Due to rapid industrialization, novel water-quality monitoring techniques for the detection of highly toxic and hazardous heavy metal ions are essential. Herein, a hybrid noble nanoparticle/DNAzyme electrochemical biosensor is proposed for the simultaneous and label-free detection of Pb²⁺ and Cr³⁺ in aqueous solutions. The sensor is based on the combination of a two-dimensional naked-platinum nanoparticle film and DNAzymes, whose double-helix configuration disassembles into smaller fragments in the presence of target-specific heavy metal ions. The electrochemical behavior of the fabricated sensor was investigated with non-faradaic electrochemical impedance spectroscopy (EIS), resulting in the successful detection of Pb²⁺ and Cr³⁺ well below their maximum permitted levels in tap water. So far, there has been no report on the successful detection of heavy metal ions utilizing the non-faradaic electrochemical impedance spectroscopy technique based on advanced nanomaterials paired with DNAzymes. This is also one of the few reports on the successful detection of chromium (III) via a sensor incorporating DNAzymes.

Reservoir computing (RC)-based neuromorphic applications exhibit extremely low power consumption, thus challenging the use of deep neural networks in terms of both consumption requirements and integration density. Under this perspective, this work focuses on the basic principles of RC systems. The ability of self-selective conductive-bridging random access memory devices to operate in two modes, namely, volatile and non-volatile, by regulating the applied voltage is first presented. We then investigate the relaxation time of these devices as a function of the applied amplitude and pulse duration, a critical step in determining the desired non-linearity by the reservoir. Moreover, we present an in-depth study of the impact of selecting the appropriate pulse-stream and its final effects on the total power consumption and recognition accuracy in a handwritten digit recognition application from the National Institute of Standards and Technology dataset. Finally, we conclude at the optimal pulse-stream of 3-bit, through the minimization of two cost criteria, with the total power remaining at 287 µW and simultaneously achieving 82.58% recognition accuracy upon the test set.

The development of disruptive artificial neural networks (ANNs) endowed with brain-inspired neuromorphic capabilities is emerging as a promising solution to deal with the challenges of the artificial intelligence era. The fabrication of robust and accurate ANNs is strongly associated with the design of new electronic devices. The intriguing properties of memristors render them suitable as building blocks within ANNs. However, the impact of the operating electrodes on the dynamics of the switching process and the relaxation effect remains elusive. It is, thus, apparent that a deep understanding of the underlying electrochemical metallization mechanism that affects the formation of the conductive filament is of great importance. Along these lines, in this work, the impact of various materials as inert electrodes (Pt NPs, ITO, n++ Si, TiN, and W) on tuning the switching mode of low power SiO2-based conductive bridge random access memory devices was systematically investigated. A comprehensive model was applied to interpret the threshold and bipolar switching patterns and shed light on the respective physical mechanisms. The model incorporated the different coefficients of thermal conductivity of the various materials and attempted to associate them with the Soret coefficient and the activation energy of thermophoresis to interpret the experimental outcomes. Our work provides valuable insight for the realization of memristive devices with tunable properties, which can be directly leveraged for implementing a variety of neuromorphic functionalities, such as synaptic plasticity and spike generation.

In this work, an optimized method was implemented for attaining stable multibit operation with low energy consumption in a two-terminal memory element made from the following layers: Ag/Pt nanoparticles (NPs)/SiO2/TiN in a 1-transistor-1-memristor (1T1M) configuration. Compared to the reference sample where no NPs were embedded, an enlarged memory window of $\sim 10^{{5}}$ was recorded in conjunction with reduced variability for both switching states ( $\boldsymbol {\sigma } $ / $\boldsymbol {\mu } \lt 0.4$ ). A comprehensive numerical model was also applied to shed light on this enhanced performance, which was attributed to the spatial confinement effect induced by the presence of the Pt NPs and its impact on the properties of the percolating conducting filaments (CFs). Although 5-bit precision was demonstrated with the application of the incremental-step-pulse-programming (ISPP) algorithm, the reset process was unreliable and the output current increased abnormally when exceeded the value of $150~\boldsymbol {\mu } $ A. As a result, the multibit operation was limited. To address this issue, a modified scheme was developed to accurately control the distance between the various resistance levels and achieve highly reliable 6-bit precision. Our work provides valuable insights for the development of energy-efficient memories for applications, where a high density of conductance levels is required.

The development of artificial nerve systems is considered of great significance for the fabrication of artificial appendages or intelligent robotics with environmental communication and awareness. Along these lines, in this work, the synaptic properties of a SiO2-based conductive bridge random access memory (CBRAM) were thoroughly investigated. Initially, a detailed study was conducted to understand and catalog the short- and long-term properties of the device, and their underlying physical mechanisms. To that end, a numerical model was introduced for interpreting the synaptic pattern within the device and its results were validated with the acquired experimental data. In addition, an artificial afferent nerve concept was demonstrated, comprising a piezoelectric sensory receptor, to generate electrical signals in response to either impulsive (“touch and go”) or continuous external stimuli (“touch and stay”), a microcontroller-emulated leaky-integrate-and-fire neural node converting the signals into spiking action potentials, and the CBRAM element as a terminal synapse, responsible for processing/attenuating the action potentials Throughout these procedures, the synaptic adaptation and the memory consolidation processes were recorded and analyzed.

Memristors and crossbar arrays are increasingly regarded as fundamental nanotechnology components for future computing and storage technologies, with promising applications in neuromorphic systems, non-volatile memories, and in-memory processing. However, their characterization and programming require precise waveform generation and reproducible signal control, which pose non-trivial challenges in experimental workflows. Developing dedicated software for waveform design in this context is particularly demanding, as it must support diverse signal types, customizable timing, and the coordination of row/column activations in crossbar architectures, while remaining intuitive for non-specialist users. This paper presents WaCPro, an open-source application that integrates waveform generation, crossbar mapping, visualization, and export functionalities into a single platform for the characterization and programming of nanoscale memristive devices and crossbar arrays. Implemented in MATLAB with a modular architecture and a graphical user interface, WaCPro enables the design and export of precisely-timed waveforms essential for the electrical stimulation of nanodevices. Export functions produce simulation- and instrumentation-ready files in widely used formats, facilitating integration into laboratory workflows, highlighting the tool's ability to bridge theory and experiment. Validation experiments demonstrate excellent waveform replication accuracy in both amplitude and timing, confirming the reliability of the proposed tool for nanoscale testing environments.