Electrochemical Metal Research Articles

Electrochemical Detection of Cd(II) in Environmental Samples Using Nano-Ities

Heavy metals such as cadmium, lead, and arsenic have caused global health concerns that continue to rise yearly, which lead to several harmful diseases that primarily target organs such as kidneys and liver among other vital organs. Because of these well-known harmful effects, regulatory authorities have prescribed “permissible” levels of these metals in food, drinking water, and other possible sources to limit human exposure. However, increasing industrialization and inefficient recycling systems for industrially produced metal wastes have led to an increase in heavy metal exposure to humans. These toxins also bioaccumulate across the food chain, leaving us vulnerable to their harmful effects. Hence it is of great interest to develop systems that can detect metals efficiently to help set up effective metal mitigation protocols.Most conventional metal detecting tools, although highly accurate, require extensive sample pre-treatment steps which alter the speciation of the metal; a critical parameter for determining its toxicity. As(III) is known to be more toxic than As(V) and as such, they have separate medical remedies targeted toward the particular metal species. However, a conventional technique such as ICPMS is unable to differentiate between them. Furthermore, these conventional techniques require equipment that is bulky, expensive, and not user-friendly and limit real-time monitoring. Consequently, the development of a low-cost, portable, and robust sensor capable of providing accurate information on metal speciation will significantly aid in establishing metal mitigation systems efficiently. Low cost and user-friendliness will ensure that the sensor is within the economic and technological reach of most of the population and the portability of the sensor will enable testing in areas that are hard to access via a stationary lab. Such attributes coupled with accurate information on metal speciation will make an ideal metal sensor that will significantly aid the fight against heavy metal exposure.This study uses ion transfer between two immiscible electrolyte solutions (ITIES) to develop a Cd(II) sensor. Electrochemistry at ITIES is less complicated than other electrochemical techniques as it is based on the transfer of ions and does not include redox reactions; making it more attractive. Our electrode is a borosilicate glass electrode that is pulled using a carbon dioxide laser puller with an inner radius of ~300 nm. The nano-scale interface of our sensor follows a hemispherical diffusion regime which allows us to have a high mass transfer rate, which is essential for fast kinetic measurements. The nano-interface can also withstand various complex matrices consistently making it ideal for field applications. An ionophore- 1-10 phenanthroline was used to facilitate the Cd(II) transfer across the nano-interface. The sensor was calibrated n various matrices such as potassium chloride and artificial seawater to show its capability to withstand complex matrices without fouling. Stability and selectivity tests were done to showcase the sensor’s performance. It can also successfully detect Cd(II) when it is present in a complex form with strong ligands such as EDTA and NTA, etc. Our sensor’s analytical performance passed the ultimate test when we were able to accurately detect the dissolved Cd(II) ion concentrations in a water sample collected from the Indian River Lagoon in Melbourne, FL. The results from this test were in close agreement with the results reported by another research group that used ICPMS to quantify the amount of Cd(II) dissolved in the same environmental sample. However, ITIES does not require any sample pretreatment steps which is required for ICPMS.Thus, this study shows great promise for the development of an ideal electrochemical metal sensor for environmental samples. To the best of our knowledge, this is the first time a nanometer-scale glass electrode with ITIES to detect Cd(II) ions in complex matrices is being reported. Future studies will focus on the detection of metals in urine and blood samples and develop this into a portable point of care device.

(Digital Presentation) Electrochemical Metallization Cell Based Memristive Neuron Chip Fabricated with 28nm CMOS Process for Real-Time Unsupervised Learning and Pattern Recognition

Spiking neurons communicate with other neurons using sparse and binary signals in a human brain, so they can achieve real-time processing of the information with ultra-low power consumption[1,2]. Thereby, spiking neurons are essential elements for building an energy-efficient biomimetic spatiotemporal system. Recently, to emulate the behavior of biological neuron, many researches for memristive device-based neurons with peripheral circuits (i.e., sense-amplifier or reset circuit)[3] and complementary metal-oxide-semiconductor (CMOS) neurons with capacitors have been reported[4]. Most of the reported memristive device-based neurons required a high operation voltage (>1.2 V) for emulating integrate function of a biological neuron. In addition, complementary metal-oxide-semiconductor-based neurons could not achieve high neuronal density due to using a capacitor in emulating integrate function.In this study, therefore, we propose an electrochemical metallization cell based memristive neuron chip fabricated with 28-nm CMOS process having a low operation voltage (<0.7 V) for emulating integrate function. In addition, since the proposed electrochemical metallization cell based memristive neuron chip does not require a capacitor for emulating integrate function, it can realize a high neuronal density. The memristive neuron chip exhibited a typical integrate-and-fire function; particularly, the frequency of generating a spiking output signal exponentially increased with the input voltage amplitude. Moreover, a spiking neural network was designed using the memristive neuron chip and the software program consisting of a crossbar synaptic memristor array, simplified spike-timing-dependent-plasticity learning rule, and current-to-voltage converters. Using the co-designed spiking neural network with software and hardware, real-time unsupervised learning was realized. Finally, using the trained spiking neural network, a real-time classification for the MNIST hand-written image taken by a live webcam was successfully performed in an inference process. Acknowledgement This research was supported by National R&D Program through the National Research Foundation of Korea(NRF) funded by Ministry of Science and ICT(2021M3F3A2A01037733). Reference Mead, C. Neuromorphic Electronic Systems. Encycl. Comput. Neurosci. 78, 1979–1979 (2015).Douglas, R. Neuromorphic Analogue VLSI. Annu. Rev. Neurosci. 18, 255–281 (1995).Tuma, T., Pantazi, A., Le Gallo, M., Sebastian, A. & Eleftheriou, E. Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693–699 (2016).Aamir, S. A., Müller, P., Hartel, A., Schemmel, J. & Meier, K. A highly tunable 65-nm CMOS LIF neuron for a large scale neuromorphic system. Eur. Solid-State Circuits Conf. 2016-Octob, 71–74 (2016). Figure 1

Au-TiO2/Ti Hybrid Coating as a Liquid and Gas Diffusion Layer with Improved Performance and Stability in Proton Exchange Membrane Water Electrolyzer.

The liquid and gas diffusion layer is a key component of proton exchange membrane water electrolyzer (PEMWE), and its interfacial contact resistance (ICR) and corrosion resistance have a great impact on the performance and durability of PEMWE. In this work, a novel hybrid coating with Au contacts discontinuously embedded in a titanium oxidized layer was constructed on a Ti felt via facile electrochemical metallizing and followed by a pre-oxidization process. The physicochemical characterizations, such as scanning electron microscopy, energy dispersive spectrometer, and X-ray diffraction results confirmed that the distribution and morphology of the Au contacts could be regulated with the electrical pulse time, and a hybrid coating (Au-TiO2/Ti) was eventually achieved after the long-term stability test under anode environment. At the compaction force of 140 N cm−2, the ICR was reduced from 19.7 mΩ cm2 of the P-Ti to 4.2 mΩ cm2 of the Au-TiO2/Ti. The corrosion current density at 1.8 V (RHE) is 0.689 μA cm−2. Both the ICR and corrosion resistance results showed that the prepared protective coating could provide comparable ICR and corrosion resistance to a dense Au coating.

Electrochemical Metal Recycling: Recovery of Palladium from Solution and In Situ Fabrication of Palladium-Carbon Catalysts via Impact Electrochemistry.

Recycling of critical materials, regeneration of waste, and responsible catalyst manufacture have been repeatedly documented as essential for a sustainable future with respect to the environment and energy production. Electrochemical methods have become increasingly recognized as capable of achieving these goals, and “impact” electrochemistry, with the advantages associated with dynamic nanoelectrodes, has recently emerged as a prime candidate for the recovery of metals from solution. In this report, the nanoimpact technique is used to generate carbon-supported palladium catalysts from low-concentration palladium(II) chloride solutions (i.e., a waste stream mimic) as a proof of concept. Subsequently, the catalytic properties of this material in both synthesis (Suzuki coupling reaction) and electrocatalysis (hydrogen evolution) are demonstrated. Transient reductive impact signals are shown and analyzed at potentials negative of +0.4 V (vs SCE) corresponding to the onset of palladium deposition in traditional voltammetry. Direct evidence of Pd modification was obtained through characterization by environmental scanning electron microscopy/energy-dispersive X-ray spectroscopy, inductively coupled plasma mass spectrometry, X-ray photoelectron spectroscopy, transmission electron microscopy, and thermogravimetric analysis of impacted particles. This showed the formation of deposits of Pd0 partially covering the 50 nm carbon black particles with approximately 14% Pd (wt %) under the conditions used. This material was then used to demonstrate the conversion of iodobenzene into its biphenyl product (confirmed through nuclear magnetic resonance) and the successful production of hydrogen as an electrocatalyst under acidic conditions (under cyclic voltammetry).

Native drift and Mott nanochannel in layered V2O5 film for synaptic and nociceptive simulation

The integration and cooperation of nociceptors, neurons and synapses in the biological nervous system empower humans to efficiently perceive and process noxious information for avoiding dangers. Inspired by biological nervous systems, current artificial demonstrations include electrolyte-gate transistors, electrochemical metallization resistive switching devices and halide perovskite-based memristors. However, these devices suffer from integration difficulties and instability issues. Herein, we introduce a complementary metal oxide semiconductor (CMOS)-compatible simple and stable Pt/V2O5/Pt sandwich structure and carefully construct and modulate the suboxide V2O5−x and Mott VO2 nanochannels in the layered V2O5 matrix to simulate brain-like processing and nervous pain perception functions, respectively. Simulation results demonstrate that the recognition accuracy of handwritten digits reaches 80% after only 5 training epochs and 89% after 52 epochs in a convolutional neural network based on the V2O5−x nanochannel synaptic device. The nociceptor with all key characteristics is perfectly imitated based on the VO2 nanochannel threshold switching device. Especially, an ultralow threshold level of 0.4 V and sub-millisecond incubation time are observed in the nociceptor simulation, which could be needed in special injury situations. The proposed drift and Mott nanochannels in one device hold a tremendous potential as synaptic and nociceptive emulators for artificial intelligence systems.

Rainer Waser – A Pioneer of Fundamentals of Resistive Switching Memories

Rainer Waser – A Pioneer of Fundamentals of Resistive Switching Memories

Strategies to Control the Relaxation Kinetics of Ag‐Based Diffusive Memristors and Implications for Device Operation

AbstractDiffusive memristors based on volatile electrochemical metallization (v‐ECM) devices are of broad interest for applications in emerging memory technologies and neuromorphic computing areas due to their interesting features such as thresholding, self‐relaxation, and energy‐efficient switching behaviors. Recently, the nonlinear threshold kinetics and the correlation of filament formation and growth with its relaxation behavior are uncovered from a physical perspective. However, the complexity of the diffusive memristors’ behavior might still hamper a straight transfer into emerging computing applications. Facing this challenge means going beyond the single device level and understanding the impact of other circuitry elements for further optimization of device operation. In this work, the effect of a series resistor on the switching dynamics of Ag/HfO2/Pt diffusive memristor devices by deploying various programming schemes with different resistances is investigated. Furthermore, all results and their implications on devices’ operation are compressively discussed. These findings help to further advance the optimization of the operating condition of diffusive memristors.

Ultrasmall ZnO Nanocrystals Confined in Honeycombed N‐Doped Carbon for High‐Performance and Stable Lithium/Sodium Ion Batteries

ZnO with high theoretical capacity, low cost, natural abundance, and environmentally friendliness is regarded as a promising anode material for lithium‐ion batteries (LIBs). Unfortunately, it suffers from low conductivity and huge volume expansion during charge/discharge, which finally leads to rapid capacity degradation and poor rate capability. To overcome these issues, the honeycombed ZnO@N‐doped carbon (HC‐ZnO@NC) composite with improved performances is prepared in this work. The composite could be easily prepared as an anode for LIBs and sodium‐ion batteries (SIBs) by cross‐linking and subsequent calcining at a target temperature. When used as an anode for LIBs, it could possess a reversible capacity of 687 mAh g−1 after 500 cycles at a rate of 0.5C. At a higher rate of 2C, 388 mAh g−1 is observed after 500 cycles. Additionally, HC‐ZnO@NC also obtains a capacity of 166 mAh g−1 after 200 cycles at 0.5C for SIBs. When the rate reaches 1C, it maintains a capacity of 126 mAh g−1 after 1000 cycles. The outstanding electrochemical metal ion storage properties might be ascribed to the synergistic effect of honeycombed carbon architecture and micro ZnO nanocrystals.

Nonvolatile Logic‐in‐Memory Computing based on Solution‐Processed CuI Memristor

AbstractMemristors are intensively studied as being regarded as the critical components to realize the in‐memory computing paradigm. A novel electrochemical metallization memristor based on solution‐processed Pt/CuI/Cu structure is proposed and demonstrated in this work, with a high resistance switching ratio of 1.53 × 107. Owing to the efficient drift paths provided by Cu vacancies for Cu cations in CuI, very small operating voltages (Vset = 0.64 V and Vreset = −0.19 V) are characterized, contributing to ultralow standby power consumption of 9 fW and per set transition of 8.73 µW. Using CuI memristor arrays, a set of Boolean logic operations and a half‐adder are implemented. Moreover, by building the model for a 75 × 48 one‐transistor‐one‐memristor array, the feasibility of hardware encryption and decryption for images is verified. All these demonstrate that solution‐processed CuI memristors possess great potential in constructing energy‐efficient logic‐in‐memory computing architectures.

Reconfigurable multivalued memristor FPGA model for digital recognition

AbstractCompared with the traditional memristor, it's more significant to research the multivalued memristor in improving the stability and reliability of memristive neural networks. Developing some multivalued memristor emulators is highly attractive since the fabrication and integration of the memristor are not mature at present. This work presents a behavioral‐level model of a general multivalued memristor FPGA that exhibits continuous or discrete behavior, similar to electrochemical metallization memories. The proposed solution has been successfully synthesized and verified on a Xilinx ZYNQ‐7000 FPGA XQ7Z020 with less than 1% hardware utilization. Additionally, to test the synaptic function of the memristor model in artificial neural networks, this paper constructs a quantized artificial neural network based on eight‐valued memristors in FPGA. In the training of the neural network, the pixel values of the images and the network weights are quantized and then mapped to the input voltage and conductance respectively of the memristor during the forward propagation. The experimental results show that the memristive network circuit achieves 92.8% recognition accuracy for 10,000 MNIST images.

(Invited, Digital Presentation) Nanostructured Perovskite Resistive RAM for Next Generation Data Storage

Resistive RAMs (Re-RAMs) have emerged as a propitious candidate among next generation non volatile memories because of their strikingly fast speed, prolonged data retention ability and high storage density. Herein, we developed an electrochemical metallization (ECM) based Re-RAM with three-dimensionally integrated single-crystalline perovskite (methyl ammonium lead halide or MAPbX3 where X=I, Br and Cl) quantum wire (PQW)/ nanowire (PNW) array embedded in a nano-engineered porous alumina membrane (PAM) serving as the switching medium. The PQWs and PNWs were grown inside the PAM template by a vapor-solid-solid-reaction (VSSR) process and were clubbed between silver (Ag) and aluminum (Al) contacts. The nanostructured perovskite Re-RAM exhibited 100 ps switching speed which is a record for perovskite Re-RAMs and also among the fastest for all types of Re-RAMs reported. The fast switching was attributed to the increased ionic (Ag+) and electronic mobility and subsequent accelerated filament formation within the body of the monocrystalline PQWs/PNWs. The PAM scaffolding imparted material and electrical stability to the environmentally delicate perovskite. As a consequence, we were able to obtain a record long (among perovskite Re-RAMs) estimated retention time > 28 years and record-high (among perovskite Re-RAMs) uninhibited cyclic endurance of 6×106 cycles. Also, utilizing the ultra-high density (2×1011/cm2) of the PQWs, a 14 nm lateral dimension ultra-small memory cell was built. In conjunction with multi-bit switching, an effective device footprint of 76.5 nm2 was achieved for a single bit storage. Further, as a concept proof, an 8×8 Re-RAM crossbar array device was fabricated which demonstrated temporally robust alphabetic data storage, with a unique metal-semiconductor-insulator-metal (MSIM) architecture to alleviate the sneaky path problem. The MSIM scheme based on quasi-self-selecting elements, demonstrated a strong potential for unhindered scalability in the future, a problem often encountered with integrating external selecting diodes and transistors. The PQW/PNW Re-RAMs also responded to light stimuli, exhibiting optical programmability among the low resistance states. In summary, these intriguing results propel perovskite Re-RAMs to the state-of-the-art standard and demonstrate an attractive potency for PQW/PNW devices to be used as an alternative technology in future storage and computing modules.

Innovation Processing of Circuit Boards with FDM Printing and Selective Electrochemical Metallization

Additive processing like fused deposition modeling (FDM) can be used for customized products or prototypes. Due to the layer-by-layer structure it is possible to work flexibly and efficiently in geometry. In the electronics industry geometrically demanding circuit carriers can be built in relatively short times, but the processes are somewhat inflexible and take a series of processing steps. Circuit boards are made from layers of fiber-reinforced plastics and then joined by pressing. Vertical interconnects are made by copper plating. Finally, electronic components added via a soldering process.With FDM it is possible to work flexibly with regard to geometry, printing different materials, and a large selection of filaments is given. For a circuit board, one needs at least two filament materials (one insulator, one electronic conductor) to realize conducting lines.In this work we use FDM with different polymers for printing the substrate plate and conductor track structures in a single process. Some of the filaments are electrically conductive and can be used as conducting path structures. However, the electrical resistance of these filaments is significantly lower than conventional copper tracks. Especially for higher currents, chemical metallization of the polymer tracks with copper and tin is necessary. In the presentation we will discuss the preliminaries for achieving selective electroless metallization on selected FDM filament materials.

Systematic Engineering of Metal Ion Injection in Memristors for Complex Neuromorphic Computing with High Energy Efficiency

Neuromorphic electronics attract significant attention as a new computing architecture. Despite much effort for achieving practical neuromorphic systems, it is still challenging to construct a synapse array ideal for complex neural networks. Herein, a novel strategy for developing a highly integrated crossbar array of a one‐selector–one‐memory (1S–1R) synapse by systematically engineering ion injection is demonstrated. In the proposed synapse, an electrochemical metallization (ECM) memristor consisting of unstable filaments and a typical ECM device with stable filaments act as a selector with a low leakage current and a stable memory device, respectively. To overcome the voltage‐matching issues in constructing the 1S–1R synapse with high integration density, ion injection related with the electrical properties is optimized in the ECM devices via the distribution of active metal nanoparticles at the interface. The developed synapse possesses a high on/off ratio, superior selectivity, low operating current, and stable multilevel conductance, compared to the previously reported devices. High feasibility for complex neuromorphic systems is demonstrated, and the neural network based on the developed synapse array exhibits reliable parallel computation with high energy efficiency. This promising concept of realizing complex neuromorphic electronics is a fundamental building block for the practical artificial intelligence.

Anodic oxidation effects at the copper/silicon oxide interface

We report on anodic oxidation effects in memristively switching Cu/SiO2/Pt and Cu/CuOx/SiO2/Pt Electrochemical Metallization Cells. In-depth x-ray photoelectron spectroscopy reveals the formation of a Cu-ion reservoir composed of Cu(OH)2 underneath the anode during oxidation. By using cyclic voltammetry partial redox reactions are observed both in SiO2- and CuOx/SiO2-based devices. From our analysis we conclude that the CuOx layer acts as a reservoir for Cu-ions, similar to the low work-function electrode in valance change mechanism devices, which acts a reservoir for oxygen vacancies. Integration of a CuOx layer may thus allow to tailor the resistive switching performance of cation based memristors.

Nanoscale Colocalized Electrochemical and Structural Mapping of Metal Dissolution Reaction.

Understanding the structure-activity relationship in electrochemical metal dissolution reactions is fundamentally important, from designing higher density batteries to mitigating corrosions. The kinetics of metal dissolution reaction is highly dependent on surface structures, including grain boundaries and local defects. However, directly probing the electrochemical activity at these sites is difficult because the conventional bulk electrochemistry measures an averaged kinetics, obscuring the structure-activity correlation. Herein, we report the colocalized mapping of an electrochemical metal dissolution reaction using Ag as a model system. The local dissolution kinetics is voltammetrically mapped via scanning electrochemical cell microscopy (SECCM), which is correlated with local structures obtained via colocalized electron backscattering diffraction (EBSD). Individual pits of ∼200 nm are formed, and their geometries suggest dissolution is fastest in the direction parallel to the {111} planes. Enhanced dissolution kinetics is observed at the high-angle grain boundaries but not at twin boundaries, which are attributed to the different binding energy of Ag atoms. Furthermore, the faster local dissolution correlates with the geometrically necessary dislocation density. The work demonstrates the importance of nanoscale local electrochemical mapping and colocalized microscopic measurement in obtaining the structure-activity relationship for electrochemical reactions at complex interfaces.

Electric Control of Exchange Bias at Room Temperature by Resistive Switching via Electrochemical Metallization.

Electric field control of exchange bias (EB) plays an important role in spintronics due to its attractive merit of lower energy consumption. Here, we propose a novel method for electrically tunable EB at room temperature in a device with the stack of Si/SiO2/Ta/Pt/Ag/Mn-doped ZnO (MZO)/Pt/FeMn/Co/ITO by resistive switching (RS) via electrochemical metallization (ECM). The device shows enhanced and weakened EB when set at high-resistance state (HRS) and low-resistance state (LRS), respectively. For the device at LRS, the aberration-corrected scanning transmission electron microscopy (STEM) characterizations unambiguously reveal that the Ag filaments grow initially from the Ag anode and then elongate toward the ITO cathode. It is inferred that at LRS, a small portion of Ag filaments have passed through the MZO and the intervening thin Pt layer and extended into the FeMn layer. After applying reverse voltage, these Ag filaments are electrochemically dissolved and ruptured near the MZO/Pt interface. This is considered to be the main mechanism responsible for RS and switchable EB as well. This work presents a new strategy for designing low-power, nonvolatile magnetoelectric random access memory devices.

Amino acids in electrochemical metal-free benzylic C[sbnd]H amidation

This report describes the mild electrochemical metal-, oxidant-, additive-free site-selective direct CH amidation of benzyl C(sp3)-H bonds in an undivided cell using α-amino acids as a source of an amide bond moiety. Practically significant amides are obtained under electrochemical mild one-pot conditions (without strong external oxidants, at room temperature), with total yields up to 73%. This electrochemical approach features a functional selectivity and a broad scope of substrates with benzyl bond and different amino acids.

Photocatalysis‐Induced Nanopores toward Highly Reliable Organic Electrochemical Metallization Memory

AbstractOrganic electrochemical metallization (ECM) memory that possesses high reliable switching performance is in great demand for the future smart wearable and flexible electronics. The resistive switching (RS) behavior of organic ECM memory is determined by the micro‐morphology evolution of metal conductive filaments (CFs) during the operating process. However, the morphology controllability of CFs is generally deteriorated by their random distribution and unexpected overgrowth. Herein, a kind of nanoporous PVC/TiO2 nanocomposite is developed using photocatalytic method for improving the RS reliability of organic ECM memory. The introduction of engineered nanopores can effectively simplify the CFs morphology to obtain excellent uniformity, good retention, and low cycling degradation. In addition, taking advantage of the multilevel RS behavior and 200 × 200 memristive array artificial neural network (ANN), alphabetic data storage and image pattern recognition are successfully realized. The proposed approach is expected to provide novel platforms for the advance of highly reliable flexible electronics and ANN for intelligence applications.

Electrochemical Metallization Process on Screen-Printed Electrode for Creatinine Monitoring Application

The formation and dissolution of the conductive paths in an electrochemical metallization (ECM) system is greatly influenced by the mobility of metal ions in the electrolyte, which can be utilized for the sensor application. Here, we report a demonstration of the ECM on the commercially available screen-printed electrode (SPE) strip, which is then utilized for monitoring the concentration of creatinine in an aqueous mixture. Prior to that, the working electrode was modified by drop casting an organic pentamer, 1,4-bis[2-(5-thiophene-2-yl)-1-benzothiopene]-2,5-dioctyloxybenzene (BOBzBT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). The electrostatic interaction between the BOBzBT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> radical cations and the creatinine molecules causes the resistance change in the electrochemical cell, influencing the variation of the ECM-induced pinched hysteresis loop. Consequently, the changes were recorded and the calibration curve was obtained. Compared to the unmodified SPE, the BOBzBT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -modified SPE demonstrated good performance in monitoring the concentration of creatinine. The sensor demonstrated 60 s response time with a sensitivity of 8.18 A dL g <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> for a linear detection range of 0.7–1.1 mg/dL. The selectivity and reproducibility of the modified sensor was also demonstrated. The setup’s simple fabrication procedures could open the way for the development of an ECM-based SPE sensor for creatinine monitoring.

Highly Reliable Synaptic Cell Array Based on Organic–Inorganic Hybrid Bilayer Stack toward Precise Offline Learning

As the use of artificial intelligence (AI) soars, the development of novel neuromorphic computing is demanding because of the disadvantages of the von Neumann architecture. Furthermore, extensive research on electrochemical metallization (ECM) memristors as synaptic cells have been carried out toward a linear conductance update for online learning applications. In most cases, however, a conductance distribution change over time has not been studied as a major issue, giving less consideration to inference‐only computing accelerators based on offline learning. Herein, organic–inorganic bilayer stacking for synaptic unit cells using poly(1,3,5‐trivinyl‐1,3,5‐trimethyl cyclotrisiloxane) (pV3D3) and Al2O3 thin films is suggested, showing highly enhanced reliability for offline learning. The bilayer structure achieves better reliability and control of the analog resistive switching and synaptic functions, respectively, through the guided formation of conductive filaments via tip‐enhanced electric fields. In addition, 5‐bit multilevel states achieve long‐term stability (&gt;104 s) following an in‐depth study on conductance‐level stability. Finally, a device‐to‐system‐level simulation is performed by building a convolutional neural network (CNN) based on the hybrid devices. This highlighted the significance of multilevel states in fully connected layers. It is believed that the study provides a practical approach to using ECM‐based memristors for inference‐only neural network accelerators.