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Record W3091347531 · doi:10.5539/jmsr.v9n3p21

Adsorption Characteristics of Zeolite A Synthesized from Wassa Kaolin for Thermal Energy Storage

2020· article· en· W3091347531 on OpenAlexvenueno aff
Linus Kweku Labik, Bright Kwakye-Awuah, Eric K.K. Abavare, Baah Sefa-Ntiri, Isaac Nkrumah, Craig D. Williams

Bibliographic record

VenueJournal of Materials Science Research · 2020
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsZeoliteAdsorptionMaterials scienceReagentChemical engineeringLangmuirThermalLangmuir adsorption modelCatalysisOrganic chemistryThermodynamicsChemistry

Abstract

fetched live from OpenAlex

Zeolites based on the numerous applications can be utilised in providing solutions to some challenges of our world. With the ability to store thermal energy as chemical potential, zeolites are able to store thermal energy for long periods. This can occur with very minimal loss of energy and indefinitely unless the zeolite comes into contact with an adsorbate. The use of zeolite - water as adsorbent - adsorbate pair in thermal energy storage (TES) applications have been studied and have shown good results. However, the cost of zeolites synthesized from reagents continue to hamper the effective use of this adsorbent. Zeolite A was synthesized from kaolin from Wassa in Ghana based on a modified synthesis route. The adsorption properties of the zeolite utilising a designed and fabricated TES system using amounts of 100g, 200g, 300g, 400g and 500g of zeolite with a 1:1.5 ratio to water. Adsorption isosteres were plotted with the temperature and pressure values recorded and results showed correlation to adsorption behaviour of zeolites. Langmuir adsorption isotherms with r-squared values greater than 90% confirmed the affinity of water for zeolites. isosteric heat of adsorption was calculated with the minimum being 5,655.84 J/g and the maximum being 8,113.44 J/g. This confirms that the Zeolite A synthesized from Was kaolin has the structural properties needed for TES applications.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.023
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.063
GPT teacher head0.314
Teacher spread0.252 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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