Combinatorial Nanoparticle-Bound ssDNA Oligonucleotide Library Synthesized by Split-and-Pool Synthesis
Bibliographic record
Abstract
Synthetic ssDNA oligonucleotides hold great potential for various applications, including DNA aptamers, DNA digital data storage, DNA origami, and synthetic genomes. In these contexts, precise control over the synthesis of the ssDNA strands is essential for generating combinatorial sequences with user-defined parameters. Desired features for creating synthetic DNA oligonucleotides include easy manipulation of DNA strands, effective detection of unique DNA sequences, and a straightforward mechanism for strand elongation and termination. In this study, we present a split-and-pool method for generating synthetic DNA oligonucleotides on nanoparticles, enabling the creation of scalable combinatorial libraries. Our approach involves coupling DNA to nanoparticles, ligating double-digested fragments for orientation-specific synthesis, and attaching a final single-digested fragment to ensure strand termination. We assess the quality of our method by characterizing both the DNA and the nanoparticles used as solid supports, confirming that our method produces scalable, combinatorial nanoparticle-bound ssDNA libraries with controllable strand lengths.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".