Selection and Screening of DNA Aptamers for Inorganic Nanomaterials
Bibliographic record
Abstract
Abstract Searching for DNA sequences that can strongly and selectively bind to inorganic surfaces is a long‐standing topic in bionanotechnology, analytical chemistry and biointerface research. This can be achieved either by aptamer selection starting with a very large library of ≈1014 random DNA sequences, or by careful screening of a much smaller library (usually from a few to a few hundred) with rationally designed sequences. Unlike typical molecular targets, inorganic surfaces often have quite strong DNA adsorption affinities due to polyvalent binding and even chemical interactions. This leads to a very high background binding making aptamer selection difficult. Screening, on the other hand, can be designed to compare relative binding affinities of different DNA sequences and could be more appropriate for inorganic surfaces. The resulting sequences have been used for DNA‐directed assembly, sorting of carbon nanotubes, and DNA‐controlled growth of inorganic nanomaterials. It was recently discovered that poly‐cytosine (C) DNA can strongly bind to a diverse range of nanomaterials including nanocarbons (graphene oxide and carbon nanotubes), various metal oxides and transition‐metal dichalcogenides. In this Concept article, we articulate the need for screening and potential artifacts associated with traditional aptamer selection methods for inorganic surfaces. Representative examples of application are discussed, and a few future research opportunities are proposed towards the end of this article.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".