High Temperature Self‐Regenerative Granular Hydrogels for Fracture Treatments During Subsurface Energy Recovery
Bibliographic record
Abstract
The uses of granular hydrogels to assemble macroscopic bulk hydrogels display numerous distinct advantages. However, prior assembly of bulk hydrogels is accomplished by interparticle linking strategy, which compromised mechanical property and thermal stability under hostile conditions. To expand their applications as engineering soft materials, self-regenerative granular hydrogels via a seamless integrating approach to regenerate bulk hydrogels is highly desirable. Herein, covalent regenerative granular hydrogels (CRHs) are prepared at low-temperature synthetic conditions and re-construct bulk seamless hydrogels at high-temperature aqueous environments. The re-formed bulk hydrogels display rubber-like viscoelastic behaviors over a wide range of temperatures from 90 to 150 °C, where the covalent re-crosslinking reactions homogeneously occurr along the periphery and in the matrix of granular hydrogels, accounting for the increased structural integrity at high temperatures. The bulk hydrogel shows increased elasticity and long-term thermal integrity at 150 °C for more than six months in the confined fractures. Moreover, regenerative granular CRH-based bulk hydrogels significantly improve mechanical robustness under destructive pressure. Thus, high temperature water induced regenerative granular hydrogels present the paradigm to treat engineering scenarios such as large fractures for hydraulic fracturing, drilling operation, and disproportionate permeability reduction under extremely hostile conditions during subsurface energy recovery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".