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Record W2039045219 · doi:10.1021/la903679x

Templated Self-Assembly of Glass Microspheres into Ordered Two-Dimensional Arrays under Dry Conditions

2009· article· en· W2039045219 on OpenAlex

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

VenueLangmuir · 2009
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMicrosphereSelf-assemblyMaterials scienceNanotechnologyChemical engineering

Abstract

fetched live from OpenAlex

This paper describes a new approach to mesoscale self-assembly in which a stream of nitrogen is used to propel micrometer-scale components toward a template of patterned liquid adhesive drops. This approach combines the use of capillary forces to hold the components in place with dry processing conditions. Eliminating the use of a liquid medium to suspend components is an important goal for mesoscale self-assembly methods because it eliminates the need for special encapsulation to protect electrically functional components. We demonstrate the dry self-assembly approach by assembling 100 microm glass microspheres into a variety of 2D patterns. A study of defects in these arrays relates parameters associated with the template--density of binding sites and volume of liquid adhesive comprising the drops--to the frequency of defects arising from the incorporation of additional microspheres into the array. Optimized template parameters and self-assembly conditions yield 2D arrays with defect rates of approximately 4-5%. We also demonstrate the versatility of this self-assembly method by producing ordered binary arrays of clear and black glass microspheres.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.516

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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