# Harnessing Small-Molecule Analyte Detection in Complex Media: Combining Molecularly Imprinted Polymers, Electrolytic Transistors, and Machine Learning

Gabrielle Coelho Lelis, Wilson Tiago Fonseca, Alessandro Henrique de Lima, Anderson Kenji Okazaki, Eduardo Costa Figueiredo, Antonio Riul Jr, Gabriel Ravanhani Schleder, Paolo Samorì, Rafael Furlan de Oliveira

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Gabrielle Coelho Lelis, Wilson Tiago Fonseca, Alessandro Henrique de Lima, Anderson Kenji Okazaki, Eduardo Costa Figueiredo, et al.. Harnessing Small-Molecule Analyte Detection in Complex Media: Combining Molecularly Imprinted Polymers, Electrolytic Transistors, and Machine Learning. ACS Applied Materials & Interfaces, 2023, 17 (9), pp.12990-13000. 10.1021/acsami.3c16699. hal-04979176

HAL Id: hal-04979176

https://hal.science/hal-04979176v1

Submitted on 6 Mar 2025

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# Harnessing Small-Molecule Analyte Detection in Complex Media: Combining Molecularly Imprinted Polymers, Electrolytic Transistors, and Machine Learning

Gabrielle Coelho Lelis†,§, Wilson Tiago Fonseca†,§, Alessandro Henrique de Lima†, Anderson Kenji Okazaki†, Eduardo Costa Figueiredo‡, Antonio Riul Jrψ, Gabriel Ravanhani Schleder†,*, Paolo Samori§, Rafael Furlan de Oliveira†,ψ,*

## Abstract

Small molecule analyte detection is key for improving the quality of life, particularly in health monitoring through the early detection of diseases. However, detecting specific markers in complex multicomponent media using devices compatible with point-of-care (PoC) technologies is still a major challenge. Here, we introduce a novel approach that combines molecularly imprinted polymers (MIPs), electrolyte-gated transistors (EGTs) based on 2D materials, and machine learning (ML) to detect hippuric acid (HA) in artificial urine, being a critical marker for toluene intoxication, parasitic infections, and kidney and bowel inflammation. Reduced graphene oxide (rGO) was used as the sensory material and molecularly imprinted polymer (MIP) as supramolecular receptors. Employing supervised ML techniques based on symbolic regression and compressive sensing enabled us to comprehensively analyze the EGT transfer curves, eliminating the need for arbitrary signal selection and allowing a multivariate analysis during HA detection. The resulting device displayed simultaneously low

<footnote>† Brazilian Nanotechnology National Laboratory (LNNano), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas 13083-100, SP, Brazil</footnote>

<footnote>‡ Faculty of Pharmaceutical Sciences, Federal University of Alfenas, 37130-001 Alfenas, MG, Brazil</footnote>

<footnote>Ψ Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin, Campinas, SP, 13083-859, Brazil</footnote>

<footnote>¥ John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, United States</footnote>

<footnote>§ Université de Strasbourg, CNRS, ISIS, 8 allée Gaspard Monge, Strasbourg 67000, France</footnote>

<footnote>§ These authors contributed equally</footnote>

<footnote>*Corresponding author:
rafael.furlan@lnnano.cnpem.br</footnote>
operating voltages (< 0.5V), rapid response times (≤ 10s), operation across a
wide range of HA concentrations (from 0.05 to 200 nmol L⁻¹), and a low limit of
detection (LoD) of 39 pmol L⁻¹. Thanks to the ML multivariate analysis we
achieved a 2.5-fold increase in the device sensitivity (1.007 µA/nmol L⁻¹) with
respect to the human data analysis (0.388 µA/nmol L⁻¹). Our method represents
a major advance in PoC technologies, by enabling the accurate determination of
small molecule markers in complex media via the combination of ML analysis,
supramolecular analyte recognition, and electrolytic transistors.

**Keywords:** Point of Care Sensors, Diagnostics, Molecularly Imprinted Polymers, Hippuric Acid, Electrolyte-gated Transistors, Machine Learning

**1. Introduction**

Detecting small molecule analytes is pivotal in various fields of science
and technology, including quality control of food and beverages, environmental
monitoring, and clinical diagnostics¹⁻³. These analytes are low molecular weight
compounds (typically < 1kDa)⁴, displaying a wide range of chemical structures,
which serve as food additives, drugs and pharmaceuticals products, pesticides,
pollutants, toxins, metabolites, etc.⁵,⁶. Small molecules are also important
biomarkers of various diseases and can be found in physiological fluids like urine,
blood, and saliva. Their precise detection and quantification are essential for
biomedical research and personalized medicine, especially for the early detection
of diseases⁷,⁸.

Because of their presence in complex media, such as environmental
samples and physiological fluids, and frequently at low concentrations (micro-
and nanomolar range), the detection and quantification of small molecules is
extremely challenging. Commonly employed approaches for their highly sensitive
detection are immunoassays, spectroscopic methods, and chromatography,
which are unfortunately complex, laborious, and expensive⁹. However, clinical
diagnostics demands point-of-care (PoC) devices for decentralized and fast
analyte determination and decision-making, which adds further complexity to the
development of new detection methods¹⁰,¹¹.

Aiming to develop new strategies for the development of PoC technologies
capable of detecting small molecule analytes in complex media we report an
unprecedented approach that synergistically combines (i) electrolyte-gated transistors (EGTs) as device platform, (ii) reduced graphene oxide (rGO) as sensory material, (iii) molecularly imprinted polymers (MIPs) as supramolecular receptors, and (iv) machine learning (ML) for advanced data analysis. Here, hippuric acid (HA) – a metabolite naturally found in the urine of healthy individuals but also a biomarker of several diseases such as toluene intoxication, parasitic infection and kidney and bowel inflammation<sup>12-15</sup> – was employed as the target small molecule analyte to be detected in artificial urine.

Electrolyte-gated transistors (EGTs) possess several advantages for chemical sensing and biosensing, which include inherent operation in aqueous medium, low working voltages (< 1V), and signal amplification ability<sup>16,17</sup>. Here, as the EGT active layer, we employed rGO since it offers additional advantages for PoC devices, such as high conductivity along with easy film processing from aqueous GO dispersions<sup>18,19</sup>. These characteristics make the rGO EGTs both simple to fabricate and efficient for chemical sensing and biosensing<sup>20-23</sup>.

To selectively detect HA in artificial urine we functionalized the gate electrode of rGO EGTs with a chemically tailored MIP. The latter offers remarkable synthetic versatility and stability, while also being more cost-effective and easier to handle in comparison to biorecognition elements<sup>24,25</sup>. Finally, ML was employed to process the complex signal data from the device response as a function of HA concentration. ML allowed the automatic extraction of essential information from the sensing experiments and a multivariate analysis of the whole EGT transfer curve. As a result, we simultaneously achieved higher accuracy and sensitivity with respect to the human data analysis.

In this work, we tackle the challenge of detecting small-molecule analytes in complex media, namely HA in artificial urine, using electronic devices compatible with PoC technologies. For this purpose, an unprecedented approach that takes full advantage of the synergistic combination of MIP receptors and supramolecular recognition of the target analyte, ambipolar EGTs based on rGO as transducers, and ML for advanced data analysis is reported. As a result, we obtained a device that shows remarkable performance with respect to traditional analytical methods and technological potential as a new diagnostic tool.

## 2. Methods
**Materials:** Silicon (p-doped)/silicon dioxide (Si/SiO₂) substrates (thermal oxide 2 µm thick, Siegert wafer), poly(diallylmethylammonium chloride) solution (PDDA, Sigma Aldrich), graphene oxide (GO, 4 mg mL⁻¹, monolayer content >95%, Graphenea), photoresist AZ5214E (Microchemicals), AZ 726 MIF developer (Merck), polydimethylsiloxane (PDMS) elastomer resin and curing agent (Dow Corning), acetone and isopropanol VLSI (Very Large System Integration).

**Fabrication of EGT test patterns:** The fabrication of the EGT test pattern initiated with the thorough cleaning of Si/SiO₂ (12 mm x 12 mm) substrates with acetone and isopropanol VLSI. A detailed overview of the steps involved in fabricating the test pattern can be found in Supporting Information (SI) - Figure S1. Maskless photolithography was used to create the layout for the device electrodes. This was followed by the e-beam deposition of a 5 nm thick Cr adhesive layer and a 40 nm thick Au layer to produce the electrodes. The source (S) and drain (D) electrodes were designed in an interdigitated fashion (IDE), featuring 24 fingers. The dimensions of the transistor channel length (L) and width (W) were set at 30 µm and 2 cm, respectively. We fabricated EGTs featuring a coplanar gate electrode (4 mm² area) positioned 2 mm away from the IDE region. The entire device fabrication process was carried out in a cleanroom environment.

**GO deposition and thermal reduction:** The device IDE region was coated using a 1% w/w PDDA aqueous solution containing 0.5 mol L⁻¹ of NaCl for 15 min, followed by a gentle rinsing with deionized water (DI) to remove the non-adsorbed material from the surface. Here, PDDA served as a primer to guarantee the GO electrostatic deposition onto the IDE surface following our previous publications²⁰,²¹. To immobilize GO onto the PDDA adhesive layer, a 1:10 dilution of the commercial GO water dispersion was carried out, resulting in a final concentration of 0.4 mg mL⁻¹. The GO suspension was sonicated for 3 min to ensure better homogeneity and then deposited on the IDE region for 30 min. After deposition, the coated substrate was rinsed with DI water and dried using N₂. Finally, the casted GO film was thermally reduced in an ultra-high vacuum chamber (10⁻¹⁰ mbar) at 350 °C for 10 min employing a heating ramp of 10°C/min, yielding a uniform rGO thin film in the channel (Figure S2).

**GO and rGO thin film characterization:** GO and rGO films were characterized by means of atomic force microscopy (AFM), X-ray photoelectron spectroscopy
(XPS), Laser Scanning Confocal Microscopy (LSCM), and Raman spectroscopy. AFM measurements were performed using a Bruker Multimode equipment in peak force tapping mode. The measurements employed a ScanAsyst-Air (Bruker) AFM tip with 0.4 N/m spring constant operating at room temperature. XPS measurements were carried out at room temperature using a Thermo Scientific K-alpha spectrometer with a microfocused Al Kα monochromator (energy = 1486.6 eV). The size of the X-ray spot was set to 400 µm. The survey and high-resolution C1s and O1s spectra were recorded with a pass energy of 200 eV (20 scans) and 50 eV (10 scans), respectively. Wide-scan surveys of all elements and high-resolution spectra for C and O were recorded at a base pressure of 10⁻⁷ mbar. LSCM was performed using a Keyence VK-X200 microscope. The Raman spectra of GO and rGO films were recorded using a confocal Raman spectrometer (Horiba, XploRA™ plus). Raman spectra were registered at 532 nm laser excitation with a 100× objective, laser power of 0.12 mW (1 % of the maximum power) using 3 s acquisition time and 10 accumulations.

**rGO EGT operation:** Electrical measurements were carried out in a probe station connected to a Keithley 2636A SourceMeter® unit at room temperature and in ambient atmosphere. A PDMS reservoir was used to confine the electrolyte the IDE and gate electrode region. We recorded the device transfer curves by applying a constant source and drain voltage (V<sub>DS</sub>) while varying the gate-source voltage (V<sub>GS</sub>) and registering the source-drain current (I<sub>DS</sub>).

**EGT functionalization:** To detect HA, the device gate electrode was modified with the appropriate MIP receptor. The employed MIP was synthesized following the protocol reported by Boscari et al.²⁶. The MIP was immobilized onto the gate electrode via electrostatic assembly with PDDA. The PDDA deposition followed the same protocol previously described here for the immobilization of GO onto the IDE region. The PDDA-coated gate electrode was modified with 5 µL of MIP aqueous suspension (10 mg mL⁻¹) for 30 min, then rinsed with DI water and dried under a gentle flux of N₂. To validate the MIP selectivity towards HA, control experiments were carried out with non-imprinted polymer (NIP) analogues. The NIP was also synthesized following the method reported by Boscari et al.²⁶ and immobilized onto the EGT gate electrode via electrostatic assembly with PDDA.
**HA sensing experiments:** HA solutions were prepared at different concentrations (from 0.05 to 200 nM) in both 1 mmol L⁻¹ KCl aqueous electrolyte and in artificial urine. Artificial urine was prepared one day before use as reported by Sarigul et al. ²⁷. The artificial urine samples contain 11.965 mmol L⁻¹ Na₂SO₄, 1.487 mmol L⁻¹ C₅H₄N₄O₃, 2.450 mmol L⁻¹ Na₃C₆H₅O₇.2H₂O, 7.791 mmol L⁻¹ C₄H₇N₃O, 249.750 mmol L⁻¹ CH₄N₂O, 30.953 mmol L⁻¹ KCl, 30.053 mmol L⁻¹ NaCl, 1.663 mmol L⁻¹ CaCl₂, 23.667 mmol L⁻¹ NH₄Cl, 0.19 mmol L⁻¹ K₂C₂O₄.H₂O, 4.389 mmol L⁻¹ MgSO₄.7H₂O, 18.667 mmol L⁻¹ NaH₂PO₄.2H₂O, and 4.667 mmol L⁻¹ Na₂HPO₄.2H₂O. Here, a stock solution (1 mmol L⁻¹) of HA was prepared in artificial urine and sonicated for 1h for better homogeneity. An aliquot of 50 µL of the HA stock solution was diluted in 1 mmol L⁻¹ aqueous KCl solution and stirred using a vortex stirrer for 10 s at room temperature. The samples were diluted in 1 mmol L⁻¹ KCl solution varying the HA concentration from 10 to 200 nmol L⁻¹. Then, 100 µL of the sample was added to the rGO EGT surface for the sensing experiments. The analytical signal of HA was obtained by monitoring the rGO EGT current response as a function of HA concentration. The sensors were subjected to successive transfer scans until a stable response was achieved by sweeping VGS from +0.5 to -0.5 V at a rate of 25 mV s⁻¹ with a constant VDS of 200 mV. Analytical curves are built considering the mean IDS,min values and standard deviation taken from three (n = 3) consecutive transfer scans.

**ML Multivariate analysis:** Three consecutive transfer curves of three distinct rGO EGTs measured at different HA concentrations in artificial urine were employed. The respective IDS and VGS were used as input. We used the sure independence screening and sparsifying operator (SISSO) algorithm that combines linear symbolic regression and compressed sensing for obtaining accurate and interpretable models²⁸. For this purpose, a 2D multitask model was employed, i.e., 2 trainable coefficients were utilized to find the correlation to obtain an explicit and accurate equation based on few features, resulting in a common equation even for different sensor datasets. We performed an exploratory model analysis with training and validation by 10-fold cross-validation, considering symbolic models that appear in different instances. Final training was executed on the complete dataset, evaluating regression performance metrics such as the root mean square error (RMSE) and the coefficient of determination (R²) between predicted and reference (true) data.
### 3. Results and Discussions

Figure 1A portrays the layout of the EGT test pattern, which exhibits three sets of co-planar electrodes. To enhance reproducibility, particularly in the sensing experiments, all three EGTs are positioned onto the substrate surface to enable the use of the same electrolyte reservoir. Figure 1B illustrates the side-view of an rGO EGT with its electrical connections. The EGT channel comprises an active PDDA/rGO film.

<figure><img src="image_2.png" /><figcaption>**Figure 1:** A) Schematic representation of a Si/SiO₂ test pattern containing 3 EGT platforms. B) Cross-section view of an rGO EGT and its electrical connections. V<sub>DS</sub>, I<sub>DS</sub>, and V<sub>GS</sub> are the source-drain voltage, source-drain current, and gate-source voltage, respectively.</figcaption></figure>

Prior to the EGT operation and sensing experiments, we evaluated the characteristics of the rGO film deposited on the device channel. Figures 2A and 2B show respectively the AFM images of the GO thin film before and after the reduction process. GO and rGO films exhibit a multilayered sheet-like topography with a root-mean-square roughness ($R_{rms}$) respectively of 3.0 and 1.3 nm, determined over a of 5 × 5 µm² area. The lateral dimension of the GO flakes exceeds ca. 5 µm, in agreement with the nominal values (viz. 10 µm) provided by the supplier (Graphenea). The smaller $R_{rms}$ of rGO films evidences the successful removal of some oxygen functionalities, thereby confirming the efficient thermal reduction of GO.

Figures 2C and 2D show the high-resolution XPS C1s spectra of GO and rGO films, respectively. The C1s spectrum was deconvoluted using three
Gaussian-Lorentzian curves to represent the main oxygen functionalities known to be present in GO<sup>21,29</sup>. The peak at ca. 284.63 eV is assigned to C-C and C=C, while the peak at ca. 286.67 eV corresponds to the oxygen groups containing C-O bonds, specifically epoxide and hydroxyl groups. Additionally, the peak at ca. 288.10 eV can be associated to C=O bonds from carbonyl and carboxylic acid groups<sup>30,31</sup>. After reduction, the major contribution in the C1s spectrum of rGO relates to C-C and C=C, with minor contributions from oxygen-containing groups. At higher binding energies (ca. 291.39 eV) one can observe the π-π* shake-up satellite peak that corresponds to the improved sp<sup>2</sup> structure of rGO, confirming successful GO thermal reduction. The atomic composition (%) of each GO and rGO chemical functionalities is reported in Table S1. The GO thermal reduction has also been confirmed by Raman spectroscopy, as discussed in detail in the SI (Figure S3).
<figure><img src="image_3.png" /><figcaption>**Figure 2:** Topographical AFM images of A) GO and B) rGO films onto the EGT channel. High-resolution XPS C1s spectra of C) GO and D) rGO films.</figcaption></figure>

Concerning the device operation, rGO EGTs typically exhibit an ambipolar behavior. This results from the tuning of the charge carriers at Dirac (pseudo)cones through VGS application and Fermi level (EF) control (Figure 3A). Here, by sweeping VGS within a |0.5V| window the device response results in a transfer curve having a broad minimum and small anticlockwise current hysteresis, as shown in Figure 3B. The broad minimum is a result of the localized states in the rGO mid-gap<sup>20,22,23</sup>. Such a low density of states makes the electronic properties of rGO very sensitive to the surroundings, which is ideal for sensing applications<sup>32</sup>. Further discussions on the device operation and the origin of the current hysteresis are found in the SI (Figures S4 and S5).
<figure><img src="image_4.png" /><figcaption>**Figure 3:** A) Energy band diagram for rGO EGTs. The application of VGS controls the Fermi level (EF), thereby modulating the type of charge carrier from (i) electrons and (ii) holes. EC and EV are, respectively, the conducting and valence bands of rGO. B) rGO EGT transfer characteristics in KCl 1 mmol L⁻¹ during 20 cycles, VDS = 200 mV. The red curves represent scans starting at positive VGS and the blue curves for VGS starting at a negative potential. C,D) Device transfer curve heatmaps. The color code illustrates the density of data points considering the 20 consecutive VGS scans. E) VCNP stability and F) IDS,min stability during 20 VGS sweeping cycles.</figcaption></figure>
An important aspect for sensing application refers to the device operational stability. Here, the rGO EGT transfer curves retrace substantially well for 20 consecutive scans performed in both directions, i.e., +VGS → -VGS and -VGS → +VGS (Figure 3B). From the Kernel density plots (i.e., heatmaps) in Figure 3C and 3D we observe an excellent retracing especially around the curvature. In this region, two important figures of merit (FoM) that characterize the device operation can be extracted, namely the charge neutrality point voltage (VCNP) and minimum current value (IDS,min).

In Figure 3E we notice stable VCNP values for curves starting at positive voltages (+VGS). However, VCNP fluctuates approximately 25 mV during the first and last two measurements when scanning from -VGS. Conversely, IDS,min reaches complete stability after approximately the two first scans regardless the starting VGS (Figure 3F). Variations of VCNP and IDS,min during the first scans may be attributed to the device conditioning, where film swelling by the electrolyte species or charge carrier trapping in the pristine device can occur. When we consider the other two rGO EGTs on the same test pattern, we observe that IDS,min is more stable than VCNP, exhibiting also more consistent values for the different tested devices (Figure S6). For this reason, we employed IDS,min as the main output signal during the sensing experiments for transfer curves registered from +VGS to -VGS.

Here, HA is chosen as the target small molecule analyte to be detected in complex media (viz. artificial urine). HA is a metabolite naturally found in the urine of healthy individuals³³. However, HA is also a marker for several diseases, including toluene intoxication¹², kidney¹⁴ and bowel inflammation¹⁵, and infections caused by *Trypanosoma brucei brucei*¹³, *Schistosoma japonicum*, *Necator americanus*³⁴, and *Echinostoma Caproni*³⁵. In certain medical conditions, variations in the normal levels of HA in urine may manifest³⁶, requiring the use of analytical methods capable of accurately quantifying small variations of HA.

To date, most procedures for detecting HA rely either on bulky and expensive laboratory-based techniques (e.g., chromatography or nuclear magnetic resonance spectroscopy-NMR) or on tests offering only qualitative or semi-quantitative analysis, i.e., determination of whether HA is below or above a predefined threshold level¹³,¹⁴,³⁷. As a result, HA diagnosis using the currently
employed methods can be unreliable and falls short in accurately determining the HA concentration in the sample, especially using a PoC tool.

To detect HA we have modified the gate electrode of rGO EGTs with a MIP designed to selectively discriminate HA. The MIP is immobilized onto the device gate electrode by means of electrostatic adsorption by using PDDA, as illustrated in Figure 4A (steps from i to iv). Prior to the sensing experiments, the successful immobilization of the MIP was validated by XPS measurements, which shows the presence of the recognition element onto the gate electrode (Figure S7). The MIP immobilization strategy was also validated by recording the device transfer curves at each functionalization step, i.e., when the gate electrode is unmodified, after PDDA coating, and after the MIP grafting (Figure 4B). When the MIP is grafted onto the PDDA-coated gate electrode a decrease of $I_{DS,min}$ is observed (Figure 4B inset).

Prior to the HA sensing in artificial urine, we first tested our approach towards the detection of standard HA solutions prepared in 1 mmol L⁻¹ KCl at concentrations ranging from 0.002 to 2.0 µmol L⁻¹ (Figure 4C). The analytical signal for HA extracted at $I_{DS,min}$ was subtracted from the signal $I_0$ obtained for the blank sample, i.e. without HA in the medium. The resulting $\Delta I_{DS,min}$ is employed to build up an analytical curve, which displays a linear response (R = 0.984) with the logarithmic concentration of HA (Figure 4C, blue curve). As a control experiment, rGO EGTs modified with the non-imprinted polymer (NIP) were also employed (Figure 4C, red curve). We observe that NIP-rGO EGTs show the same response as non-functionalized devices, i.e., same trend with similar curve slopes namely -0.754 µA/µmol L⁻¹ for unmodified and -0.867 µA/µmol L⁻¹ for NIP-modified devices (Figure 4C, black curve). The respective correlation coefficient of the two linear fitting amounting R = 0.962 (black curve) and R = 0.977 (red curve). These results confirm that rGO EGTs modified with MIP are sensitive and selective to HA.
<figure><img src="image_5.png" /><figcaption>**Figure 4.** A) Gate functionalization steps to immobilize MIP in rGO EGTs: i) deposition of PDDA solution onto the gate electrode, ii) formation of a thin PDDA film on the gate electrode, iii) addition of MIP aqueous suspension, and iv) formation of the MIP-coated gate electrode for HA recognition. B) Transfer curves at V<sub>DS</sub> = 200 mV obtained for devices having a bare gate electrode (black curve), after PDDA coating (green curve), and after the MIP immobilization (blue curve). C) ΔI<sub>DS,min</sub> as a function of HA concentration using unmodified (black curve) and modified devices with NIP (red curve) and MIP (blue curve), (n = 3). D) Transfer curves at V<sub>DS</sub> = 200 mV obtained at different concentrations of HA in 1 mmol L<sup>-1</sup> KCl. E) Analytical curve obtained for HA detection in KCl for concentrations spanning from 0.05 to 10.000 nmol L<sup>-1</sup>.</figcaption></figure>

To challenge the device towards more robust sensing applications, we
verified the performance of MIP rGO EGTs stored for two months and against a
wider range of HA concentration. Figure 4D shows the transfer characteristics of the devices as a function of HA for concentrations spanning from 0.05 to 10.000 nmol L⁻¹ prepared in 1 mmol L⁻¹ KCl. The blank sample was evaluated in the same way as the different HA concentrations but without the addition of HA. Figure 4E shows the analytical curve for the HA detection in KCl. The sensor provides an excellent linear response in a wide range of concentrations spanning over 4 orders of magnitude, complying with the equation: ΔI<sub>DS,min</sub> (µA) = 0.151 (µA) + 0.032 (µA nmol L⁻¹) × log[HA] (nmol L⁻¹), with R = 0.972. The LoD is calculated employing the method recently proposed by Kong, D. et al.<sup>38</sup> when the analytical signal shows a logarithmic relationship with the analyte concentration. The calculated LoD uses the equation: 3.3 δ/LoD = SA/CA, where δ is the standard deviation of four measurements of the blank solution (1 mmol L⁻¹ KCl in the absence of HA), SA is the analyte signal intensity, and CA is the concentration of the analyte. We found LoD of 20 pmol L⁻¹, which is consistent since it falls just below the first experimental point in the analytical curve (Figure 4E).

Based on the excellent results obtained for the HA sensing in KCl solution, the device was finally employed to detect HA in complex media, namely artificial urine. The detection of small molecule analytes in complex media is challenging due to matrix effects, where the existing chemical species can act as interfering agents or jeopardize the sensor integrity and reliability, e.g., fouling on the sensor surface<sup>39</sup>. Here, in order to monitor HA in artificial urine we followed a common practice in clinical laboratory analysis which is the serial dilution of the physiological fluid<sup>40</sup>. We employed KCl as diluent to vary the HA concentration from 0.05 to 200 nmol L<sup>-1</sup>, as illustrated in Figure 5A. No filtration or other more intricate sample preparation method were necessary.
<figure><img src="image_6.png" /><figcaption>**Figure 5.** A) Artificial urine sample containing HA and several other compounds diluted in KCl for sensing experiments using the MIP-rGO EGTs. B) Device transfer curves at different concentrations of HA in artificial urine ranging from 0.05 to 200 nmol L⁻¹. C) ΔI<sub>DS,min</sub> as a function of HA concentration in artificial urine (n = 3).</figcaption></figure>

Figure 5B shows the MIP-rGO EGT transfer characteristics at different HA concentration in artificial urine. The shape of the device transfer curve is slightly distorted in respect to the curves obtained in pure KCl possibly due to non-specific interactions on the rGO surface. Figure 5C displays the analytical curve obtained for HA in artificial urine with an excellent linear response using MIP-rGO EGT (blue curve), resulting in R = 0.996 and sensitivity of 0.388 µA nmol L⁻¹. Since the continuous dilution of HA in artificial urine increases the amount of KCl in medium, here we have normalized the device signal to attenuate the background signal. Following again the formalism of Kong, D. et al.³⁸ for logarithmic relationship between sensor signal and analyte concentration, we found a LoD of 40 pmol L⁻¹ for HA in artificial urine. Once again, the calculated LoD is consistent since it lies slightly below the first experimental point in the analytical curve shown in Figure 5C.
To validate the sensor selectivity in artificial urine, control experiments were carried out employing devices modified with a NIP (Figure 5C, red curve). NIP-rGO EGT exhibited a poor linearity of the current response with the HA concentration (R = 0.660). Here, since the current normalization is adopted to attenuate the background signal from the complex media, the device response employing NIP shows a positive slope. We verify that at high matrix concentrations, i.e., undiluted artificial urine (10⁻³ mol L⁻¹), the device response is significantly masked possibly due to screening effects (Figure S8). Finally, we also evaluate the sensor reliability by analyzing its recovery response via the addition of HA at different concentrations. From Table 1 we observe the recovery values range from 82 to 118% for HA concentrations within 10 to 200 nmol L⁻¹, indicating that MIP-rGO EGTs are promising devices for the detection of HA in complex media.

<table><caption>**Table 1.** Determination of HA in artificial urine using MIP-rGO EGTs (n=3).</caption><thead><tr><th>Conc. HA added /<br>nmol L<sup>-1</sup></th><th>Conc. HA found /<br>nmol L<sup>-1</sup></th><th>Recovery<br>Percentage / %</th><th>RSD*<br>/(%)</th></tr></thead><tbody><tr><td>10</td><td>9 ± 1</td><td>90 ± 10</td><td>11.11</td></tr><tr><td>25</td><td>29 ± 3</td><td>112 ± 12</td><td>10.34</td></tr><tr><td>50</td><td>59 ± 1</td><td>118 ± 2</td><td>1.69</td></tr><tr><td>75</td><td>84 ± 3</td><td>112 ± 4</td><td>3.57</td></tr><tr><td>200</td><td>163 ± 31</td><td>82 ± 16</td><td>19.02</td></tr></tbody></table>

One question that arises during HA detection is whether $I_{DS,min}$ is the sole and best device FoM that can be used as the sensor signal. Here, $I_{DS,min}$ is employed as output signal due to its stable response during the device operation. However, EGTs are frequently claimed to be multiparametric sensors, where more than one device FoM could be employed as output signal⁴¹. Here, we verify that $V_{CNP}$ shows no clear changes upon variations of the HA concentration, even in simple electrolyte such as KCl (Figure S9). Depending on the selected $I_{DS}$ some correlation with the HA concentration is found (Figure S10). Nonetheless, such arbitrary choice of $I_{DS}$ compromises the quality of the data analysis and the sensor response in a practical application.

To address this point, here we employ a multivariate analysis of the sensor response using ML. Typically, EGTs sensors based on graphene-related

<footnote>*RSD – relative standard deviation for n = 3</footnote>
materials present one of the responses illustrated in Figure 6A-6C, namely
changes in I<sub>DS</sub> (either in n/p branches, at CNP, or both), V<sub>CNP</sub> shifts, or changes
in the device transconductance (g<sub>m</sub>)<sup>42</sup>. Each type of response strongly depends
on the phenomena underlying the sensing mechanism. For example, if the
analyte recognition event alters the doping level of graphene (or rGO) one may
expect a V<sub>CNP</sub> shift<sup>43</sup>. Conversely, if the analyte binding to the device surface acts
as a scattering site this may result in g<sub>m</sub> changes<sup>44</sup>. Finally, I<sub>DS</sub> changes may
relate to variations of the density of charge carriers during sensing and it is co-
dependent on both the other two effects<sup>42</sup>.

Even if each response shown in Figure 6A-6C can be observed in many
experimental works<sup>42</sup>, very frequently the mechanisms underlying the analyte
recognition compete among each other, leading to complex device responses
(Figure 6D). Here we can hypothesize that the HA binding in the recognition site
of the MIP involves no charge transfer processes since no redox peaks are
observed in the device gate leakage current (Figure S11). Rather, local changes
in the electrical potential as a result of the analyte supramolecular recognition
may occur, leading to changes the effective gate potential which are function of
the HA concentration. However, the shape and behavior of the EGT transfer
curves are both very sensitive to surface-analyte-medium interactions, leading to
complex device responses that differ from the well-behaved curves shown in
Figure 6A-6C.

To take into account all information contained in the device transfer curve
and to eliminate arbitrary choices of output signals we employ ML to perform a
multivariate analysis of the sensor response. Here, every single I<sub>DS</sub> value at each
V<sub>GS</sub> is considered for the data processing, i.e. from I<sub>1</sub>(V<sub>1</sub>) to I<sub>n</sub>(V<sub>n</sub>), as depicted in
Figure 6D. Using principal component analysis (PCA), we verify that the input
data contains information for distinguishing the samples (Figure S12).
Furthermore, it shows the need for models that go beyond linear discrimination
for obtaining accurate predictions, such as the SISSO method we employ here.
<figure><img src="image_7.png" /><figcaption>**Figure 6:** Typical ideal responses of rGO EGTs showing sole changes of (A) $I_{DS,min}$, (B) $V_{CNP}$, and (C) transconductance ($g_m$) upon a sensing event. (D) Typical transfer curve of rGO EGTs upon a sensing event where no clear trend or a complex response is observed, requiring the use of ML for multivariate analysis. (E) Relationship between true concentration values of HA in artificial urine employed in the experiments and the HA concentrations found by the ML multivariate analysis. (F) New calibration curve for HA detection using the best signal predictor.</figcaption></figure>

Figure 6E shows that the HA concentration values predicted by the ML model agrees significantly with the real HA concentration in artificial urines. The ML processing provides several predictors for the logarithm of the sensor signal (Table S2). The most accurate predictor found is $C_1 \times D_1 + C_2 \times D_2$, where $C_1$ and $C_2$ are constants and $D_1$ and $D_2$ are linear combinations of $I_{DS}$ values at specific $V_{GS}$. Here, $D_1 = |(V_{21} - V_{31})| - (V_8 + V_{20})$ and $D_2 = (V_1 + V_{17}) + (V_{35} - V_{40})$, where $V_n$ is the nth $V_{GS}$ value from the rGO EGT transfer curve registered from +0.5 to -0.5 V employing a step voltage of 25 mV. The SISSO model display
high accuracy, with a correlation coefficient R of 0.998, and a RMSE of 0.0829 log(concentration).

Finally, using the SISSO model, we built a new calibration curve for the device response (Figure 6F). The ML processing provides a similar LoD (0.039 nmol L⁻¹) to the one found employing the human analysis (Figure 5C), however, with a sensitivity 2.5 times higher (viz. 1.007 µA/nmol L⁻¹). In addition, no arbitrary choices were made for selecting a given output signal and all possible metrics frequently reported for such type of devices were considered all at once (Figure 6A-6C).

To assess whether the characteristics of our device are competitive for PoC technology, we compare our findings with other methods employed for the detection of HA in complex media, as shown in Table 2.

<table><caption>**Table 2.** Analytical methods reported in the literature for HA detection in complex media.</caption><thead><tr><th>Method</th><th>Sensitivity / ΔS /ΔConc.HA</th><th>LoD / nmol L<sup>-1</sup></th><th>Range / nmol L<sup>-1</sup></th><th>MDT / s</th><th>Reference</th></tr></thead><tbody><tr><td>EP</td><td>1.3 × 10<sup>4</sup></td><td>0.012</td><td>0.05–500</td><td>>240</td><td>45</td></tr><tr><td>HPLC</td><td>1.9 × 10<sup>15</sup></td><td>1.7</td><td>5.6–5.6 × 10<sup>3</sup></td><td>>300</td><td>46</td></tr><tr><td>RS</td><td>–</td><td>–</td><td>–</td><td>20</td><td>47</td></tr><tr><td>FT-IR</td><td>6.2 × 10<sup>10</sup></td><td>2.38 × 10<sup>3</sup></td><td>5 × 10<sup>3</sup>–50 × 10<sup>3</sup></td><td>–</td><td>48</td></tr><tr><td>LB</td><td>1.1 × 10<sup>4</sup></td><td>1.02 × 10<sup>3</sup></td><td>20.0 × 10<sup>3</sup>–90.9 × 10<sup>3</sup></td><td>20</td><td>49</td></tr><tr><td>NMR</td><td>–</td><td>–</td><td>–</td><td>–</td><td>50</td></tr><tr><td>SCE</td><td>73</td><td>1.4 × 10<sup>4</sup></td><td>2.79 × 10<sup>5</sup>–9.3 × 10<sup>6</sup></td><td>>69</td><td>51</td></tr><tr><td>MEKC</td><td>0.025</td><td>2.79 × 10<sup>6</sup></td><td>2.79 × 10<sup>6</sup>–2.79 × 10<sup>7</sup></td><td>10</td><td>26</td></tr><tr><td>rGO</td><td>1.007 × 10<sup>3</sup></td><td>0.039</td><td>0.05–200</td><td>10</td><td>This work</td></tr></tbody></table>

MDT: Mean detection time. EP: electrochemical polymerization. ΔS: Signal normalized. ΔConc.HA: HA concentration expressed in mol L⁻¹. HPLC: High-Performance Liquid Chromatography. RS: Raman Spectroscopy. FT-IR: Fourier Transform Infrared Spectroscopy. LB: Luminescent Biosensor. NMR: Nuclear Magnetic Resonance. SCE: Short-Capillary Electrophoresis. MEKC: Micellar Electrokinetic Chromatography.

We observe that several analytical methods for HA detection require expensive, complex, and non-portable instrumentation (e.g., HPLC or NMR) and/or time-consuming analysis. Despite these works can exhibit high sensitivity for HA detection, most methods have limited working range for HA concentration or even higher LoD. Our device provides HA detection with high sensitivity 1.007 µA/nmol L⁻¹ and LoD of 39 pmol L⁻¹, along with other characteristics that are crucial for PoC sensing.
Our method employs small sample volumes (100 µL), without requiring pre-concentration and elution steps for HA determination. Additionally, the reported device operates across a wide range of HA concentrations (0.05-200 nmol L⁻¹) and it can be powered with low working voltages (< 0.5 V) yielding rapid responses (≤ 10 s per sample). The mean detection time (MDT) to determine a single HA concentration value corresponds to the time required to record one device transfer curve, i.e., ~10s. The HA detection can be even faster if one registers the transfer curve at a narrower voltage window. The reported characteristics make our approach a promising strategy towards the development of novel PoC sensors for clinical diagnostics, especially for the determination of small-molecule analytes in complex media.

## 4. Conclusion

In this study, by combining MIPs, rGO-based EGTs and ML we have developed an ultrasensitive, highly selective, and fast-responding device capable of detecting a representative small molecule analyte, i.e. HA, in a complex medium such as artificial urine. HA serves as metabolite naturally found in the urine of healthy individuals and a biomarker of several diseases predominantly analyzed through expensive and non-PoC compliant methods. Our MIP-functionalized rGO EGTs displayed remarkable characteristics and performance, such as detection across a broad HA concentration range (0.05 to 200 nmol L⁻¹), low operating voltages (< 0.5V), rapid response (≤ 10s), and a low LoD of 39 pmol L⁻¹. The unprecedented use of ML on EGT data enabled a multivariate analysis with a specific focus on the device transfer curves by eliminating arbitrary sensor signal choices. This resulted in a 2.5-fold increase in the device sensitivity (1.007 µA/nmol L⁻¹) with respect to the human data analysis (0.388 µA/nmol L⁻¹) and higher accuracy. Our strategy holds promise for advancing healthcare technologies by enabling accurate and highly sensitive PoC diagnostics for various small molecule biomarkers found in complex media.

## Supporting Information

The Supporting Information is available free of charge at https://pubs.acs.org/doi/ Device fabrication steps, XPS and Raman Characterization of GO and rGO films, rGO EGT operational characteristics and stability, XPS analysis of the device
gate electrode after modification with MIP, and Multivariate machine learning analysis.

## Acknowledgments

The authors acknowledge financial support from the São Paulo Research Foundation FAPESP (Grant number 202106238-5, 2019/14949-9, 2023/03501-2), CNPq (301465/2022-3), and SisNANO Programme. We thank Pedro H. S. A. Mamud for the assistance with the thermal reduction of GO. G.R.S. acknowledges support from CNPq - INCT (National Institute of Science and Technology on Materials Informatics, grant n. 371610/2023-0). R.F.O. acknowledges further support from INCT/INEO and the facilities at LNNano for AFM and XPS measurements.

## Conflict of Interest

The authors declare no conflict of interest.

## Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon request.

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