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Record W2134966954 · doi:10.1039/c3nr06127b

Towards waste free organic synthesis using nanostructured hybrid silicas

2014· article· en· W2134966954 on OpenAlexaff
Rosaria Ciriminna, Laura M. Ilharco, Valerica Pandarus, Alexandra Fidalgo, François Béland, Mario Pagliaro

Bibliographic record

VenueNanoscale · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsSiliCycle (Canada)
FundersCentre National de la Recherche Scientifique
KeywordsOrmosilMaterials scienceAmorphous solidCatalysisNanoparticleNanotechnologyChemical engineeringHybrid materialSol-gelOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

As catalysis and organic synthesis come together again, the need for stable, selective and truly heterogeneous solid catalysts for clean and efficient synthetic organic chemistry has increased. Hybrid silica glasses obtained by the sol-gel nanochemistry approach can be successfully used for the waste-free synthesis of valued chemicals in various applications. This success derives from the deliberate chemical design of hybrid nanostructures capable of immobilizing and stabilizing organocatalytic species and unstable metal nanoparticles. The highly selective activity along with a broad scope and ease of application of these mesoporous materials to high-throughput reactions opens the route to faster, cleaner and more convenient processes for both small and large scale manufacturing of useful molecules.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2014
Admission routes1
Has abstractyes

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