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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".