Behavior Of Devices Research Articles

Characterization and Performance Evaluation of ESP32 for Real-time Synchronized Sensor Networks

In recent years, technological advancement characterized by heightened connectivity and digitization has revolutionized our interaction with the world. This evolution has been exemplified by the emergence of the Internet of Things, which has introduced a new era of digitization and automation, primarily propelled by devices based on the ESP32 microcontroller. This study seeks to comprehensively evaluate and characterize a fully functional sensor network built upon the ESP32 microcontroller. The evaluation covers three key aspects: network performance, the ESP32’s built-in ADC peripheral, and the data acquisition system. The ESP32 device has demonstrated its capability to consistently sample sensors at a high rate with remarkable stability. The ESP32’s built-in ADC exhibited undesirable behavior for analog devices, attributed to noise and non-linearity. As a result, in applications requiring precision with analog sensors, the utilization of a cost-effective external ADC, as employed in this study, is recommended. Finally, during the network analysis of the sensor network, no issues were identified. The data acquisition system exhibited no bottlenecks, and packet losses remained minimal, regardless of the sampling period.

One-pot synthesis of a network of Mn2O3–MnO2–poly(m-methylaniline) composite nanorods on a polypyrrole film presents a promising and efficient optoelectronic and solar cell device

Abstract A one-pot synthesis approach was employed to create a composite nanorod structure (Mn2O3–MnO2–poly(m-methylaniline) or Mn2O3–MnO2–PmMA) on a polypyrrole (Ppy) film. Optoelectronic and solar cell thin film devices were constructed using n- and p-type layers of Mn2O3–MnO2–PmMA and Ppy, respectively. The optical characteristics of the n-layer, coupled with the morphological behavior of its nanorods, enhance optical absorbance efficiency, reducing the bandgap value to 2.48 eV. The behavior of the optoelectronic device is chiefly determined by the significantly higher current density (J ph) value of 0.017 mA/cm2 compared to the dark current density (J o) value of 0.002 mA/cm2. Additionally, the linear dynamic range is 85 dB, and the noise ratio is 12%. The optimized values for the photosensitivity (R) and detectivity (D) at 340 nm are 0.22 mA W−1 and 0.47 × 10⁸ Jones, respectively. Even at 540 nm, these values decrease to 0.08 and 0.2 × 10⁸ Jones, respectively. For solar cell behavior, the short-circuit current (J SC) and open-circuit voltage (V OC) at 540 nm are determined, yielding values of 0.001 mA/cm2 and 0.98 V, respectively. The outstanding performance of this optoelectronic device, coupled with its solar cell behavior, positions the thin film material for a dual role in various industrial applications.

Enhanced synaptic behavior of neuromorphic device based on organic electrochemical transistors by adding Aminosilane

Herein, we propose a strategy to enhance the synaptic behavior of neuromorphic devices based on organic electrochemical transistors (OECT) by introducing (3-aminopropyl)triethoxysilane (APT) into the conjugated polymer film channel. Various film analyses utilizing optical and atomic force microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy revealed that the addition of APT to the poly (3-hexylthiophene) (P3HT) film facilitated the formation of highly crystalline P3HT aggregation, thereby suppressing film degradation induced by repetitive ion injection and ejection under gate bias. Furthermore, electrical characterization of the OECT devices incorporating P3HT channels demonstrated an expanded hysteresis region and improved memory characteristics upon APT addition, which contains functional groups with high electron density, favorably influencing synaptic plasticity features such as short-term plasticity (STP), long-term plasticity (LTP), short-term memory (STM), and long-term memory (LTM). Consequently, it led to the fortification and resilience of synaptic behavior in OECTs, positioning them as a promising candidate for neuromorphic devices.

Heavy ions and alpha particles irradiation impact on III-V broken-gap gate-all-around TFET

This report deals with the impact of radiation on III-V and Si gate-all-around tunnel field effect transistors (GAA TFET). The highly energetic particles (HEPs) such as heavy ions and alpha particles are allowed to strike the device at several locations along the channel to determine the sensitive location of the device. Once the sensitive location is determined, then the device is irradiated to different energies of heavy ions and alpha particles. Further, to get a proximate match as a practically exposed device, the device has been irradiated to several incident angles. Before analyzing the device's behaviour, the models used for the analysis are calibrated with the transfer characteristics of an experimentally published report on III-V GAA TFET. Thereafter, analysis of charge collection, transient current, hole generation in the channel, and different incident angles are analyzed. Finally, it is concluded that III-V GAA TFET shows better immunity towards different incident angles under a radiation environment when compared to Si GAA TFET.

Trustworthy Precision Medicine: An Interpretable Approach to Detecting Anomalous Behavior of IoT Devices.

The growing integration of Internet of Things (IoT) technology within the healthcare sector has revolutionized healthcare delivery, enabling advanced personalized care and precise treatments. However, this raises significant challenges, demanding robust, intelligible, and effective monitoring mechanisms. We propose an interpretable machine-learning approach to the trustworthy and effective detection of behavioral anomalies within the realm of medical IoT. The discovered anomalies serve as indicators of potential system failures and security threats. Essentially, the detection of anomalies is accomplished by learning a classifier from the operational data generated by smart devices. The learning problem is dealt with in predictive association modeling, whose expressiveness and intelligibility enforce trustworthiness to offer a comprehensive, fully interpretable, and effective monitoring solution for the medical IoT ecosystem. Preliminary results show the effectiveness of our approach.

Ultrafast nanometric resolution spatiotemporal measurement of ambipolar diffusivity and carrier-phonon coupling in doped and undoped silicon

Understanding and determination of the ambipolar diffusivity of a semiconductor is fundamental for predicting device behavior and optimizing its performance. Ultrafast pump-probe measurements allow for the determination of energy carrier dynamics with sub-picosecond resolution. Due to the inherent diffusive nature of carriers, measurements with a high spatial resolution are needed, in addition to the traditional pump-probe system, to determine not only the carrier dynamics but also the spatial extent caused by carrier diffusion as well. In this work, a spatiotemporal measurement system with ultrafast temporal and nanometric spatial resolution, together with a comprehensive transport model, is used to determine the ambipolar diffusivity and carrier-phonon energy coupling time in both undoped and doped silicon. The results show that as the carrier density increases, the measured ambipolar diffusivity decreases with minimal variation in the carrier-phonon energy coupling time. In general, this work demonstrates an optical-based method for determining ambipolar diffusivity in a semiconductor material.

Enhancing electric field calculation in HTS tape simulations for currents exceeding the critical limit using full HTS tape modeling

Superconducting devices are an interesting technological option for improvement of efficiency of energy systems. High-temperature superconducting tapes are among the best choices for power applications. Because of their high non-linearity, the design of devices with superconducting tape requires specific simulation models and methods to correctly represent the superconducting behavior. The J-A formulation has been investigated as an efficient tool to represent the electromagnetic behavior of such devices. It is based on the current density (J) and magnetic vector potential (A), and able to represent both superconducting materials and ferromagnetic materials. With regards to the tapes, the use of thin-film approximation can be successful with the J-A formulation. Usually, only representations considering the superconducting layer of tapes have been investigated. This becomes a problem for operations where the current in the superconductor surpasses the critical current, for example in transient analysis. This paper presents an analysis of the effects the inclusion of all of the tapes’ layers have on simulations with the J-A formulation with thin-film approximations. By considering all layers of the tape, one represents the transition between superconducting and high-loss states of the tape. A simple system, consisting of a single tape with applied current, has been studied as benchmark and simulated with J-A and the more established T-A formulation in two cases: considering all layers; and only the superconducting layers. Results show that results for the J-A and T-A formulations are compatible and the inclusion of all layers improves the stability of the simulation and provides correct assessment of the electric field in the tapes.

Advanced Biomimetic and Biohybrid Magnetic Micro/Nano‐Machines

AbstractBiomimetic and biohybrid micro/nano‐structures involve the replication and creation of technologies, structures, and materials based on biological systems at the micrometer and nanometer scale. These strategies harness the natural biological principles to develop innovative treatment methods and advanced microstructure devices for noninvasive therapies. In this study, a detailed overview of fabrication processes, magnetically assisted locomotive techniques, and potential applications of biomimetic and biohybrid micro/nano‐machines are presented. The latest advancements in magnetically actuated biomimetic structures, such as annelid‐worm‐like microswimmers, jellyfish‐shaped microparticles, fish‐shaped microswimmers, and walnut‐shaped micromotors are explored. Additionally, the magnetic biohybrid systems, including sunflower seed‐based micro‐perforators, nanomotors extracted from the bamboo stem, sperm cell‐based micromotors, bacteria‐based robots, scaffold‐based microrobots, DNA‐based micromotors, microalgae‐based microswimmers, and red blood cell‐based microswimmers are also examined. A thorough investigation of the magnetically assisted locomotive behavior of these microstructure devices in biological Newtonian fluids, featuring cork‐screw motion, undulatory motion, surface wrinkling motion, traveling wave‐like motion, and ciliary stroke motion is discussed. Furthermore, unprecedented and innovative treatment methods developed using these minuscule devices such as cervical cancer treatment using tetrapod hybrid sperm micromotors, tissue regeneration using silk fibroin protein‐based magnetic microscale scaffolds, and doxorubicin drug delivery using mushroom‐based microrobots is extensively presented.

A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics.

Government agencies are now encouraging industries to enhance their security systems to detect and respond proactively to cybersecurity incidents. Consequently, equipping with a security operation center that combines the analytical capabilities of human experts with systems based on Machine Learning (ML) plays a critical role. In this setting, Security Information and Event Management (SIEM) platforms can effectively handle network-related events to trigger cybersecurity alerts. Furthermore, a SIEM may include a User and Entity Behavior Analytics (UEBA) engine that examines the behavior of both users and devices, or entities, within a corporate network. In recent literature, several contributions have employed ML algorithms for UEBA, especially those based on the unsupervised learning paradigm, because anomalous behaviors are usually not known in advance. However, to shorten the gap between research advances and practice, it is necessary to comprehensively analyze the effectiveness of these methodologies. This paper proposes a thorough investigation of traditional and emerging clustering algorithms for UEBA, considering multiple application contexts, i.e., different user-entity interaction scenarios. Our study involves three datasets sourced from the existing literature and fifteen clustering algorithms. Among the compared techniques, HDBSCAN and DenMune showed promising performance on the state-of-the-art CERT behavior-related dataset, producing groups with a density very close to the number of users.

Construction and volumetric benchmarking of quantum computing noise models

The main challenge of quantum computing on its way to scalability is the erroneous behaviour of current devices. Understanding and predicting their impact on computations is essential to counteract these errors with methods such as quantum error mitigation. Thus, it is necessary to construct and evaluate accurate noise models. However, the evaluation of noise models does not yet follow a systematic approach, making it nearly impossible to estimate the accuracy of a model for a given application. Therefore, we developed and present a systematic approach to benchmarking noise models for quantum computing applications. It compares the results of hardware experiments to predictions of noise models for a representative set of quantum circuits. We also construct a noise model containing five types of quantum noise and optimize its parameters using a series of training circuits. We compare its accuracy to other noise models by volumetric benchmarks involving typical variational quantum circuits. The model can easily be expanded by adding new quantum channels.

Comparison of p-n and p-i-n vertical diodes based on p-PMItz/n-Si, p-PMItz/n-4HSiC and p-PMItz/i-SiO2/n-Si heterojunctions

In this paper, we present a comprehensive comparison study between p-n and p-i-n vertical diodes employing diverse heterojunction configurations under dark conditions. The diodes are fabricated utilizing p-PMItz as the organic semiconductor layer interfacing with different inorganic substrates, including n-Si (n-type silicon), n-4HSiC (n-type 4H silicon carbide), and incorporating an intrinsic SiO2 (silicon dioxide) layer in the p-PMItz/i-SiO2/n++-Si configuration. The current–voltage and dielectric characteristics are analyzed to discern the performance discrepancies among these diode configurations. The influence of heterojunction interfaces and band alignments on device behavior is investigated, shedding light on the charge transport mechanisms within these structures. Our findings reveal distinct trends in device characteristics for p-n and p-i-n diodes, highlighting the significance of heterojunction design in optimizing device performance. This comparative analysis offers valuable insights for the development of efficient organic–inorganic hybrid diodes tailored for various optoelectronic applications.

Manipulation and diagnosis of femtosecond relativistic electron bunch using terahertz-driven resonators

Using strong electromagnetic fields generated by lasers to interact with electrons for precise diagnosis and manipulation of electron beams represents a recent focal point in accelerator technology. This approach surpasses the limitations of conventional RF technology, such as low electric field gradients and timing jitters, effectively enhancing the accuracy of ultrafast electron beam diagnostics and manipulations. As demands for precision continue to rise, the precise diagnosis of crucial parameters of ultrafast electron beams remains challenging. This study delves into the electromagnetic behavior of THz-driven devices and proposes an all-optical method utilizing single-cycle THz radiation to compress and characterize a 3 MeV electron beam. Particle tracking simulations demonstrate an astonishing compression effect, reducing the bunch length from 54.0 fs to 4.3 fs, and achieving sub-femtosecond bunch length measurement resolution. Moreover, when combined with an orthogonal THz streak camera, this method shows even greater potential in multi-bunch scenarios.

Fabrication perspective of Fe3O4-based cross-cell memristive device for synaptic applications

Neurotransmitter release in chemical synapses plays a pivotal role in a wide range of essential brain functions, including neural activity (potentiation/depression), learning, cognition, emotion, perception, and consciousness. In this study, we have presented the fabricated cross-cell memristive device that exhibits an analog resistive switching (ARS) device, with Silver (Ag) as active and Platinum (Pt) as inert metal electrodes. The energy bandgap, crystal structure, surface morphology, elemental composition, and electronic properties of the deposited metal-oxide thin film were examined by using UV–Vis spectroscopy, Glancing Angle X-ray diffraction (GAXRD), Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive X-ray (EDX), and Raman spectroscopy, respectively. The electrical characteristics of the fabricated resistive switching (RS) device have been studied by the Keithley 4200A SCS parameter analyzer by low triangular DC sweep voltage (-2V/+2V) at room temperature (RT). Furthermore, we have evaluated the outstanding performance of the fabricated cross-cell RS device at a read voltage of 0.1V, and we have also discussed its remarkable linearity. This work will aid researchers in realizing the synaptic behavior of cross-cell devices for neuromorphic computing applications.

Numerical Simulation, Electrostatic and Physical Compact Modeling of C8-BTBT-C8 Organic Thin Film Transistor

This paper presents numerical simulation and compact modeling of 2,7-Dioctyl {1} benzothieno {3,2-b}{1} benzothiophene (C8-BTBT-C8) organic semiconductor-based TFT. It shows the entire modeling process flow of this organic semiconductor (OSC) and tests the device realization using a ring oscillator. The paper comprises OSC characterisation, band-gap modeling, electrostatic modeling, and capacitance modeling. The TCAD model consists of the Hopping and Pool Frenkel premise and characterizes the Density of State (DOS) for traps in deep and tail states. The findings from this research provide valuable information for improving OTFT models, enhancing their predictive accuracy, and advancing the understanding of organic semiconductor device behavior. The electrostatics demonstrate device structure dependency

Influence of Increased Online Information on Consumption Patterns during COVID-19: Case of Priority Medical Devices

Influence of Increased Online Information on Consumption Patterns during COVID-19: Case of Priority Medical Devices

Security intrusion monitoring model for Internet of Things (IoT) using sniffing tools on wireless sensor networks

The Internet of Things (IoT) has revolutionized the way devices interact and share data over wireless sensor networks (WSN), enabling seamless connectivity and automation. However, the proliferation of IoT devices has raised serious security and privacy risks concerns due to their inherent vulnerabilities. This paper proposes a model for security intrusion monitoring by analyzing the existing literature and providing insights into the design, implementation, and effective deployment of the proposed model to detect intrusion in IoT using sniffing tools for network traffic analysis in real-time within WSN. The model passively monitors network traffic and identifies anomalous patterns, unauthorized access attempts, and abnormal device behavior. The review findings highlight the significance of the proposed model in enhancing the security of IoT systems. By detecting anomalous behavior and potential security breaches. The model enables timely response and mitigation actions to ensure the confidentiality, integrity and availability (CIA) of IoT devices data. The model includes consideration of network architecture, deployment of intrusion detection algorithms, and establishment of response mechanisms. It identifies various types of security threats, such as unauthorized access attempts, Denial-of-service, Distributed DoS, Brute-force, Heartbleed, Botnet, Inside Infiltration and device tampering, thereby providing response mechanisms that include generating alerts, isolating compromised devices, or blocking suspicious network traffic. The model incorporates a feedback loop to continuously update the detection mechanisms and adapt to evolving security threats in real-time. Series of experiments and simulations to be conducted using various IoT devices and network scenarios to evaluate model effectiveness. The model to comprise of wireless Router, MatLab for Deep Neural Network (DNN) training, Raspberry Pi, Wireshark setup and an array of Internet of Things (IoT) devices. The researcher to use dataset by extracting intrinsic, host-based and time-based attributes from Wireshark Sniffing tool. The datasets generated shall be piped by tshark to an output text file saved as a csv. Under-sampling technique to be used to address class imbalance of datasets. The model shall then be trained using the dataset to be able to detect intrusion in IoTs. The results is expected to demonstrate the model's ability to detect a wide range of security intrusions with high accuracy and minimal false positives. In conclusion, the model offers a proactive approach to safeguard IoT deployment. By leveraging sniffing tools and advanced analysis techniques, the model enhances the detection and response capabilities, enabling efficient protection against emerging threats in IoT. However, challenges associated with the model are identified, including the complexity of network monitoring and potential privacy concerns.

Emission current enhancement from quasi-freestanding epitaxial graphene microstructure electron emitters through surface layered silicon dioxide

Enhanced electron emission from oxide-encapsulated quasi-freestanding bilayer epitaxial graphene devices is reported, including one emission current of 9.4 µA and successful emission even with oxide thicknesses of up to 1.25 µm. The low operating temperature (215 °C), and applied electric fields under which the devices operate indicate electron emission is due to phonon-assisted electron emission, wherein forward-scattering hot phonons impart the necessary energy for the electrons to escape the graphene as an emission current. A suite of device structures and behaviors are cataloged, and various emission behaviors are demonstrated through encapsulating oxide layers. Emission current enhancement due to electron multiplication in the oxide layers is observed across multiple devices and oxide thicknesses.

Automatically Discovering Mechanical Functions From Physical Behaviors via Clustering

Abstract Computational design methods provide opportunities to discover novel and diverse designs that traditional optimization approaches cannot find or that use physical phenomena in ways that engineers have overlooked. However, existing methods require supervised objectives to search or optimize for explicit behaviors or functions—e.g., optimizing aerodynamic lift. In contrast, this article unpacks what it means to discover interesting behaviors or functions we do not know about a priori using data from experiments or simulation in a fully unsupervised way. Doing so enables computers to invent or re-invent new or existing mechanical functions given only measurements of physical fields (e.g., fluid velocity fields) without directly specifying a set of objectives to optimize. This article explores this approach via two related parts. First, we study clustering algorithms that can detect novel device families from simulation data. Specifically, we contribute a modification to the hierarchical density-based spatial clustering of applications with noise algorithm via the use of the silhouette score to reduce excessively granular clusters. Second, we study multiple ways by which we preprocess simulation data to increase its discriminatory power in the context of clustering device behavior. This leads to an insight regarding the important role that a design’s representation has in compactly encoding its behavior. We test our contributions via the task of discovering simple fluidic devices and show that our proposed clustering algorithm outperforms other density-based algorithms, but that K-means clustering outperforms density-based algorithms, as measured by adjusted Rand score. However, the device types may have an even stronger impact on the clustering. This opens up new avenues of research wherein computers can automatically derive new device functions, behaviors, and structures without the need for human labels or guidance.

Computational design and experimental investigation of novel thiophene-based organic dyes with N,N-dimethylaminophenyl and morpholinophenyl donors for dye-sensitized solar cells

A new series of thiophene-based organic chromophores (4–7 a, b) has been developed through structural engineering of the electron donor (D) group, incorporating N,N-dimethylaminophenyl and morpholinophenyl, and acceptor moieties such as benzonitrile and flurobenzonitrile. Additionally, a π-linkage of phenylthiophene was inserted. The effects of various donating and accepting groups, as well as the spacer, on the geometrical, energetic, absorption, and photovoltaic properties of these sensitizers were investigated using Density Functional Theory (DFT) and Time-Dependent DFT. The sensitizers were optimized in their ground state using selected functional methods. Furthermore, key properties such as absorbance, frequency, energies of Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO), energy gap, dipole moment, and global reactivity descriptors were calculated using quantum chemical calculations. These spectral properties and quantum chemical calculations provide valuable insights into the potential use of these sensitizers in photonic devices. The results obtained from analyzing light harvesting efficiency (LHE), free energy injection (ΔGinj), and open circuit voltage (Voc) demonstrate that these dyes show promising characteristics for future experimental studies and offer guidelines for improving the performance of dye-sensitized solar cells. By employing structural modifications and investigating the electronic properties of these newly developed chromophores, this study contributes to the understanding of their behavior and potential applications in photonic devices. The findings provide a foundation for further experimental investigations and offer valuable insights for the development of more efficient dye-sensitized solar cells.

Reversible Redox‐Driven Crystallization in a Paracyclophane Monolayer at a Solid–Liquid Interface

AbstractThe development and integration of cyclophanes into future functional materials require a detailed understanding of the physicochemical principles that underlie their properties, phase behavior, and in particular the relationship between structure and function. Here, electrochemically switchable crystallization of a ferrocene‐bearing 3D Janus tecton (M‐Fc) at the interface between highly oriented pyrolytic graphite (HOPG) and an electrolyte solution is demonstrated. The M‐Fc adlayer is successfully visualized under both ambient and electrochemical conditions using scanning tunneling microscopy. Voltammetric measurements show a surface‐confined redox process for the M‐Fc modified surface that drives the phase transition between a visible 2D ordered linear phase (M‐Fc0, with ferrocene in the neutral state) and an invisible gas‐like adsorption layer with high mobility when ferrocene is oxidized, M‐Fc+, and a “square scheme” mechanism explains the data. Analogous experiments in a ferrocene‐free tecton adlayer show no phase transition and confirm that the dynamics in M‐Fc are redox‐driven. On‐surface 3D nanoarchitectures are also demonstrated by forming inclusion complexes between M‐Fc and β‐cyclodextrin and device behavior through electrochemical scanning tunneling spectroscopy (STS). These results showcase the functional potential of this class of cyclophanes, which can find use in actuators, optical crystals, and other smart materials.