Natural zeolites as host matrices for the development of low-cost and stable thermochemical energy storage materials
Bibliographic record
Abstract
Abstract Advanced thermal energy storage technologies based on physical adsorption and chemical reactions of thermochemical materials (TCMs) are capable of storing large shares of renewable energy with high energy density. Further research and development is required to improve the performance and reduce the cost of these materials. A promising approach to developing low-cost TCM is to use natural zeolite adsorbents as host matrices in the development of salt-loaded composite TCM. In this study, the thermal properties of various species of low-cost zeolites from natural deposits across Canada were investigated. Two high purity crystal (HPC) zeolites from the Trans Canada (TC-HPC) and Juniper Creek (J-HPC) deposits in British Columbia were determined to have the highest water uptake capacity (0.145 g/g and 0.113 g/g, respectively) and enthalpy of adsorption (408 J/g and 304 J/g, respectively). Despite having approximately half of the water uptake capacity and adsorption enthalpy of the commercially available synthetic zeolite 13X, the cost of thermal energy storage ($CAD/kWhth) of the natural zeolites was determined to be 72–79% lower than that of the synthetic zeolite. Repeated adsorption and desorption experiments demonstrated the hydrothermal stability of the HPC zeolites over multiple charge and discharge cycles. Overall, the experimental results and cost analysis indicate that Canadian HPC zeolites are promising alternatives to synthetic zeolites in the pursuit of low-cost and stable TCM.
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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".