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Record W1971036128 · doi:10.1021/cm071382b

Block Copolymer Templated Chemistry for the Formation of Metallic Nanoparticle Arrays on Semiconductor Surfaces

2007· article· en· W1971036128 on OpenAlexaff
Masato Aizawa, Jillian M. Buriak

Bibliographic record

VenueChemistry of Materials · 2007
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceMonolayerSemiconductorCopolymerNanoparticleNanotechnologyNanostructureMetalChemical engineeringPolymerOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

Precise positioning of metallic nanostructures on semiconductor surfaces is important for applications such as photovoltaics, photoelectrocatalysis, metal interconnects, sensing platforms, and many others. In this paper, we demonstrate the utilization of self-assembling diblock copolymer monolayer films, made up of polystyrene- block -poly(2- or 4-vinylpyridine) (PS- b -P2VP or PS- b -P4VP), to spatially direct an aqueous metal reduction reaction on semiconductor surfaces, a process we call galvanic displacement. The diblock copolymer forms hexagonal arrays of spherical micelles consisting of a P2VP or P4VP core surrounded by a PS corona. Two approaches were developed, termed method 1 and method 2, to deliver metal ions to the semiconductor interface in a spatially defined manner utilizing the diblock template. In method 1, a metal complex preloaded into the P4VP cores is spontaneously reduced on the surface to form hexagonally ordered metallic nanoparticles whose structures mirror the parent polymer templates. This approach was employed to produce ordered Ag nanoparticles on Ge(100), InP(100), and GaAs(100) surfaces. Method 2, on the other hand, involves coating the semiconductor surface with an unloaded self-assembled block copolymer monolayer, followed by immersion in a solution of the metal ions and additional reagents, if required. Method 2 is particularly useful to pattern semiconductor surfaces that require the presence of hydrofluoric acid (HF) as an etchant to initiate the galvanic displacement, including Si(100). Using method 2, Cu, Au, Pt, and Pd nanoparticles were patterned on the semiconductor surfaces. In addition, the apparent order of the self-assembled monolayers is better as compared to that of the preloaded block copolymers (prepared via method 1). Since the self-assembling nanostructures of the PS- b -P2VP or PS- b -P4P diblock copolymers can be inverted to a PS core surrounded by a P2VP or P4VP corona (the so-called core−corona inversion) in the presence of HF, patterns of the resulting metallic structures are influenced by this morphological shift. The effects of polymer morphology on the galvanic displacement is described, and as an alternative approach, metal ion reduction and polymer removal with hydrogen/argon plasma is outlined.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.001

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 head0.021
GPT teacher head0.256
Teacher spread0.236 · 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

Citations198
Published2007
Admission routes1
Has abstractyes

Explore more

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