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Record W4400299885 · doi:10.26434/chemrxiv-2024-0mhg1

A Thin Film Approach to Rapid, Quantitative Measurements of Mixed-Gas Adsorption Equilibrium in Nanoporous Materials

2024· preprint· en· W4400299885 on OpenAlexaff
Jessica C. Moreton, Rajamani Krishna, Jasper M. van Baten, Nicholas Fylstra, Michel Chen, Thomas Carr, Kamalani Fielder, Kristi Chan, George K. H. Shimizu, S. Y. Yamamoto

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNanoporousAdsorptionMaterials scienceNanotechnologyChemical engineeringThermodynamicsChemical physicsChemistryPhysical chemistryPhysicsEngineering

Abstract

fetched live from OpenAlex

Nanoporous adsorbent materials are a key part of many industrial processes, including the rapidly-expanding carbon capture industry. Development of advanced sorbents requires an assessment of the sorbent’s performance under mixed-gas conditions. Existing measurement techniques tend to be slow, material-intensive, and have limited ability to measure competitive mixed-gas sorption. We have developed a novel technique that measures thin films of sorbents deposited onto sensitive micro-electromechanical system (MEMS) transducers. This technique is fast, requires very little material, and enables real-time monitoring of binary gas sorption. We report measurements of CO2/H2O mixed-gas isotherms at three different temperatures on the carbon capture MOF CALF-20. The measured experimental data on CO2/H2O mixture adsorption in CALF-20 demonstrate the severe limitations of the Ideal Adsorbed Solution Theory (IAST) in providing a quantitative estimation of the component loadings. Departures from the IAST are quantified by introduction of activity coefficients and use of the Real Adsorbed Solution Theory (RAST).

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)0.005
metaresearch head score (Gemma)0.001
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 score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.064
GPT teacher head0.302
Teacher spread0.238 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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