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Record W6902926494 · doi:10.1021/acs.iecr.1c00469.s001

Rapid Cycling Thermal Swing Adsorption Apparatus:\nCommissioning and Data Analyses for Water Adsorption of Zeolites 4A\nand 13X Over 2000 Cycles

2021· article· en· W6902926494 on OpenAlexaff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAdsorptionThermogravimetric analysisZeoliteSwingThermalThermal analysisThermal stability

Abstract

fetched live from OpenAlex

Evaluation of adsorbent\nintegrity over thousands of cycles is necessary\nto establish the service time and sustainability of adsorbents employed\nin industrial dehydration. Herein, an adsorption apparatus for rapidly\ncycling multiple adsorbents through a thermal swing adsorption process\nis introduced with results for 2000 cycles. This apparatus has eight\nsample cells arranged in parallel, which are embedded in an aluminum\nblock for rapid heating and cooling. At the outlet of each cell, the\nwater content and temperatures are measured using capacitance relative\nhumidity sensors, which incorporate resistance thermometers. The analysis\nof the breakthrough curves generated for each adsorbent gives inference\ninto the change in water uptake capacity over continuous cycling.\nTo handle the large sets of data generated by this instrument, an\nautomated analysis program was implemented. To demonstrate the functionality\nof the instrument, zeolites 4A and 13X were cycled in a thermal swing\nprocess over 2000 cycles and the change in the uptake capacity was\nmonitored by the analysis of the breakthrough plots for each cycle.\nFurthermore, the results of the breakthrough analyses were verified\nwith the thermogravimetric analysis of the adsorbents. From these\nexperiments, zeolites 4A and 13X were observed to lose 7 ± 3\nand 19 ± 7% of the adsorption capacity, respectively.

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 categoriesInsufficient payload (model declined to judge)
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 score0.377
Threshold uncertainty score0.992

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)0.0090.000

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 head0.101
GPT teacher head0.324
Teacher spread0.224 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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