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Record W2312814220 · doi:10.1021/jp403712x

Room Temperature Nanojoining of Triangular and Hexagonal Silver Nanodisks

2013· article· en· W2312814220 on OpenAlexafffund
Ehsan Marzbanrad, Anming Hu, Boxin Zhao, Y. Zhou

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHigh-resolution transmission electron microscopyMaterials scienceMicroscale chemistryNanoscopic scaleHexagonal crystal systemMolecular dynamicsChemical physicsNanotechnologyCrystallographyLattice (music)NanocrystalCrystal structureAtmospheric temperature rangeTransmission electron microscopyChemistryThermodynamics

Abstract

fetched live from OpenAlex

Room temperature nanojoining is an important phenomenon that has to be understood well for use in different applications, for example, for assembly of nanoscale building blocks into nanoscale and microscale structures and devices. However, the mechanism for nanoparticle joining at room temperature is not well established. In this research, we employed molecular dynamics simulation to explain how and why silver nanodisks are joined/assembled but with their original shape unchanged. To support our theoretical observations, we compared our simulation results to SEM and HRTEM observations of joined silver nanodisks. It was found that joining at a wide temperature range (1–500 K) can be done through short movement and rearrangement of surface atoms and subsequent elastic or plastic deformation of the particles, resulting in perfect crystal alignment at the joint interface. Our simulation shows the crystal defects such as dislocations due to initial lattice mismatch of the crystals can be sintered out to yield a perfect crystalline structure at the interface between joined particles, which is supported by the experimental observations.

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 score0.001
Threshold uncertainty score0.229

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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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

Citations44
Published2013
Admission routes2
Has abstractyes

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