1. Introduction
Molecularly imprinted polymers (MIPs) are synthetic materials that mimic the selective interaction of biological receptors to their substrates and are therefore often referred to as artificial antibodies. Compared to biomolecules, MIPs have several advantages ranging from better stability in various media and larger tolerance regarding temperature and pH to improved storage and reusability. Additionally, it is cheaper to produce them; the process is scalable and can be adjusted to a wide range of applications. Molecular imprinting results in binding sites that are complementary to the analyte in size and shape. Functional monomers are capable of interacting with the template and polymerize around it, while a cross-linker stabilizes the matrix. After template removal, stable cavities remain in the polymer that can selectively rebind the target analyte [1][2][3].
By definition, a chemical sensor is a “device that transforms chemical information ranging from the concentration of a specific sample component to total composition analysis, into an analytically useful signal. The chemical information, mentioned above, may originate from a chemical reaction of the analyte or from a physical property of the system investigated” [4]. In general, sensors are small, inexpensive, and portable devices that do not require a large laboratory. Every sensor consists of two main parts: receptor and transducer. The receptor selectively recognizes and binds the analyte, whereas the transducer transforms the information from the binding event into a measurable signal [4]. To date, vinyl, acrylate, and silane-based polymers are among the most commonly used for MIP synthesis [5].
Combining conductive polymers (or their abilities) with the selective recognition of MIPs merges the advantages of two well-established techniques. This makes it possible to fabricate sensing devices, which are not available with non-conducting MIPs [6]. The cMIPs directly change their electrical properties on the binding sites upon analyte interaction and allow for direct detection of this event due to the intrinsically present conductivity. The specific binding sites of the cMIPs increase the affinity of the electrochemical sensor toward the desired analyte. The combination of cMIPs as both a receptor and an electrochemical transducer can, for example, reduce interference of structurally similar compounds, since they would not only differ in their interaction with the binding sites of the MIP but also in their electrochemical signal [7]. cMIPs present the same advantages as non-conductive MIPs compared to conventional receptors of electrochemical sensors. cMIP sensors can be prepared for analytes in liquid as well as in gas phase.
2. Conductive MIPs
2.1 Electropolymerization
Electropolymerization is the classical and most straightforward way to obtain cMIPs.This technique leads to a thin polymer film directly on the electrode. The template is usually added to the monomer solution and is incorporated by the polymer matrix. Polymerization proceeds by applying electrochemical methods, such as cyclic voltammetry (CV) or chronoamperometry. The amount of charge transferred during synthesis controls the film thickness. However, electropolymerization requires the presence of electroactive moieties in the monomers. Hence, pyrrole, aniline, and 3-aminophenylboronic acid are among the most frequently used monomers for that purpose [8]. Table 1 presents an overview of cMIP sensors prepared by electropolymerization.
Table 1. Overview: cMIP sensors prepared using electropolymerization. All LOD values were converted into mol/L (if possible) to allow for better comparison. DPV = differential pulse voltammetry, EIS = electrochemical impedance spectroscopy, CFU = colony forming unit, QCM = quartz crystal microbalance, SWV = square wave voltammetry, SPR = surface plasmon resonance, CV = cyclic voltammetry, SAW = surface acoustic wave, EG-FET = extended-gate field-effect transistor, ECS = electrochemical capacitance spectroscopy.
Monomer |
Analyte |
Transducer |
LOD [mol/L] |
Ref. |
Pyrrole |
Doxycycline |
DPV |
44 × 10−6 |
[9] |
Pyrrole |
Sulfadimethoxine |
Amperometric |
0.5 × 10−3 |
[10] |
Pyrrole |
Sulfadimethoxine |
Amperometric |
70 × 10−6 |
[11] |
Pyrrole |
L-tryptophan |
DPV |
17 × 10−6 |
[12] |
Pyrrole |
Caffeine |
Pulsed potential |
10 × 10−6 |
[13] |
Pyrrole |
HSA |
DPV EIS |
0.25 × 10−9 12.1 × 10−6 |
[14] |
Pyrrole |
Glycoprotein (gp51) (bovine leukemia virus) |
Pulsed amperometry |
~20 × 10−6 B |
[15] |
Pyrrole |
SARS-CoV-2 spike glycoprotein |
Pulsed amperometry |
12 × 10−9 B |
[16] |
Pyrrole |
Hazelnut Cor a 14-allergen |
SWV SPR |
~1.6 × 10−15 ~1.2 × 10−15 |
[17] |
Pyrrole |
Bacteria |
QCM |
1 × 10 9 CFU/mL A |
[18] |
Pyrrole |
Yeast |
Thermal resistance |
10 1.25 ± 0.09 CFU/mL |
[19] |
Pyrrole, pyrrole-3-carboxylic acid |
Formaldehyde |
Resistive and optical fiber |
~7 ppm (res.) 4.25 ppm (opt.) B |
[20][21] |
Pyrrole, pyrrole-3-carboxylic acid |
Cardiac troponin T |
DPV |
0.25 × 10−12 |
[22] |
Pyrrole, pyrrole-3-carboxylic acid |
Theophylline |
QCM |
-- |
[23] |
OTPylFc, pyrrole |
Dopamine |
DPV |
1.7 × 10−6 D |
[24] |
Aminophenylboronic acid |
Lysozyme/cytochrome c |
CV |
7 × 10−9 (lys.) B |
[25] |
Aniline |
BSA |
DPV |
0.59 × 10−6 |
[26] |
Aniline, metanilic acid |
Cortisol, progesterone, testosterone, 17β-estradiol |
CV |
C: 5.52 × 10−18 T: 34.67 × 10−18 P: 7.95 × 10−18 E: 33.03 × 10−18 |
[27] |
Aniline, polyvinylsulphonic acid |
p-nitrophenol |
DPV |
1 × 10−6 |
[28] |
Aniline, acrylic acid |
Melamine |
DPV |
17.2 × 10−3 |
[29] |
3-acetic acid thiophene, EDOT |
Atrazine |
CV |
1.0 × 10−9 |
[30] |
Poly(hydroxymethyl 3,4-ethylenedioxythiophene) |
α-synuclein |
CV |
6.5 × 10−15 |
[31] |
2,2′-bithiophene-5-carboxylic acid |
p-synephrine |
DPV EIS |
12.2 × 10−9 5.7 × 10−9 |
[32] |
2,20-bithiophene-5-carboxylic acid and p-bis(2,20-bithien-5-yl)-methylbenzo-18-crown-6 |
Tyramine |
DPV |
159 × 10−6 |
[33] |
3-aminothiophene, ATh-γ-PGA |
Lysozyme |
DPV |
0.1 × 10−9 B |
[34] |
4-bis(2,2′-bithien-5-yl)methyl-benzoic acid glycol ester |
Oxytocin nonapeptide |
EIS |
60 × 10−6 |
[35] |
p-phenylenediamine |
Methyl paraben |
DPV |
10 × 10−6 |
[36] |
m-phenylenediamine |
Erythromycin |
DPV |
0.1 × 10−9 |
[37] |
m-phenylenediamine |
Sulfamethizole |
SAW/DPV |
0.9 × 10−9 (DPV) |
[38] |
Triphenylamine rhodanine- 3-acetic acid |
Metalloproteinase-1 (MMP-1) |
EG-FET |
20 × 10−9 (epitope 1) 60 × 10−9 (epitope 2) |
[39] |
Toluidine blue |
Prostate specific antigen (PSA) |
DPV |
29.4 × 10−15 C |
[40] |
Bismarck Brown Y |
Uric acid |
EIS/ECS |
0.160 × 10−6 |
[41] |
2.2 Electropolymerization + Additives
To enhance certain properties, electropolymerized MIPs can be combined with a wide range of additives. For example, the electrical response can be further increased by
introducing 2D materials such as graphene [42][43], carbon nanotubes (CNTs) [44][45], or MXenes [46]. Additives such as nanoparticles [47][48][49] can also increase affinity toward the analyte and enhance sensitivity by increasing the active surface area. Sensors prepared by electropolymerization with additives are presented in Table 2.
Table 2. Overview of cMIP sensors prepared using electropolymerization-including additives. All LOD values were converted into mol/L (if possible) to allow better comparison. DPV = differential pulse voltammetry, EIS = electrochemical impedance spectroscopy, SWV = square wave voltammetry, CV = cyclic voltammetry, LSV = linear sweep voltammetry.
Monomer |
Additive |
Analyte |
Transducer |
LOD [mol/L] |
Ref. |
Pyrrole |
CS2-functionalized GO |
Cadmium |
DPV |
2 × 10−9 |
[42] |
Pyrrole |
Dopamine@graphene |
Olaquindox |
DPV |
7.5 × 10−9 |
[43] |
Pyrrole |
MWCNT/GAs |
Dopamine |
DPV |
1.67 × 10−9 |
[44] |
Pyrrole |
MXene/NH2-CNTs |
Fisetin |
DPV |
1.0 × 10−9 |
[46] |
Pyrrole |
Prussian-Blue-porous carbon-CNT hybrids |
Cysteine |
DPV |
6 × 10−15 |
[50] |
Pyrrole |
Au-NPs |
Caffeine |
DPV |
0.9 × 10−9 |
[51] |
Pyrrole |
Coomassie BB |
Ricin (chain A) |
EIS |
3.13 × 10−12 |
[52] |
Pyrrole |
Copper oxide |
Tyrosine |
DPV |
4.0 × 10−9 |
[53] |
Cysteine |
Biochar |
Pb2+, Cd2+ |
Differential pulse anodic stripping voltammetry |
5.86 × 10−15 (Pb2+) 0.883 × 10−18 (Cd2+) |
[54] |
Aniline |
Copper nanoparticles |
Nitrate |
LSV, EIS |
31 × 10−6 (EIS) 5 × 10−6 (LSV) |
[48] |
Aniline, metanilic acid |
WS2 |
17β estradiol |
CV |
0.2 × 10−18 |
[55] |
Aniline |
C-dots |
L-ascorbic acid, D-ascorbic acid |
DPV |
0.00016 × 10−9 (D) 0.00073 × 10−9 (L) |
[56] |
Aniline, m-aminobenzenesulfonic acid |
MXene (e.g., Ti2C) |
C-reactive protein |
CV |
1.67 × 10−21 |
[57] |
Aniline |
PMB/MWCNTs |
Cardiac troponin |
DPV |
1.7 × 10−15 |
[58] |
Aniline or 2-methoxyaniline |
GO |
Amoxicillin |
SWV |
2.6 × 10−6 6.1 × 10−7 |
[59] |
Aminophenol |
Carbon ink |
Carcinoembryonic antigen |
EIS |
16.7 × 10−12 |
[60] |
Aminophenol |
Perovskite quantum dots |
Prometryn |
Electroluminescence |
0.2 × 10−6 0.010 µg/kg (fish) |
[61] |
o-phenylenediamine |
poly(p-aminobenzene sulfonic acid) |
Paracetamol |
DPV |
4.3 × 10−8 |
[62] |
o-phenylenediamine |
Au-NPs |
Tetracycline |
DPV |
0.32 × 10−9 |
[63] |
o-phenylenediamine |
UCNPs@ZIF-8 |
Imidacloprid |
Electroluminescence |
39.1 × 10−15 |
[64] |
o-phenylenediamine |
MPA-Cu NCs |
Enrofloxacin |
Electroluminescence |
27 × 10−12 |
[65] |
p-ATP |
Au-NPs |
Aspirin |
DPV |
0.3 × 10−9 |
[47] |
p-ATP |
N,S co-doped GQDs, Au-NPs |
Sofosbuvir |
DPV |
0.36 × 10−9 |
[49] |
p-ATP |
Au-NPs |
Chlorpyrifos |
CV |
0.33 × 10−6 |
[66] |
Aniline, m-aminobenzenesulfonic acid |
WS2 |
α-synuclein |
CV |
0.04 × 10−15 |
[67] |
Phenylboronic acid |
RGO |
Fructose |
DPV |
3.2 × 10−15 |
[68] |
3-thiopheneacetic acid |
Au-NPs |
Adenine |
DPV |
0.99 × 10−9 |
[69] |
3-thiopheneboronic acid |
Au NPs |
Epinephrine |
DPV |
76 × 10−9 |
[70] |
2,2′-bithio-phene-5-carboxylic acid |
bis-(2,2′-bithienyl)-4-ferrocenylphenyl methane |
p-synephrine |
DPV |
0.57 × 10−9 |
[71] |
Hydroxymethyl-3,4-ethylenedioxythiophene |
MXene/carbon nanohorn |
Adrenaline |
DPV |
0.3 × 10−9 |
[72] |
Triphenylamine rhodanine-3-acetic acid, EDOT |
MoS2 |
Matrix metalloproteinase-1 |
CV |
18.52 × 10−18 |
[73] |
para-aminobenzoic acid |
MoS2/NH2-MWCNT@COF |
Sulfamerazine |
DPV |
0.11 × 10−6 |
[74] |
4-aminobenzoic acid |
MWCNTs |
Cefquinome |
SWV |
50 × 10−9 A |
[75] |
o-phenylenediamine, 3-aminophenylboronic acid monohydrate |
graphene-Au NPs |
BSA |
Electrochem. oxidation of grafted 6-ferrocenyl-hexanthiol |
0.1 × 10−12 |
[76] |
Phenol |
carbon ink |
3-nitrotyrosine |
DPV |
22.3 × 10−9 |
[77] |
Phenol |
CNTs |
Human ferritin, human papillomavirus derived E7 protein |
DPV |
~0.21 × 10−18 (hFtn) <0.91 × 10−18 (E7) |
[45] |
It is also possible to form conductive polymers using chemical oxidative polymerization. This approach usually relies on ammonium persulfate as an oxidizing agent [78]. As can be seen from the few examples below (Table 3), this technique is far less common than electropolymerization for cMIP preparation. In the publications below, mainly PANI was prepared with this method. Oxidative polymerization can be useful for fabricating cMIPs on non-conductive materials, which is not possible using electropolymerization [79].
Table 3. Overview of cMIP sensors prepared using oxidative polymerization. All LOD values were converted into mol/L (if possible) to allow better comparison. DPV = differential pulse voltammetry, CV = cyclic voltammetry, OCP = open circuit potential.
Monomer |
Analyte |
Transducer |
LOD [mol/L] |
Ref. |
Aniline |
Glucose |
Resistive |
1.0048 × 10−3 |
[80] |
Aniline |
Aflatoxin B1 Fumonisin B1 |
DPV |
1.00 × 10−12 (AFB1) 44.61 × 10−12 (FuB1) |
[81] |
Aniline, metanilic acid |
Testosterone |
CV |
~3 × 10−6 |
[82] |
4,4′-methylenedianiline |
1-benzothiophene |
CV |
67.06 × 10−6 |
[83] |
3-aminophenylboronic acid |
N-(1-desoxy-ß-D-fructopyranose-1-yl)-L-valine |
OCP |
10 × 10−3 A |
[84] |
2.4 MIPs + Conductive Nanomaterials
Acrylic or vinylic monomers are a popular choice for conventional MIPs. However, the resulting polymers are usually not electrically conductive. Therefore, those kinds of MIPs are typically combined with gravimetric or optical transducers. To make them accessible to electrochemical sensors, additives that increase conductivity of the material are necessary. Composites of non-conductive MIPs and nanomaterials can be seen in Table 4.
Table 4. Overview of cMIP sensors with non-conductive MIPs and conductive nanomaterials. LOD values for gaseous analytes are given in ppm. All other LOD values were converted into mol/L to allow better comparison. The upper part of the table presents only sensors for gaseous analytes, the lower part applications in solution. DPV = differential pulse voltammetry, SWV = square wave voltammetry.
Monomer |
Additive |
Analyte |
Transducer |
LOD [ppm] |
Ref. |
MAA |
AuNPs |
Acetone |
Resistive |
66 |
[85] |
MAA |
AuNPs |
Nonanal |
Resistive |
4.5 |
[86] |
MAA |
Carbon black |
Toluene |
Resistive |
0.8 |
[87] |
MAA |
MWCNTs |
Ethanol |
Resistive |
0.5 |
[88] |
MAA, vinyl benzene |
Graphene |
Nitrobenzene |
Resistive |
0.2 |
[89] |
MAA |
MWCNTs |
Hexanal |
Resistive |
10 |
[90] |
Polyacrylic acid |
Carbon black |
Acid gases A |
Resistive |
-- |
[91] |
Polyacrylic acid |
Carbon black |
Hexanoic acid |
Resistive |
100 B |
[92] |
Monomer |
Additive |
Analyte |
Transducer |
LOD [mol/L] |
Ref. |
MAA |
GO |
L-serine |
Thin film transistor |
0.19 × 10−3 |
[93][94] |
MAA |
CoN nanowires |
Tylosin |
DPV |
5.5 × 10−12 |
[95] |
MAA |
Au@Fe3O4@RGO-MIPs |
Ractopamine |
DPV |
0.02 × 10−9 |
[96] |
MAA |
Cu-MOF |
Carbendazim |
DPV |
2 × 10−9 |
[97] |
Tetraethylene Glycol 3-morpholin propionate acrylate |
MWCNTs |
BSA |
DPV |
0.36 × 10−9 |
[98] |
Acrylamide (AA) |
MWCNTs |
Insulin |
SWV |
33 × 10−15 |
[99] |
4-vinyl pyridine |
MXene, AuNPs |
Tetrabromobisphenol A (TBBPA) |
DPV |
14.4 × 10−12 |
[100] |
Chitosan |
MWCNTs |
Tryptophan |
Second-order derivative linear sweep voltammetry |
1.0 × 10−9 |
[101] |
Polyvinylphenol |
SWCNTs |
Cotinine |
Resistive |
0.28 × 10−6 |
[102] |
Sodium p-styrenesulfonate, dopamine |
MWCNTs, AgNPs |
Sulfonamides |
DPV |
4 × 10−9 |
[103] |
2.5 Blending MIPs with Conductive Polymers
Another way to introduce conductivity into MIP-based sensing layers is the combination of conventional, non-conductive MIPs with non-imprinted (semi)conductive polymers
(Table 5).
Table 5. Overview of cMIP sensors with polymer blends. LOD values for gaseous analytes are given in ppm. All other LOD values were converted into mol/L to allow better comparison. QCM = quartz crystal microbalance, DPV = differential pulse voltammetry.
Monomer |
Cond. Polymer |
Analyte |
Transducer |
LOD [ppm] |
Ref. |
MAA |
PANI |
Terpenes A |
Resistive |
~50 B |
[104] |
Styrene |
P3HT |
Limonene |
QCM Resistive |
50 B |
[105] |
PVA |
PPy |
2,4-DNT |
Resistive |
0.1 |
[106] |
Monomer |
Additive |
Analyte |
Transducer |
LOD [mol/L] |
Ref. |
AA |
FUN-PANI |
Parathion |
DPV |
1.13 × 10−8 |
[107] |
AA |
MWCNT/PANI |
Nalbuphine |
Potentiometric |
1.1 × 10−7 |
[108] |
3. Transducers
Conventional (non-conductive) MIPs are often combined with mass-sensitive transducers. Those devices rely on the piezoelectric effect and react to adsorbed mass with a change in frequency. The most frequently used piezoelectric transducer is the quartz crystal microbalance (QCM), which is also useful with cMIPs
[109][77][110][81]. SAW resonators represent a different kind of piezoelectric sensor, which can be combined with cMIPs
[38]. However, those transducers do not require electrically conductive polymers. cMIPs reveal their full potential when combining them with electrochemical transducers: electrically conductive polymers or composites enable electron transfer to the electrodes. However, they are not only useful to detect electroactive analytes. In that case, it is necessary to add an electroactive probe (sometimes referred to as “electrochemical indicator”) to the sample.
Voltammetric methods are very selective since they identify the analyte via specific oxidation and reduction peaks
[111]. Popular detection methods include DPV (e.g.,
[12][22][33]) and CV (e.g.,
[27][55][73]). Other examples utilize square wave voltammetry
[17][59][75][99] or linear sweep voltammetry
[48]. Amperometric sensors comprising cMIPs
[10][11][15][16] are less common. Those devices are a subgroup of voltammetric methods in the sense that they operate at a fixed potential. The analyte binds to the imprints and becomes reduced or oxidized, which, in turn, generates a current proportional to analyte concentration
[112]. There are also a few examples of potentiometric detection
[84][108], electric impedance spectroscopy (e.g.,
[14][32][35]), and resistive devices
[85][86][87][88][89][90][91][92]. EIS devices measure the impedance of the system. They are sensitive to capacitive and inductive effects
[112]. In resistive sensors, the binding event causes a change in electric resistance, or in other words, conductivity, of the receptor material. Such sensor types mainly rely on composites consisting of (acrylic) MIPs and conductive nanomaterials. Adsorption of the analyte causes swelling of the polymer film, which means that the conductive parts of the material move further apart. Those devices are usually not suitable for measuring in buffers due to the high ionic strength of the medium. Therefore, they are mainly used for gas sensing applications
[113]. Other, less common transducers mentioned in this review include thermal resistance measurements
[19], optical fiber
[20][21], EG-FET
[39], thin film transistor
[93][94], and electroluminescence
[61][64][65].
4. Application Areas
Although cMIP-based sensing systems are potentially useful for a wide range of applications, most reports focus on food safety, medical applications, and environmental monitoring. Within these areas, sensors have been developed for all sizes of analytes ranging from heavy metals, to pharmaceuticals and proteins, to microbiological systems.
4.1. Food Safety
Sulfadimethoxine is a sulfonamide antibiotic that is used in animal husbandry for food products of animal origin that may contain residues of the compound and, thus, cause adverse health effects for the consumer. To detect sulfadimethoxine, a sensing system based on electropolymerized PPy was reported
[10] and further optimized, which lowered the LOD
[11] (
Table 1). Doxycyclin is a similar antibiotic: it is also used in veterinary medicine and aquaculture. Residues can be found in food products, such as meat, eggs, and milk, and can be detected with a cMIP-based sensor.
[9] (
Table 1). Other sensors for antibiotics in food include sulfonamide detection in chicken, pork, and egg
[103] (
Table 3) as well as detecting tetracycline antibiotic residues in milk and meat
[63] (
Table 2). As an example, for animal growth promoters, a sensing system for olaquindox was developed. Olaquindox contamination in food products and water sources may negatively affect humans, animals, and the environment. The sensor relies on imprinted PPy on dopamine@graphene. It is able to measure contamination in spiked fish and feedstuff
[43] (
Table 2). Melamine has been used as a fake protein source in infant formula and pet food in China. It is connected to acute renal failure in animals and humans due to kidney stone formation. Regasa et al. prepared a sensor for this compound and successfully analyzed melamine in spiked infant formula and raw milk
[29] (
Table 1).
Organophosphate pesticides are widely used to limit infestations on agricultural products. Liang et al. prepared a sensor for parathion by imprinting the pesticide on functionalized PANI nanoparticles, which made it possible to detect it in vegetable samples
[107] (
Table 4). Prometryn is a triazine herbicide commonly used for controlling weeds and algae in aquaculture. However, the compound is rather stable, accumulates in aquatic products, and is potentially harmful for human health and the environment. Zhang et al. developed a MIP sensor comprising quantum dots to detect prometryn in fish and water samples
[61] (
Table 2). Carbendazim is a fungicide frequently used in agriculture. It is suspected to cause cancer. Beigamoradi et al. developed a sensor for this substance and tested it in various food samples, including tangerine, tomato, apple, and cucumber
[97] (
Table 3).
Tyramine is a well-known marker for rottenness; a sensor to detect it in different food samples was successfully established
[33] (
Table 1). Terpenes such as limonene constitute another relevant marker for degradation of organic matter. It is possible to detect them in the gas phase with a cMIP sensor
[105] (
Table 4). Besides markers and contaminations, whole cell systems such as yeast, which are relevant to food, can be monitored using heat transfer detection with electropolymerized MIPs
[19] (
Table 1).
4.2. Medical Applications
Sensors based on cMIPs are often used to detect protein biomarkers in different media. This starts with heart-related markers, such as HSA, which can be monitored in serum to prevent liver and heart diseases
[14] (
Table 1). Cardiac troponin T is another important example: it helps to diagnose and treat myocardial infarction
[22] (
Table 1). Another troponin T sensor achieved results in diluted human blood plasma
[58] (
Table 2). C-reactive protein is another marker for coronary heart disease, inflammatory diseases, and viral infections, for which a cMIP-based sensor for detection in serum was developed
[57] (
Table 2).
Besides heart related markers, other important protein biomarkers were successfully used to prepare cMIP-based sensors. A prominent example is a sensor based on imprinted epitopes of matrix metalloproteinase-1 (MMP-1). The latter is an idiopathic IPF marker and not yet fully understood, making the sensor a valuable tool
[39] (
Table 1). A second sensor for the same analyte was developed by electropolymerizing a peptide-imprinted polymer
[73] (
Table 2). A sensor for α-synuclein was successfully applied in in culture medium of midbrain organoids
[31][67] (
Table 1 and
Table 2). PSA levels are associated with prostate cancer; a corresponding sensor for detecting it in human serum exists in the literature
[40] (
Table 1). Lysozyme is found in body fluids, and unusual levels may indicate pathological conditions, which was the reason for developing different sensors for Lysozyme
[25][34] and cytochrome c
[25] (
Table 1). As an example for addressing viral markers, a sensor for the SARS-CoV-2 spike glycoprotein, which has been of crucial importance since the beginning of the coronavirus pandemic was developed
[16] (
Table 1). Although Cor a 14 allergen is an allergen and not directly a biomarker, it has to be mentioned here, since the respective sensor remarkably demonstrated a higher selectivity of the MIP compared to Cor than a 14 IgG produced in rabbits
[17] (
Table 1).
Aside from large protein-based markers, one can find a similar number of applications to detect small-molecule biomarkers. Examples thereof are sensors for L-tryptophan
[12][101] (
Table 1 and
Table 3), testosterone
[82] (
Table 4), oxytocin nonapeptide
[35] (
Table 1), or multichannel monitoring of the hormones 17β estradiol, cortisol, progesterone, and testosterone
[27] (
Table 1). Additionally, devices to detect dopamine have been developed. High levels of dopamine can cause ADHD and schizophrenia in children, whereas low levels lead to Parkinson’s and Alzheimer´s disease in elderly people
[24][44] (
Table 1 and
Table 2).
Furthermore, one can find some applications regarding drugs. Sensors were also developed for p-synephrine, a dietary supplement for weight loss, which comes with serious side effects such as high blood pressure, myocardial infarction, and sudden death
[32][71] (
Table 1 and
Table 2). Nalbuphine hydrochloride is a phenanthrene derivative of opioid analgesics and is used for treating pain. It comes with a range of side effects, such as nausea, dehydration, and dizziness. A sensor to monitor the compound in pharmaceutical drugs and spiked urine samples was established
[108] (
Table 3). In addition, examples for cMIP sensors for well-known drugs include an example each for the antibiotic amoxicillin
[59] and the painkiller paracetamol
[62] (
Table 2). Not only drugs, but also other health relevant molecules, such as endocrine disrupting compounds, can be assessed. For example, 4-hydroxybenzoic acid esters, which are frequently used as antimicrobial additives in cosmetics and pharmaceutical products, were successfully measured in real samples with a novel sensor
[36] (
Table 1).
In addition to the mentioned applications, cMIPs potentially play a crucial role in future breath analysis. Breath analysis is an attractive alternative to invasive diagnosis, and it has already led to the development of some sensors for breath biomarkers. These include acetone, which is present in the exhaled air of diabetes patients
[84], and nonanal
[85] and hexanal
[89], which are both breath biomarkers for lung cancer (
Table 3).
4.3. Environmental Applications
A wide range of environmental pollutants are known; monitoring them becomes an increasingly important issue due to the strong worldwide population increase. For instance, heavy metal pollution from industrial processes may contaminate food or water and affect human health
[42]. Pb
2+ and Cd
2+ sensors with nanobiochar and electropolymerized L-cysteine were developed to enable monitoring of such pollutants in real-life water samples
[54]. (
Table 2).
Ractopamine is a β-androgenic leanness-enhancing agent usually fed to bred animals to boost muscle tissue growth; it can cause harm to human health by influencing the cardiovascular and central nervous system. For monitoring the compound, a cMIP sensor based on Au@Fe
3O
4@RGO-MIPs was developed
[96] (
Table 3). Para-nitrophenol is a toxic pesticide that pollutes soil and wastewater. It is known to have carcinogenic and mutagenic effects
[28]. (
Table 1). Tetrabromobisphenol A is a flame retardant often used in industrial manufacturing. It tends to accumulate in water and poses risks for the environment and human health
[100]. (
Table 4).
Besides direct environmental contamination by toxic compounds, bacterial resistance to antibiotics is a growing and crucial challenge, which requires monitoring antibiotic levels in water. For this purpose, cMIP-based sensors to detect erythromycin in tap water
[37] (
Table 1) and tylosin in real surface water and soil samples were developed
[95] (
Table 3). Another example is a sensor for sulfamethizole, which was developed by the same group
[38] (
Table 1).
In terms of indoor contaminations, volatile organic compounds (VOCs) are an issue of growing concern. Additionally for this application, cMIP-based sensing systems such as a formaldehyde
[20] (
Table 1) or toluene sensors
[87] (
Table 3) are valuable to extend the application range toward more VOCs.
5. Summary and Conclusion
The most widespread monomers used for molecular imprinting, such as acrylic or vinylic compounds, result in electrically insulating polymers. This limits the applications of the materials in chemical sensing. Methods that require direct conduction of electrons between the binding sites or direct monitoring of electrical changes in the receptor film cannot be integrated with conventional MIPs. cMIPs fill this gap as they combine the advantages of both methods: the imprints in the material provide selective recognition of the analyte, and the conductive polymer and/or additive allows for integrating them into a wider range of transducers. Combining MIPs with conductive additives often also enhances affinity and sensitivity of the sensors by increasing surface area or conductivity. As can be seen in the examples above, cMIP sensors have already been developed for a wide range of analytes. So far, cMIP sensors are, to the best of our knowledge, not used in commercial devices. As of now, most reported systems achieve highly promising results in controlled laboratory conditions. However, in order to move cMIPs toward commercial implementation, a thorough assessment of their performance in real-life samples and complex matrices is a necessary next step. Given that the field is relatively young and rapidly developing, cMIPs bear a huge potential for future applications. The most important upcoming issue will be to develop or identify systems that are stable and reliable for prolonged periods. This is often only a side aspect in publications, but the described cMIPs with the different fabrication and modification possibilities are a highly promising toolbox to close this gap.
This entry is adapted from the peer-reviewed paper 10.3390/chemosensors11050299