Preparation of adaptive bifunctional reconfigurable polymers and their sand carrying and drag reduction behaviour
Bibliographic record
Abstract
Abstract In order to solve the problem of drag reduction at the front end of shale fractures and sand carrying at the tail end with increased viscosity, the molecular dynamics simulation (MD) method was used to design polymer molecules and simulate the steric resistance, interaction potential energy, mean square displacement, and radial distribution function of the polymer. The polymer AM‐AMPS‐LMA‐DiC 12 AM (ASLC12) with better solubility, diffusion, and resistance reduction potential was obtained and synthesized. By scanning electron microscope (SEM) and viscoelastic analysis, ASLC12 has a stable mesh structure, good viscoelasticity, and shear resistance, and the mesh structure formed by it is in a dynamic equilibrium state of fracture‐reorganization under shear. We then analyzed the drag reduction, sand carrying, and salt resistance of ASLC12. When the concentration of ASLC12 is 0.09%, the sand‐carrying requirement is satisfied. When the concentration is 0.05%, the drag reduction rate can reach 74.1%, and the resistance reduction rate of ASLC12 in salt ion solution can still reach more than 62%. This shows that the polymer ASLC12 has better sand carrying, drag reduction, and salt resistance.
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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) | 0.001 | 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".