Utilization of residual fatty acids in matter organic non-glycerol from a soy biodiesel plant in filaments used for 3D printing
Bibliographic record
Abstract
Matter organic non-glycerol (MONG) is a considerable waste output (20%−25% of crude glycerol) typically landfilled by soy biodiesel plants. In this work, soy MONG was characterized for potential use as a copolymer to produce filaments for 3D printing with an intent to add value and redirect it from landfills. As a copolymer, MONG was evaluated to reduce the synthetic polymer content of the natural fiber composites (NFC). Even though the general thermal behavior of the MONG was compared to that of a thermoplastic polymer in composite applications, it is dependent on the composition of the MONG, which is a variable depending on plant discharge waste. In order to improve the thermal stability of MONG, we evaluated two pretreatments (acid and acid + peroxide). The acid + peroxide pretreatment resulted in a stabilized paste with decreased soap content, increased crystallinity, low molecular weight small chain fatty acids, and a stable blend as a copolymer with a thermoplastic polymer. This treatment increased formic acid (17.53%) in MONG, along with hydrogen peroxide, led to epoxidation exhibited by the increased concentration of oxirane (5.6%) evaluating treated MONG as a copolymer in polymer processing and 3D printing.
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".