Programmed self-assembly of microscale components using biomolecular recognition through the avidin–biotin interaction
Bibliographic record
Abstract
Following continuing trends in nanofabrication, the near future may see the requirement to integrate and assemble devices and integrated circuits that are below the scale that conventional robotic pick-and-place systems can successfully accommodate. Presented here is a protein–ligand based approach to self-assembling micronscale components onto specific patterned locations on a substrate. Other than the benefits in scale, this integration method may be advantageous for its parallel nature, 3D capabilities, and the ability to integrate devices made from incompatible processing technologies into a single platform (heterogeneous integration). Five micrometer square silicon microtiles were fabricated as model devices for microscale integrated circuits. They were fabricated from a silicon-on-insulator substrate and released into solution by bath ultrasonication after the buried oxide layer underneath them was underetched. A silicon target substrate was also patterned with gold pads for the microtiles to assemble onto. Self-assembled monolayers were employed to functionalize both the microtiles and the gold pads with biotin and avidin, respectively. Due to the very strong protein–ligand binding between avidin and biotin, the functionalized microtiles in solution were able to attach onto the target gold pads with a high selectivity. In this demonstration, for 5 μm square microtiles assembling onto square gold pads of the same size, 2.0% of the gold pads were covered by the microtiles and a selectivity (microtiles assembling onto the gold pads as opposed to the silicon substrate) of 7.3:1 was achieved.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".