SERS Active Nanobiosensor Functionalized by Self-Assembled 3D Nickel Nanonetworks for Glutathione Detection
Bibliographic record
Abstract
We introduce a “non-noble metal” based SERS active nanobiosensor using a self-assembled 3D hybrid nickel nanonetwork. A tunable biomolecule detector fabricated by a bottom-up approach was functionalized using a multiphoton ionization energy mechanism to create a self-assembled 3D hybrid nickel nanonetwork. The nanonetwork was tested for SERS detection of crystal violet (CV) and glutathione (GSH) at two excitation wavelengths, 532 and 785 nm. The results reveal indiscernible peaks with a limit of detection (LOD) of 1 picomolar (pM) concentration. An enhancement factor (EF) of 9.3 × 10 8 was achieved for the chemical molecule CV and 1.8 × 10 9 for the biomolecule GSH, which are the highest reported values so far. The two results, one being the CV molecule proved that nickel nanonetwork is indeed SERS active and the second being the GSH biomolecule detection at both 532 and 785 nm, confirm that the nanonetwork is a biosensor which has potential for both in vivo and in vitro sensing. In addition, the selectivity and versatility of this biosensor is examined with biomolecules such as l -Cysteine, l -Methionine, and sensing GSH in cell culture medium which mimics the complex biological environment. The functionalized self-assembled 3D hybrid nickel nanonetwork exhibits electromagnetic and charge transfer based SERS activation mechanisms.
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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 | 0.001 | 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".