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Record W3046550734 · doi:10.5539/apr.v12n4p75

Electricity Generation Using a Hybridized Zeolite Adsorption Heat Pump and Heat Engine

2020· article· en· W3046550734 on OpenAlexvenueno aff
Linus Kweku Labik, Bright Kwakye-Awuah, Baah Sefa-Ntiri, Eric K.K. Abavare, Isaac Nkrumah, Craig D. Williams

Bibliographic record

VenueApplied Physics Research · 2020
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsHeat pumpAdsorptionStirling engineMaterials scienceZeoliteElectricityThermal energy storageChemical engineeringEnvironmentally friendlyHeat engineThermodynamic cycleProcess engineeringThermodynamicsEnvironmental scienceNuclear engineeringChemistryOrganic chemistryCatalysisHeat exchangerElectrical engineering

Abstract

fetched live from OpenAlex

The use of adsorption in Thermal Energy Storage has gained considerable research interest of late. Some applications have focused on the use of TES for transformation of low temperature heat in applications such as cooling and heating. Zeolite and water have been studied as suitable materials. Their characteristics as environmentally friendly materials and high affinity makes them conspicuous. The unique properties of zeolites to hold adsorbed water/heat with very minimal loss is also significant. With the aid of a dynamo, a Stirling engine as heat engine and the adsorption energy storage system serving as heat pump was used to generate electricity. The relationship between electricity generation and temperature was investigated. The obtained average temperature and pressure of the zeolite - water adsorption heat pump was also compared with the basic adsorption cycle.

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 score0.705
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.104
GPT teacher head0.309
Teacher spread0.205 · 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
Published2020
Admission routes1
Has abstractyes

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