A statistical approach to develop biocomposites from epoxy resin, poly(furfuryl alcohol), poly(propylene carbonate), and biochar
Bibliographic record
Abstract
ABSTRACT Epoxy composites are typically petroleum based and prone to fracture. Increasing concerns about climate change have motivated scientists to find green alternatives. To address both drawbacks, effect of addition of poly(propylene carbonate) polyol (PPC), a polyol derived from carbon dioxide, and biochar, a byproduct of pyrolysis in an epoxy/poly(furfuryl alcohol) (PFA) network was studied. It is hypothesized that addition of PPC will increase impact strength while addition of inexpensive biochar will offset cost of final product. In addition, incorporation of PFA, PPC, and biochar can increase biobased content of thermoset. A statistical approach was used to find a systematic correlation between constituent contents and mechanical properties of biocomposites. Mixture design of experiment and backward elimination regression were used to model mechanical properties of biocomposites. The fitted models showed a great ability to predict mechanical properties of new formulations. Addition of 10% biochar increased tensile strength and toughness by 13% and 34%, respectively. Biochar also increased modulus while it had adverse effect on impact strength. Promising effect of PPC on toughening of matrix was proved and it was found that addition of 30% of PPC increased impact strength by fivefolds. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 45307.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".