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Record W1969493428 · doi:10.1021/cm021728c

Investigation of the Evolution of Intermediate Phases of AlPO<sub>4</sub>-18 Molecular Sieve Synthesis

2003· article· en· W1969493428 on OpenAlexafffund
Yining Huang, Bryan A. Demko, Christopher W. Kirby

Bibliographic record

VenueChemistry of Materials · 2003
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroporous materialMolecular sieveMaterials scienceAmorphous solidCrystallizationHydrothermal synthesisHydrothermal circulationCrystallographyPhase (matter)Chemical engineeringAluminosilicateMagic angle spinningMineralogyNuclear magnetic resonance spectroscopyChemistryCatalysisStereochemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

In the present work, we examined the intermediate phases formed during the hydrothermal synthesis of microporous material, AlPO 4 -18. The evolution of the long-range ordering of the gel samples as a function of crystallization time was followed by powder X-ray diffraction. The development of the local environments of P and Al atoms was monitored by 31 P and 27 Al magic angle spinning (MAS) NMR. Several representative intermediate phases were further characterized by 27 Al multiple quantum MAS and 27 Al → 31 P cross-polarization experiments. Our results show that the formation of AlPO 4 -18 undergoes several stages. Mixing phosphorus and aluminum sources together with a template at room temperature yielded a mixture containing an ordered aluminophosphate (AlPO) phase and a phosphate material. These materials were converted to an amorphous AlPO phase under hydrothermal conditions. Continuous heating resulted in the transformation of the amorphous phase into a crystalline microporous material, AlPO 4 -5. A further increase in heating time led to the disappearance of AlPO 4 -5 and the formation of AlPO 4 -18.

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 score0.003
Threshold uncertainty score0.420

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 head0.013
GPT teacher head0.241
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

Citations53
Published2003
Admission routes2
Has abstractyes

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