3-D Fibrous Network of TiO2 Nanoparticles: Raman Sensor Development
Bibliographic record
Abstract
A 3-D nano-fiber particle network of TiO2 nanoparticles is synthesized by pulsed femtosecond laser irradiation of a pure Ti substrate. This study investigated the properties of the resulting nanostructure for chemical and biomolecular detection by Raman spectroscopy. Controlled tuning of surface roughness, porosity and depth of the 3-D network were found to directly influence Raman detection. The presented findings support a previously unrealized detection capacity by TiO2. Crystal violet was used to test the Surface-Enhanced Raman Spectroscopy (SERS) performance of the developed TiO2 sensor pads. The corresponding Raman enhancement factor was determined to be 1.3x106 which is directly comparable to commercial Ag and Au based Raman substrates. Bisphenol-A and diclofenac sodium salt were introduced into drinking water and tested with various sensor pads to develop a Raman detection map. The results suggest an affinity towards uniform TiO2 3-D nanofibrous networks.
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 | 0.000 | 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 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".