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Record W4402512755 · doi:10.1016/j.renene.2024.121338

A novel membrane-based absorber for sorption heat transformers: A comparative experimental study of a thin-film absorbent and salt-impregnated adsorbents

2024· article· en· W4402512755 on OpenAlexafffund
Mahyar Ashouri, Salman Hassanabadi, Callum Chhokar, Ilya S. Girnik, Majid Bahrami

Bibliographic record

VenueRenewable Energy · 2024
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsPacific Institute for Climate SolutionsSimon Fraser University
FundersWestern Economic Diversification CanadaBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationSimon Fraser UniversityPacific Institute for Climate Solutions
KeywordsSorptionAdsorptionMaterials scienceMembraneTransformerChemical engineeringSalt (chemistry)Composite materialChemistryEngineeringOrganic chemistryElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

The present study proposes a novel membrane-based sorber bed with stationary solution film for oscillatory sorption heat transformers. A custom-built gravimetric large pressure jump setup is used to experimentally compare the performance of the novel membrane-based sorber bed with a Lithium Bromide/Silica gel solid composite sorbent synthesized using the salt-impregnation method. It is shown that the current membrane-based sorber bed provides up to about four times the specific cooling power and more than twice the cooling power density of solid sorbents, demonstrating the capability of the present sorber bed as a promising alternative to solid sorbents. The specific cooling power, cooling power density, and energy storage density of about 2.8 kW/kg, 470 kW/m 3 , and 269 MJ/m 3 are experimentally obtained, respectively. According to experimental results, it is observed that the film thickness plays a significant role in sorption dynamics, while the effect of membrane mass transfer resistance on sorption dynamics is less than 10%. In addition, the present membrane-based sorber bed is analytically modeled, and the results are validated with present experimental data. The present analytical model is also used to investigate the effects of design parameters on the performance metrics.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.975

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 head0.025
GPT teacher head0.262
Teacher spread0.237 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes2
Has abstractyes

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