Entrapment of Fluorescence Signaling DNA Enzymes in Sol−Gel-Derived Materials for Metal Ion Sensing
Bibliographic record
Abstract
Three fluorescence signaling DNA enzymes (deoxyribozymes or DNAzymes) were successfully immobilized within a series of sol-gel-derived matrixes and used for sensing of various metal ions. The DNAzymes are designed such that binding of appropriate metal ions induces the formation of a catalytic site that cleaves a ribonucleotide linkage within a DNA substrate. A fluorophore (fluorescein) and a quencher (DABCYL, [4-(4-dimethylaminophenylazo)benzoic acid]) were placed on the two deoxythymidines flanking the ribonucleotide to allow the generation of fluorescence upon the catalytic cleavage at the RNA linkage. In general, all DNAzymes retained at least partial catalytic function when entrapped in either hydrophilic or hydrophobic silica-based materials, but displayed slower response times and lower overall signal changes relative to solution. Interestingly, it was determined that maximum sensitivity toward metal ions was obtained when DNAzymes were entrapped into composite materials containing approximately 40% of methyltrimethoxysilane (MTMS) and approximately 60% tetramethoxysilane (TMOS). Highly polar materials derived from sodium silicate, diglycerylsilane, or TMOS had relatively low signal enhancements, while materials with very high levels of MTMS showed significant leaching and low signal enhancements. Entrapment into the hybrid silica material also reduced signal interferences that were related to metal-induced quenching; such interferences were a significant problem for solution-based assays and for polar materials. Extension of the solid-phase DNAzyme assay toward a multiplexed assay format for metal detection is demonstrated, and shows that sol-gel technology can provide new opportunities for the development of DNAzyme-based biosensors.
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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) | 0.000 | 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".