Electrical impedance tomography image reconstruction for lung monitoring based on ensemble learning algorithms.

Electrical impedance tomography (EIT) is a promising non-invasive imaging technique that visualizes the electrical conductivity of an anatomic structure to form based on measured boundary voltages. However, the EIT inverse problem for the image reconstruction is nonlinear and highly ill-posed. Therefore, in this work, a simulated dataset that mimics the human thorax was generated with boundary voltages based on given conductivity distributions. To overcome the challenges of image reconstruction, an ensemble learning method was proposed. The ensemble method combines several convolutional neural network models, which are the simple Convolutional Neural Network (CNN) model, AlexNet, AlexNet with residual block, and the modified AlexNet model. The ensemble models' weights selection was based on average technique giving the best coefficient of determination (R2 score). The reconstruction quality is quantitatively evaluated by calculating the root mean square error (RMSE), the coefficient of determination (R2 score), and the image correlation coefficient (ICC). The proposed method's best performance is an RMSE of 0.09404, an R2 score of 0.926186, and an ICC of 0.95783 using an ensemble model. The proposed method is promising as it can construct valuable images for clinical EIT applications and measurements compared to previous studies.

Laser-induced fibers and copper phthalocyanine modified laser-induced graphene electrodes for sensitive and selective electrochemical detection of nitrite.

We have recently reported laser-induced fibers (LIF) as a promising nanomaterial that possesses good electrochemical activity and are easily manufacturable. In this paper, for the first time, the application of LIF as functionalization materials on laser-induced graphene (LIG) electrodes for the detection of nitrate is demonstrated. The as-fabricated LIF surfaces on Kapton were extracted by ultrasonication as a dispersion and were used to modify the surface of the LIG electrode. An enhancement in active surface area from 0.669 cm2 for bare LIG to 0.83 cm2 for LIF-modified LIG was observed. Similarly, the heterogeneous electron transfer rate increased from 0.190 to 0.346 cm s-1 for LIF/LIG electrodes. The electrochemical detection of nitrite was achieved by modifying the LIG with a nanocomposite of LIF and copper phthalocyanine (CuPc). The presence of CuPc provided the desired catalytic activity towards the oxidation of nitrite, and the LIF enhanced the electron transfer to the electrode. Such a synergetic combination of the LIF embedded with CuPc enabled reaching a low limit of detection (LoD) of 0.12 µM, a large linear range from 10 to 10 000 µM and good selectivity in the presence of potential interferants. The sensor had a long shelf life of 30 days and good analytical capability to detect nitrite in mineral, tap, and groundwater. The potential of LIF is largely unexplored and the findings reported here on the fibers would open manifold opportunities for realizing novel applications.

Equivalent Electrical Circuit Approach to Enhance a Transducer for Insulin Bioavailability Assessment.

The equivalent electrical circuit approach is explored to improve a bioimpedance-based transducer for measuring the bioavailability of synthetic insulin already presented in previous studies. In particular, the electrical parameter most sensitive to the variation of insulin amount injected was identified. Eggplants were used to emulate human electrical behavior under a quasi-static assumption guaranteed by a very low measurement time compared to the estimated insulin absorption time. Measurements were conducted with the EVAL-AD5940BIOZ by applying a sinusoidal voltage signal with an amplitude of 100 mV and acquiring impedance spectra in the range [1-100] kHz. 14 units of insulin were gradually administered using a Lilly's Insulin Pen having a 0.4 cm long needle. Modified Hayden's model was adopted as a reference circuit and the electrical component modeling the extracellular fluids was found to be the most insulin-sensitive parameter. The trnasducer achieves a state-of-the-art sensitivity of 225.90 ml1. An improvement of 223 % in sensitivity, 44 % in deterministic error, 7 % in nonlinearity, and 42 % in reproducibility was achieved compared to previous experimental studies. The clinical impact of the transducer was evaluated by projecting its impact on a Smart Insulin Pen for real-time measurement of insulin bioavailability. The wide gain in sensitivity of the bioimpedance-based transducer results in a significant reduction of the uncertainty of the Smart Insulin Pen. Considering the same improvement in in-vivo applications, the uncertainty of the Smart Insulin Pen is decreased from [Formula: see text]l to [Formula: see text]l.Clinical and Translational Impact Statement: A Smart Insulin Pen based on impedance spectroscopy and equivalent electrical circuit approach could be an effective solution for the non-invasive and real-time measurement of synthetic insulin uptake after subcutaneous administration.

Explainable Feature Engineering for Multi-Modal Tissue State Monitoring Based on Impedance Spectroscopy.

One of the most promising approaches to food quality assessments is the use of impedance spectroscopy combined with machine learning. Thereby, feature selection is decisive for a high classification accuracy. Physically based features have particularly significant advantages because they are able to consider prior knowledge and to concentrate the data into pertinent understandable information, building a solid basis for classification. In this study, we aim to identify physically based measurable features for muscle type and freshness classifications of bovine meat based on impedance spectroscopy measurements. We carry out a combined study where features are ranked based on their F1-score, cumulative feature selection, and t-distributed Stochastic Neighbor Embedding (t-SNE). In terms of features, we analyze the characteristic points (CPs) of the impedance spectrum and the model parameters (MPs) obtained by fitting a physical model to the measurements. The results show that either MPs or CPs alone are sufficient for detecting muscle type. Combining capacitance (C) and extracellular resistance (Rex) or the modulus of the characteristic point Z1 and the phase at the characteristic frequency of the beta dispersion (Phi2) leads to accurate separation. In contrast, the detection of freshness is more challenging. It requires more distinct features. We achieved a 90% freshness separation using the MPs describing intracellular resistance (Rin) and capacitance (C). A 95.5% freshness separation was achieved by considering the phase at the end of the beta dispersion (Phi3) and Rin. Including additional features related to muscle type improves the separability of samples; ultimately, a 99.6% separation can be achieved by selecting the appropriate features.

Electromechanical Performance and Figure of Merit Optimization for Flexible Lead-Free PDMS-BaTiO 3 Piezocomposites.

In the modern era of the Internet of Things, the potential role of flexible piezoelectric generators (PEG) reflects the rapid increase in self-powered devices and wearable technologies. In this study, a casting process to elaborate the polydimethylsiloxane (PDMS)/barium titanate (BaTiO 3) composite has been presented. The addition of 15 wt % BaTiO 3 microparticles into the PDMS polymer greatly enhances the piezoelectric coefficient (d 31 = 24 pC N-1), leading to an increased output voltage of approximately 4 V under finger tapping force. The proposed flexible microgenerator yielded an excellent piezoelectric figure of merit (FoM 31 = 13.1 × 10-12 m2 N-1), significantly enhancing successfully the energy-harvesting performance (power density of 35 nW/cm2). Furthermore, the fabricated lead-free PEG exhibited an excellent flexibility figure of merit (fFoM) due to the low young modulus values (Maximum E = 3.4 MPa). These results indicate efficient energy conversion and demonstrate a favorable balance between the flexibility and piezoelectric properties of the composite, highlighting its potential for a wide range of applications in self-powered wearable sensors able to collect different human motions in applications such as gesture tracking and finger motion detection.

Ferrocenyl-triazole complexes and their use in heavy metal cation sensing.

Complexes tris((1-ferrocenyl-1H-1,2,3-triazol-4-yl)methyl)amine (3), bis((1-ferrocenyl-1H-1,2,3-triazol-4-yl)methyl)amine (6), bis((1-ferrocenyl-1H-1,2,3-triazol-4-yl)methyl)ether (7), and 1-ferrocenyl-1H-1,2,3-triazol-4-yl)methanamine (9) were synthesized using the copper-catalyzed click reaction. Complexes 3, 6, 7, and 9 were characterized using NMR (1H and 13{1H}) and IR spectroscopy, elemental analysis, and mass spectrometry. Structures of 3, 7, and 9 in the solid state were determined using single-crystal X-ray diffraction. It was found that the triazole rings were planar and slightly twisted with respect to the cyclopentadienyl groups attached to them. Chains and 3D network structures were observed due to the presence of π⋯π and C-H⋯N interactions between the cyclopentadienyl and triazole ligands. A reversible redox behavior of the Fc groups between 239 and 257 mV with multicycle stability was characteristic for all the compounds, revealing that the electrochemically generated species Fc+ remained soluble in dichloromethane. Electrochemical sensor tests demonstrated the applicability of all the complexes to enhance the quantification sensing behavior of the screen-printed carbon electrode (SPCE) toward Cd2+, Pb2+, and Cu2+ ions.

Toward Humidity-Independent Sensitive and Fast Response Temperature Sensors Based on Reduced Graphene Oxide/Poly(vinyl alcohol) Nanocomposites.

Flexible temperature sensors are becoming increasingly important these days. In this work, we explore graphene oxide (GO)/poly(vinyl alcohol) (PVA) nanocomposites for potential application in temperature sensors. The influence of the mixing ratio of both materials, the reduction temperature, and passivation on the sensing performance has been investigated. Various spectroscopic techniques revealed the composite structure and atomic composition. These were complemented by semiempirical quantum chemical calculations to investigate rGO and PVA interaction. Scanning electron and atomic force microscopy measurements were carried out to evaluate dispersion and coated film quality. The temperature sensitivity has been evaluated for several composite materials with different compositions in the range from 10 to 80 °C. The results show that a linear temperature behavior can be realized based on rGO/PVA composites with temperature coefficients of resistance (TCR) larger than 1.8% K-1 and a fast response time of 0.3 s with minimal hysteresis. Furthermore, humidity influence has been investigated in the range from 10% to 80%, and a minor effect is shown. Therefore, we can conclude that rGO/PVA composites have a high potential for excellent passivation-free, humidity-independent, sensitive, and fast response temperature sensors for various applications. The GO reduction is tunable, and PVA improves the rGO/PVA sensor performance by increasing the tunneling effect and band gap energy, consequently improving temperature sensitivity. Additionally, PVA exhibits minimal water absorption, reducing the humidity sensitivity. rGO/PVA maintains its temperature sensitivity during and after several mechanical deformations.

Energy Harvesting Technologies and Applications for the Internet of Things and Wireless Sensor Networks.

The field of Internet of Things (IoT) technologies is advancing rapidly, driven by the critical need for autonomous and sustainable wireless sensor networks [...].

Comparative Study of sEMG Feature Evaluation Methods Based on the Hand Gesture Classification Performance.

Effective feature extraction and selection are crucial for the accurate classification and prediction of hand gestures based on electromyographic signals. In this paper, we systematically compare six filter and wrapper feature evaluation methods and investigate their respective impacts on the accuracy of gesture recognition. The investigation is based on several benchmark datasets and one real hand gesture dataset, including 15 hand force exercises collected from 14 healthy subjects using eight commercial sEMG sensors. A total of 37 time- and frequency-domain features were extracted from each sEMG channel. The benchmark dataset revealed that the minimum Redundancy Maximum Relevance (mRMR) feature evaluation method had the poorest performance, resulting in a decrease in classification accuracy. However, the RFE method demonstrated the potential to enhance classification accuracy across most of the datasets. It selected a feature subset comprising 65 features, which led to an accuracy of 97.14%. The Mutual Information (MI) method selected 200 features to reach an accuracy of 97.38%. The Feature Importance (FI) method reached a higher accuracy of 97.62% but selected 140 features. Further investigations have shown that selecting 65 and 75 features with the RFE methods led to an identical accuracy of 97.14%. A thorough examination of the selected features revealed the potential for three additional features from three specific sensors to enhance the classification accuracy to 97.38%. These results highlight the significance of employing an appropriate feature selection method to significantly reduce the number of necessary features while maintaining classification accuracy. They also underscore the necessity for further analysis and refinement to achieve optimal solutions.

Towards solving the reproducibility crisis in surface-enhanced Raman spectroscopy-based pesticide detection.

Growing concerns about pesticide residues in agriculture are pushing the scientific community to develop innovative and efficient methods for detecting these substances at low concentrations down to the molecular level. In this context, surface-enhanced Raman spectroscopy (SERS) is a powerful analytical method that has so far already undergone some validation for its effectiveness in pesticide detection. However, despite its great potential, SERS faces significant difficulties obtaining reproducible and accurate pesticide spectra, particularly for some of the most widely used pesticides, such as malathion, chlorpyrifos, and imidacloprid. Those inconsistencies can be attributed to several factors, such as interactions between pesticides and SERS substrates and the variety of substrates and solvents used. In addition, differences in the equipment used to obtain SERS spectra and the lack of standards for control experiments further complicate the reproducibility and reliability of SERS data. This review systematically discusses the problems mentioned above, including a comprehensive analysis of the challenges in precisely evaluating SERS spectra for pesticide detection. We not only point out the existing limitations of the method, which can be traced in previous review works, but also offer practical recommendations to improve the quality and comparability of SERS spectra, thereby expanding the potential applications of the method in such an essential field as pesticide detection.

Nonlinear recurrence analysis of piezo sensor placement for unmanned aerial vehicle motor failure diagnosis.

This paper is focused on the diagnostics of multicopter UAV propulsion system, in which the temporary transient states occur during operation in faulty conditions (eg. not all motor phases working properly). As a diagnostic sensor, the piezo strip has been used, which is very sensitive to any vibrations of the multi-rotor frame. The paper concerns the precise location of the sensor for more effective monitoring of the propulsion system state. For this purpose, a nonlinear analysis of the vibration times series was carefully presented. The obtained non-linear time series were studied with the recurrence analysis in short time windows, which were sensitive to changes in Unmanned Aerial Vehicle motor speeds. The tests were carried out with different percentage of the pulse width modulation signal used for the operation of the brushless motor and for different locations of the piezosensor (side and top planes of the multicopter arm). In the article, it was shown that the side location of the piezosensor is more sensitive to changes in the Unmanned Aerial Vehicle propulsion system, which was studied with the Principal Component Analysis method applied for four main recurrence quantifications. The research presented proves the possibility of using nonlinear recurrence analysis for propulsion system diagnostics and helps to determine the optimal sensor location for more effective health monitoring of multicopter motor.

Multifaceted experiments and photothermal simulations based analysis of laser induced graphene and its fibers.

The interaction of CO2 laser with polyimide results in the formation of laser-induced graphene (LIG) and other morphological transitions based on laser parameters, such as Laser-induced fibers (LIF) on the surface. However, a fundamental investigation of LIF, its properties and potential have not been explored until now. We aim therefore to provide novel insights into the LIF by characterization of its structural, electrical, electrochemical, and mechanical properties. Four different morphologies were identified depending on the laser parameters and the temperature required for their formation were quantified by FEM model. Minimum temperatures of 1800 K were required to form LIG and around 2600 to 5000 K to form LIF. High heterogeneity of the LIF along thickness due to temperature gradients, and the existence of sheet structures underneath the fibers were identified. Due to the loosely bound nature of fibers, LIF dispersion was prepared by ultrasonication to functionalize the carbon electrode for electrochemical characterization. The modification with LIF on the electrodes enhanced the electrochemical response of the electrode towards standard redox couple which confirmed the conductive nature of the fibers. This work provides a solid basis for the versatile tuning of the behavior and properties of LIF for potential applications.

Inkjet-Printed Multiwalled Carbon Nanotube Dispersion as Wireless Passive Strain Sensor.

In this study, a multiwalled carbon nanotube (MWCNT) dispersion is used as an ink for a single-nozzle inkjet printing system to produce a planar coil that can be used to determine strain wirelessly. The MWCNT dispersion is non-covalently functionalized by dispersing the CNTs in an anionic surfactant, namely sodium dodecyl sulfate (SDS). The fabrication parameters, such as sonication energy and centrifugation time, are optimized to obtain an aqueous suspension suitable for an inkjet printer. Planar coils with different design parameters are printed on a flexible polyethylene terephthalate (PET) polymer substrate. The design parameters include a different number of windings, inner diameter, outer diameter, and deposited layers. The electrical impedance spectroscopy (EIS) analysis is employed to characterize the printed planar coils, and an equivalent electrical circuit model is derived based on the results. Additionally, the radio frequency identification technique is utilized to wirelessly investigate the read-out mechanism of the printed planar MWCNT coils. The complex impedance of the inductively coupled sensor undergoes a shift under strain, allowing for the monitoring of changes in resonance frequency and bandwidth (i.e., amplitude). The proposed wireless strain sensor exhibits a remarkable gauge factor of 22.5, which is nearly 15 times higher than that of the wireless strain sensors based on conventional metallic strain gauges. The high gauge factor of the proposed sensor suggests its high potential in a wide range of applications, such as structural health monitoring, wearable devices, and soft robotics.

Detection of H. pylori outer membrane protein (HopQ) biomarker using electrochemical impedimetric immunosensor with polypyrrole nanotubes and carbon nanotubes nanocomposite on screen-printed carbon electrode.

Helicobacter pylori (H. pylori) is classified as a class I carcinogen that colonizes the human gastrointestinal (GI) tract. The detection at low concentrations is crucial in combatting H. pylori. HopQ protein is located on H. pylori's outer membrane and is expressed at an early stage of contamination, which signifies it as an ideal biomarker. In this study, we presented the development of an electrochemical impedimetric immunosensor for the ultra-sensitive detection of HopQ at low concentrations. The sensor employed polypyrrole nanotubes (PPy-NTs) and carboxylated multi-walled carbon nanotubes (MWCNT-COOH) nanocomposite. PPy-NTs were chosen for their excellent conductivity, biocompatibility, and redox capabilities, simplifying sample preparation by eliminating the need to add redox probes upon measurement. MWCNT-COOH provided covalent binding sites for HopQ antibodies (HopQ-Ab) on the biosensor surface. Characterization of the biosensor was performed using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), contact angle measurements, and electrochemical impedance spectroscopy (EIS), complemented by numerical semiempirical quantum calculations. The results demonstrated a dynamic linear range of 5 pg/mL to 1.063 ng/mL and an excellent selectivity, with the possibility of excluding interference using EIS data, specifically charge transfer resistance and double-layer capacitance as multivariants for the calibration curve. Using two EIS components, the limit of detection is calculated to be 2.06 pg/mL. The biosensor was tested with a spiked drinking water sample and showed a signal recovery of 105.5% when detecting 300 pg/mL of HopQ. This novel H. pylori biosensor offers reliable, simple, portable, and rapid screening of the bacteria.

Biocompatibility Testing for Implants: A Novel Tool for Selection and Characterization.

This review article dives into the complex world of biocompatibility testing: chemical, mechanical, and biological characterization, including many elements of biocompatibility, such as definitions, descriptive examples, and the practical settings. The focus extends to evaluating standard documents obtained from reliable organizations; with a particular focus on open-source information, including FDA-USA, ISO 10933 series, and TÜV SÜD. We found a significant gap in this field: biomaterial scientists and those involved in the realm of medical device development in general, and implants in particular, lack access to a tool that reorganizes the process of selecting the appropriate biocompatibility test for the implant being examined. This work progressed through two key phases that aimed to provide a solution to this gap. A straightforward "yes or no" flowchart was initially developed to guide biocompatibility testing decisions based on the previously accumulated information. Subsequently, the Python code was employed, generating a framework through targeted questions. This work reshapes biocompatibility evaluation, bridging theory and practical implementation. An integrated approach via a flowchart and the Python code empowers stakeholders to navigate biocompatibility testing effortlessly. To conclude, researchers are now better equipped for a safer, more effective implant development, propelling the field towards improved patient care and innovative progress.

An Improved Wake-Up Receiver Based on the Optimization of Low-Frequency Pattern Matchers.

Wake-up receivers are gaining importance in power-aware wireless sensor networks, as they significantly reduce power consumption during RF reception, enabling asynchronous communication with low latency. However, the performance of wake-up receivers still lags behind that of off-the-shelf RF transceivers. There is a growing demand for higher sensitivity, enhanced reliability, and lower latency while maintaining the lowest power consumption. In this article, our goal is to advance the performance of wake-up receivers based on off-the-shelf components and low-frequency pattern matchers. Through a systematic investigation, we proposed multiple improvements aimed at enhancing wake-up receiver performance and reliability. We introduced an improved passive envelope detector and realized a wake-up receiver for the 868 MHz band, which achieves a power consumption of 5.71 µW and latency of 9.02 ms. Our proposed wake-up receiver is capable of detecting signals down to an average power level of -61.6 dBm. These achievements represent significant advancements compared to the existing state of research on wake-up receivers based on low-frequency pattern matchers. Recent articles have not been able to attain such improved values in signal detection, power consumption, and latency.

Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study.

Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles. For the experimental studies, first, compact wireless sensor nodes were developed for real-time measurement of sEMG on the gastrocnemius, flexor carpi ulnaris, biceps brachii, and quadriceps muscles on the right side and the left side. For the classification of the seizure, a machine learning model has been elaborated. The 16 common sEMG time-domain features were first extracted and examined with respect to discrimination and redundancy. This allowed the features to be classified into irrelevant features, important features, and redundant features. Redundant features were examined with the Big-O notation method and with the average execution time method to select the feature that leads to lower complexity and reduced processing time. The finally selected six features were explored using different machine learning classifiers to compare the resulting classification accuracy. The results show that the artificial neural network (ANN) model with the six features: IEMG, WAMP, MYOP, SE, SKEW, and WL, had the highest classification accuracy (99.95%). A further study confirms that all the chosen eight sensors are necessary to reach this high classification accuracy.

Ultra-Sensitive and Fast Humidity Sensors Based on Direct Laser-Scribed Graphene Oxide/Carbon Nanotubes Composites.

In this paper, the relative humidity sensor properties of graphene oxide (GO) and graphene oxide/multiwalled nanotubes (GO/MWNTs) composites have been investigated. Composite sensors were fabricated by direct laser scribing and characterized using UV-vis-NIR, Raman, Fourier transform infrared, and X-ray photoemission spectroscopies, electron scanning microscopy coupled with energy-dispersive X-ray analysis, and impedance spectroscopy (IS). These methods confirm the composite homogeneity and laser reduction of GO/MWNT with dominant GO characteristics, while ISresults analysis reveals the circuit model for rGO-GO-rGO structure and the effect of MWNT on the sensor properties. Although direct laser scribing of GO-based humidity sensor shows an outstanding response (|ΔZ|/|Z| up to 638,800%), a lack of stability and repeatability has been observed. GO/MWNT-based humidity sensors are more conductive than GO sensors and relatively less sensitive (|ΔZ|/|Z| = 163,000%). However, they are more stable in harsh humid conditions, repeatable, and reproducible even after several years of shelf-life. In addition, they have fast response/recovery times of 10.7 s and 9.3 s and an ultra-fast response time of 61 ms when abrupt humidification/dehumidification is applied by respiration. All carbon-based sensors' overall properties confirm the advantage of introducing the GO/MWNT hybrid and laser direct writing to produce stable structures and sensors.

Novel Sensitive Electrochemical Immunosensor Development for the Selective Detection of HopQ H. pylori Bacteria Biomarker.

Helicobacter pylori (H. pylori) is a highly contagious pathogenic bacterium that can cause gastrointestinal ulcers and may gradually lead to gastric cancer. H. pylori expresses the outer membrane HopQ protein at the earliest stages of infection. Therefore, HopQ is a highly reliable candidate as a biomarker for H. pylori detection in saliva samples. In this work, an H. pylori immunosensor is based on detecting HopQ as an H. pylori biomarker in saliva. The immunosensor was developed by surface modification of screen-printed carbon electrodes (SPCE) with MWCNT-COOH decorated with gold nanoparticles (AuNP) followed by HopQ capture antibody grafting on SPCE/MWCNT/AuNP surface using EDC/S-NHS chemistry. The sensor performance was investigated utilizing various methods, such as cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and scanning electron microscope (SEM) coupled with energy-dispersive X-ray spectroscopy (EDX). H. pylori detection performance in spiked saliva samples was evaluated by square wave voltammetry (SWV). The sensor is suitable for HopQ detection with excellent sensitivity and linearity in the 10 pg/mL-100 ng/mL range, with a 2.0 pg/mL limit of detection (LOD) and an 8.6 pg/mL limit of quantification (LOQ). The sensor was tested in saliva at 10 ng/mL, and recovery of 107.6% was obtained by SWV. From Hill's model, the dissociation constant Kd for HopQ/HopQ antibody interaction is estimated to be 4.60 × 10-10 mg/mL. The fabricated platform shows high selectivity, good stability, reproducibility, and cost-effectiveness for H. pylori early detection due to the proper choice of biomarker, the nanocomposite material utilization to boost the SPCE electrical performance, and the intrinsic selectivity of the antibody-antigen approach. Additionally, we provide insight into possible future aspects that researchers are recommended to focus on.

Towards Embedded Electrochemical Sensors for On-Site Nitrite Detection by Gold Nanoparticles Modified Screen Printed Carbon Electrodes.

The transition of electrochemical sensors from lab-based measurements to real-time analysis requires special attention to different aspects in addition to the classical development of new sensing materials. Several critical challenges need to be addressed including a reproducible fabrication procedure, stability, lifetime, and development of cost-effective sensor electronics. In this paper, we address these aspects exemplarily for a nitrite sensor. An electrochemical sensor has been developed using one-step electrodeposited (Ed) gold nanoparticles (EdAu) for the detection of nitrite in water, which shows a low limit of detection of 0.38 µM and excellent analytical capabilities in groundwater. Experimental investigations with 10 realized sensors show a very high reproducibility enabling mass production. A comprehensive investigation of the sensor drift by calendar and cyclic aging was carried out for 160 cycles to assess the stability of the electrodes. Electrochemical impedance spectroscopy (EIS) shows significant changes with increasing aging inferring the deterioration of the electrode surface. To enable on-site measurements outside the laboratory, a compact and cost-effective wireless potentiostat combining cyclic and square wave voltammetry, and EIS capabilities has been designed and validated. The implemented methodology in this study builds a basis for the development of further on-site distributed electrochemical sensor networks.