Novel production of basalt nanosheets with ultrahigh aspect ratios and their assembly with nylon membranes for high-performance electrical insulating composite membranes
Bibliographic record
Abstract
The rapid advancement of modern electrical equipment has significantly increased the demand for advanced electrical insulating materials. Traditional silicate nanosheets are widely used as fillers in electrical insulating composites. However, their low aspect ratio restricts their effectiveness in high-performance electrical insulating applications. Here, a three-step liquid exfoliation strategy is proposed to prepare basalt nanosheets (BSNs) with an ultrahigh aspect ratio (up to 1397) from basalt scales (BS). This process involves cation exchange on BS to generate lamellar structures, serving as the basis for the preparation of lithium ion-exchanged BSNs (LBSNs). Next, oxalic acid/hydrogen peroxide treatment breaks the chemical bonds within the LBS sediment, producing chemically treated BSNs (CBSNs). Finally, BSNs are prepared by mixing LBSNs and CBSNs, followed by differential centrifugation to isolate BSNs with an ultrahigh aspect ratio (BSNs-1000). Subsequently, inspired by the overlapping structure of pangolin skin, BSNs-1000 are assembled onto nylon membranes, forming biomimetic nylon/BSNs-1000 (N/B-1000) composite membranes with an overlapping surface structure. This structure forms an effective physical barrier, impeding charge and crack propagation, thereby significantly enhancing their electrical insulating and mechanical properties. The novel exfoliation method and biomimetic strategy provide effective approaches for developing advanced electrical insulating membranes for high-performance electrical equipment.
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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) | 0.000 | 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 | 0.001 |
| 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".