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Record W2037916674 · doi:10.1021/cm0005743

Preparation and Performance of Pd Particles Encapsulated in Block Copolymer Nanospheres as a Hydrogenation Catalyst

2000· article· en· W2037916674 on OpenAlex
Royale S. Underhill, Guojun Liu

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

VenueChemistry of Materials · 2000
Typearticle
Languageen
FieldMaterials Science
TopicDendrimers and Hyperbranched Polymers
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCatalysisMethanolCopolymerAcrylic acidNanoparticleHydrazine (antidepressant)Yield (engineering)Polymer chemistryMaterials sciencePolymerChemical engineeringChemistryOrganic chemistryNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Triblock nanospheres with hydroxylated polyisoprene (PHI) coronas, cross-linked poly(2-cinnamoyloxyethyl methacrylate) shells, and poly(acrylic acid) (PAA) cores were prepared following a method described previously. Equilibrating such nanospheres with a PdCl 2 solution in methanol enabled the loading of Pd 2+ into the PAA cores of the nanospheres. After the excess PdCl 2 in the methanol phase was removed, Pd(II) inside the nanosphere cores was reduced with hydrazine to yield Pd nanoparticles. Such encapsulated Pd particles were dispersed in methanol or water, which solubilized PHI. Like Pd black, the nanosphere-encapsulated Pd nanoparticles catalyzed the hydrogenation of alkenes. The need for the reactant(s) to diffuse into and products to diffuse out of the encapsulating nanospheres expectedly slowed the catalytic reactions. The more interesting aspect had been in our ability to modify the activity of the Pd catalyst via changing the pH and thus the conformation of the encapsulating polymer chains.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.004
Threshold uncertainty score0.997

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)0.0040.000

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 head0.006
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