Fabrication and Dielectric Properties of Soft-core Helical Particles Using Spirulina Platensis as Templates
Bibliographic record
Abstract
Aiming at the lightweight filler particles with good dielectric properties in the composites, helical Spirulina platensis were chosen as templates to produce microscopic helical soft-core filler particles by an electroless deposition technique. The morphology and appearance of the coated Spirulina platensis was analysed with optical microscopy and scanning electron microscopy respectively, the result showed that the particles were successfully coated with a uniform metal coating and their initial helical shape were perfectly replicated. The dielectric properties of these helical soft-core filler particles embedded in epoxy resin were studied in detail, which showed that with the coating thickness increase, the real and imaginary part of permittivity of the composites both increase in a frequency of 2–18 GHz. These soft-core metallised helical microorganisms are lightweight and have good dielectric properties. The metal content in the composites is only 6.6 vol% when the percolation threshold occurs. Such low metal content can reach percolation point is attributed to the filler particles’ soft-core structure and long helical shape advantage. Keywords: Microorganism; bio-replicated forming; soft-core helical particle; electroless deposition; dielectric property
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 | 0.000 |
| 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 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".