Addressable Nanowell Arrays Formed Using Reversibly Sealable Hybrid Elastomer-Metal Stencils
Bibliographic record
Abstract
There are two major array formats used in life science research and biomedical analysis. The first is the microwell plate format with millimeter-sized wells each with microliter capacity addressed individually and repeatedly during experiments. The second is the microarray format with micrometer-sized spots that are patterned initially but not addressable individually thereafter. Here, we present an addressable nanoliter-well plate with micrometer sized wells that combines the advantages of the two array formats. The nanowells are formed by reversibly sealing a steel stencil featuring an array of micrometer-scale openings to an optically transparent substrate. The nanowells have a capacity of approximately 1 nL, are approximately 140 microm in diameter, and are arrayed at a density of 1600 wells cm(-2). A soft polymer is patterned photolithographically around each opening so as to form a microgasket for pressure sensitive, liquid tight, and reversible sealing to any type of smooth substrate, either hydrophilic or hydrophobic. The rigidity of the steel prevents the distortion that occurs in soft, all-polymeric stencils and permits accurate registration across the entire array, which in turn allows for repeated, individual addressing of wells using an inkjet spotter. The stencils are used to pattern cells, make protein microarrays, and create nanowells on surfaces to study reverse transfection by first spotting plasmids encoding fluorescent proteins into the wells, seeding cells, and monitoring the transfection of the cells in real time using time-lapse imaging. The hybrid elastomer-metal stencils (HEMSs) are versatile and useful for multiplexed analysis of drugs, biomolecules, and cells with microarray density.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".