Evaluation of a Microencapsulated Phase Change Slurry for Subsurface Energy Recovery
Bibliographic record
Abstract
Geothermal energy offers the potential to provide continuous baseload renewable energy. Unlike conventional geothermal approaches, emerging closed-loop geothermal can employ specialized fluids that improve thermal energy recovery and expand the range of applications. However, the requirements for candidate fluids are challenging. The fluids must be physically and chemically stable under high-temperature, -pressure, and -shear conditions. Their effective heat capacity must be high and their viscosity must also be low, factors that motivated us to use a high-latent heat phase change material within a low-viscosity carrying fluid. Here, we prepare the microencapsulated phase change material-based slurries (PCSs) and perform a comprehensive thermal and hydrodynamic characterization to assess their suitability for use in a closed-loop geothermal operation. The thermophysical properties of the PCSs show promising results with minimal change in onset temperature (2.14 °C) and low supercooling (2.21 °C) during charging and discharging, respectively. A suitably low viscosity (in the range 0.01–0.05 Pa·s at 300 s–1) could be obtained with PCS concentrations in the range 20–30 wt %. Higher concentrations resulted in higher viscosities that would incur pumping energy costs exceeding the potential thermal storage benefits. At 30 wt % PCM, this fluid offers the potential for approximately 30% more stored energy than water alone for the 80 °C system. With respect to robustness under combined thermal and shear stresses, a PCS with 30 wt % phase change material showed no visible separation ratio and no shell rupturing or chemical changes when cycled 10× from 20 to 80 °C while being sheared at operation-relevant rates of 10–300 s–1. This study delineates and de-risks PCSs as geo-fluids, exploiting both sensible and latent heat storage with physical and chemical stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 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".