Adsorption Characteristics of Zeolite A Synthesized from Wassa Kaolin for Thermal Energy Storage
Bibliographic record
Abstract
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.
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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) | 0.001 | 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".