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Record W2291966185 · doi:10.1080/15583724.2015.1110167

Bimetallic Dendrimer-encapsulated Nanoparticle Catalysts

2016· article· en· W2291966185 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.

Bibliographic record

VenuePolymer Reviews · 2016
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBimetallic stripDendrimerCatalysisNanotechnologyMaterials scienceNanoparticleOrganic chemistryPolymer chemistryChemistry

Abstract

fetched live from OpenAlex

Bimetallic dendrimer-encapsulated nanoparticles (DENs) have been receiving a significant amount of attention due to their promising properties, unique characteristics, and novel applications in catalysis and other advanced “nano-” science and technology areas. Bimetallic DENs catalysts, as reviewed here, have shown a higher catalytic activity than the monometallic DENs in various catalytic systems. In this review, a general background for the dendrimer is first presented, which is then followed by an introduction of two major routes that are most often adopted in the preparation of dendrimers: divergent method and convergent method. Then, recent research advances in the synthesis, characterization, and catalytic applications of bimetallic DENs are summarized and highlighted in this article. A conclusion is then provided.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.160
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0050.020

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 head0.028
GPT teacher head0.265
Teacher spread0.237 · 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