Valorization of soybean oil residue through advanced technology of graphene oxide modified membranes for tocopherol recovery
Bibliographic record
Abstract
Abstract Soybean oil deodourization distillate (SODD) is a source of tocopherols used to produce vitamin E, in which α‐tocopherol is the isomer with higher biological activity and value‐added for the food industry. Hence, the aim of the present study was to modify the membrane surface with sulphonic groups, polyethyleneimine, and graphene oxide nanoparticles functionalized with tannic acid to recover SODD α‐tocopherol. First, the SODD saponification step was conducted followed by a liquid–liquid separation, which performed a pre‐concentration of α‐tocopherol approximately 28 times. The unsaponifiable material was dried and diluted in hexane, obtaining the feed solution for the membrane filtration processes. HPLC‐UV/DAD analysis showed that the modified membrane containing 1.12 mg of graphene oxide and 4.50 mg of tannic acid (M3) allowed to recover 82.00% of α‐tocopherol in the concentrate (14.16 wt.% of α‐tocopherol). From the characterizations of the modified membrane, it was observed that M3 presented hydrophobic properties and was able to be reused in two filtration cycles with a 64.39% flow recovery rate and a 71.96% recovery of α‐tocopherol. The results showed that the membrane modification with graphene oxide is a compelling methodology with low energy consumption and is eco‐friendly for the recovery and purification of α‐tocopherol from SODD.
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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) | 0.000 | 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 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".