Bibliographic record
Abstract
Abstract Developments in design and technology in the engineering and medical fields necessitate the use of smart and high-performance materials to meet higher engineering specifications. The general requirements of such materials include a combination of high stiffness and strength with significant weight savings, resistance to corrosion, chemical resistance, low maintenance, and reduced costs. Over the last three decades, it has been demonstrated that auxetic materials offer a huge potential for the fields of engineering, natural sciences, and biomedical engineering, and for many other industries, including the aerospace and defense industries, through their unique deformation mechanism and measured enhancements in mechanical properties. To meet future engineering challenges, auxetic materials are increasingly being recognized as integral components of smart and advanced materials. Although materials with a negative Poisson’s ratio have been known since the early 1900s, they did not capture researchers’ attention until the late 1980s. Since 1991, these materials have been known as auxetic materials. Since then, their benefits and applications have been expanded to all major classes of materials such as metals, ceramics, polymers, and composites, and they are also now being used in engineering applications. The goal of this review was to present the development of auxetic polymers, which were first fabricated in the form of polyurethane foam approximately three decades ago and are now used in the fabrication of non-woven nano/micropolymeric structures. This review could provide useful information for the future development of auxetic polymers.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".