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Record W3000711956 · doi:10.1021/jacs.9b10156

Polymer Stereocomplexation as a Scalable Platform for Nanoparticle Assembly

2020· article· en· W3000711956 on OpenAlexfundno aff
Allison Abdilla, Neil D. Dolinski, Puck de Roos, Jing Ren, Erika van der Woude, Soyoung E. Seo, Manuel Sanchez Zayas, Jimmy Lawrence, Javier Read de Alaniz, Craig J. Hawker

Bibliographic record

VenueJournal of the American Chemical Society · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsnot available
FundersArmy Research OfficeNatural Sciences and Engineering Research Council of CanadaDivision of Materials ResearchVeskiLouisiana State University
KeywordsNanomaterialsNanoparticlePolymerNanotechnologyMethyl methacrylateQuantum dotNanoscopic scaleTacticityChemistrySelf-assemblyCopolymerNanostructureNanometreMethacrylateMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

DNA-mediated assembly of inorganic particles has demonstrated to be a powerful approach for preparing nanomaterials with a range of interesting optical and electrical properties. Building on this inspiration, we describe a generalizable gram-scale method to assemble nanoparticles through the formation of poly(methyl methacrylate) (PMMA) triple-helices. In this work, alkene-terminated syndiotactic (st-) and isotactic (it-) PMMA polymers were prepared and subsequently functionalized to afford nanoparticle ligands. Nanoparticles with complementary st- and it-PMMA ligands could then be spontaneously assembled upon mixing at room temperature. This process was robust and fully reversible through multiple heating and cooling cycles. The versatility of PMMA stereocomplexation was highlighted by assembling hybrid structures composed of nanoparticles of different compositions (e.g., Au and quantum dots) and shapes (e.g., spheres and rods). These initial demonstrations of nanoparticle self-assembly from inexpensive PMMA-based materials present an attractive alternative to DNA-based nanomaterials.

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 score0.008
Threshold uncertainty score0.224

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 head0.017
GPT teacher head0.285
Teacher spread0.269 · 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

Citations37
Published2020
Admission routes1
Has abstractyes

Explore more

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