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Record W1981802376 · doi:10.1002/aic.14205

Amine‐functionalization of the nanotitanate ETS‐2

2013· article· en· W1981802376 on OpenAlexafffund
Brenden Tanchuk, James A. Sawada, Steven M. Kuznicki

Bibliographic record

VenueAIChE Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Alberta
FundersHelmholtz-Alberta InitiativeCanada Research Chairs
KeywordsAmine gas treatingAdsorptionMonolayerSurface modificationSubstrate (aquarium)DesorptionChemistryThermogravimetryAnhydrousGravimetric analysisChemical engineeringOrganic chemistryInorganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

A series of nonporous, amine‐functionalized sodium titanates was prepared and the thermal and adsorptive behavior of the samples were characterized. Engelhard titanosilicate 2 was chosen as a substrate for its high surface area (∼300 m 2 /g), native surface hydroxyl concentration, and lack of microporosity; eliminating the risk of fouling the adsorbent under certain process conditions. Aminosilanes containing a single (N1), two (N2), and three (N3) amine groups were chemically grafted to the surface of the substrate and the adsorption capacity for CO 2 measured through thermogravimetry‐mass spectroscopy (TG‐MS) desorption, volumetric adsorption, and gravimetric adsorption/desorption cycling. The N3 sample displayed complete monolayer coverage and was capable of adsorbing five times as much atmospheric CO 2 as the N1 sample. Testing under anhydrous conditions only engages the primary amine on the tether and the data consistently suggests a correlation between amine utilization and the proportion of monolayer coverage for these adsorbents. © 2013 American Institute of Chemical Engineers AIChE J , 59: 4727–4734, 2013

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.230

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 head0.004
GPT teacher head0.162
Teacher spread0.157 · 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 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

Citations3
Published2013
Admission routes2
Has abstractyes

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