Producing Covalent Microarrays of Amine‐Conjugated DNA Probes on Various Functional Surfaces to Create Stable and Reliable Biosensors
Bibliographic record
Abstract
Abstract Oligonucleotide‐based microarrays are ideal tools for genetic testing and diagnostics due to their stability, ease of synthesis, and high specificity for the target of interest. This study reports on the effectiveness of several coupling strategies to covalently attach single‐stranded nucleic acids to surfaces, focusing on the robustness of the attachment in various environmental conditions, such as pH and temperature. Various characteristics of DNA microarrays produced using amine‐conjugated DNA probes on five different functional surfaces, commonly used for immobilization of biomolecules, namely, epoxy, carboxyl, amine, aldehyde, and N‐hydroxysuccinimide‐coated substrates, are investigated. Immobilization efficiency, changes in surface energy, as well as the stability of the conjugated DNA upon exposure to various environmental conditions are measured. Finally, in order to study the postimmobilization viability of the developed biosensors, microarrays of synthetic RNA cleaving probes (DNAzyme) are immobilized onto these surfaces and their functionality is evaluated through their ability to detect Escherichia coli at various temperatures. Results show that epoxy‐coated plastic surfaces are most ideal for the creation of DNA‐based biosensing chips. This study provides a guideline for producing oligonucleotide‐based microarrays on glass and plastic substrates and can be used for developing microarray‐based biosensors, suitable for long‐term storage.
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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".