Photolithographic Synthesis of High-Density DNA and RNA Arrays on Flexible, Transparent, and Easily Subdivided Plastic Substrates
Bibliographic record
Abstract
The photolithographic fabrication of high-density DNA and RNA arrays on flexible and transparent plastic substrates is reported. The substrates are thin sheets of poly(ethylene terephthalate) (PET) coated with cross-linked polymer multilayers that present hydroxyl groups suitable for conventional phosphoramidite-based nucleic acid synthesis. We demonstrate that by modifying array synthesis procedures to accommodate the physical and chemical properties of these materials, it is possible to synthesize plastic-backed oligonucleotide arrays with feature sizes as small as 14 μm × 14 μm and feature densities in excess of 125 000/cm(2), similar to specifications attainable using rigid substrates such as glass or glassy carbon. These plastic-backed arrays are tolerant to a wide range of hybridization temperatures, and improved synthetic procedures are described that enable the fabrication of arrays with sequences up to 50 nucleotides in length. These arrays hybridize with S/N ratios comparable to those fabricated on otherwise identical arrays prepared on glass or glassy carbon. This platform supports the enzymatic synthesis of RNA arrays and proof-of-concept experiments are presented showing that the arrays can be readily subdivided into smaller arrays (or "millichips") using common laboratory-scale laser cutting tools. These results expand the utility of oligonucleotide arrays fabricated on plastic substrates and open the door to new applications for these important bioanalytical tools.
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 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 | 0.001 |
| 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".