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Record W7125410700 · doi:10.26434/chemrxiv-2024-nx3vg

Experimental Demonstration of Humid Post-Combustion CO2 Capture by Vacuum Swing Adsorption Using CALF-20

2024· article· W7125410700 on OpenAlexaffabout
Tai Nguyen, George K. H. Shimizu, Arvind Rajendran

Bibliographic record

VenueChemRxiv · 2024
Typearticle
Language
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsRelative humidityCabin pressurizationHumidityAdsorptionWork (physics)Vacuum swing adsorptionSwingDry gas

Abstract

fetched live from OpenAlex

Calgary Framework 20 (CALF-20) is a metal-organic framework deployed for industrial post-combustion CO2 capture. This work explores capturing CO2 from a humid stream using CALF-20. A four-step vacuum swing adsorption (VSA) cycle incorporating a light product pressurization step was examined. Two columns packed with structured CALF-20 were used to perform VSA experiments over a wide range of relative humidity (RH) values (13%, 25%, 45% and 70% RH). Key process performance indicators, purity, recovery and productivity were measured and compared with the dry case basis. At low to intermediate relative humidity (13%-45% RH), the difference between the dry and the wet VSA cycle was minimal. The purity and recovery were approximately 95% and 71% in Case Study 1, and 92% and 81% in Case Study 2, respectively. The temperature and composition histories were similar to the dry. At high relative humidity (70% RH), while CALF-20 could still achieve similar purity, recovery and productivity, reaching low pressure during the evacuation step was difficult due to the water condensation. Each experiment was run for several days (hundreds of cycles) to confirm the long-term stability of the material. CALF-20 also showed good cyclic durability; minimal loss in the CO2 capacity from the used CALF-20 sample (~10,000 cycles) was observed

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 categoriesMeta-epidemiology (narrow)
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 score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.237
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

Citations2
Published2024
Admission routes2
Has abstractyes

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