Laser-Generated Au–Ag Nanoparticles For Plasmonic Nucleic Acid Sensing
Bibliographic record
Abstract
The development of nanoplasmonic sensing approaches for DNA detection based on the localized plasmonic properties of different metallic NPs fabricated by femtosecond laser ablation with an application for synthetic oligonucleotides as specific probes for genetic sequence variations is presented. The planar surface plasmon resonance (SPR) technique has been used to test oligonucleotide probes specific to rpoB genes of Mycobacterium tuberculosis . Optimal experimental conditions providing efficiency of hybridization between immobilized probe and cDNA target and performance of the SPR method were obtained and applied to the nanoplasmonic biosensing based on colloidal nanoparticles. Gold and silver/gold alloy nanoparticles were fabricated by the “pure” laser ablation method and have shown faster conjugation to thiol-modified DNA and higher stability in hybridization buffer than nanoparticles produced by chemical synthesis. Nanoparticle-enhanced and spectral SPR methods were used to confirm the efficiency of DNA-modified laser-generated gold nanoparticles in biosensing. Numerical estimation shows a higher sensitivity of nanoalloy materials application in dimer aggregate configurations. The described approaches could be proposed as a basis for an optical biosensor for sensitive and real-time detection of nucleic acid samples, for example, nucleotide sequences related to drug-resistant tuberculosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".