Self‐Assembly of Nucleobase, Nucleoside and Nucleotide Coordination Polymers: From Synthesis to Applications
Bibliographic record
Abstract
Abstract With multiple metal binding sites, nucleobases, nucleosides, and nucleotides form various coordination polymers (CPs) with metal ions. These self‐assembled materials are very interesting for their simplicity to prepare, highly tunable structure and properties, and excellent biocompatibility. Polymerization is achieved under ambient conditions without the need of free radical initiators or UV light. Different nucleobases have different metal‐binding preferences, but in general most of their coordination sites prefer borderline or soft metals, while the phosphate groups in the nucleotides prefer hard Lewis acids. In this Focus Review, recent developments in this field are summarized starting from the synthesis and characterization of these CPs. Various metal ions including gold, silver, lanthanides, zinc, copper, and iron are individually reviewed, and each metal brings its own property into the CP materials. While most of these materials are non‐crystalline nanoparticles, under certain conditions crystalline metal‐organic frameworks (MOFs) and hydrogels can also be prepared. The applications of such CPs are then described including enzyme entrapment, drug loading and delivery, biosensor development, catalysis, coating of nanoparticles, and luminescence. Finally, future research opportunities of this field are discussed.
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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".