Bibliographic record
Abstract
Knowing the basic properties of nanofibers (such as morphology, molecular structure and mechanical properties) is crucial for the scientific understanding of nanofibers and for the effective design and use of nanofibrous materials. In order to evaluate and develop the manufacturing process, the composition, structure and physical properties must be characterized to decide whether the produced fibers are suitable for their particular application. Evaluation of the various production parameters in processes such as electrospinning is a critical step towards production of nanofibers commercially. Many common techniques used to characterize conventional engineering materials, as well as some not so common techniques, have been employed in the characterization of nanofibers. Table 6.1 shows the scales of fibers and the corresponding characterization techniques. To provide an overall understanding, some of the general characterization techniques for structural, chemical, mechanical, thermal and other properties will be introduced in this chapter. Structural characterization of nanofibers The morphological characterization techniques briefly discussed herein are: optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM) and scanning tunneling microscopy (STM). These methods characterize the morphology and determine fiber diameter, pore size and porosity, all of which are necessary to evaluate the various production parameters. The techniques for characterization of order/disorder of molecular structures using X-ray diffraction (XRD) are also covered in this section. Furthermore, mercury porosimetry, a special technique for porosity measurement, is introduced.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".