Plasmonic photo‐assisted electrochemical sensor for detection of trace lead ions based on Au anchored on two‐dimensional g‐C <sub>3</sub> N <sub>4</sub> /graphene nanosheets
Bibliographic record
Abstract
Abstract In this paper, a plasmonic photo‐assisted electrochemical sensor, Au anchored on two‐dimensional (2D) graphitic carbon nitride (g‐C 3 N 4 )/reduced graphene oxide (rGO) nanosheets (Au/g‐C 3 N 4 /rGO), was facile fabricated. The morphology and structure of the composite are characterized by transmission electron microscope, X‐ray photoelectron spectroscopy, X‐ray diffraction and ultraviolet–visible spectrophotometer (UV–Vis). Based on the semiconductor of g‐C 3 N 4 and optical properties of surface plasmon resonance for Au, the as‐prepared Au/g‐C 3 N 4 /rGO showed high sensitivity for the detection of trace lead ion [Pb(II)] by differential pulse anodic stripping voltammetry in the presence of visible light illumination. Under optimized conditions, the limit of detection (signal‐to‐noise ratio ( S / N ) = 3) Pb(II) detection can be low to 0.1 nmol·L −1 . In addition, the interference research and real soil sample detection were measured to confirm the possibility of practical applications.
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 | 0.001 | 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".