Impedance Signal Research Articles

Nondestructive differential eddy current testing for corrosion detection on coated aluminium alloys

PurposeThe objective of this study is to use non-destructive testing of corrosion on coated aluminium alloys using differential eddy current detection (DECD), with the aim of elucidating the relationship between the characteristics of corrosion defects and the detection signal.Design/methodology/approachPitting corrosion defects of varying geometrical dimensions were fabricated on the surface of aluminium alloy plates, and their impedance signals were detected using DECD to investigate the influence of defect diameter, depth, corrosion products and coating thickness on the detection signals. Furthermore, finite element analysis was used to ascertain the eddy current distributions and detection signals under different parameters.FindingsThe size of the defect is positively correlated with the strength of the detection signal, with the defect affecting the latter by modifying the distribution and magnitude of the eddy current. An increase in the diameter and depth of corrosion defects will enhance the eddy current detection (ECD) signal. The presence of corrosion products in the corrosion defects has no significant effect on the eddy current signal. The presence of a coating results in a decrease in the ECD signal, with the magnitude of this decrease increasing with the thickness of the coating.Originality/valueThe objective is to provide experimental and theoretical references for the design of eddy current non-destructive testing equipment and eddy current testing applications.

Monitoring performance of a new smart geosynthetic under drawing test

Geosynthetics are widely used in civil engineering reinforcements owing to their high strength, acceptable toughness, and ease of transportation. However, traditional geosynthetics do not have the capability to monitor damage inside the soil. Therefore, in this paper, a new sensor-enabled piezoelectric geobelt (SPGB) is developed to measure the deformation of reinforced-soil structures. In-soil drawing tests are conducted to investigate the sensing performance of the SPGB. Variations in the voltage and impedance signals of the SPGB with the drawing displacement under different damage conditions are investigated. The results show that with the increase of drawing displacement, SPGB undergoes tensile deformation followed by pullout damage. In tensile deformation, the signal response of SPGB is related to strain. As the strain increases, the output voltage first increases and then decreases, and the impedance gradually decreases. In the pullout damage phase, the signal response of SPGB is related to the contact area between SPGB and soil. As the drawing displacement increases, the contact area between SPGB and soil gradually decreases, the output voltage gradually decreases, and the impedance gradually increases. Therefore, the SPGB, as a sensor-enabled geosynthetic, provides a reinforcing function to the soil body and simultaneously performs in-soil catastrophe identification.

Neural Network-Enabled Multiparametric Impedance Signal Templating for High throughput Single-Cell Deformability Cytometry Under Viscoelastic Extensional Flows.

Cellular biophysical metrics exhibit systematic alterations during processes, such as metastasis and immune cell activation, which can be used to identify and separate live cell subpopulations for targeting drug screening. Image-based biophysical cytometry under extensional flows can accurately quantify cell deformability based on cell shape alterations but needs extensive image reconstruction, which limits its inline utilization to activate cell sorting. Impedance cytometry can measure these cell shape alterations based on electric field screening, while its frequency response offers functional information on cell viability and interior structure, which are difficult to discern by imaging. Furthermore, 1-D temporal impedance signal trains exhibit characteristic shapes that can be rapidly templated in near real-time to extract single-cell biophysical metrics to activate sorting. We present a multilayer perceptron neural network signal templating approach that utilizes raw impedance signals from cells under extensional flow, alongside its training with image metrics from corresponding cells to derive net electrical anisotropy metrics that quantify cell deformability over wide anisotropy ranges and with minimal errors from cell size distributions. Deformability and electrical physiology metrics are applied in conjunction on the same cell for multiparametric classification of live pancreatic cancer cells versus cancer associated fibroblasts using the support vector machine model.

Temperature compensation method for impedance signals of bolt loosening under temperature varying conditions using TransUnet

Abstract Piezoelectric transducer based electro-mechanical impedance technology is an effective and reliable detection method for bolt structure loosening under external load. However, temperature change usually causes some non-loosening factors to change the impedance spectrum of bolt structure. In order to reduce the misjudgment of loosening damage caused by temperature change, it is necessary to construct a temperature compensation model for impedance spectrum. In this paper, the convolutional structure in U-net and Transformer are effectively combined to form a TransUnet deep neural network structure suitable for input and output of one-dimensional data. Using impedance data between 10 °C and 50 °C and temperature as input to the network. After convolution operation, the convolutional block attention module is embedded in the U-net to optimize the encoder transmission characteristics and enhance the performance of the skip connection in the traditional U-shaped structure. The temperature compensation rate (TCR) is defined to measure the effect of temperature compensation. Then the lightweight convolutional neural network structure is used to recognize the bolt loosening damage of the compensated impedance signal. The generalization ability of the TransUnet was tested using impedance data that is not in the training dataset. The results show that the TransUnet proposed in the paper can realize the temperature compensation of multi-peak impedance signals. The TCR and recognition accuracy of bolt loose damage reaches reach 0.003 and 95.7%, respectively, which is 11% higher than that without temperature compensation. At 60 °C, the TCR and the identification accuracy of loosening damage can still reach 0.0055 and 90.2%, respectively, which show that the TransUnet has strong generalization ability.

Evaluation of sensor-enabled piezoelectric geoelectric cable in cyclic shear tests of subgrade soil under vertical cyclic loads

Of late, deformation of subgrade soil has led to an increasing number of road subsidence diseases. Real-time monitoring of subgrade deformation is critical to ensure the safety of subgrade operations. In this paper, a sensor-enabled piezoelectric geoelectric cable (SPGC) with impedance strain effect and piezoelectric effect is tested. The SPGC impedance and voltage signals obtained by cyclic shear test under vertical static load and cyclic shear test under vertical cyclic load are used to evaluate the monitoring effect. The results showed that normal stress had the greatest effect on the shear strength of the soil, whereas the normal stress and horizontal shear displacement amplitude significantly influenced the strain in the soil. Varying the normal and horizontal shear frequencies had little effect on the shear strength and strain of the soil. The normalized impedance and voltage of the SPGC, respectively, decreased and increased rapidly during the initial stage of the shear cycle; these changes were relatively small during the middle and late stages of the shear cycle. The SPGC voltage waveform revealed the changes in the shear stress and vertical displacement under different normal and horizontal shear frequencies, from which the stability of the subgrade soil under the aforementioned conditions could be evaluated. The variations in the SPGC impedance and effective voltage from the cyclic shear tests under both vertical static and vertical cyclic loads remained essentially consistent with the number of cycles. However, there was a difference in that the trough of the SPGC impedance under the vertical cyclic load was larger than that under the vertical static load; likewise, the effective SPGC voltage under the cyclic load was larger than that under the static load. Through an analysis of the SPGC impedance and voltage signals in the subgrade soil, the consistency of the SPGC-normalized impedance and effective voltage with shear stress was clarified; this helped us evaluate the health of the subgrade and monitor the characteristics of the precursor signals before a slide were to occur, thereby affording us an opportunity to issue timely warnings.

Arduino-based investigation of transmission lines and impedance matching

Abstract Transmission lines play a crucial role in radio-frequency energy transmission between source and load. These systems connect essential physical phenomena like the formation of standing waves and the attenuation of electromagnetic waves in material media with practical considerations such as impedance matching and signal integrity. Understanding these systems requires knowledge of fundamental concepts like inductance, capacitance, and impedance. The absence of impedance matching between the line and load leads to a modulated amplitude in the resulting signal owing to interference between the injected and reflected signals. Here, we present an experiment utilizing an Arduino platform as a signal generator and a data acquisition interface to analyze a radio frequency transmission line. By employing a few-meter-long coaxial cable as the transmission line and a digital oscilloscope with a high sampling rate (several giga-samples per second), we demonstrate how to determine the reflection coefficient as a function of the load impedance. The reflection coefficient enables determination of the return loss, underscoring the significance of impedance matching for optimal performance among the source, transmission line, and the load.

Temperature Estimation of Lithium-Ion Batteries Using Electrochemical Techniques

With their high energy and high power density, lithium-ion batteries (LIBs) are widely used as power sources for electric vehicles and energy storage systems, and their demand continues to grow. However, the increasing energy and power density of LIBs leads to significant heat generation issues, and reflecting these safety concerns there have been a number of recent fires of high-performance batteries. Under these circumstances, it is increasingly important to have a reliable assessment of battery safety through precise diagnostics of battery state of health (SoH). As a matter of fact, the battery management system diagnoses and evaluates battery SoH by analyzing various parameters such as voltage, current, and surface temperature of battery, and among these there is no doubt that battery temperature is a critical safety factor. As LIBs become more high-performance and large-scale, the temperature distribution inside the battery becomes more uneven, which possibly degrades performance and eventually leads to failure, so it is very important to secure the uniformity of the battery temperature and at the same time to analyze the temperature inhomogeneity and sudden temperature changes inside the battery.A typical way to estimate battery temperature is to insert a temperature sensor into the battery or attach it to the battery surface. While these methods enable direct temperature measurement, inserting a sensor can mechanically affect the internal structure of the battery, and attaching a sensor to a surface can result in delayed detection of internal cell temperature changes and may not fully reflect the battery temperature especially if there is a large temperature distribution. As an alternative, temperature estimation using electrochemical impedance spectroscopy (EIS) has received significant attention. Although EIS analysis is an indirect method of obtaining internal cell information through the electrochemical signal, it has the great advantage that it is a non-destructive method that does not affect the cell structure, and the internal cell situation is fully reflected in the electrochemical signal. Nevertheless, typical temperature estimation methods using EIS are time-consuming because they require deriving temperature-related impedance components from impedance data measured over a wide frequency range (kHz to Hz). To solve this problem, Srinivasan pioneered a method that utilizes impedance values at a single frequency for temperature estimation, which uses a specific response signal that has a clear temperature dependence but is largely unaffected by other factors (such as state of charge (SoC)). While this method can effectively reduce the cost and time spent on temperature estimation using EIS and is one of the most promising methods of estimating cell internal temperature known to date, it has been reported that the accuracy of temperature estimation is low at high temperatures for reasons that are not yet fully understood.In this study, we diagnose the problems with the existing method of estimating the internal temperature of batteries through EIS and propose a way to improve the accuracy of temperature estimation at high temperatures. For this purpose, first, we measured the impedance at various SoCs and derived the characteristic frequency representing the reaction at the solid electrolyte interphase (SEI), which is the main reaction used for temperature estimation in the previous work. In addition, we thoroughly analyzed the impedance change with temperature to more clearly understand the resistance components contributing to the impedance at the characteristic frequency. The analysis showed that impedance value at characteristic frequency, which was thought to represent only one reaction at the SEI, contained signals from other reactions (e.g. interfacial charge transfer) at higher temperatures. This situation becomes more severe as the temperature increases, and is believed to be the reason why traditional impedance-based temperature estimation methods have limited accuracy at high temperatures. We also observed that increasing the characteristic frequency to avoid this signal overlap prevented signal disturbance due to charge transfer reactions, but instead introduced another signal disturbance due to stray inductance. Based on these analyses, this study proposes a new frequency value to be used for battery temperature estimation through EIS, and at the same time presents an impedance correction method that can reliably take only the impedance signal of a specific reaction with temperature dependence without interference from other reaction signals at that frequency. It is confirmed that the temperature estimation accuracy is greatly improved when using the method presented in this study compared to the existing temperature estimation method. In this presentation, we will critically discuss why existing methods have poor temperature estimation accuracy at high temperatures, and propose an impedance-based temperature estimation method that can achieve high accuracy over a wide operating temperature range.

(Invited) Experimentally Measuring the Role of Ion-Phonon and Ion-Electron Correlations in Solid-State Li Ion Conductors

In superionic conductors, ion hopping involves a complex interplay of ion-phonon, ion-electron, and ion-ion correlations that is challenging to measure experimentally. In this presentation, we present a new experimental technique that can directly measure ultrafast ion hopping on its inherent picosecond and longer timescale. The technique works by measuring the time-resolved perturbation to a GHz impedance signal when potential ion-coupling interactions are driven with UV to THz irradiation. High-bandwidth, real-time electronics allow synchronization of the impedance measurement to ultrafast laser pulses for varying carrier frequencies. The result of the measurement is the relative strength of different correlations in the many-body ion hopping Hamiltonian. We demonstrate the technique on Li0.5La0.5TiO3 (LLTO) and single crystalline Li7La3Zr2O12 (LLZO), both solid-state Li+ conductors. The ultrafast laser-driven impedance measurements reveal that the dominant ion hopping mechanism in LLTO is through a coupled phonon-ion THz rocking mode. Although this higher frequency mode is less than one-quarter of the overall phonon density of states, it leads to the majority of ion hops as compared to other optical and acoustic phonon modes. Metastable states lasting tens of minutes were also measured for ion-electron perturbations. Further work on extending the technique to measure ion-ion correlations is currently underway. In general, the new technique applies to any complex ion conducting system such as polymers, fuel cells, supercapacitors, and membranes.

Interfaces Comprehension at Electrodes on Sulfide-Based Solid State Batteries

The increasing demand for safe, highly efficient, and cost-effective energy storage systems has accelerated the development of solid-state batteries (SSBs) with lithium metal (LiM) anodes. This technology offers remarkable advantages over conventional lithium-ion batteries with liquid electrolytes; from improved safety with non-flammable electrolyte to higher gravimetric and volumetric energy density enabled using LiM anode along with the multilayered bipolar stacking cell fabrication. The combination of solid electrolyte (SE) mechanical strength, flexibility, and safety against self-ignition allows for optimized battery design to meet the specific requirements.1–4 In recent years, soft, flexible, and easily deformable sulfide-based solid electrolytes have received significant attention due to their attractive mechanical properties, high conductivity, and feasibility of processing at ambient temperature, which makes them appealing candidates for upscaling the technology to large format devices.2,4,5 Despite the high ionic conductivity and attractive mechanical properties of sulfide-based SSBs, this chemistry still faces key challenges to encompass fast rate and long cycling performance, mainly arising from the dynamic and complex solid-solid interfaces. Solid-state materials are characterized by a significant impact of interface-related phenomena on their functional characteristics, such as mechanical properties, conductivity mechanisms, or electrochemical performance. For SSBs with ceramic or glass-ceramic electrolytes, the stacking of the composite cathode, the SE and the LiM anode leads to multiple interfaces from voids, cracks, secondary phases from chemical and electrochemical reactions and grain boundaries.6,7 As electrochemical kinetics is governed by the contact area at the interface, ensuring the adhesion between the electrolyte and active electrode materials is the key to obtain high performance devices, especially at fast rates.The goal of the present work is to provide understanding on the complex solid-solid interfaces regulating sulfide-based battery performance. As shown in Figure 1, cells under different configurations with LPSCl, Li or In metal anodes, and LiNi0.6Mn0.2Co0.2O2 (NMC622)-based composite cathodes have been systematically studied aiming to bring insights on the dynamic interfaces at component level dominating processes during cell operation. From a multi-configurational approach and an advanced deconvolution of electrochemical impedance signals into distribution of relaxation times, we disentangle intricate underlying interfacial processes taking place at the battery components and that play major role on the overall performance. For the Li metal solid-state batteries, the cycling performance is highly sensitive to the chemomechanical properties of the cathode active material, the formation of the SEI, and processes ascribed to Li diffusion in the cathode composite and in the space-charge layer. The outcomes of this work aim to facilitate the design of sulfide solid-state batteries and provide methodological inputs for battery ageing assessment.(1) Zheng, F.; Kotobuki, M.; Song, S.; Lai, M. O.; Lu, L. Review on Solid Electrolytes for All-Solid-State Lithium-Ion Batteries. Journal of Power Sources 2018, 389, 198–213. https://doi.org/10.1016/j.jpowsour.2018.04.022.(2) Randau, S.; Weber, D. A.; Kötz, O.; Koerver, R.; Braun, P.; Weber, A.; Ivers-Tiffée, E.; Adermann, T.; Kulisch, J.; Zeier, W. G.; Richter, F. H.; Janek, J. Benchmarking the Performance of All-Solid-State Lithium Batteries. Nat Energy 2020, 5 (3), 259–270. https://doi.org/10.1038/s41560-020-0565-1.(3) Schnell, J.; Günther, T.; Knoche, T.; Vieider, C.; Köhler, L.; Just, A.; Keller, M.; Passerini, S.; Reinhart, G. All-Solid-State Lithium-Ion and Lithium Metal Batteries – Paving the Way to Large-Scale Production. Journal of Power Sources 2018, 382, 160–175. https://doi.org/10.1016/j.jpowsour.2018.02.062.(4) Kim, K. J.; Balaish, M.; Wadaguchi, M.; Kong, L.; Rupp, J. L. M. Solid‐State Li–Metal Batteries: Challenges and Horizons of Oxide and Sulfide Solid Electrolytes and Their Interfaces. Advanced Energy Materials 2021, 11 (1), 2002689. https://doi.org/10.1002/aenm.202002689.(5) Liu, S.; Zhou, L.; Han, J.; Wen, K.; Guan, S.; Xue, C.; Zhang, Z.; Xu, B.; Lin, Y.; Shen, Y.; Li, L.; Nan, C. Super Long‐Cycling All‐Solid‐State Battery with Thin Li 6 PS 5 Cl‐Based Electrolyte. Advanced Energy Materials 2022, 12 (25), 2200660. https://doi.org/10.1002/aenm.202200660.(6) Pesci, F. M.; Bertei, A.; Brugge, R. H.; Emge, S. P.; Hekselman, A. K. O.; Marbella, L. E.; Grey, C. P.; Aguadero, A. Establishing Ultralow Activation Energies for Lithium Transport in Garnet Electrolytes. ACS Appl. Mater. Interfaces 2020, 12 (29), 32806–32816. https://doi.org/10.1021/acsami.0c08605.(7) Chen, R.; Li, Q.; Yu, X.; Chen, L.; Li, H. Approaching Practically Accessible Solid-State Batteries: Stability Issues Related to Solid Electrolytes and Interfaces. Chem. Rev. 2020, 120 (14), 6820–6877. https://doi.org/10.1021/acs.chemrev.9b00268. Figure 1

Research on wireless monitoring system and algorithms for preload force utilizing machine learning and electromechanical impedance

The root mean square deviation is a common way to measure the electromechanical impedance of piezoceramic transducers that are used for traditional preload monitoring. However, due to the impedance signal’s high sensitivity to temperature, most current research is carried out under the same temperature conditions to avoid its effects. Even so, it is impossible to ignore the temperature factor in practical engineering, which hinders the application of impedance technology. Therefore, this paper proposes a novel approach for preload monitoring in actual engineering: using a wireless impedance signal acquisition platform to collect long-distance impedance signals and then establishing a predicted model based on machine learning (ML) considering the temperature. The wireless impedance signal acquisition platform includes a smart washer, a piezoelectric impedance wireless sensing device, and the host computer software. This test established a dataset under various operating conditions and developed a machine-learning-based predictive model. After comparing five typical ML algorithms, it was discovered that XGBoost performed better in prediction accuracy and generalization ability. Moreover, the Shapley additive explanation method is utilized to further analyze and interpret the XGBoost model. It indicates that ML primarily relies on numerical features (such as the area of each subinterval) to identify the impedance signal and predict the prestress, whereas information features (such as temperature value, peak, etc.) have little influence on the model’s output. Finally, the results above demonstrate that the ML-based models can predict the preload at different temperatures, effectively reducing temperature interference.

Integrating the Capsule-like Smart Aggregate-Based EMI Technique with Deep Learning for Stress Assessment in Concrete.

This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses. Thirdly, a CSA-embedded cylindrical concrete structure is experimented with to acquire EMI responses under various compressive loading levels. Finally, the feasibility and robustness of the 1D CNN model are evaluated for noise-contaminated EMI data and untrained stress EMI cases.

Investigation of the Electrical Impedance Signal Behavior in Rolling Element Bearings as a New Approach for Damage Detection

The opportunities of impedance-based condition monitoring for rolling bearings have been shown earlier by the authors: Changes in the impedance signal and the derived features enable the detection of pitting damages. Localizing and measuring the pitting length in the raceway direction is possible. Furthermore, the changes in features behavior are physically explainable. These investigations were focused on a single bearing type and only one load condition. Different bearing types and load angles were not considered yet. Thus, the impedance signals and their features of different bearing types under different load angles are investigated and compared. The signals are generated in fatigue tests on a rolling bearing test rig with conventional integrated vibration analysis based on structural borne sound. The rolling bearing impedance is gauged using an alternating current measurement bridge. Significant changes in the vibration signals mark the end of the fatigue tests. Therefore, comparing the response time of the impedance can be compared to the vibration signal response time. It can be shown that the rolling bearing impedance is an instrument for condition monitoring, independently from the bearing type. In case of pure radial loads, explicit changes in the impedance signal are detectable, which indicate a pitting damage. Under combined loads, the signal changes are detectable as well, but not as significant as under radial load. Damage-indicating signal changes occur later compared to pure radial loads, but nevertheless enable an early detection. Therefore, the rolling bearing impedance is an instrument for pitting damage detection, independently from bearing type and load angle.

Superimposed Positive Sequence Impedance for Detecting Unintentional Islanding in Microgrid

Incorporation of environmentally friendly energy sources (RESs) into the electricity grid has many benefits, including economic, technological, and environmental. However, excessive renewable energy sources (RES) in the power grid provide technical problems, including equipment protection, DG operation, and islanding detection. One of the most serious challenges is the islanding phenomenon. Islanding can cause several problems, such as frequency instability and voltage fluctuations resulting in damage to electrical equipment or threatening utility workers who may be working/accessing the equipment. This research proposes an efficient islanding detection algorithm to lessen the impact of such threats. This novel passive islanding detection scheme is based on superimposed positive sequence impedance (SPSI). For calculating the superimposed positive sequence impedance (SPSI), the voltage and current signals are obtained from targeted DG points. The scheme’s performance is tested on multiple bus systems across islanding and non-islanding conditions using a MATLAB/Simulink environment. It is shown that even in the presence of noise, the algorithm can determine an islanding decision with high accuracy and a short detection time of 84 ms. In comparison to other algorithms, it operates at zero power mismatch (ZPM) and does not affect power quality.

Practical Implementation of the Sensory Utilization of Rolling Bearing Impedance Measurement: A Case Study in Machine Tool Spindle Bearings

Research into sensory utilization of the impedance measurement of rolling bearings has been intensifying for several years. However, publications in this area are mainly limited to theoretical models and experiments under laboratory conditions. In this contribution, the concept of measuring the impedance of rolling bearings for sensory utilization is implemented within a machine tool spindle. Machine tools can be found in almost all manufacturing companies for the production of components, complete products or prototypes and are therefore one of the most important and relevant production systems. The most common cause of failure is component failure, especially the rolling bearings of the spindle. Failure of the rolling bearings is often the result of incorrect operation and/or overloading. A preventative approach is the sensory monitoring of operating variables. In this work, the spindle design is modified to implement the impedance measurement of rolling bearings and then tests are carried out at free-running and under load. The impedance measurement is analyzed in time and frequency domain. Ultimately, an initial suggestion is made for features in the impedance signal that can be related to various operating parameters.

Development of rapid and efficient enterovirus typing and classification platform for real-time diagnostics

Enteroviruses are highly prevalent among children and adults worldwide. Accurate detection and genotyping of enteroviruses can be achieved using consensus-degenerate hybrid oligonucleotide primer polymerase chain reaction (PCR)-based methods. Nevertheless, the popularized use of such methods for enterovirus detection and genotyping is limited by their relatively long processing time (>2 days), high processing cost, and the requirement for specialized and expensive instrumentation. Here, we report a relatively simple asymmetric PCR-electrochemical biosensing method that can detect enteroviruses and accurately characterize them into four distinct genotypic subgroups in less than 2 h. Specifically, genes of interest in the enteroviruses were amplified via asymmetric PCR (asy-PCR) using the designed PCR primers. The asy-PCR products were hybridized with enterovirus genotype-specific single-stranded DNA primer probes. Changes in response to electrochemical impedance signals can rapidly clarify the genotypes of enteroviruses. We observed that this method could not only effectively amplify the target gene within minimal PCR-amplified cycles of consensus-degenerate hybrid oligonucleotide (COHOP) products from the specimen but also allowed the rapid differentiation of the four subgroups of enteroviruses in 2 h. In summary, we developed an efficient and rapid electrochemical biosensing platform for enterovirus subtyping diagnostics that can be applied to other emergent infectious diseases.

Design and optimization of microchannel for enhancement of the intensity of induced signal in particle/cell impedance measurement.

The measured impedance signal of a single particle or cell in a microchannel is of the μA level, which is a challenge for measuring such weak signals. Therefore, it is necessary to improve the intensity for expanding the applications of impedance measurement. In this paper, we analyzed the impact of geometric parameters of microchannel on output signal intensity by using the three-dimensional finite element method. In comparison to conventional microchannels, which are distributed at a uniform height, the microchannels in this design use the height difference to enhance the signal intensity. By analyzing the effects of the geometric dimensions of the constriction channel, main channel height, radius of particles, types of cells, shapes of particles with different ellipticities, and particles spacing on the current signal, we concluded the optimal dimensions of these parameters to improve the intensity of the induced current signal. Through the fabrication of the optimized size of device and experimental demonstration, it is verified that the current signal intensity caused by the particle with a diameter of 10 µm is nearly twice that of the conventional structure with a height of 20 µm, which proves the correctness of the optimization results and the feasibility of this work. In addition, the performance of the device was verified by measuring the mixtures of different size particles as well as non-viable and viable yeast cells.

Label-free electrochemical biosensor based on dual amplification of gold nanoparticles and polycaprolactones for CEA detection

Carcinoembryonic Antigen (CEA), an acidic glycoprotein with human embryonic antigen properties, is found on the surface of cancer cells that have differentiated from endodermal cells. This paper presents a label-free electrochemical immunoassay for the dual amplification detection of CEA using gold nanoparticles loaded with polypyrrole polydopamine (Au/PPy-PDA) and polymerized polycaprolactone (Ng-PCL) prepared by ring-opening polymerization (ROP). First, the composite Au/PPy-PDA was adhered to the electrode surface. Then, gold nanoparticles form a Au–S bond with the sulfhydryl group in Apt1 to secure it on the electrode surface. Subsequently, the non-specific binding sites on the electrodes surface are closed by bovine serum albumin (BSA). Next, CEA is dropped onto the electrode surface, which is immobilized by antigen-antibody specific recognition, and the carboxyl-functionalized Apt2 forms a "sandwich structure" of antibody-antigen-antibody by specific recognition. Polymeric Ng-PCL is adhered to the electrode surface, leading to an increase in the electrochemical impedance signal, resulting in a complete chain of signal analysis. Finally, the response signal is detected by electrochemical impedance spectroscopy (EIS). Under optimal experimental conditions, the method has the advantages of high sensitivity and wide linear range (1 pg mL−1∼100 ng mL−1), and the lower limit of detection (LOD) is 0.234 pg mL−1. And it has the same high sensitivity, selectivity and interference resistance for the real samples detection. Thus, it provides a new way of thinking about biomedical and clinical diagnosis.

Unsupervised autoencoders with features in the electromechanical impedance domain for early damage assessment in FRP-strengthened concrete elements

This paper presents the development of a robust automatic diagnosis technique that uses raw Electro-Mechanical Impedance (EMI) signals and deep autoencoder models to detect damage in fiber-reinforced-polymers (FRP) strengthened reinforced concrete (RC) elements, for which the most common failure modes occur in a sudden and brittle way by debonding. The contribution of this work is threefold: First, for the first time, two autoencoder models, convolutional and fully connected, based on an unsupervised learning framework supplemented by appropriate pre-processing techniques, are proposed for effective tracking of FRP-strengthened RC elements from raw EMI response variations in different locations of the auscultated structure; their implementation is also extensively investigated. The proposed framework consists of two main components, namely, dimensionality reduction and relationship learning. The first component is to reduce the dimensionality of the raw EMI signal while preserving the necessary information required, and the second component is to perform the relationship learning between the features with the reduced dimensionality and the stiffness reduction parameters of the structure. The approach is beneficial as only the EMI spectrum from the healthy structure state is considered for the training of the autoencoders. Second, the superior performance of the proposed framework is demonstrated. The results show that the proposed technique can accurately detect minor damage in its earliest stages for this kind of strengthened structures, while removing the need for manual or signal processing-based damage sensitive feature extraction from EMI signals for damage diagnosis. Finally, research presented in this work can potentially open up new opportunities for successful condition monitoring of this type of strengthened structures.

Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.

Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non-invasive and cost-effective detection method for early-stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on-skin sensor with low electrode-skin contact impedance for early-stage NAFLD detection is fabricated. A method is developed to synthesize platinum nanoparticles and reduced graphene quantum dots onto the on-skin sensor to reduce electrode-skin contact impedance by increasing double-layer capacitance, thereby enhancing detection accuracy. Furthermore, an attention-based deep learning algorithm is introduced to differentiate impedance signals associated with early-stage NAFLD in high-fat-diet-fed low-density lipoprotein receptor knockout (Ldlr-/-) mice compared to healthy controls. The integration of an adhesive, soft on-skin sensor with low electrode-skin contact impedance and the attention-based deep learning algorithm significantly enhances the detection accuracy for early-stage NAFLD, achieving a rate above 97.5% with an area under the receiver operating characteristic curve (AUC) of 1.0. The findings present a non-invasive approach for early-stage NAFLD detection and display a strategy for improved early detection through on-skin electronics and deep learning.

AuPt/NF prepared with the lattice induction of substrate was applied to construct the electrochemical immunosensor for PCT detection

For the electrodeposition, the conductivity and lattice structure of substrate is important to the morphology and lattice of the deposited material. In this study, gold-platinum (AuPt) nanopartical was deposited on nickel foam (NF) based on the lattice induced orientation of the Ni substrate, and the obtained AuPt/NF was applied to construct electrochemical impedimetric immunosensor for procalcitonin (PCT) detection. As a new immunosensor matrix, NF with higher electrical conductance, flexibility and specific surface area, which can improve the plasticity, sensitivity and universality of the immunoelectrode. Due to the lattice matching between Au and Ni, ultrathin AuPt nanolayer with good biocompatibility and large surface area can be modified on the NF surface, which can bind more biomolecules and amplifies the change of impedance signal. Based on the synergistic effect between AuPt and NF, PCT detection based on this electrochemical impedimetric immunosensor with a wide linear range (0.2 pg mL−1 to 20 ng mL−1) and low detection limit (0.11 pg mL−1). In addition, this impedimetric immunosensor exhibits high recovery in the PCT detection of serum samples. This work provides a new thought and method for the construction of electrochemical immunosensor.