Templated Self-Assembly of Glass Microspheres into Ordered Two-Dimensional Arrays under Dry Conditions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".