Preparation and Characterization of Exfoliated PHBV Nanocomposites to Enhance Water Vapor Barriers of Calendared Paper
Bibliographic record
Abstract
The water vapor transmission rates (WVTRs) of poly(3-hydroxybutyrate- co -4-hydroxybutyrate) (PHBV) and PHBV/nanocomposite-coated papers were measured at various levels of relative humidity and temperature. The coating of PHBV on base paper was performed with two different methods, and the more effective one for lowering WVTR values was utilized for coating the PHBV/nanocomposites on the paper. Nanocomposites of PHBV were prepared with a commercial montmorillonite Cloisite30B (CS30B), and a novel modified clay was obtained via a solution-blending process. To prepare the series of modified clays, which were coined here as HPGC, MPGC, and LPGC, we intercalated β-butyrolactone in the Cloisite30B clay by a ring-opening polymerization. The morphology of the clay and nanocomposites was revealed by X-ray diffraction. Additionally, the dispersion and phase behavior of the clay in the PHBV matrix was observed using a transmission electron microscope. It was found that the coating method and clay exfoliation were the most important factors affecting water vapor permeability. The water vapor barrier of the coated papers was improved significantly if the surface of the base substrate was prespray-coated with a PHBV suspension prior to the laminate coating of the PHBV film with a hot press. The papers coated with exfoliated PHBV/nanocomposites exhibited even lower WVTR values. Overall, PHBV, PHBV/CS30B, and PHBV/HPGC coating treatments lowered the WVTR values by 46, 56, and 118 times, respectively. The resulting coated paper is promising as a green-based packaging material due to an improved moisture barrier.
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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 | 0.000 |
| 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) | 0.001 | 0.000 |
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".