Synthesis, characterization, and rheological behavior of HPG graft poly (AM-co-AMPS)/GO nanocomposite hydrogel system for enhanced oil recovery
Bibliographic record
Abstract
The purpose of this study is to investigate the synthesis of graft copolymer nanocomposite based on hydroxypropyl guar (HPG) graft acrylamide (AM) and 2-acryloamido-2-methyl propane sulfonic acid (AMPS), reinforced with graphene oxide (GO), and study its suitability for the development of the copolymer-based hydrogel systems by chromium triacetate crosslinker to use in oil recovery applications. The characterization outcomes acknowledged the grafting of AM and AMPS onto HPG in the attendance of the GO. The uniform dispersion of GO at 0.1 wt.% was observed in the morphology analysis. Moreover, not only the viscosity but also, the storage and loss modulus of graft copolymer nanocomposites hydrogel improved by adding GO. The effect of graft copolymer nanocomposite and crosslinker concentrations on the performance of hydrogel was also evaluated and optimized by a rheological test. These results showed the outstanding performance of crosslinked hydrogel structure of HPG-g-poly (AM-co-AMPS)/GO, owing to the linking of AM with AMPS and grafting on the HPG chains and the surface of GO in attendance of chromium triacetate. This makes the copolymer hydrogel system more stable against salinity, shearing, and high temperatures. Therefore, this nanocomposite hydrogel system is potentially useful for oil recovery applications.
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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) | 0.000 | 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 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".