{1 5 0} \boldsymbol{-} \boldsymbol{\mu} \mathbf{m}$LDPE under$100 \text{kV} / \text{mm} \text{DC}$at$23 \pm 2{ }^{\circ} \mathrm{C}$, we compare symmetric (OSL-LDPE-OSL) and asymmetric (ISL-LDPEOSL) stacks based on EVA/carbon-black screens. Symmetric screens markedly suppress space-charge accumulation-peak density$\mathbf{2. 3 8 ~ C} \cdot \mathbf{m}^{-\mathbf{3}}$vs$\mathbf{2 9. 9 6 ~ C} \cdot \mathbf{m}^{-\mathbf{3}}$-and slow packet propagation-transit 900 s vs 500 s-with earlier onsets in the asymmetric case (150 s) than in the symmetric (450 s). Intermediate behavior appears for ISL-LDPE-ISL$(9.38 \mathrm{C} \cdot \mathrm{m}^{-\mathbf{3}}$; 600 s) and OSL-LDPE-ISL ($17.25 \mathrm{C} \cdot \mathrm{m}^{-3}; 600 ~\mathrm{s}$). PEA maps for symmetric stacks are broader and less skewed, consistent with more balanced injection barriers and more effective bipolar recombination. Without altering the polymer matrix, interfacial symmetry emerges as a simple, scalable lever to attenuate spacecharge accumulation and enhance dielectric reliability in medium-voltage cable insulation.
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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".