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Record W2520796725 · doi:10.1002/cite.201600052

Adsorption Behavior of Halogenated Anesthetic and Water Vapor on Cr‐Based MOF (MIL‐101) Adsorbent. Part II. Multiple‐Cycle Breakthrough Tests

2016· article· en· W2520796725 on OpenAlexaff
Hua Ye, Nicola Gargiulo, Antonio Peluso, Paolo Aprea, Mladen Eić, Domenico Caputo

Bibliographic record

VenueChemie Ingenieur Technik · 2016
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsAdsorptionWater vaporChemistryRelative humidityChromiumHumidityChromatographyMetal-organic frameworkOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract Multiple‐cycle breakthrough tests of a binary gas mixture of the halogenated anesthetic sevoflurane (SF) and water vapor were performed on both the synthesized chromium‐based metal organic framework (Cr‐MOF) and a conventionally used reference adsorbent. At 1 vol % SF and 50 % relative humidity mixture composition, the Cr‐MOF (MIL‐101) showed a significantly higher SF adsorption capacity, lower water vapor adsorption capacity, and no roll‐up effect compared to the reference sample. After each adsorption measurement, the saturated column was regenerated. MIL‐101 showed higher stability with minor SF capacity loss after 14 cycles, while the reference sample lost about a half of the SF capacity after 17 cycles.

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), Insufficient 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 score0.008
Threshold uncertainty score1.000

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)0.0010.000

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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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