Automated Microcontact Bioprinting for High-Throughput Manufacturing of Lubricant-Infused Microarrays
Bibliographic record
Abstract
Microarrays are widely used for detecting target analytes and biomarkers, with fabrication methods ranging from non-contact to contact bioprinting techniques. Microcontact bioprinting (μCP), which utilizes elastomeric stamps to transfer biorecognition molecules (bioinks) onto substrates, offers advantages such as customizability, cost-effectiveness, and versatility in handling bioinks with high viscosities. Despite its prevalent use in laboratory settings, μCP faces challenges in achieving the repeatability and reproducibility required for industrial manufacturing. In this study, we address these limitations by developing and optimizing a μCP protocol using industrial techniques. A key innovation in our approach is the combination of microcontact printing with fluorosilanization, enabling the use of lubricant-infused surfaces to prevent non-specific attachment. Additionally, we enhance biomolecule immobilization through covalent attachment using a modified bioink formulation. We identify and mitigate high-risk failure modes including bioink formulation, application and removal, environmental conditions, and force application during stamping. Furthermore, we integrate an automated syringe pump and a standardized force application system, taking critical steps toward industrial scalability. Using lubricant-infused substrates, our optimized μCP protocol demonstrates significant improvements in repeatability and reproducibility, achieving intra-assay and inter-assay coefficients of variance below 10% and signal-to-noise ratios exceeding 15. These advancements validate our μCP method for high-throughput, scalable microarray fabrication, paving the way for its implementation in industrial manufacturing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".