Bibliographic record
Abstract
In nanotechnology, polymers play a very important role as one of most often employed materials, especially in the fields of nanofibers and nanocomposites. Hundreds of polymers, including natural and synthetic polymers, have been fabricated into nanofibers and nanocomposites in the past 20 years. Thus a fundamental understanding of polymers, especially fiber-making polymers, is essential for people in various fields such as the biological, medical, electrical and material areas that are converging with nanotechnology. Polymeric materials The first polymers to be exploited were natural products such as wood, leather, cotton and grass for fiber, paper, construction, glues and other related materials. Then came the modified natural polymers. Cellulose nitrate was the one that first attained commercial importance for stiff collars and cuffs as celluloid in around 1885. Notably, cellulose nitrate was later used in Thomas Edison's motion picture film. Another early natural polymer material was Chardonnet's artificial silk, made by regenerating and spinning of cellulose nitrate solution, which eventually led to the viscose process that is still in use today. The first synthetic polymer was Bakelite, manufactured from 1910 onward for applications ranging from electrical appliances to phonograph records. Bakelite is a thermoset, that is, it does not flow after the completion of its synthesis. The first generation of synthetic thermoplastics (materials that could flow above their glass transition temperatures) are polyvinyl chloride (PVC), poly(styrene–stat–butadiene), polystyrene (PS), and polyamide 66 (PA66). Other breakthrough polymers include high modulus aromatic polyamides, known as Kevlar™, and a host of high temperature polymers. Table 2.1 lists some of the polymers currently often encountered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.061 | 0.048 |
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".