Reviewing experimental studies on chemical thermal energy storage in Cementitious composites: report of the RILEM TC 299-TES
Bibliographic record
Abstract
Thermochemical energy storage (TCES) is a method of storing energy by using reversible chemical reactions to absorb and release heat. TCES materials generally possess the highest volumetric energy density and negligible heat losses during cyclic charging/discharging when compared with sensible and latent heat storage materials. The controllable charging/discharging processes in the TCES materials make them suitable for long-term or seasonal thermal energy storage, which can help improve the resilience of the existing energy system and built environment. In recent years, there has been a growing number of studies on the use of cementitious materials as low-cost and low-carbon thermochemical energy storage materials, including ettringite, calcium aluminate cements, and geopolymers. In this study, the state-of-the-art development using cementitious materials for thermo-chemical energy/heat storage applications is reviewed and systematically compared in terms of their compositions, energy storage operating conditions, and energy storage performance. Technical recommendations are proposed for standardised characterisation and testing protocols of these cementitious (composite) materials used for thermochemical heat storage. The current research challenges and future research needs in this field are also discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".