Hybrid Green Bionanocomposites of Bio-based Poly(butylene succinate) Reinforced with Pyrolyzed Perennial Grass Microparticles and Graphene Nanoplatelets
Bibliographic record
Abstract
microparticles (biocarbon) and graphene nanoplatelets to create a hybrid bionanocomposite. Pyrolyzed biomass, known as biocarbon, was incorporated into a BioPBS matrix to improve the thermo-mechanical properties of the bioplastic while simultaneously increasing the value of this co-product. Biocomposites loaded with 25 wt % biocarbon showed 57, 13, and 32% improvements in tensile modulus, heat deflection temperature, and thermal expansion, respectively. Further improvements were found when graphene nanoplatelets (GnPs) were added to the biocomposite, forming a hierarchical hybrid bionanocomposite. Two processing methods were used to incorporate graphene into the composites: (I) graphene, BioPBS, and biocarbon were added together and directly compounded, and (II) a masterbatch of graphene and BioPBS was processed first and then diluted to the same ratios as those used in the direct compounding method I. The two methods resulted in different internal morphologies that subsequently impacted the mechanical properties of the composites; little change was observed in the thermal properties studied. Bionanocomposites processed using the direct compounding technique showed the greatest increase in tensile strength and modulus: 17 and 120%, respectively. Bionanocomposites processed using a masterbatch technique had slightly lower strength and modulus but showed almost twice the impact strength compared with the direct compounding method. This masterbatch technique was found to have a superior balance of stiffness and toughness, likely due to the presence of superclustered graphene platelets, confirmed through a scanning electron microscope and a transmission electron microscope.
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 | 0.000 | 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".