Multiscale Manufacturing of Three-Dimensional Polymer-Based Nanocomposite Structures
Bibliographic record
Abstract
There is currently a worldwide effort for advances in micro and nanotechnologies due to their high potential for technological applications in fields such as microelectromechanical systems (MEMS), organic electronics and high-performance structures for aerospace. In these fields, nanoparticle-filled composites, i.e. nanocomposites, represent an interesting material option compared to conventional resins due to their enhanced properties and multi-functional potential. However, several significant scientific and technological challenges must be first overcome in order to fabricate nanocomposite-based structures and devices rapidly and cost-effectively. Various fabrication techniques of one or twodimensional (1D/2D) nanocomposite structures have been developed, but few techniques are available for three-dimensional (3D) nanocomposite structures. The capability to manufacture complex 3D structures will greatly expand the practical applications of nanocomposite materials and enable the development of novel devices such as a 3D nanocomposite micro-coil spring. This chapter will present an overview of the challenges in multiscale fabrication and the current manufacturing techniques available to create 3D structures of polymer-based nanocomposites.
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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 | 0.001 |
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".