Lateral Flow Assays Biotesting by Utilizing Plasmonic Nanoparticles Made of Inexpensive Metals – Replacing Colloidal Gold
Bibliographic record
Abstract
Nanoparticles (NPs) can be conjugated with diverse biomolecules and employed in biosensing to detect target analytes in biological samples. This proven concept was primarily used during the COVID-19 pandemic with gold NPs-based lateral flow assays (LFAs). Considering the gold price and its worldwide depletion, here we show that novel plasmonic nanoparticles (NPs) based on inexpensive metals, titanium nitride (TiN) and copper covered with a gold shell (Cu@Au), perform comparable or even better than gold nanoparticles. After conjugation, these novel nanoparticles provided high figures of merit for LFA testing, such as high signals and specificity and robust naked-eye signal recognition. To the best of our knowledge, our study represents the 1st application of laser-ablation-fabricated nanoparticles (TiN) in the LFA and dot-blot biotesting. Since the main cost of the Au NPs in commercial testing kits is in the colloidal synthesis, our development with TiN is very exciting, offering potentially very inexpensive plasmonic nanomaterials for various bio-testing applications. Moreover, our machine learning study showed that the bio-detection with TiN is more accurate than that with Au.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".