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Record W3177352588 · doi:10.11159/icnnfc21.lx.001

Functional Nanoparticles via “Living” Crystallization-Driven Self-Assembly

2021· article· en· W3177352588 on OpenAlexaffvenue
Ian Manners

Bibliographic record

VenueProceedings of the World Congress on Recent Advances in Nanotechnology · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCrystallizationNanoparticleSelf-assemblyNanotechnologyMaterials scienceChemical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Molecular, and more recently, macromolecular synthesis has evolved to an advanced state allowing the creation of remarkably complex organic molecules and well-defined polymers with typical dimensions from 0.5 nm -10 nm. In contrast, the ability to prepare materials in the 10 nm -100 micron size regime with controlled shape, dimensions, and structural hierarchy is still in its relative infancy and currently remains the virtually exclusive domain of biology. In this talk recent developments concerning a promising "seeded growth" route to well-defined 1D and 2D nano-and microparticles termed "living" crystallization-driven self-assembly (CDSA), will be described. Living CDSA can be regarded as a type of "living supramolecular polymerization" that is analogous to living covalent polymerizations of molecular monomers but on a much longer length scale (typically, 10 nm -5 microns). Living CDSA also shows analogies to biological "nucleation-elongation" processes such as amyloid fiber growth. The building blocks or "monomers" used for living CDSA consist of a rapidly stacking molecules with a wide variety of chemistries. The seeds used as "initiators" for living CDSA are usually prepared from preformed polydisperse 1D or 2D micelles by sonication. Recent results indicate that living CDSA is scalable, which will help enable applications in areas such as optoelectronics, catalysis, and biomedicine, and recent examples of work by our group and our collaborators in these areas will be discussed.1-4.

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 score0.004

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.247
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

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

Citations0
Published2021
Admission routes2
Has abstractyes

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Same venueProceedings of the World Congress on Recent Advances in NanotechnologySame topicPickering emulsions and particle stabilizationFrench-language works237,207