Rheological and thermal stability of interpenetrating polymer network hydrogel based on polyacrylamide/hydroxypropyl guar reinforced with graphene oxide for application in oil recovery
Bibliographic record
Abstract
Abstract The purpose of the present work is to enhance the thermal stability and rheological properties of semi-interpenetrating polymer network (IPN) hydrogel based on partially hydrolyzed polyacrylamide/hydroxypropyl guar (HPAM/HPG) nanocomposite reinforced with graphene oxide (GO), at temperatures (200 and 240 °F) for use in oil recovery applications. FTIR spectra of the IPN nanocomposite hydrogels revealed interactions of GO with HPAM/HPG chains. An increase in the viscosity is also observed from the rheological study. Moreover, IPN and its nanocomposite hydrogels exhibited non-Newtonian behavior. The decline of viscosity of IPN nanocomposite hydrogels was observed with an increase in the temperature from 200 to 240 °F but was still higher than IPN hydrogel without GO. Dispersion of GO through the HPAM/HPG hydrogel matrix was evaluated by SEM morphology and electrical conductivity. The IPN nanocomposite hydrogels showed high viscosity stability, thermal stability, and flow activation energy as compared to IPN hydrogel without GO. Therefore, the addition of 0.1 wt.% of GO to the HPAM/HPG matrix is suitable to create a cross-linked polymer solution with improved properties which may be beneficial for use in 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) | 0.001 | 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".