Covalent Functionalizations of Poly(vinyl chloride) in Tune with Applications: An Update
Bibliographic record
Abstract
Poly(vinyl chloride), PVC, stands as one of the best polymer candidates as far as polymeric materials are strongly sought for in our today’s life. Functionalization of poly(vinyl chloride) (PVC) remains an appropriate way to fashion materials for specific applications. Molecules of different functionalities and sizes, up to macromolecules, were affixed to PVC matrix. Graft polymerization led to functionalized PVC with several properties for different applications. Some covalently modified PVCs, mainly with heteroatom-containing and cyclic molecules, proved to be biologically active and efficient scaffolds for enzyme/protein immobilization. Suitable functionalizations of PVC even ensured the effectiveness of the polymers as separative, ion-selective electrode, and fuel cell membranes. Some modifying agents incorporated in PVC made the polymeric materials convenient and reliable for solar cells design. Reactions of PVC with metal chelating molecules engendered PVC-metal complexes that were efficient polymer-supported catalysts for Heck, Sonogashira, and Suzuki-Miyaura coupling reactions. Heavy metal sorbents were also made by tailored functionalization of PVC. Modifications of PVC with allotropic carbon nanoparticles, including fullerene C60, carbon nanotubes, and graphene and their applications in the nanocomposites making are herein discussed. The newly emerged “click chemistry” and “living controlled radical polymerization, LCRP” were exploited in the functionalization of poly(vinyl chloride).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".