Stabilization of nickel nanoparticle suspensions with the aid of polymer and surfactant: static bottle tests and dynamic micromodel flow tests
Bibliographic record
Abstract
Abstract Nickel nanoparticles can work as catalyst for the aquathermolysis reactions between water and heavy oil. A homogeneous and stable suspension is needed to carry the nickel nanoparticles into deeper reservoirs. This study conducts a detailed investigation on how to achieve stabilized nickel nanoparticle suspensions with the use of surfactant and polymer. To stabilize the nickel nanoparticle suspension, three surfactants including sodium dodecyl sulfate, cationic surfactant cetyltrimethylammonium bromide and polyoxyalkalene amine derivative (Hypermer) along with xanthan gum polymer were introduced into the nickel nanoparticle suspension. Static stability tests and zeta potential measurements were conducted to determine the polymer/surfactant recipes yielding the most stable nickel nanoparticle suspensions. Dynamic micromodel flow tests were also conducted on three suspensions to reveal how the nickel nanoparticles would travel and distribute in porous media. Test results showed that when the injection was initiated, most nickel nanoparticles were able to pass through the gaps between the sand grains and produced in the outlet of the micromodel; only a small number of the nickel nanoparticles were attached to the grain surface. A higher nickel concentration in the suspension may lead to agglomeration of nickel nanoparticles in porous media, while a lower concentration can mitigate this agglomeration. Moreover, clusters tended to form when the nickel nanoparticle suspension carried an electrical charge opposite to that of the porous media. Follow-up waterflood was initiated after the nanofluid injection. It was found that the waterflood could not flush away the nanoparticles that were remaining in the micromodel.
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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 | 0.001 | 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) | 0.001 | 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".