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.
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 | 0.000 | 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".