Bibliographic record
Abstract
Molecular imprinting refers to templated polymerization with rationally designed monomers, and this is a general method to prepare stable and cost-effective ligands. This attractive concept however suffers from low affinity, low specificity, and limited signaling mechanisms for binding. Acrydite-modified DNA oligonucleotides can be readily copolymerized into acrylic polymers. With molecular recognition and catalytic functions, such functional DNAs are recently shown to enhance the performance of molecularly imprinted polymers (MIPs) in a few ways. First, DNA aptamers are used as macromonomers to enhance binding affinity and specificity of MIPs. Second, DNA can help produce optical signals to follow binding events. Third, imprinting can also improve the performance of catalytic DNA by enhancing its activity and specificity toward the template substrate. Finally, MIP is shown to help aptamer selection. Bulk imprinting, nanoparticle imprinting, and surface imprinting are all demonstrated with DNA. Since both DNA and synthetic polymers are cost effective and stable, their hybrid materials still possess such properties while enhancing the function of each component. This review covers recent developments on the abovementioned aspects of DNA-containing MIPs, a field just emerged in the last five years, and future research directions are discussed toward the end.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".