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Record W4415127582 · doi:10.1617/s11527-025-02803-w

Reviewing experimental studies on chemical thermal energy storage in Cementitious composites: report of the RILEM TC 299-TES

2025· article· en· W4415127582 on OpenAlexaff
Lorena Skevi, Xinyuan Ke, Stéphane Ginestet, Claudiane Ouellet‐Plamondon, Fernando Gomes, Martin Cyr

Bibliographic record

VenueMaterials and Structures · 2025
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
FundersEngineering and Physical Sciences Research Council
KeywordsCementitiousThermal energy storageEnergy storageThermal energyLatent heatThermalSolid mechanics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.254
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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