Unveiling the role of Janus nanoparticle shape in trapped oil displacement: A molecular perspective
Bibliographic record
Abstract
Janus nanoparticles (JNPs) exhibit significant promise for enhancing oil recovery (EOR). However, their large-scale field deployment remains challenging. A key challenge lies in the insufficient understanding of how the physical characteristics of JNPs influence their transport behavior and microscopic oil displacement mechanisms in porous media. In this study, molecular dynamics (MD) simulations are employed to systematically investigate the displacement dynamics of oil trapped on rough surfaces mediated by JNPs of various geometries. The results reveal that particle shape critically affects both the pinning resistance encountered at groove edges and the accumulation patterns along lateral walls. These shape-dependent adsorption configurations in turn modulate local wettability and ultimately dictate the efficiency of oil removal from nanoscale grooves. Spherical and ellipsoidal JNPs demonstrate superior displacement performance when the groove surface is coated with a thin oil film. However, under conditions involving thick oil films, spherical JNPs exhibit limited penetration into narrow grooves due to their stable orientation at the oil–water interface, which reflects strong interfacial stability. In contrast, disc, rod, and ellipsoidal JNPs effectively disrupt thick oil films via a cooperative mechanism termed “aggregation and flipping”. Among all evaluated geometries, ellipsoidal JNPs consistently deliver optimal EOR performance across various oil film conditions. These findings provide molecular-level insights into shape-governed JNP performance in EOR, offering valuable guidance for the rational design and application of shape-optimized JNPs in oilfield operations.
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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 | 0.001 |
| 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".