Continuously Tunable Nucleotide/Lanthanide Coordination Nanoparticles for DNA Adsorption and Sensing
Bibliographic record
Abstract
Metal-organic coordination polymers (CPs) have attracted great research interest because they are easy to prepare, porous, flexible in composition, and designable in structure. Their applications in biosensor development, drug delivery, and catalysis have been explored. Lanthanides and nucleotides can form interesting CPs, although most previous works have focused on a single type of metal ligand. In this work, we explored mixed nucleotides and studied their DNA adsorption properties using fluorescently labeled oligonucleotides. Adenosine monophosphate (AMP) and guanosine monophosphate (GMP) formed negatively charged CP nanoparticles with most lanthanides, and thus a salt was required to adsorb negatively charged DNA. DNA adsorption was faster and reached a higher capacity with lighter lanthanides. Desorption of pre-adsorbed DNA by inorganic phosphates, urea, proteins, surfactants, and competing DNA was successively carried out. The results suggested the importance of the DNA phosphate backbone, although hydrogen bonding and DNA bases also contributed to adsorption. The AMP CPs adsorbed DNA more strongly than the GMP ones, and using mixtures of AMP and GMP, continuous tuning of DNA adsorption affinity was achieved. Such CPs were also used as a sensor for DNA detection based on the different affinities of single- and double-stranded DNA, and a detection limit of 0.9 nM target DNA was achieved. Instead of tuning DNA adsorption by varying the length and sequence of DNA, the composition of CPs can also be controlled to achieve this goal.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".