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Record W1965414716 · doi:10.1021/jp904599t

Infrared and Computational Studies on Interactions of Carbon Dioxide and Titania Nanoparticles with Acetate Groups

2009· article· en· W1965414716 on OpenAlexaff
Ruohong Sui, John M. H. Lo, Paul A. Charpentier

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2009
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of CalgaryWestern University
Fundersnot available
KeywordsCalcinationFourier transform infrared spectroscopyNanoparticleSupercritical carbon dioxideInfrared spectroscopyInfraredMoleculeChemistryDensity functional theoryDenticityAbsorbanceSupercritical fluidLewis acids and basesInorganic chemistryMetalChemical engineeringMaterials scienceCatalysisNanotechnologyOrganic chemistryComputational chemistryChromatography

Abstract

fetched live from OpenAlex

Understanding the nature of the interactions between growing colloidal nanoparticles and CO 2 molecules is of importance for designing and synthesizing well-defined oxide nanoarchitectures during sol−gel processing in supercritical CO 2 (scCO 2 ). In this research, attenuated total reflective Fourier transform infrared (ATR-FTIR) spectrometry was used for studying the interactions between CO 2 molecules and metal acetate bidentate groups. Freshly synthesized aerogels formed using Ti and Zr alkoxides were treated with CO 2 and subsequently depressurized to remove the strong infrared absorbance of bulk CO 2 . The IR spectra from the depressurized TiO 2 and ZrO 2 samples were compared to the IR spectra from the respective calcined samples showing that the ν 2 bending peak of the CO 2 split due to Lewis acid and base interactions. Further verification of the spectral assignments and the bonding analysis were obtained by theoretical calculations using the density functional theory (DFT) method and TiO 2 nanocluster models. This research demonstrates that the metal acetate group on the Ti and Zr polycondensates is CO 2 -philic, and therefore these colloidal particles can be stabilized in scCO 2 .

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.144

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 head0.009
GPT teacher head0.237
Teacher spread0.229 · 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

Citations28
Published2009
Admission routes1
Has abstractyes

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