Detection of volatile organic compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array of 3 component metal oxides that give maximal cross-sensitivity and can successfully use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array comprises NiO-Au (ohmic), CuO-Au (Schottky), and ZnO–Au (Schottky) sensors made by the DC reactive sputtering method and having a film thickness of 80–100 nm. The NiO and CuO films have ultrafine particle sizes of < 50 nm and rough surface texture, while ZnO films consist of nanoscale platelets. This array was subjected to various VOC concentrations, including ethanol, acetone, toluene, and chloroform, one by one and in a pair/mix of gases. Thus, the response values show severe interference and departure from commonly observed power law behavior. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with an R² value of more than 0.99 and LOD of 0.012 ppm, 0.015 ppm, 0.014 ppm, and 0.025 ppm for predicting the concentrations of acetone, toluene, ethanol, and chloroform, respectively, in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring. Graphical Abstract

Electrochemical sensors based on antibody-antigen recognition events are commonly used for the rapid, label-free, and sensitive detection of various analytes. However, various parameters at the bioelectronic interface, i.e., before and after the probe (such as an antibody) assembly onto the electrode, have a dominant influence on the underlying detection performance of analytes (such as an antigen). In this work, we thoroughly investigate the dependence of the bioelectronic interface characteristics on parameters that have not been investigated in depth: the antibody density on the electrode’s surface and the antigen incubation time. For this important aim, we utilized the sensitive non-faradaic electrochemical impedance spectroscopy method. We showed that as the incubation time of the antigen-containing drop solution increased, a decrease was observed in both the solution resistance and the diffusional resistance with reflecting boundary elements, as well as the capacitive magnitude of a constant phase element, which decreased at a rate of 160 ± 30 kΩ/min, 800 ± 100 mΩ/min, and 520 ± 80 pF × s^(α-1)/min, respectively. Using atomic force microscopy, we also showed that high antibody density led to thicker electrode coating than low antibody density, with root-mean-square roughness values of 2.2 ± 0.2 nm versus 1.28 ± 0.04 nm, respectively. Furthermore, we showed that as the antigen accumulated onto the electrode, the solution resistance increased for high antibody density and decreased for low antibody density. Finally, the antigen detection performance test yielded a better limit of detection for low antibody density than for high antibody density (0.26 μM vs 2.2 μM). Overall, we show here the importance of these two factors and how changing one parameter can drastically affect the desired outcome.

Antipsychotic clozapine is the most effective medication currently available for schizophrenia. However, clozapine is dramatically underutilized due to its harsh side effects that are not effectively monitored. By continuously monitoring clozapine blood levels, such as use of an implantable glucometer, which has transformed diabetes management, the treatment can be optimized and side effects will be minimized. Currently, none of the methods for clozapine detection show the ability to repeatedly measure clozapine in whole blood without pretreatment steps. Here we propose using a microelectrode modified with reduced graphene oxide-a material that was used for repeatable measurements in implantable electrochemical devices. We present the successful direct electrodeposition of reduced-graphene oxide coating onto microelectrodes. Systematic characterization of the electrodeposition technique parameters (i.e., the technique scan rate and the number of cycles) revealed their effect on the electrochemical activity and the structural properties (the film thickness and roughness) of the films. The developed reduced-graphene oxide-modified microelectrode exhibited the feasibility to detect clozapine in microliters-volume-samples of whole blood with a limit-of-detection and a sensitivity of 0.64 ± 0.04 μM and 19.6 ± 1.3 μA/cm2μM, respectively. Moreover, the reduced graphene oxide-modified microelectrodes exhibited high repeatability (retaining 94.6% of the electrochemical signal after 10 repeats), reproducibility (3.6% relative standard deviation), and storage stability (retaining 89% of the electrochemical signal after 4 weeks). Finally, relative recovery studies of 0.5, 1, and 2 μM clozapine concentrations resulted in 108 ± 4.0%, 112 ± 3.5%, and 103 ± 2.2%, respectively. Future studies should investigate the microelectrode fouling mechanisms in whole blood and explore methods to overcome fouling.

This Review provides a comprehensive overview of 3D printing techniques to fabricate implantable microelectrodes for the electrochemical detection of biomarkers in the early diagnosis of cardiovascular and neurodegenerative diseases. Early diagnosis of these diseases is crucial to improving patient outcomes and reducing healthcare systems' burden. Biomarkers serve as measurable indicators of these diseases, and implantable microelectrodes offer a promising tool for their electrochemical detection. Here, we discuss various 3D printing techniques, including stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), and two-photon polymerization (2PP), highlighting their advantages and limitations in microelectrode fabrication. We also explore the materials used in constructing implantable microelectrodes, emphasizing their biocompatibility and biodegradation properties. The principles of electrochemical detection and the types of sensors utilized are examined, with a focus on their applications in detecting biomarkers for cardiovascular and neurodegenerative diseases. Finally, we address the current challenges and future perspectives in the field of 3D-printed implantable microelectrodes, emphasizing their potential for improving early diagnosis and personalized treatment strategies.

The antipsychotic clozapine is the most effective medication available for schizophrenia and it is the only antipsychotic with a known efficacious clinical range. However, it is dramatically underutilized due to the inability to test clozapine blood levels in finger‐pricked patients' samples. This prevents obtaining immediate blood levels information, resulting in suboptimal treatment. The development of an electrochemical microsensor is presented, which enables, for the first time, clozapine detection in microliters volume whole blood. The sensor is based on a microelectrode modified with micrometer‐thick biopolymer chitosan encapsulating carbon nanotubes. The developed sensor detects clozapine oxidation current, in the presence of other electroactive species in the blood, which generate overlapping electrochemical signals. Clozapine detection, characterized in whole blood from healthy volunteers, displays a sensitivity of 32 ± 3.0 µA cm⁻² µmol⁻¹ L and a limit‐of‐detection of 0.5 ± 0.03 µmol L⁻¹. Finally, the developed sensor displays a reproducible electrochemical signal (0.6% relative standard deviation) and high storage stability (9.8% relative standard deviation after 8 days) in serum samples and high repeatability (9% relative standard deviation for the 5th repetition) in whole blood samples. By enabling the rapid and minimally invasive clozapine detection at the point‐of‐care, an optimal schizophrenia treatment is provided.

Label-free field-effect transistor-based immunosensors are promising candidates for proteomics and peptidomics-based diagnostics and therapeutics due to their high multiplexing capability, fast response time, and ability to increase the sensor sensitivity due to the short length of peptides. In this work, planar junctionless field-effect transistor sensors (FETs) were fabricated and characterized for pH sensing. The device with SiO₂ gate oxide has shown voltage sensitivity of 41.8 ± 1.4, 39.9 ± 1.4, 39.0 ± 1.1, and 37.6 ± 1.0 mV/pH for constant drain currents of 5, 10, 20, and 50 nA, respectively, with a drain to source voltage of 0.05 V. The drift analysis shows a stability over time of −18 nA/h (pH 7.75), −3.5 nA/h (pH 6.84), −0.5 nA/h (pH 4.91), 0.5 nA/h (pH 3.43), corresponding to a pH drift of −0.45, −0.09, −0.01, and 0.01 per h. Theoretical modeling and simulation resulted in a mean value of the surface states of 3.8 × 10¹⁵/cm² with a standard deviation of 3.6 × 10¹⁵/cm². We have experimentally verified the number of surface sites due to APTES, peptide, and protein immobilization, which is in line with the theoretical calculations for FETs to be used for detecting peptide-protein interactions for future applications.