E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing
Abstract
:1. Introduction
1.1. Polythiophene and Derivatives
1.2. Polyaniline
1.3. Polypyrrole
1.4. Electronic Systems: Electronic Tongues and Noses
1.4.1. Sensing Unit: Electrochemical Sensor Arrays
1.4.2. Processing of the Collected Data: Multivariate Methods
1.4.3. Novelty of the Work
2. Electronic Tongues (E-Tongues) Based on CPs
2.1. Sensing Unit: Electrochemical Sensors
2.2. Analytical Application of E-Tongues
3. Electronic Noses (E-Noses) Based on CPs
3.1. Sensing Unit: Chemiresistors
3.2. Analytical Application of E-Noses
4. Future Perspectives: Integration of E-Tongues and E-Noses in Commercial Systems
4.1. Commercial Prototypes of E-Tongues
4.2. Commercial Prototypes of E-Noses
4.3. Final Remarks: Challenges of Electrochemical/Gas Sensing Devices
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Electrochemical Device | Analyte | Working Media | Sample | Analytical Parameters | Ref. | |
---|---|---|---|---|---|---|
LD (µM) | LR (µM) | |||||
PTh | ||||||
MWCNT/PTh/Pt | BPA | PBS pH 7.5 | Water | 0.009 | 0.05–0.4 | [62] |
MnO2/PTh/rGO/GCE | MP | PBS pH 7 | Human urine and blood | 0.0057 | 0.5–10 | [63] |
GO-4-ATP-Au-PTh/Au GCE | Nicotine | PBS pH 7 | Serum, urine, cigarette | 0.17 | 1.0–30 | [64] |
PTh-AgBr | Glucose | NaOH | Human blood plasma | 0.31 | 4–5000 | [65] |
PTh-Ag/GCE | L-Tryp | PBS pH 7 | Soybeans extract | 0.020 | 0.2–400 | [66] |
PEDOT | ||||||
PEDOT/IL/GCE | DA | PBS pH 7.4 | Human urine | 0.033 | 0.2–328 | [67] |
UiO-66-NH2@PEDOT/GA/GCE | PCMC | ABS pH 6 | Tap water | 0.2 | 0.6–18 | [68] |
PEDOT/AG/GCE | AC | PBS pH 7 | Local tablets | 0.041 | 0.15–5881 | [69] |
Cu2O/PEDOT/MWCNT | Glucose | NaOH | Human blood serum | 0.04 | 0.495–374 | [70] |
GC/PEDOT-AuNPs-SV | CA | PBS pH 7 | Juice | 4.24 | 10–1000 | [71] |
PEDOT-Tyr/SNG-C | CA | PBS pH 7 | Wine, beer | 4.33 | 10–300 | [72] |
PEDOT/PEDOT-SH/Au | Nitrite | PBS pH 6.9 | Tap water, milk | 0.051 | 0.15–1000 | [73] |
PEDOT/Au | UA | PBS pH 6.6 | Milk | 7.0 | 6–200 | [74] |
GCE/PEDOT-MC/AgNPs | Rutin | PBS pH 3 | Tablets | 0.0035 | 0.005–0.5 | [75] |
Pt/PEDOT-PBNPS | H2O2 | ABS pH 5.5 | Human blood | 1.4 | 5–1000 | [76] |
PANI | ||||||
Co3O4@PANINFs/GCE | Glucose | PBS pH 7.4 | Human serum | 60 | 100–8000 | [77] |
TiO2@PANI@Au/GCE | Hydrazine | NH3/NH4+ pH 9 | Power plant sewage | 0.15 | 0.9–1200 | [78] |
PANI/SnO2/GCE | Nitrite | PBS pH 6 | - | 0.04 | 0.12–7777 | [79] |
GCE/PANI-Fe3O4 | DA | PBS pH 7 | Water | 0.176 | 0.2–2.4 | [80] |
GCE/PANI-NiO | DA | PBS pH 7 | Water | 0.166 | 0.2–2.4 | [80] |
α-Fe2O3/PANI/GCE | UA | PBS pH 7 | Human urine | 0.038 | 0.01–5 | [81] |
NiO-NPs@PANINS/SPE | Glucose | NaOH | Human blood serum | 0.06 | 1–3000 | [82] |
MeGO/PANI | AA | PBS pH 7.4 | - | 2.0 | 8–5000 | [83] |
PPy | ||||||
Fe3O4@PPy/MWCNTs/GE | AT | BR pH 4 | Serum, tablets | 0.0230 | 0.0314–201 | [84] |
AuNP/PPy/GCE | L-dopa | PBS pH 7 | Urine | 0.075 | 0.1–6.0 | [85] |
PDA/PPy/GCE | UA | PBS pH 8 | Human serum, urine | 0.11 | 0.5–40 | [86] |
PGE/CuO-NPs/PPy | TR | PBS pH 8.5 | Tablets | 0.001 | 0.005–380 | [87] |
PPy:LAC | Lactate | KNO3 | Human tear, rat blood | 81.0 | 100–10,000 | [88] |
AuCu/PPy/Cu-TCCP | H2O2 | PBS pH 8 | Medical H2O2 solution | 0.0067 | 0.71–24,100 | [89] |
Sensor Array | Sample | Use | Multivariate Calibration | Ref. | |
---|---|---|---|---|---|
No CP Sensor | CP Sensor | ||||
SNG-C | PEDOT/Pt | Musts | Discrimination of samples collected at different ripening times | PCA iPLS PLS | [114] |
- | PEDOT/Pt | Red wines | Classification of different samples and origin | PCA PLS | [115] |
Pt Au | PEDOT/Pt | Fruit juice | Discrimination between samples from different fruits | PCA PLS-LDA | [116] |
IDE PA6/IDE | PA6/PANI/IDE (0.25–5.0% PANI) | Bovine milk | Discrimination of samples according to tetracycline residue content | PCA | [117] |
CE AuCE rGO-CE rGO-AuCE | PANI-CE PANI-AuCE | Vinegar, sugar | Multiflavor detection | PCA | [118] |
C/SPE NiO/C/SPE MWCNT/C/SPE SWCNT/C/SPE Pt | PANI/C/SPE | Red wine | Phenolic content | PCA | [119] |
SWCNT/SPCE MWCNT/SPCE | PPy-DSA/SPCE | White wine | Discrimination according to varietal origin | PCA LDA | [120] |
CPE-CoPc CPE-LuPc2 CPE-LuPc2 | PPy-dopant/Au Dopant: SO4, DSA, FCN, AQDS, PWA, TSA | Red wine | Evaluation of chemical adulteration | PCA PLS | [121] |
GdPc2/CSPE DyPc2/CSPE CSPE | PPy-dopant/CSPE Dopant: FeCN, NP, Mo | Beef | Determination of ammonia and putresceine | PCA PLS-LDA | [122] |
- | PPy- dopant/Pt Dopant: DSA, H2SO4, FCN, AQDS, PWA, TSA | Beer | Evaluation of bitterness and alcoholic strength | PCA PLS | [123] |
- | PPy-dopant/Pt Dopant: FCN, NP, PWA, H2SO4, MO, AQS | Olive oil | Evaluation of bitterness | PCA PLS | [124] |
- | PPy-dopant/SPCE Dopant: DSA, SO4, FCN | Wine | Classification of wines according to vintage year | PCA LDA | [125] |
Graphite-epoxy PtNPs CuNPs | PANI PPy | Wine | Classification of wines and recognition of the oxygenation effect | PCA | [126] |
Gas Sensor Device | Target Gas | Range (ppm) | Sensing Performance | Ref. | ||
---|---|---|---|---|---|---|
Gas Conc. (ppm) | Recovery Time (s) | Response Time (s) | ||||
SnO2/PTh | NO2 | 10–200 | 10 | - | 2.07 | [153] |
P3CT/CNT | NMPEA | 0.004–0.032 | 0.004 | 40 | 20 | [154] |
PEDOT:PSS/FeCl3 | NH3 | 0.2–200 | 0.5 | - | 20 | [155] |
WO3-PEDOT:PSS | LPG | 500–3000 | 500 | 54 | 29.4 | [156] |
PANI/PVDF | NH3 | 0.2–5 | 0.2 | 235 | 174 | [157] |
PANI/SnO2 | NO2 | 5–55 | 37 | 25 | 17 | [158] |
SnO2/rGO/PANI | H2S | 0.05–10 | 2 | 78 | 82 | [159] |
PANI-NF | LPG | 100–1000 | 700 | 200 | 50 | [160] |
PPy/rGO | NH3 | 1.0–4.0 | 1.0 | 300 | 60 | [161] |
PPy thin film | NO2 | 10–100 | 10 | 374 | 218 | [162] |
PPy nanoribbons | CH3CH2OH | - | 100 | 31 | 2 | [163] |
PPy-Ag | CH3COCH3 | 25–600 | 580 | 150 | 175 | [164] |
PPy-CNT | H2 | 1–100 | 10 | - | >1.0 | [165] |
PANI Sensor Array | Sample | Use | Multivariate Calibration | Ref. |
---|---|---|---|---|
PANI-dopant/IDGEs Dopant: CSA, DBSA, HCl | Strawberry Grape Apple | Discrimination of samples according to aromatic substances | PCA | [177] |
PANI-HCl/PGIEs PANI-HCl/IDEs | Strawberry Grape Apple | Detection of different aromas | PCA | [178] |
PANI-dopant/IDGEs Dopant: HCl, TSA, CSA, MSA | Cow’s estrus | Determination of estrus times of cows | PCA | [179] |
PANI-dopant/IDEs Dopant: HCl, TSA, CSA, MSA | Bananas | Monitoring of bananas ripeness | PCA | [180] |
PANI-dopant/PGIEs Dopant: CSA, HCl, DBSA | Gummy candies | Monitoring of aromas during candy storage | PCA | [181] |
PANI-CSA/Chitosan PANI-DBSA/TiO2 PANI-DBSA/CNT | Simulated human breath | Preliminary diagnoses of kidney disease | PCA LDA | [182] |
PANI/AuNPs | Human breath | Early diagnoses of renal diseases | PCA LDA | [183] |
PANI-dopant/MWCNT PANI-dopant/GO Dopant: CSA, DBSA, HCl | Essential oils | Determination of quality of essential oils | PCA | [184] |
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Sierra-Padilla, A.; García-Guzmán, J.J.; López-Iglesias, D.; Palacios-Santander, J.M.; Cubillana-Aguilera, L. E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing. Sensors 2021, 21, 4976. https://doi.org/10.3390/s21154976
Sierra-Padilla A, García-Guzmán JJ, López-Iglesias D, Palacios-Santander JM, Cubillana-Aguilera L. E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing. Sensors. 2021; 21(15):4976. https://doi.org/10.3390/s21154976
Chicago/Turabian StyleSierra-Padilla, Alfonso, Juan José García-Guzmán, David López-Iglesias, José María Palacios-Santander, and Laura Cubillana-Aguilera. 2021. "E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing" Sensors 21, no. 15: 4976. https://doi.org/10.3390/s21154976
APA StyleSierra-Padilla, A., García-Guzmán, J. J., López-Iglesias, D., Palacios-Santander, J. M., & Cubillana-Aguilera, L. (2021). E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing. Sensors, 21(15), 4976. https://doi.org/10.3390/s21154976