Metallic Nanoparticle−Carbon Nanotube Composites for Electrochemical Determination of Explosive Nitroaromatic Compounds
Bibliographic record
Abstract
Metal nanoparticles (Pt, Au, or Cu) together with multiwalled and single-walled carbon nanotubes (MWCNT and SWCNT) solubilized in Nafion have been used to form nanocomposites for electrochemical detection of trinitrotoluene (TNT) and several other nitroaromatics. Electrochemical and surface characterization by cyclic voltammetry, AFM, TEM, SEM, and Raman spectroscopy confirmed the presence of metal nanoparticles on CNTs. Among various combinations tested, the most synergistic signal effect was observed for the nanocomposite modified glassy carbon electrode (GC) containing Cu nanoparticles and SWCNT solubilized in Nafion. This combination provided the best sensitivity for detecting TNT and other nitroaromatic compounds. Adsorptive stripping voltammetry for TNT resulted in a detection limit of 1 ppb, with linearity up to 3 orders of magnitude. Selectivity toward the number and position of the nitro groups in different nitroaromatics was very reproducible and distinct. Reproducibility of the TNT signal was within 7% (n = 8) from one electrode preparation to another, and the response signal was stable (+/-3.8% at 95% confidence interval) for 40 repeated analyses with 10 min of preconditioning. The Cu-SWCNT-modified GC electrode was demonstrated for analysis of TNT in tap water, river water, and contaminated soil.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".