Ultrasensitive Detection of Water Contaminants, Biomarkers and illegal Drugs Using Active 3D Metallic Nanostructures
Bibliographic record
Abstract
Three-dimensional metallic nanostructures produced via laser interference support the generation of surface plasmon polaritons. This type of nanostructures are well-suited for pointof-use (bio)sensing applications, are extremely costeffective, and can be easily fabricated on practically any flat surface. Here, we present a new generation of metallic nanostructures fabricated using holographic laser-inscription that are capable of producing accurate photonic signals that can be employed as label-free (bio)sensors for the rapid, in situ detection and identification of biomarkers of diseases, illegal drugs, water contaminants and terrorism agents. These biosensing platforms consist of a network of threedimensional metallic nanostructures with thickness in the order of tens of nanometers, which can be employed for both surface plasmon resonance (SPR) and surface-enhanced Raman scattering (SERS). The platform utilize smartphoneanalogous, off-the-shelf inexpensive optical components for the generation and detection of the photonic signal. We demonstrate sensing of solutions with different refractive indices and real-time detection of biologically relevant analytes including proteins, water contaminants, illicit drugs and pathogenic bacteria with a sensitivity of ~103 PIU/RIU. This work presents a significant advancement towards the development of fully-integrated, handheld portable (bio)sensing platform for point-of-use applications requiring (bio)detection in real-time.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".