Conductive Molecularly Imprinted Polymers (cMIPs): Comparison
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Please note this is a comparison between Version 1 by Adriana Feldner and Version 3 by Jessie Wu.

Molecularly imprinted polymers (MIPs) have proven useful as receptor materials in chemical sensing and have been reported for a wide range of applications. Based on their simplicity and stability compared to other receptor types, they bear huge application potential related to ongoing digitalization. This is the case especially for conductive molecularly imprinted polymers (cMIPs), which allow easy connection to commercially available sensing platforms; thus, they do not require complex measuring setups.

  • molecularly imprinted polymers
  • conductive polymers
  • sensors
  • electropolymerization
  • composites

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 Molecularly Imprinted PolymerPs

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]
A No LOD given. Only one concentration measured. B No LOD given. Lowest concentration of calibration curve. C No LOD given. Lower limit of quantification. D No LOD given. Lowest concentration of linear range.

 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]
A No LOD given. Lowest detectable concentration.

2.3 Oxidative Polymerization

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]
A No LOD given. Only this concentration measured.

2.4 Molecularly Imprinted PolymerPs + 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]
A Propanoic acid, hexanoic acid, heptanoic acid, and octanoic acid. B No LOD. Only this concentration measured.

2.5 Blending Molecularly Imprinted PolymerPs 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]
A α-Pinene, limonene, linalool, and geraniol; B lowest measured concentration.
 

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 resviearchw 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]. Pb2+ and Cd2+ 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@Fe3O4@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.

References

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