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Record W4381194114 · doi:10.11159/ehst23.121

Vermiculite/LiCl Composite for Adsorption Thermal Energy Storage

2023· article· en· W4381194114 on OpenAlexafffund
Suboohi Shervani, Curtis Strong, F. Handan Tezel

Bibliographic record

VenueProceedings of the International Conference of Energy Harvesting, Storage, and Transfer · 2023
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsUniversity of Ottawa
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsVermiculiteAdsorptionComposite numberMaterials scienceEnergy storageThermalThermal energy storageComposite materialChemistryPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Sorption based thermal energy storage systems are amassing more attention of the researchers, due to their high energy storage density, stability and cyclic performance.Several materials have been used for adsorption based thermal energy storage technology.One promising group of materials are hygroscopic salts (e.g.metal chlorides), due to their high energy storage density and water vapour sorption capacity.However, hygroscopic salts are difficult to handle due to deliquescence and issues of cyclic stability.One method of stabilizing hygroscopic salts is to impregnate a host material with them to form a salt-in-matrix composite.Vermiculite is considered to be a good candidate as a host material for salt-in-matrix composites for thermal energy storage due to its high porosity, high stability, and low cost.Pure vermiculite has low energy storage density in comparison to traditional adsorbents.However, when vermiculite is impregnated with LiCl energy storage density values of up to 159 kWh/m 3 can be achieved at 50% relative humidity after a regeneration temperature of 120C.This paper includes the synthesis and characterization, e.g.structural and thermal energy storage properties of the vermiculite based composite.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.575

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 head0.024
GPT teacher head0.221
Teacher spread0.197 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the International Conference of Energy Harvesting, Storage, and TransferSame topicAdsorption and Cooling SystemsFrench-language works237,207