Bibliographic record
Abstract
Abstract The chapter reviews and illustrates with examples the diverse applications of vibrational spectroscopy to the study of polymer composites, which are defined as materials consisting of a polymeric (thermoplastic or thermoset) matrix reinforced with fibers (carbon, glass, polymer) or inorganic particles. After some discussion of the experimental difficulties that must be dealt with in obtaining spectra of these heterogeneous materials, the literature is reviewed under the following subject categories: quality control of polymer composites; studies of reinforcements (carbon, glass, polymer fibers, inorganic particulate fillers, cellulose‐based fillers), their surface treatments, and interphases; thermoplastic matrices (crystallinity, orientation); thermoset matrices (the study and monitoring of curing by mid‐infrared, near‐infrared, and Raman spectroscopies); environmental degradation. The chapter concludes with a brief coverage of newer types of composite materials: nanocomposites based on layered silicates (nanoclays), sol–gel preparation methods, and carbon nanotubes; molecular composites; and polymer‐dispersed liquid crystals.
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) | 0.007 | 0.002 |
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".