Functionalization of Carbon Nanotubes with Water‐Insoluble Porphyrin in Ionic Liquid: Direct Electrochemistry and Highly Sensitive Amperometric Biosensing for Trichloroacetic Acid
Bibliographic record
Abstract
A functional composite of single-walled carbon nanotubes (SWNTs) with hematin, a water-insoluble porphyrin, was first prepared in 1-butyl-3-methylimidazolium hexafluorophosphate ([BMIM][PF(6)]) ionic liquid. The novel composite in ionic liquid was characterized by scanning electron microscopy, ultraviolet absorption spectroscopy, and electrochemical impedance spectroscopy, and showed a pair of direct redox peaks of the Fe(III)/Fe(II) couple. The composite-[BMIM][PF(6)]-modified glassy carbon electrode showed excellent electrocatalytic activity toward the reduction of trichloroacetic acid (TCA) in neutral media due to the synergic effect among SWNTs, [BMIM][PF(6)], and porphyrin, which led to a highly sensitive and stable amperometric biosensor for TCA with a linear range from 9.0x10(-7) to 1.4x10(-4) M. The detection limit was 3.8x10(-7) M at a signal-to-noise ratio of 3. The TCA biosensor had good analytical performance, such as rapid response, good reproducibility, and acceptable accuracy, and could be successfully used for the detection of residual TCA in polluted water. The functional composite in ionic liquid provides a facile way to not only obtain the direct electrochemistry of water-insoluble porphyrin, but also construct novel biosensors for monitoring analytes in real environmental samples.
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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) | 0.001 | 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 | 0.001 | 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".