Novel Biocomposites from Biobased Epoxy and Corn-Based Distillers Dried Grains (DDG)
Bibliographic record
Abstract
The growing ethanol production around the world demands more value-added applications for its main byproduct, distillers grains. The present work reports the study of using corn based distillers dried grains (DDG) as biofillers for biobased epoxy resin and the mechanical, physical and thermal properties of the composites. The biobased epoxy resin is the blend of diglycidylether of bisphenol-A based resin and epoxidized soybean oil. The incorporation of DDG into the biobased resin accelerated the curing process as evident from the differential scanning calorimetry and temperature-modulated Fourier transform infrared spectroscopy. The 40 wt% DDG filled composite showed an excellent retention of flexural strength of up to 94 % of that of the neat epoxy. Furthermore, the composite filled with the DDG treated with epoxy functionalized oligomeric silsesquioxane showed even better flexural properties thanks to the improved wettability of the biobased resin with the filler. Dynamic mechanical analysis showed a 12 % increment in storage modulus for the surface-treated DDG composites over the biobased resin. The improved interfacial adhesion between the DDG and matrix through the surface modification was also observed in the morphological characterization with electron microscope. The study demonstrates the viability of combining DDG biofiller with the epoxy bioresin to produce novel biomaterials at low cost.
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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 | 0.000 | 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) | 0.000 | 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".