Method for Converting Demetallization Products into Dispersed Metal Oxide Nanoparticles in Heavy Oil
Bibliographic record
Abstract
Metallic heteroatoms deactivate expensive catalyst and, thus, should be removed at early stages during crude oil processing. Electro- and biological demetallization are examples of two emerging techniques which remove the metallic heteroatoms—mainly nickel and vanadium—into ions or ionic complexes ultimately residing in the aqueous phase of a two-phase water/oil system. This work investigates the conversion of the aqueous metallic species into metal oxide nanoparticles, which are effective upgrading catalysts, dispersed in the oil phase. The conversion step commenced in situ within a water-in-oil emulsion structure, and the resultant nanoparticles remain very well dispersed in the heavy oil phase. The product nanoparticles were characterized, after successful collection from the oil phase, using X-ray diffraction (XRD), transmission electron microscopy (TEM), and energy dispersive X-ray spectroscopy (EDX). Despite the complexity of the heavy oil system, results confirmed the in situ formation of NiO and V 2 O 5 nanoparticles with mean sizes of 20 and 15 nm, respectively. Some aggregates have, nevertheless, formed, due to the relatively high temperature requirement of the method.
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 | 0.001 | 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".