Utilizing limestone calcined clay cement (LC3) to develop eco-friendly grouts for shallow geothermal energy applications—a laboratory study
Bibliographic record
Abstract
Utilizing low-carbon, cement-based grouts is essential for promoting a more sustainable approach to harnessing shallow geothermal energy. This study aims to develop eco-friendly, cement-based grouts utilizing limestone calcined clay cement (LC 3 ) for shallow geothermal energy applications. The development of the grouts involved two steps. First, the grout composed of 90% LC 3 , 5% CSA cement, and 5% gypsum was identified as the optimum binder system (named LC 3 -90 in the mixture design) based on tests for density, thermal conductivity, volumetric stability, and compressive strength. Secondly, based on the optimum binder system, different contents of graphite (3, 6, 9, and 12 wt.% of binders) were incorporated into the grout to enhance its thermal performance. The results indicate that the developed grouts achieved thermal conductivity up to 1.862 W/mK in dry states and 3.184 W/mK when fully saturated, with a negligible shrinkage of -0.0091% and a high compressive strength of 26.04 MPa. Finally, a life cycle analysis (LCA) revealed that the greenhouse gas emission of the developed grouts is only around 377 kg CO 2 eq per 1 m 3 grout, which is up to 36% lower than traditional OPC-based grout. In conclusion, the developed grouts provide excellent heat transfer and sealing capacities, improving the overall performance of shallow geothermal systems while significantly reducing environmental impacts. • Limestone calcined clay cement (LC 3 ) was first used to develop grouts for BHEs. • The developed grouts show high thermal conductivity, up to 3.184 W/mK. • The developed grouts show excellent sealing capacities with negligible shrinkage. • LCA was performed to assess the environmental benefits of the developed grouts. • The developed grouts reduce CO 2 emissions by 36% compared with traditional grouts.
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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".