Bibliographic record
Abstract
Chemiresistors are solid state devices that change their electronic properties (more specifically, the resistance of a conductive thin film or percolation network) as a result of chemical interactions with their environment. They are a well-established and widely commercialized technology for gas or vapor sensor applications. The active layer may consist of metal oxides, polymers, nanomaterials or composites. In most cases, chemisorption or catalytic activity involving the analyte results in surface doping of the active layer, although other mechanisms (such as conductivity changes due to swelling) have also been reported. A significant part of the sensing literature is taken up by reports of ChemFETs, in which case the conductivity of the active layer can also be modulated by an applied gate voltage. Gate voltage modulation is helpful for establishing the sensing mechanism and - on occasion - for distinguishing multiple simultaneous target analytes. In most cases, however, the actual sensor operation occurs at zero gate voltage, thus reducing the ChemFET to a chemiresistor. [1] Gas sensors can be operated at high voltages and without shielding the contacts to the film from gas exposure, two simplifications that are not afforded to sensors operating in aqueous environments. Water quality sensors are a surprisingly underserved area of sensor applications.[2] Important chemical water quality parameters include pH, dissolved gases, common ions and a range of toxic trace contaminants which may be ionic or uncharged, inorganic or organic. All these water quality parameters are usually monitored using colorimetric sensors, electrochemical sensors and large lab-based instruments. None of these lend themselves to low maintenance, reagent free, low power continuous operation for online monitoring. In particular, colorimetric sensors need a resupply of reagents and electrochemical sensors require reference electrodes. Chemiresistors have the potential to eliminate all these disadvantages, but there has been slow progress in adapting them to aqueous analytes. They are simple and economical to manufacture, and can operate reagent-free and with low or no maintenance. Unlike electrochemical sensors they do not require reference electrodes. Challenges include the need to prevent electrical shorts through the aqueous medium and the need to keep the sensing voltage low enough to avoid electrochemical reactions at the sensor. We have built a chemiresistive sensing platform for aqueous media. The active sensor element consists of a percolation network of low-dimensional materials particles that form a conducting film, e.g. from carbon nanotubes, pencil trace, exfoliated graphene or MoS2. The first members of that platform were free chlorine sensors,[3-5] but we have also demonstrated pH sensitive films [6,7] and cation sensors.[8] While there are some challenges associated with expanding the range of accessible analytes,[9] we have recently expanded the applicability of our platform, in particular anions and cations that are commonly present as pollutants in surface and drinking water. Our sensors can be incorporated into a variety of systems and will also be suitable for online monitoring in remote and resource-poor locations. References: [1] A. Zubiarrain-Laserna and P. Kruse, Graphene-Based Water Quality Sensors. J. Electrochem. Soc. 167 (2020) 037539. [2] P. Kruse, Review on Water Quality Sensors. J. Phys. D 51 (2018) 203002. [3] L. H. H. Hsu, E. Hoque, P. Kruse, and P. R. Selvaganapathy, A carbon nanotube based resettable sensor for measuring free chlorine in drinking water. Appl. Phys. Lett. 106 (2015) 063102. [4] E. Hoque, L. H. H. Hsu, A. Aryasomayajula, P. R. Selvaganapathy, and P. Kruse, Pencil-Drawn Chemiresistive Sensor for Free Chlorine in Water. IEEE Sens. Lett. 1 (2017) 4500504. [5] A. Mohtasebi, A. D. Broomfield, T. Chowdhury, P. R. Selvaganapathy, and P. Kruse, Reagent-Free Quantification of Aqueous Free Chlorine via Electrical Readout of Colorimetrically Functionalized Pencil Lines. ACS Appl. Mater. Interfaces 9 (2017) 20748-20761. [6] D. Saha, P. R. Selvaganapathy and P. Kruse, Peroxide-Induced Tuning of the Conductivity of Nanometer-Thick MoS2 Films for Solid State Sensors. ACS Appl. Nano Mater. 3 (2020) 10864-10877. [7] S. Angizi, E. Y. C. Yu, J. Dalmieda, D. Saha, P. R. Selvaganapathy and P. Kruse, Defect Engineering of Graphene to Modulate pH Response of Graphene Devices. Langmuir 37 (2021) 12163-12178. [8] J. Dalmieda, A. Zubiarrain-Laserna, D. Ganepola, P. R. Selvaganapathy and P. Kruse, Chemiresistive Detection of Silver Ions in Aqueous Media. Sens. Actuators B:Chem 328 (2021) 129023. [9] J. Dalmieda, A. Zubiarrain-Laserna, D. Saha, P. R. Selvaganapathy and P. Kruse, Impact of Surface Adsorption on Metal-Ligand Binding of Phenanthrolines. J. Phys. Chem. C 125 (2021) 21112-21123. Figure 1
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.028 |
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".