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Record W2336675361 · doi:10.1088/2053-1591/3/4/045019

Application of halloysite nanotubes for carbon dioxide capture

2016· article· en· W2336675361 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMaterials Research Express · 2016
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsHalloysitePolyethylenimineSorbentAdsorptionTriethoxysilaneDesorptionAmine gas treatingSurface modificationCarbon dioxideChemistryChemical engineeringNuclear chemistryChromatographyOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Halloysite is a naturally occurring clay, with physical structure represented by halloysite nanotubes (HNTs). We investigated the potential applicability of HNTs for carbon dioxide (CO2) capture, using two amine-functionalized HNTs: (3-aminopropyl) triethoxysilane (APTES)-grafted HNTs and polyethylenimine (PEI)-impregnated HNTs. APTES-HNTs and PEI-HNTs resulted in 5.6 and 30 wt. % (in sorbent) in functionalization onto HNTs, respectively. Capture efficiency was higher in APTES-HNTs at lower temperatures, while it was maximum in PEI-HNTs at 70°C–75 °C. At 75 °C, adsorption/desorption tests showed that 95% of the two reactions occurred within 30 min, and exhibited 0.15 and 0.21 millimole of CO2 adsorption capacity per millimole of amine group for APTES-HNTs and PEI-HNTs, respectively. During 10 cycles of CO2 adsorption/desorption, there was no significant decrease in sorbent weight and adsorption capacity in both HNTs. These results show that inherent structural features of HNTs can be easily tailored for the development of operational condition-specific CO2 capture system.

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.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.048
GPT teacher head0.355
Teacher spread0.306 · 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