Charge Modulation Research Articles

Vibrotactile Gloves for Parkinson's Disease Patients

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder marked by motor impairment due to the loss of dopamine-producing neurons. This paper presents the development of a vibrotactile glove designed to provide specific vibrational feedback through fingertip-based vibration motors, aimed at stimulating neuronal activity and potentially delaying PD symptoms. Constructed using an Arduino Nano, vibration motors, lithium-ion battery, slide switch, and a charging module, this glove prototype allows controlled vibrations at predefined frequencies and patterns. Preliminary testing suggests that this device may offer significant benefits in improving motor function and coordination, with potential implications as a preventive tool for those at risk of PD.

Application of machine learning for reconstruction of multiphase fluid structure measured by a capacitance multi-electrode sensor

Non-invasive real-time measurements of phase content in the reservoir fluid are highly advantageous in the oil and gas industry and remain a current research topic. The paper presents an innovative, self-designed multi-electrode capacitance meter intended for detecting multiphase flow patterns in a low-permittivity medium, such as the reservoir fluid. The ca-pacitance sensor is built with delta-sigma charge modulators capacitance-to-digital converters. Machine learning is applied to convert the capacitance measurements into a tomographic image of the flow pattern. At present, the meter is built with eight electrodes. It is shown that the measurements are repeatable and have a good signal-to-noise ratio. The implemented neural network is capable of correctly reconstructing the tomographic images for a test tube filled with reservoir fluid and placed in various locations inside the test section.

Absence of bulk charge density wave order in the normal state of UTe2

A spatially modulated superconducting state, known as pair density wave (PDW), is a tantalizing state of matter with unique properties. Recent scanning tunneling microscopy (STM) studies revealed that spin-triplet superconductor UTe2 hosts an unprecedented spin-triplet, multi-component PDW whose three wavevectors are indistinguishable from a preceding charge-density wave (CDW) order that survives to temperatures well above the superconducting critical temperature, Tc. Whether the PDW is the mother or a subordinate order remains unsettled. Here, based on a systematic search for bulk charge order above Tc using resonant elastic X-ray scattering (REXS), we show that the structure factor of charge order previously identified by STM is absent in the bulk within the sensitivity of REXS. Our results invite two scenarios: either the density-wave orders condense simultaneously at Tc in the bulk, in which case PDW order is likely the mother phase, or the charge modulations are restricted to the surface.

Development of inorganic phase change material and cold charging performance analysis based on cold storage refrigerator

Phase change cold storage refrigerators are a core of low-carbon development in cold chain logistics. This study is dedicated to optimizing the performance of phase-change cold storage refrigerators for the refrigerated transport of fruits and vegetables. Firstly, a high-performance inorganic phase change material was developed. By selecting a ratio of 2 wt% Carboxymethyl cellulose and 0.25 wt% Fumed Silica to solve the defects of supercooling and phase separation of this material, Subsequently, an experimental platform was built based on this material to test the charging and cooling characteristics of the phase change module. It was found that the opening of the expansion valve, the compressor’s speed, and the condenser fan’s speed were positively correlated to the charging and cooling efficiencies. Still, the effects of the first two on the system performance coefficient had optimal value points. However, there is an optimum point for the impact of the first two on the system performance coefficient. The optimized system coefficient of performance is increased to 1.62, and the charging time is reduced by 47.6 %, which is a good balance between energy efficiency and transport efficiency. In addition, it was found that the radial thermal resistance of the evaporator tubes in the charging module was more significant than the axial direction, and a new type of finned tube was designed to enhance the heat transfer, which resulted in a further reduction of 29 percent in the charge time.

Local Charge Modulation Induced the Formation of High‐Valent Nickel Sites for Enhanced Urea Electrolysis (Adv. Energy Mater. 41/2024)

Local Charge Modulation Induced the Formation of High‐Valent Nickel Sites for Enhanced Urea Electrolysis (Adv. Energy Mater. 41/2024)

Modulation of charge and spin circular currents in a ring-wire hybrid setup

Modulation of charge and spin circular currents in a ring-wire hybrid setup

Stability Challenges in Industrialization of Perovskite Photovoltaics: From Atomic‐Scale View to Module Encapsulation

AbstractPerovskite photovoltaics have attracted significant attention in both academia and industry, benefiting from the superiorities of high efficiency, low cost, and simplified fabrication process. Importantly, long‐term stability is essential for practical industrialization; however, the stability challenge remains a significant impediment. Notably, stability is an essential prerequisite for practical applications. Unfortunately, as the device area increases, even to the module level, the efficiency gradually diminishes, and the stability deteriorates. This review summarizes the advances in perovskite photovoltaic technology stability from comprehensive perspectives, including the atomic‐scale, grain boundary, film morphology, interface, charge transport layer, electrode, laser etching, and module encapsulation. First, the review highlights the ongoing importance of stability in the industrialization of perovskite photovoltaics. Then, the review presents the stability challenge and explores the relationship between efficiency and stability in large‐area photovoltaic modules, shedding light on the stability issue. Later, the review explains the stability issue in terms of structure, chemistry, interfaces, device design, operation, and external environment, and proposes stability strategies ranging from the atomic‐scale to module encapsulation. Finally, the review emphasizes various improvement strategies, particularly multilevel synergistic optimization, offering fundamental guidance for the industrialization of perovskite photovoltaics.

Simultaneous modulation of pulse charge and burst period elicits two differentiable referred sensations

Objective.To investigate the feasibility of delivering multidimensional feedback using a single channel of peripheral nerve stimulation by complementing intensity percepts with flutter frequency percepts controlled by burst period modulation.Approach.Two dimensions of a distally referred sensation were provided simultaneously: intensity was conveyed by the modulation of the pulse charge rate inside short discrete periods of stimulation referred to as bursts and frequency was conveyed by the modulation of the period between bursts. For this approach to be feasible, intensity percepts must be perceived independently of frequency percepts. Two experiments investigated these interactions. A series of two alternative forced choice tasks (2AFC) were used to investigate burst period modulation's role in intensity discernibility. Magnitude estimation tasks were used to determine any interactions in the gradation between the frequency and intensity percepts.Main results.The 2AFC revealed that burst periods can be individually differentiated as a gradable frequency percept in peripheral nerve stimulation. Participants could correctly rate a perceptual scale of intensity and frequency regardless of the value of the second, but the dependence of frequency differentiability on charge rate indicates that frequency was harder to detect with weaker intensity percepts. The same was not observed in intensity differentiability as the length of burst periods did not significantly alter intensity differentiation. These results suggest multidimensional encoding is a promising approach for increasing information throughput in sensory feedback systems if intensity ranges are selected properly.Significance.This study offers valuable insights into haptic feedback through the peripheral nervous system and demonstrates an encoding approach for neural stimulation that may offer enhanced information transfer in virtual reality applications and sensory-enabled prosthetic systems. This multidimensional encoding strategy for sensory feedback may open new avenues for enriched control capabilities.

Two Distinct Charge Orders in Infinite-Layer PrNiO2+δ Revealed by Resonant X-Ray Diffraction

Abstract Research of infinite-layer nickelates has unveiled a broken translation symmetry, which has sparked significant interest in its root, its relationship to superconductivity, and its comparison to charge order in cuprates. In this study, resonant x-ray scattering measurements were performed on thin films of infinite-layer PrNiO2+δ . The results show significant differences in the superlattice reflection at the Ni L 3 absorption edge compared to that at the Pr M 5 resonance in their dependence on energy, temperature, and local symmetry. These differences point to two distinct charge orders, although they share the same in-plane wavevectors. It is suggested that these dissimilarities could be linked to the excess oxygen dopants, given that the resonant reflections were observed in an incompletely reduced PrNiO2+δ film. Furthermore, azimuthal analysis indicates that the oxygen ligands likely play a crucial role in the charge modulation revealed at the Ni L 3 resonance.

Modular Customized Biomimetic Nanofluidic Diode for Tunable Asymmetric Ion Transport.

Artificial ion diodes, inspired by biological ion channels, have made significant contributions to the fields of physics, chemistry, and biology. However, constructing asymmetric sub-nanofluidic membranes that simultaneously meet the requirements of easy fabrication, high ion transport efficiency, and tunable ion transport remains a challenge. Here, a direct and flexible in situ staged host-guest self-assembly strategy is employed to fabricate ion diode membranes capable of achieving zonal regulation. Coupling the interfacial polymerization process with a host-guest assembly strategy, it is possible to easily manipulate the type, order, thickness, and charge density of each module by introducing two oppositely charged modules in stages. This method enables the tuning of ion transport behavior over a wide range salinity, as well as responsive to varying pH levels. To verify the potential of controllable diode membranes for application, two ion diode membranes with different ion selectivity and high charge density are coupled in a reverse electrodialysis device. This resulted in an output power density of 63.7W m-2 at 50-fold NaCl concentration gradient, which is 12 times higher than commercial standards. This approach shows potential for expanding the variety of materials that are appropriate for microelectronic power generation devices, desalination, and biosensing.

Enhanced Performance for Li & Na Ion Battery Anodes by Charge Modulation of Interface Through SiC4 and SiN1C3 Active Centers on Silicon Nitrogen Co‐Doped Graphene

AbstractHeteroatom doping of graphene is a powerful approach to develop high‐performance Li and Na ion battery anodes. In this study, silicon‐nitrogen co‐doped graphene (Si−N−GN) nanostructures were successfully synthesized via a rational and scalable solvothermal process. The performances of Si−N−GN in Na+ and Li+ half‐cells were investigated in detail by advanced electrochemical techniques and the obtained results were analyzed in terms of Si−N−GN surface properties, reaction kinetics, and electrode/interface interactions. Si−N−GN exhibited a superior capacity of 540 mAh g−1 at a current density of 0.5 A g−1 for Li+ and improved rate capability for both Li+ and Na+ which are linked with the increased interlayer spacing and enlarged graphene sheets upon Si‐doping. Importantly, the capacity increased with the number of cycles owing to surface electron density redistribution and Si−N−GN/SEI interactions, supported by DFT.

Ultrahigh-performance heterojunction bipolar phototransistor enabled by bilateral piezoelectric charge modulation effect

Heterojunction photodiodes based on p-n junction with enhanced piezo-phototronic effect have been extensively studied. However, the photoresponsivity of conventional photodiodes is small due to the natural zero current gain. Heterojunction phototransistor usually has greater photoresponsivity due to the non-zero current gain. More importantly, heterojunction phototransistor owns two interfaces, offering the opportunity to introduce the bilateral piezoelectric charge modulation effect for further performance enhancement compared to the conventional single-interface piezo-phototronic effect in photodiode. In this work, a base-floating heterojunction bipolar phototransistor (FHPT) based on n-ZnO/p-Si/n-ZnO structure with a spectral detection range from ultraviolet (UV)–visible is demonstrated. The positive piezoelectric charge generated at the two interfaces of the collector junction and the emitter junction, combined with the optimal base width and low resistance base doping concentration of the FHPT, leads to an excellent photoresponsivity (18046 A/W, 365 nm), high detectivity (5.7×1012 Jones), and a wider modulation (7120 %). The physical mechanism leading to the ultrahigh performance is attributed to the cooperative positive piezoelectric charge at the two interfaces, simultaneously reducing the effective base width and lowering the emitter junction barrier. This work illustrates the regulation of heterojunction phototransistor performance by the bilateral piezoelectric charge modulation effect, providing insightful guidance to high-performance phototransistor design.

Directed Assembly of Proteinaceous-Polysaccharide Nanofibrils to Fabricate Membranes for Emerging Contaminant Remediation.

Emerging contaminants, including per- and polyfluoroalkyl substances and heavy metals, are threatening the health of humans and ecosystems. Their removal from the environment remains challenging. Here, we designed silk fibroin-cellulose nanocrystal (silk-CNC) nanofibrillar and nanoporous membranes for emerging contaminant remediation. The protein-polysaccharide nanofibrils were fabricated by templating the assembly of silk fibroin using CNCs. Silk fibroin polymorphic nature combined with surface charge modulation of CNCs produced cationic silk-CNC(+) and anionic silk-CNC(-) nanofibrils that can target a broad spectrum of contaminants. Silk-CNC(+) nanofibrils and membranes exhibited antimicrobial properties and captured both short-chain heptafluorobutyric acid, perfluorobutanesulfonic acid, and long-chain perfluorooctanoic acid by virtue of hydrophobic attraction from β-sheeted silk fibroin and electrostatic interactions with CNC(+). Silk-CNC(-) provided the opportunity to target cations such as heavy metal cocontaminants. The nanofabrication of biopolymer-based membranes combines high performance with environmentally benign and cost-effective removal of emerging contaminants for water purification, wastewater treatment, and remediation.

Intelligent MPPT for High Gain Zeta Converter for Standalone PV Application with Galvanic Isolation

In this paper, Standalone PV system is designed to operate without the need of electric utility grid which are commonly designed to supply specific DC electric loads. These type of PV system are generally powered using photovoltaic array and consist of solar charging modules, controllers and regulators. The objective of this work is to implement the Radial Basis Function Neural Network (RBFNN) based Maximum Power Point Tracking (MPPT) technique to attain constant DC link voltage of the ZETA converter. This method utilizes a ZETA converter to achieve better voltage gain and lower ripple in the output current and voltage. Using an Artificial Neural Network (ANN) controller to operate a high frequency converter, which improves power conversion efficiency. To ensure safety and prevent electrical faults, an isolation transformer provides galvanic isolation between the high frequency converter and the PV system. Galvanic isolation avoids electrical faults from being transmitted and safeguards connected loads. The rectifier is used to supply the Direct Current (DC) load with the output from the isolation transformer. The rectifier changes the transformer’s Alternating Current (AC) output into DC current to power DC loads. An intelligent MPPT is employed to stabilize the converter’s output voltage and address the intermittent characteristics of PV, and also assists in sustaining the DC link voltage without alterations and ripples from the converter. The efficiency of this work is authenticated via MATLAB simulation 2021a.

Local Charge Modulation Induced the Formation of High‐Valent Nickel Sites for Enhanced Urea Electrolysis

AbstractNi‐based electrocatalysts are considered to be significantly promising candidates for electrocatalytic urea oxidation reaction (UOR). However, their UOR activity and stability are severely enslaved by the inevitable Ni group self‐oxidation phenomenon. In this study, the glassy state NiFe LDH with uniform Cu dopant (Cu‐NiFe LDH) by a simple sol–gel strategy is successfully synthesized. When served as the UOR catalyst, Cu‐NiFe LDH required a 123 mV lower potential for UOR at both 10 and 100 mA cm−2 in comparison with the conventional anodic OER. It can also operate steadily for more than 300 h at 10 mA cm−2. The in‐depth investigation reveals that Cu incorporation can optimize the local electronic structure of Ni species to induce high‐valent Ni sites. The high‐valent Ni sites would act as the active center during the proposed energetically favorable UOR route, which directly reacts on the high‐valent Ni sites without self‐oxidation inducing the formation of NiOOH species, resulting in a boosted electrocatalytic UOR activity and stability.

Neuromorphic engineering in GaN HEMTs exploiting dendritic dislocations for neuromodulation behaviors and adaptive intelligent power forecasting systems

Gallium nitride (GaN), a prominent III-V semiconductor, plays a pivotal role in the advancement of sophisticated power systems. This study presents an innovative neuromorphic engineering approach utilizing the unique dendritic dislocations extending from bulk to channel within GaN High Electron Mobility Transistors (HEMTs) for three-dimensional charge modulation. This approach dynamically alters charge states and channel potentials in response to multiple parametric stimuli, effectively mimicking neural processing and transmission functions, as well as emulating direct and indirect neuromodulation behaviors observed in human nervous system. The advanced neuromodulation emulation enhances biomimetic diversity, laying a solid foundation for the design of intelligent systems. Comprehensive material and electrical analyses reveal the charging and discharging behaviors of the defect energy levels, underlying the neuromorphic working mechanisms. Additionally, by incorporating artificial neural network algorithms, an intelligent power forecasting system that leverages the device’s diverse neuromorphic traits is developed for real-time and adaptive power management, optimizing efficiency and accuracy in balancing supply and demand, thereby enhancing the cost-effectiveness and sustainability of power systems. The findings highlight the neuromorphic capabilities of GaN devices and open avenues for further exploration into the intelligent power management.

ANFIS-based control of resonant converters for optimized charging system of electric vehicle (EV) batteries

This paper presents a resonant converter-based Electric Vehicle (EV) battery charging module utilizing Proportional-Integral (PI) and Adaptive Neuro-Fuzzy Inference System (ANFIS) control for an optimized charging system. The EV charging module is integrated with a resonant converter comprising a full bridge, HFTF, and DBR. The module utilizes a Resonant Converter which reduces the switching loss incurred during converter operation at high frequency by offering ZCS or ZVS at the switching time. A standard PI Controller manages the duty ratios of the primary full bridge switches with tuned gains. The CC and CV controllers each have their own PI Controller for current and voltage, respectively. To enhance the performance of the EV System, the standard PI Controllers in both the CC and CV control systems are replaced with ANFIS Controllers which are trained as per the data generated by the CC and CV control using an optimization technique that controls the duty ratio of the switches. The proposed ANFIS-based and PI-based control strategy provides an adaptive and flexible approach to control the battery voltage and current by intelligent adjustment of Constant Current (CC) and Constant Voltage (CV) operation modes and the passive elements switching across specific ranges of State-Of-Charge (SOC) to enhance the performance and safety of the charging system. MATLAB Simulation results demonstrated that the proposed ANFIS-based control reduces current ripple content compared to PI-based control. The ANFIS Controller improves overall battery performance, reliability, and stability, which makes it a better choice for next-generation EV charging systems.

How battery capacities are correctly estimated considering latent short-circuit faults

Capacity is a key factor in assessing battery health. Traditional capacity estimation methods assume by default the battery is in a normal state. When there is a latent short-circuit fault, the measured current deviates from the actual current flowing into or out of the battery unit, leading to errors in capacity estimation. To address this challenge, we have constructed an end-cloud collaborative framework to accurately estimate the module's capacity considering the latent short-circuit faults. The core scheme is to estimate the module's charging capacity and discharging capacity simultaneously, obtain the module's operating mode based on the historical relationship between the two, conduct quantitative diagnosis for the short-circuit fault, and feedback the accurate capacity value. This framework addresses the coupled issue of erroneous capacity estimation in the presence of latent short-circuit faults, and the inability to diagnose module external short-circuit faults due to the lack of a comparison. This has profound significance for achieving more reliable battery safety management.

Thermal optimization design of high-power charging station charging module

Abstract When facing significant heat generation from a 30 kW high-power charging station, traditional air-cooling forced convection methods are insufficient to meet the cooling requirements. In this study, based on an in-depth analysis of power modules, we designed a key component of the liquid cooling system, the cold plate, through theoretical calculations. Using simulation techniques, we analyzed the temperature distribution within the cold plate and performed design optimization under conditions of a 4C charging rate and a thermal load of 1598 W. Considering the actual operating environment of the charging equipment, we selected a 50% volume fraction of ethylene glycol-water solution as the coolant and simulated its performance under extreme outdoor conditions of 50°C. Simulation results showed that the optimized liquid-cooled cold plate module achieved a junction temperature of 69.73°C, significantly lower compared to the initial temperature of 109.9°C, resulting in an approximately 36% improvement in heat dissipation efficiency. This significant enhancement not only highlights the advantages of the liquid cooling system in managing the thermal load of high-power charging stations but also addresses the issue of excessive heat load faced by traditional cooling methods. The outcomes of this research provide important theoretical and practical foundations for the thermal design of charging equipment, which is expected to play a critical role in improving the performance and reliability of charging stations.

SolarBot: A Dual-Mode, Bluetooth-Enabled Two-Wheel Robot Powered by Renewable Energy

This research paper explores the development and deployment of a two-wheel drive robot car that leverages renewable solar energy stored in lithium-ion batteries. The system is designed with an L298N motor driver, an Arduino microcontroller, and an HC-05 Bluetooth module, providing the robot with both autonomous and manual control capabilities. The solar panel, integrated with a charging module and voltage regulator, is mounted on a zero PCB, ensuring a sustainable and uninterrupted power supply. The robot is equipped with ultrasonic sensors, enabling it to detect and navigate around obstacles automatically, ensuring a safe and efficient operation. In its autonomous mode, the robot can navigate predefined paths, making real-time adjustments based on sensor input to avoid collisions. This feature is particularly useful for applications such as automated plant watering, where the robot can move through a garden and irrigate plants while avoiding obstacles. Additionally, the robot can be utilized as a floor cleaning device, capable of maneuvering around furniture and other objects. The RC Bluetooth app facilitates manual control, allowing users to operate the robot remotely for various tasks. The project demonstrates a practical application of renewable energy in robotics, emphasizing the benefits of sustainable energy solutions and versatile operational modes. The use of a wooden platform provides a sturdy base for the robot's components, ensuring durability and reliability. This work contributes to the field of robotics by showcasing how solar power can be effectively harnessed to create energy-efficient, multifunctional robotic systems.