Carbon Nanotube-Based Chemiresistive Sensor Array for Dissolved Gases
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Dissolved gases such as oxygen (DO) and ammonia (dNH 3 ) are among the most consequential parameters for the assessment of water quality. Since the concentrations of DO and dNH 3 are interdependent through the nitrogen cycle, simultaneous monitoring can be useful in many applications. For example, in wastewater treatment, aeration baths are used to adjust the rate of removal of ammonia by the bioactive sludge. Here, we have developed a sensing array which can monitor dissolved molecular oxygen (DO) and dissolved un-ionized ammonia (dNH 3 ) continuously and simultaneously. This was achieved by functionalizing two sensors made from single-walled carbon nanotube (SWCNT) films with two different molecules: phenyl-capped aniline tetramer (PCAT) and iron phthalocyanine (II) (FePc). It was found that the FePc-doped SWCNT (FePc@SWCNT) sensors demonstrated good sensitivity and selectivity to DO, compared to dNH 3 . Conversely, we found that the PCAT-doped SWCNT (PCAT@SWCNT) sensors demonstrate greater sensitivity to ammonia. Investigating the effect of different PCAT salts as a dopant, we describe the following series of sensor responses to ammonia: chloride < crotonate < fumarate. Additionally, we coated our sensors with thin PDMS membranes, which are selectively permeable to gases, over ionic species. Finally, using principal component analysis (PCA) and partial least-squares discriminant analysis, it was possible to discriminate between responses to DO and dNH 3, with 100% accuracy. As a result, here, we have developed a compelling proof-of-concept for the use of a single sensing substrate, doped with molecules with distinct mechanisms of interaction with two different analytes, to simultaneously monitor concentrations of two dissolved gases, in this example, DO and dNH 3 .
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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) | 0.000 | 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) | 0.001 | 0.001 |
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".