Device Parameters Research Articles

Design and optimization of high-performance eco-friendly perovskite solar cells: Utilizing FAMASnGeI3 and CsGeI3 as absorbers and tuning HTL and interface parameters

In this work, several designs of lead-based and lead-free perovskite solar cells (PSCs) have been developed and investigated. For the proposed designs, CH3NH3PbI3 (lead-based), FAMASnGeI3, and CsGeI3 (lead-free) are used as absorber materials, [Formula: see text] and NiO have been used as Hole Transport Layer (HTL) materials and TiO2 as Electron Transport Layer (ETL) materials. ETL materials, in general, have more concern with stability issues and HTL materials have more issues with efficiency improvements. The effect of changing thickness, doping density and defect density of the absorber layer, as well as HTL, defect density of absorber/HTL interface and work functions of front and back contacts on the performance of the proposed devices, are investigated. To enhance the device performance, optimization of the device parameters is performed. After optimization of different parameters, it is observed that the lead-based device structure TiO2/CH3NH3PbI3/NiO has a maximum efficiency of 29.94%. Even the corresponding lead-free device structure TiO2/CsGeI3/NiO exhibits a maximum efficiency of 29.19%. Additionally, this study delved into the influence of altering series and shunt resistances, as well as temperature on the operational characteristics of the lead-free optimized device. Such eco-friendly and cost-effective alternatives as lead-free perovskite cells can be very promising for future work.

Unveiling heterogeneity of hysteresis in perovskite thin films

The phenomenon of current–voltage hysteresis observed in perovskite-based optoelectronic devices is a critical issue that complicates the accurate assessment of device parameters, thereby impacting performance and applicability. Despite extensive research efforts aimed at deciphering the origins of hysteresis, its underlying causes remain a subject of considerable debate. By employing nanoscale investigations to elucidate the relationship between hysteresis and morphological characteristics, this study offers a detailed exploration of photocurrent–voltage hysteresis at the nanoscale within perovskite optoelectronic devices. Through the meticulous analysis of localized I–V curve arrays, our research identifies two principal hysteresis descriptors, uncovering a predominantly inverted hysteresis pattern in 87% of the locations examined. This pattern is primarily attributed to the energetic barrier encountered at the interface between the probe and the perovskite material. Our findings underscore the pronounced heterogeneity and grain-dependent variability inherent in hysteresis behavior, evidenced by an average Hysteresis Index value of 0.24. The investigation suggests that the localized hysteresis phenomena cannot be exclusively attributed to either photocharge collection processes or organic cation migration at grain boundaries. Instead, it appears significantly influenced by localized surface trap states, which play a pivotal role in modulating electron and hole current dynamics. By identifying the key factors contributing to hysteresis, such as localized surface trap states and their influence on electron and hole current dynamics, our findings pave the way for targeted strategies to mitigate these effects. This includes the development of novel materials and device architectures designed to minimize energy barriers and enhance charge carrier mobility, thereby improving device performance and longevity. This breakthrough in understanding the microscale mechanisms of hysteresis underscores the critical importance of surface/interface defect trap passivation in mitigating hysteretic effects, offering new pathways for enhancing the performance of perovskite solar cells.

Study of the influence of robotic gripper parameters on apple fruit damage

The article discusses the design of a robotic device intended for effective apple fruit harvesting with minimum damage. The research was conducted in the Federal Scientific Agroengineering Center VIM from 2020 to 2023. The developed device is equipped with specialized mechanisms and sensors designed to reduce the negative eff ects on apples during harvesting. The study aims to justify the parameters of the robotic device for apple fruit picking and to conduct laboratory experimental studies of the grip strength eff ect on fruit damage during robotic fruit harvesting. A classification of gripping devices was developed based on the analysis of their design parameters and types. A concentric three-fingered gripper tool with rotating and sliding elements added to the gripping device was selected as the prototype device. As a result of studying the size and weight parameters of apples of the ‘Jonathan’ and ‘Granny Smith’ cultivars, data were obtained that enable one to describe the characteristics of these fruits more fully. Th e grapho-analytical method was employed to select the optimal geometric parameters of the gripper claws. We designed a robotic gripper which has several components, including gripper claws and a movable base. A laboratory setup was developed to simulate the operation of the manipulator and conduct experiments. Th e setup makes it possible to create conditions close to the actual manipulator operation and study the processes of grasping and holding fruits. The three-factor experiment allowed us to analyze the impact of the grip strength of the gripper claws, as well as the distance from the fruit to the gripper on the damage to fruits. It has been determined that these parameters have a signifi cant eff ect on the process of grasping and holding fruits. Entirely optimal values of these parameters contribute to reliable holding of a fruit in the gripper claws with minimal damage. As a result of analyzing the size-mass parameters of fruits, we found the average size and weight of apples of the Jonathan and Granny Smith cultivars. Th e design parameters of the robotic device were justifi ed. A 3D robotic gripper model was developed. We also manufactured an experimental robotic gripper model which underwent laboratory tests. As a result, the parameters of the grip strength and the distance from the fruit to the gripper were identified.

High-Performance Memristive Synapse Composed of Ferroelectric ZnVO-Based Schottky Junction.

In pursuit of realizing neuromorphic computing devices, we demonstrated the high-performance synaptic functions on the top-to-bottom Au/ZnVO/Pt two-terminal ferroelectric Schottky junction (FSJ) device architecture. The active layer of ZnVO exhibited the ferroelectric characteristics because of the broken lattice-translational symmetry, arising from the incorporation of smaller V5+ ions into smaller Zn2+ host lattice sites. The fabricated FSJ devices displayed an asymmetric hysteresis behavior attributed to the ferroelectric polarization-dependent Schottky field-emission rate difference in between positive and negative bias voltage regions. Additionally, it was observed that the magnitude of the on-state current could be systematically controlled by changing either the amplitude or the width of the applied voltage pulses. Owing to these voltage pulse-tunable multi-state memory characteristics, the device revealed diverse synaptic functions such as short-term memory, dynamic range-tunable long-term memory, and versatile rules in spike time-dependent synaptic plasticity. For the pattern-recognition simulation, furthermore, more than 95% accuracy was recorded when using the optimized experimental device parameters. These findings suggest the ZnVO-based FSJ device holds significant promise for application in next-generation brain-inspired neuromorphic computing systems.

LiNbO3-based memristors for neuromorphic computing applications: a review

Neuromorphic computing is a promising paradigm for developing energy-efficient and high-performance artificial intelligence systems. The unique properties of lithium niobate-based (LiNbO3)-based memristors, such as low power consumption, non-volatility, and high-speed switching, make them ideal candidates for synaptic emulation in neuromorphic systems. This study investigates the potential of LiNbO3-based memristors to revolutionize neuromorphic computing by exploring their synaptic behavior and optimizing device parameters, as well as harnessing the potential of LiNbO3-based memristors to create efficient and high-performance neuromorphic computing systems. By realizing efficient and high-speed neural networks, this literature review aims to pave the way for innovative artificial intelligence systems capable of addressing complex real-world challenges. The results obtained from this investigation will be crucial for future researchers and engineers working on designing and implementing LiNbO3-based neuromorphic computing architectures.

Electrical Characterization and Ammonia Gas Response of a p‐Si/n‐poly[benzimidazobenzophenanthroline] Thin‐Film Junction Diode

The electrical characterization and ammonia vapor (NH3) response of a p‐Si/n‐poly[benzimidazobenzophenanthroline] (n‐BBL) thin‐film junction diode are reported. The presence of a depletion layer at the n‐BBL/p‐Si interface is verified via capacitance–voltage measurements, and the built‐in potential is ≈1.8 V. Using the standard diode equation for data analysis, the turn‐on voltage, rectification ratio, and ideality parameter are found to be 2 V, 16, and 6, respectively. The diode is also tested in the presence of NH3 vapor where it retained its asymmetric J–V behavior with increased currents and an insignificant change in device parameters. NH3 is believed to interact with the adsorbed O2− species on the n‐BBL surface liberating electrons that enhance the diode current. The response time, recovery time, and sensitivity of the diode are 65 s, 121 s, and 52%, respectively. The removal of the gas restores the diode characteristics to their near original shape making it reusable. The diode is also electrically characterized as a function of temperature and is found to retain its rectifying behavior down to 150 K. The rectifying and gas‐sensing features make the diode multifunctional, which expands the range of applications of this ladder‐type conducting polymer.

Design and Experiment of Oil-Electric Hybrid Air-Suction Sorghum Plot Seeder

To tackle the issues of low seeding accuracy and seed injury caused by the seeders utilized at a small scale and in the plot seeding of sorghum in mountainous or hilly regions, this study presents the design of an oil–electric hybrid air suction sorghum plot seeder. The main working parts of the seeder are described, and the performance of the seed-mixing device is simulated using EDEM software. An oil–electric hybrid drive mode is used to provide power for operation and to the seed-metering device and fan. Additionally, a sowing control and monitoring system is designed using a single-chip microcomputer controller to ensure uniform plant spacing at different forward speeds. A multi-factor experiment is conducted using the central synthesis method to determine the optimal operating parameters of the seed-metering device through bench tests. The results show that a profile hole diameter of 2.5 mm on the seed tray, a negative-pressure chamber vacuum of 8.0 kPa, and a seed-metering device speed of 28 r/min result in a 95.95% pass rate, 0.5% missing rate, and 3.55% reseeding rate. The deviation between the experimental and analytical results that validate the optimum parameters is kept within acceptable limits. Field tests are conducted at different forward speeds using the optimum parameter combinations, and a comparison is made with the widely used duckbill planter. The results show pass, missing, and reseeding rates of 94.41%, 2.3%, and 3.29%, respectively. The missing monitoring error is less than 7.19%. All of the indices of the oil–electric hybrid air suction sorghum plot seeder are superior to those of the duckbill planter; thus, it fulfills the agronomic requirements for seeding a sorghum plot.

Low-cost, stable all-inorganic charge transporting layer based FAPI perovskite solar cells achieving ≈ 26% efficiency using SCAPS-1D

The superior opto-electronic properties of metal halide perovskites have uncovered great potential as light harvesters for thin-film photovoltaics, capturing the dominant research trend for over a decade now. While Perovskite Solar Cells (PSCs) depict promising results of a successful photovoltaic technology, further replacement of conventional charge transporting layers (CTLs) with inorganic counterparts can impart sustainable device stability and cost-effectiveness. Here, we represent a theoretical study aimed to analyse the performance of Formamidinium Lead Iodide (FAPI) based PSCs using SCAPS-1D simulation tool (version 3.3.09). The solar cell is designed into a novel, and cost-effective inverted p-i-n architecture comprising of inorganic transporting layers. The introduction of a thin CuI buffer layer accompanied with the hole transporting layer (HTL) was found to boost the device performance greatly. Furthermore, a systematic analysis of the device parameters was carried out based on crucial factors like thickness of the absorber, CTLs, and buffer layer, interfacial defect densities (Nit), series resistances (RS) and working temperature (T). In addition to this, we also conducted a study on the combined effect of absorber defect density (Nt) and thickness on our simulated PSC. Our optimized device configuration ITO/CuI/Cu2O/FAPI/ZnO/Al, resulted in an ideal device efficiency of ≈ 26% and a reasonably high efficiency of 21.58% adopting a more realistic approach. This study therefore focuses on an economical architectural design strategy with an emphasis on device efficiency, and its long-term sustainability.

Self-powered flexible piezoelectric motion sensor with spatially aligned InN nanowires

The power output of piezoelectric sensors based on nanowires (NWs) is governed by various factors, but largely depends on the alignment of the NWs. However, aligning the NWs has proved to be difficult. We successfully developed self-powered and flexible piezoelectric motion sensors (PMSs) with InN NWs as a response medium. The device performance was maximized by spatially controlling the alignment of the InN NWs by applying a magnetic field, which was the first step in the development of the NW-PMSs. The output voltage of the PMS with the InN NWs aligned along the bending direction was measured to be 3.05 V, which was a significant improvement of 2.44 times compared to that with randomly-distributed NWs. A systematic analysis of the extent to which the performance of the PMSs depend on the device parameters, such as the length of the NWs, bending frequency, operation time (up to 30 days), relative humidity, and bending cycle, indicates that the device performance is sufficient for real-life applications. For example, attachment of the self-powered PMSs to human joints such as the finger, wrist, elbow, and knee revealed that these motion sensors are highly effective, thereby indicating the possibility of detecting the various motions of the human body. The self-powered PMSs developed in this work are expected to contribute to rehabilitation, disease prevention, and human–machine interaction.

A machine learning approach for optimizing and accurate prediction of performance parameters for stacked nanosheet transistor

In this article, the possibilities of accurate prediction of wide range of parameters and optimizing the same through machine learning (ML) approach have been demonstrated for the multi stacked nanosheet transistor (NSFET). The machine is trained by the generated data of the tedious calibrated technology computer aided (TCAD) simulations. An innovative strategy is employed that combines ML with device simulations. Numerous devices are simulated with different geometric parameters like height, width, length and equivalent oxide thickness of the channel. The input, output, and CV characteristics are extrapolated from the simulation which is predicted by ML models. The DC, Analog and RF parameters are derived with a domain expertise approach. The device parameters are anticipated with actual values by the ML approach. Random forest regression, linear regression, polynomial regression and decision tree regression are employed for the prediction of the performance parameters. Random forest regression models provide significant R2 score with minimal error percentage. It indicates that TCAD-augmented ML can be considered an alternative to device simulation due to its reduction in computational cost. This work can also be treated as a benchmark for accurate prediction of the NSFET.

Two-dimensional analytical model for a non-lightly doped drain SOI MOSFET

A two-dimensional (2D) analytical model considering the effects of the gate oxide region, channel region, and buried oxide region for a non-lightly doped drain (LDD) SOI MOSFET is proposed. The top and bottom surface potential distributions have been derived on the basis of solving 2D Poisson’s equation and using an evanescent mode analysis. The potential distribution, threshold voltage, and threshold voltage roll-off have been verified by Silvaco ATLAS simulated results for the proposed device with different device parameters. The model agrees well with the simulation results under the above-mentioned conditions. Therefore, the analytical model provides the basic designing guidance for non-LDD SOI MOSFETs.

Application of magnetic flux leakage method in the crimping inspection of carbon fiber conductor

Compared with steel core aluminum strand wire, carbon fiber composite core wire has many excellent characteristics such as large flow capacity, good heat resistance, light weight, but its mechanical properties are poor, and the unqualified crimping quality is easy to lead to wire breakage accidents, which greatly affects its popularization. For this reason, a scheme for the detection of crimping defects of carbon fiber composite mandrel based on magnetic leakage method is proposed. Firstly, the finite element analysis model of the clamping fixture was established, and then the magnetic pole distance k and radial air gap n of the magnetic leakage testing device were explored. Based on the signal-to-noise ratio under the two typical defect conditions of fracture and low pressure, the most suitable excitation structure parameters of the magnetic leakage device were obtained. The results show that when k=55mm and n=3.5mm, the signal-to-noise ratio of MFL detection device reaches the best effect. The research results are of great significance to the quality inspection of compacts of carbon fiber composite mandrel.

MATERIALS FOR OPTICAL SYSTEMS LIGHTING DEVICES

The design of lighting devices, the properties of the materials and components used in them, as well as light sources determine the possibility of operating the lamps when exposed to certain environmental factors. All materials have different physical and chemical properties, knowing which, you can correctly determine under what operating conditions the device will function reliably. The choice of the optical system of a lighting device is the most important and one of the first stages of design, which largely determines the overall layout of the light fixture, its dimensions, weight and all the main characteristics. The basis for choosing an optical system of a joint venture is a set of requirements for quality indicators and parameters of the joint venture, primarily lighting, presented in the technical specifications for the design of the joint venture. In this case, external factors and their influence on the parameters of the lighting device must be taken into account, as well as the properties of materials, the specifics of the production technology and the features of using the lighting device in a lighting installation.

Representation learning algorithms for inferring machine independent latent features in pedestals in JET and AUG

Variational autoencoder (VAE)-based representation learning algorithms are explored for their capability to disentangle tokamak size dependence from other dependencies in a dataset of thousands of observed pedestal electron density and temperature profiles from JET and ASDEX Upgrade tokamaks. Representation learning aims to establish a useful representation that characterizes the dataset. In the context of magnetic confinement fusion devices, a useful representation could be considered to map the high-dimensional observations to a manifold that represents the actual degrees of freedom of the plasma scenario. A desired property for these representations is organization of the information into disentangled variables, enabling interpretation of the latent variables as representations of semantically meaningful characteristics of the data. The representation learning algorithms in this work are based on VAE that encodes the pedestal profile information into a reduced dimensionality latent space and learns to reconstruct the full profile information given the latent representation. Attaching an auxiliary regression objective for the machine control parameter configuration, broadly following the architecture of the domain invariant variational autoencoder (DIVA), the model learns to associate device control parameters with the latent representation. With this multimachine dataset, the representation does encode density scaling with device size that is qualitatively consistent with Greenwald density limit scaling. However, if the major radius of the device is given through a common regression objective with the other machine control parameters, the latent state of the representation struggles to clearly disentangle the device size from changes of the other machine control parameters. When separating the device size as an independent latent variable with dedicated regression objectives, similar to separation of domain and class labels in the original DIVA publication, the latent space becomes well organized as a function of the device size.

Reciprocal dynamics in nonlinear systems

Nonreciprocal vibration transmission is an important problem from a fundamental perspective and because of the additional functionalities that it enables in mechanical or acoustic devices. A common realization of nonreciprocal dynamics relies on implementation of nonlinear internal forces within the system. In this talk, we identify and discuss different manifestations of nonreciprocity in nonlinear systems. As a necessary condition, nonreciprocal dynamics is realized in nonlinear systems with broken mirror symmetry. We show that a second symmetry-breaking parameter can counteract the original asymmetry and ultimately restore reciprocal dynamics in a system with broken mirror symmetry, even near the system resonances. Thus, breaking the mirror symmetry is a necessary but insufficient condition for realizing nonreciprocity in nonlinear systems. We also highlight the contribution of phase to nonreciprocal vibration transmission by discussing response regimes that are characterized by nonreciprocal phase shifts. Our findings showcase the potential of asymmetry to serve as an additional design parameter for devices that operate based on nonreciprocity.

Specific Instructions Are Important: A Cross-sectional Study on Device Parameters and Instruction Types While Walking With a Robot in Children and Adolescents.

The aim of the study is to evaluate how gait kinematics and muscle activity during robot-assisted gait training are affected by different combinations of parameter settings and a number of instruction types, ranging from no instructions to goal-specific instructions. Robots for gait therapy provide a haptic guidance, but too much guidance can limit the active participation. Therapists can stimulate this active participation either with instructions or by adapting device parameters. How these two factors interact is still unknown. In the present study, we test the interaction of three different parameter settings and four instruction types in a cross-sectional study with 20 children and adolescents without impairment. Gait kinematics and surface electromyography were measured to evaluate the immediate effects. We found that only goal-specific instructions in combination with a low guidance led to a moderate but significant change in gait kinematics. The muscle activity was altered by all instructions, but the biggest effect was found for goal-specific instructions with a 2.5 times higher surface electromyography amplitude compared to no instruction. Goal-specific instructions are a key element of robot-assisted gait therapy interventions and device parameter adjustments may be used to modulate their effects. Therapists should pay close attention to how they instruct patients.

The Application of Convolutional Neural Networks in Organ-on-a-Chip Technology: A Review

Recent developments in microfluidics and biomaterials have enabled the creation of organs-on-chips (OoCs), which provide a controlled and lifelike in vitro microenvironment resembling an actual organ. Organ-on-a-chip technology offers a tremendous opportunity for efficient, cost-effective, and ethical drug testing and research. However, the high throughput and large quantities of data these complex microenvironments produce make it difficult for researchers to effectively analyze the data and draw valuable conclusions. Convolutional Neural Networks (CNNs) are most aptly positioned to address many of OoC’s challenges because of their ability to interpret microscopic images and facilitate the analytical process. Despite the growing field of AI, there have been a limited number of studies summarizing the various applications of CNNs in OoCs. This review aims to provide 1) an overview of the technology involved with CNNs and OoCs 2) an insight into the state-of-the-art applications of CNNs in OoCs including device parameters, predicting and tracking cell trajectories, super-resolution image segmentation, and image classification, and 3) an overview of existing challenges and opportunities ahead for clinical translation of this technology. Various applications of CNNs have been classified by the type of task. Different CNN models such as Faster R-CNNs, fully convolutional networks (FCNs), and Mask R-CNNs are explained and highlighted. This review article can be used as a resource for a better understanding of the potential of CNNs in biomedical research and clinical applications, particularly OoCs.

Design and Test of Seed–Fertilizer Replenishment Device for Wheat Seeder

In view of existing problems, such as the seed and fertilizer supply link for wheat seeders still relying on manual installation and the lack of practical application equipment, a seed–fertilizer replenishment device based on the three-degree-of-freedom mechanical arm and screw conveying principle is designed using the seed box installation and supply as the operation scenario to replace the manual installation process. Combined with the requirements of the seed box replenishment operation, the key parameters of the replenishment robot arm and the screw conveyor auger are determined. Then, the kinematic model of the replenishment robot arm is established based on the modified D-H method, forward and inverse kinematics calculations are performed, and the workspace is analyzed using the Monte Carlo method. Based on this, the robotic arm task path is designed, the fifth-degree polynomial interpolation method is used to complete the trajectory planning, and MATLAB R2016a software is used to simulate the motion trajectories of each joint, verifying the feasibility of the trajectory planning solution. Finally, a prototype is trial-produced and quadratic regression orthogonal testing and response surface analyses are conducted to obtain the optimal working parameters of the replenishment device. The verification test shows that when the angular velocity of the lumbar joint of the replenishment device is 4°/s, the speed of screw conveyor is 90 r/min, and the angle of the big arm is 12°, the conveying loss rate is 3.98%, and the conveying efficiency is 0.833 kg/s. The relative errors with the theoretical optimal values are 4.2% and 2.4%, respectively, both less than 5%. The supply trajectory is reasonable, and the robot arm runs smoothly. This study can provide reference for the design of seed–fertilizer replenishment device for wheat seeders.

Exploring statistical approaches for accessing the reliability of Y2O3-based memristive devices

Memristive devices have emerged as promising alternatives to traditional complementary metal-oxide semiconductor (CMOS)-based circuits in the field of neuromorphic systems. These two-terminal electronic devices, known for their non-volatile memory properties, can emulate synaptic behavior within artificial neural networks, offering remarkable advantages, including scalability, energy efficiency, rapid operation, compact size, and ease of fabrication. They hold the potential to serve as fundamental components for artificial neurons, revolutionizing neuromorphic computing systems by closely mimicking biological neurons. However, the integration of resistive random-access memory (RRAM) into commercial production faces challenges due to substantial variations in resistive switching (RS) parameters, which include cycle-to-cycle (C2C) and device-to-device (D2D) fluctuations. These variations are rooted in the stochastic nature of RS, linked to physical mechanisms like diffusion and redox reactions. Nonetheless, limitations exist in the current analytical approaches, emphasizing the need for more standardized tools to assess memristive device reliability consistently. Weibull distribution is widely used to analyze RRAM variability and many further studies are based on it. However, this distribution may not work well for some memristive devices. In such cases, one can use other statistical distributions available in the literature. In the present work, statistical distributions, namely Weibull, Exponential, Log-Normal, Gamma, and Logistic distributions, are employed to scrutinize memristive devices device parameters, shedding light on their performance and reliability. Also, analytical methods namely maximum likelihood estimates for parameter estimation and Kolmogorov-Smirnov test for assessing goodness of fit of the distributions are used. This study aims to provide an approach with a deeper understanding of memristive device parameters and analysis techniques.

Analysis of the optical and electronic characteristics of Al/NiPc complex/p-Si diode

The primary goal of this work is to explore how the introduction of the Nickel (II) phthalocyanine tetrasulfonic acid tetrasodium salt (NiTsPc) organic interlayer influences the performance of conventional metal/semiconductor diodes. Firstly, the optical features of the NiTsPc organic film formed onto glass substrate were investigated. For this, the UV–vis spectroscopic data were used to determine various optical parameters like absorption coefficient (α), extinction coefficient (k), and refractive index (n). Then, Al/NiTsPc/p-Si diode was produced by forming ohmic and rectifier contact. The current–voltage (I-V) measurements were analyzed taking into account thermionic emission (TE) approach at room temperature. Device parameters such as ideality factor (η), barrier height (Φb), and resistance were investigated with the help of I-V technique. The Al/p-Si structure containing NiTsPc film showed good rectifying properties. In this analysis, Φb and η values were determined as 0.83 eV and 1.41, respectively, at room temperature. The device represents photovoltaic features with open-circuit voltage (Voc) of 0.37 V and a short-circuit current (Isc) of 8.17 μA under illumination of 100 mWcm−2. The results represent that the produced junction can be utilized in different photoelectric applications.