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Record W3127231969 · doi:10.1002/slct.202003836

Synthesis of Hierarchical Porous Silica by Sol‐Gel of Sodium Silicate and Nanoemulsion Templating: Effective Combination Conditions

2021· article· en· W3127231969 on OpenAlexaff
Manal Hessien, Éric Prouzet

Bibliographic record

VenueChemistrySelect · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceChemical engineeringScanning electron microscopePorositySodium silicateCalcinationAdsorptionMicrostructureSpecific surface areaSol-gelTransmission electron microscopySmall-angle X-ray scatteringSilicateNanotechnologyScatteringComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Hierarchical porous materials have attracted a considerable attention owing to the increased interest in their applications. Hierarchical Porous Silica (HPS) was synthesized by combining the sol‐gel of sodium silicate (SS) and oil in water nanoemulsion (O/W‐NE) templating. The oil droplets of NE acted as pore forming agent and the sol‐gel built the silica framework. The O/W‐NE was prepared by a low energy method, i. e., the phase inversion composition (PIC) method. The influence of pH of SS and NE on HPS was studied. The volume of ammonia, used to induce gelling, was studied as a factor influencing the HPS. The calcined samples were characterized by scanning electron microscope (SEM), transmission electron microscope (TEM), FTIR, N 2 adsorption and small‐angel X‐ray scattering (SAXS). The results show that the microstructure is highly affected by pH and can be; macropores inserted in a dense matrix; a blend of a dense and a porous structure; or fully hierarchical porous silica. HPS has a specific surface area of 240 m 2 /g and a large pore volume (1.5 cm 3 /g) and a surface roughness of 2.95.

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)0.001
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 score0.001
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.005
GPT teacher head0.231
Teacher spread0.225 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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