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Record W2129518628 · doi:10.1039/c5sm00997a

Investigation of splashing phenomena during the impact of molten sub-micron gold droplets on solid surfaces

2015· article· en· W2129518628 on OpenAlex
Daozhi Shen, Guisheng Zou, Lei Liu, W. W. Duley, Y. Zhou

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

VenueSoft Matter · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSolid surfaceMaterials scienceMolten metalNanotechnologyChemical physicsChemical engineeringMetallurgyChemistryEngineering

Abstract

fetched live from OpenAlex

The dynamics of splashing accompanying the impact of molten 800 nm diameter gold droplets on silicon, gold coated silicon, gold coated glass and polished solid gold surfaces has been studied. A novel method based on laser induced forward transfer has been developed to generate single submicron molten gold droplets. Splashing morphology has been characterized using Scanning Electron Microscopy (SEM) and Focused Ion Beam (FIB) techniques. It is found that the splashing of submicron gold droplets upon impact is enhanced by high droplet impact energy achieved by reducing the droplet flight distance and that an air layer resulting in a bubble becomes trapped under the impacting droplets even when the size of the droplet is less than one micron. Our results show that, under these conditions, heat transfer between the submicron droplet and the solid substrate is more important than surface roughness and surface tension in the evolution of splashing. A theoretical model has been developed to simulate the splashing characteristics of submicron gold droplets during impact. Both the experimental data and the analytical model show that splashing is enhanced by high heat transfer rates to the surface.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.331

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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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