Voltage Difference Research Articles

Tricuspid regurgitation in atrial fibrillation: correlation to left atrial myopathy and improvement post ablation

Abstract Background Atrial fibrillation (AF) closely correlates to left atrial (LA) myopathy, which in turn can cause functional mitral regurgitation (FMR). The correlation of LA myopathy to tricuspid regurgitation (TR) is still unknown. Data on the dynamic change of TR post AF ablation is still limited. Purpose We sought to investigate the correlation of LA myopathy to TR and furthermore if TR can be improved by AF ablation. Methods Consecutive patients presenting for first time AF ablation were prospectively enrolled. All patients underwent transthoracic echocardiography at baseline as well as 6 months after the ablation procedure. TR was graded as follows: minimal, mild, moderate and severe (I-IV). All study participants underwent high density bipolar voltagemaps prior to AF ablation to characterize the presence of atrial myopathy. Results Our final study cohort consisted of 143 patients (age 64±11 years, 47% female, 48% persistent AF). 15% had no, 21% minimal, 35% mild, 19% moderate and 9% severe TR, respectively. High density maps with 1325±736 mapping points and with an average bipolar voltage of 1.58±0.89mV were created. A significant decline of average LA bipolar voltage was obvious, when comparing patients without TR to any severity level of TR (p=0.015) (Figure 1), however there was no significant difference in LA bipolar voltage, when comparing different TR severity levels (Figure 1). After a follow up of 6 months a significant improvement of TR was observed (p<0.001) with a median level of improvement by one level. 27% had no, 21% minimal, 31% mild, 18% moderate and 2% severe TR, respectively. Conclusion In patients presenting for AF ablation TR closely correlates with LA myopathy according to decreased levels of LA bipolar voltage. AF ablation results in highly significant TR level improvement.

Dilation framing camera with the dual-pulse excitation technique

In an inertial confinement fusion (ICF) ultrafast diagnostic system that is based on electron beam time-dilation, an ultrafast electrical pulse is used to excite a microstrip photocathode (PC), which generates a varying PC voltage to obtain a photoelectron velocity that varies with emission time. The photoelectron beam achieves time-dilation through the drift process and is then detected by a time-resolved sensor, thereby increasing the temporal resolution of the diagnostic system. A pulse time-dilation diagnostic system is simulated, while the sensor is a gated microchannel plate (MCP) detector with a temporal resolution of 100 ps and an excitation pulse on a PC with a slope of 3 V/ps; the diagnostic system achieves a temporal resolution of 11.12 ps. However, the excitation pulse creates a voltage difference across the PC. A voltage difference of 900 V can be acquired for a PC length of 60 mm, which yields a nonuniform spatial resolution ranging from 30.4 µm to approximately 3000 µm. Furthermore, the voltage difference across the PC also limits the frame size to 2.2 mm along the pulse propagation direction according to the simulation results. To achieve a uniform spatial resolution and a larger frame size, a dual-pulse excitation technique on a PC is presented, which is the technique to symmetrically apply voltage pulses at both ends of the PC microstrip. The theoretical results show that this technique will improve the uniformity of the PC voltage spatial distribution. When the PC pulse slope is 3 V/ps and the dual-pulse excitation technique is employed, the diagnostic system has a temporal resolution of 5.91 ps and a uniform spatial resolution of 30.4 µm. Furthermore, the frame size along the pulse propagation direction is improved to the effective length of the microstrip PC.

High‐Gain Boost Converter Based Energy Harvesting Method for the Ultra‐Low Voltage Difference of HVDC Transmission Line

ABSTRACTOnline monitoring sensors are crucial for ensuring the long‐term stable operation of high‐voltage direct current (HVDC) transmission systems. However, ensuring a sustainable and reliable energy supply for these sensors in the HVDC transmission line is extremely challenging. Energy harvesting is an efficient solution to meet the energy supply needs of self‐powered online monitoring sensors. This work proposes a novel method for harvesting energy from the ultra‐low voltage difference of HVDC transmission line using a high‐gain boost converter. Firstly, the structure of the energy harvesting system is proposed. Then, the mechanism by which the gain of the boost converter is easily affected by parasitic parameters of the circuit and device under ultra‐low input voltage is analyzed. A method for automatically stabilizing the output voltage of the boost converter using a post‐stage energy management circuit is proposed. This method avoids the use of closed‐loop control circuits, which would otherwise increase losses. Finally, the boost converter with high gain is designed to elevate the ultra‐low voltage difference to a higher voltage, providing sufficient power for the online monitoring sensors. The effectiveness of the proposed method has been verified through simulations and experiments, demonstrating that the energy harvesting system can extract energy from a voltage difference as low as 35 mV. Additionally, the system can produce a stable output power of up to 128 mW when the voltage difference is 100 mV.

Low-Energy Desalination Techniques, Development of Capacitive Deionization Systems, and Utilization of Activated Carbon

Water desalination technology has emerged as a critical area of research, particularly with the advent of more cost-effective alternatives to conventional methods, such as reverse osmosis and thermal evaporation. Given the vital importance of water for life and the scarcity of potable water for agriculture and livestock—especially in the Kingdom of Saudi Arabia—the capacitive deionization (CDI) method for removing salt from water has been highlighted as the most economical choice compared to other techniques. CDI applies a voltage difference across two porous electrodes to extract salt ions from saline water. This study will investigate water desalination using CDI, utilizing a compact DC power source under 5 volts and a standard current of 2 amperes. We will convert waste materials like sunflower seeds, peanut shells, and rice husks into activated carbon through carbonization and chemical activation to improve its pore structure. Critical parameters for desalination, including voltage, flow rate, and total dissolved solids (TDS) concentration, have been established. The initial TDS levels are set at 2000, 1500, 1000, and 500 ppm, with flow rates of 38.2, 16.8, and 9.5 mL/min across the different voltage settings of 2.5, 2, and 1.5 volts, applicable to both direct and inverse desalination methods. The efficiency at TDS concentrations of 2000, 1500, and 1000 ppm remains between 18% and 20% for up to 8 min. Our results indicate that the desalination process operates effectively at a TDS level of 750 ppm, achieving a maximum efficiency of 45% at a flow rate of 9.5 mL/min. At voltages of 2.5 V, 2 V, and 1.5 V, efficiencies at 3 min are attained with a constant flow rate of 9.5 mL/min and a TDS of 500 ppm, with the maximum desalination efficiency reaching 56%.

Active Battery Voltage Equalization Based on Chain-Loop Comparison Strategy

This paper describes active battery balancing based on a bidirectional buck converter, a flyback converter, and battery cells by using the proposed chain-loop comparison strategy. The role of the bidirectional buck converter is to charge/discharge the battery pack. During the charging period, the converter is in buck mode, and its output is controlled by constant current/voltage; during the discharging period, the converter is in boost mode, and its output is controlled by constant voltage. The role of the flyback converter is voltage equalization of the battery pack, and its output is controlled by constant current. A chain-loop comparison strategy is used to control battery voltage equalization. In this work, three equalization modes, namely, charging balance, discharging balance, and static balance, were considered. The voltage difference between the maximum and minimum is 0.007 V after a balancing time of 19.75 min, 0.005 V after a balancing time of 24 min, and 0.007 V after a balancing time of 20 min for charging balance, discharging balance, and static balance, respectively.

Design of spintronic devices based on adjustable half-metallicity induced by electric field in A-type antiferromagnetic bilayer NiI2

Exploring the attainment of half-metallic behavior in two-dimensional (2D) materials through external perturbations is a popular area of current research. In this work, we demonstrate, using first-principles calculations, that bilayer NiI2 (bi-NiI2) is an A-type antiferromagnetic (AFM) semiconductor with an indirect bandgap of 0.86 eV, with the most stable configuration being the AB stacking mode. Upon the application of a vertical electric field, the material transforms from its original semiconducting state into a half-metallic state. Moreover, the spin polarization reverses its orientation whenever the direction of the electric field is altered. This intriguing behavior has inspired us to design a spintronic device based on the A-type AFM bi-NiI2. By employing nonequilibrium Green's function (NEGF) combined with density functional theory (DFT) calculations, we find that the device achieves ON/OFF switching by applying vertical electric fields in parallel or anti-parallel configurations in the two leads. The device displays 100 % spin polarization in the parallel configuration (PC) scenario, driven by bias voltage or temperature differences. Utilizing either the parallel or antiparallel configuration (APC) for ON/OFF switching enables the device to exhibit tunneling magnetoresistance (TMR) of up to 1.45 × 1010 % due to bias voltage and up to 1011 % thermal TMR arising from temperature differences between the leads. These findings highlight the potential of NiI2 and A-type AFM bilayers in the design of spintronic devices.

An 11 nW, ＋0.34 °C/−0.38 °C inaccuracy self-biased CMOS temperature sensor at sub-thermal drain voltage

This paper presents a low-power, high-accuracy self-biased full CMOS temperature sensor based on sub-threshold currents at sub-thermal drain voltage. The sensor achieves high accuracy and minimal corner dependence by generating sub-threshold current ratios using NMOS transistors of different sizes operating at sub-thermal drain voltage. The proposed self-biased all-CMOS temperature sensing architecture enhances sensitivity by up to seven times and improves linearity. The overall stability under temperature fluctuations is significantly enhanced by utilizing a substrate diode structure that maintains constant current variation. Additionally, a high-threshold comparator with a fast response compresses the oscillator reset voltage difference, enabling ultra-low power operation. Timing logic control is employed to discard unstable cycle outputs, thereby reducing errors and achieving high-accuracy outputs. Operating at 1 V, the circuit consumes only 11 nW at 27 °C in a 180 nm CMOS process. It achieves a peak-to-peak inaccuracy of ＋0.34 °C/−0.38 °C from −10 to 100 °C after two-point calibration, with a resolution of 40 mK and a resolution FoM as low as 3.7 pJK2.

Revealing the intricacies of natural convection: A key factor in aqueous zinc battery design

Aqueous zinc-based batteries offer high safety, abundant resources, low cost, and environmental friendliness, making them a promising alternative to lithium-ion batteries for large-scale energy storage markets. However, the natural convection in the electrolyte during operation, which can greatly affect the battery performance, is overlooked for a long time. Through particle tracing experiments, this study demonstrates that in the vertical placement of the battery, natural convection occurs due to the variation of electrolyte density, whereas it does not happen when the cathode is on top. Besides, there is a significant difference in electrochemical performance between these two configurations, with a voltage difference of up to 0.25 V at 10 mA cm⁻² and a peak current difference of up to 3.4 mA in linear sweep voltammetry. Simulation results show that convective mass transfer can be up to 100 times greater than diffusive mass transfer in a bulk solution. This highlights the importance of considering natural convection to improve model accuracy. Further, to increase energy density, batteries are scaled up in practical applications to reduce the weight of the casing. This work simulates mass transfer conditions under various scenarios, illustrating the trade-off between current density and battery size, and offering new guidance for the design of aqueous zinc-based batteries.

A-257 Revolutionizing Fungal Infection Diagnosis: A Sensitive, Cost-Effective, and Time-Efficient Solution

Abstract Background Fungal infection detection is essential for optimizing therapeutic strategies, preventing complications, enhancing overall health, and alleviating the financial burden, and preventing transmission. According to the CDC, fungal illness resulted in 75,000 hospital admissions and 9 million outpatient visits annually, with 7,199 deaths predicted in the United States in 2021. The rising prevalence of fungal infections, influenced by factors such as population expansion and evolving treatment strategies poses a growing challenge to achieving an early and accurate diagnosis. There is the crucial need for improved detection methods to promptly identify various fungal infections, including Candida species, spanning from skin issues to potentially fatal systemic diseases, ultimately reducing associated morbidity and mortality. Severe and profound infections with life-threatening potential are common in individuals with compromised immune systems and those who are hospitalized. This risk is notably elevated among patients with organ transplants, immunosuppressive treatments, diabetes, recent broad-spectrum antibiotic usage, catheter utilization, and extended hospitalization periods. Our advancement seeks to introduce innovative methods leveraging carbon-based nanomaterials for the detection of fungal infections, offering distinct advantages including cost-effectiveness, ease of operation, and time efficiency compared to conventional techniques. Methods Our technology utilizes carbon-based nano materials, a promising advancement in nanotechnology to detect fungal infections, especially Candida manifestations. The samples are brought into contact with pre-probed carbon-based nano materials sensors maintained at a specific annealing temperature, facilitating nucleic acid hybridization. Subsequently, alterations in the carbon nanotube's electrical signal are observed, indicating a change in conductivity. This change can allow for the measurement of a voltage difference across a continuous flow of electricity, correlating with the concentration of targets present in the sample, thereby enabling the detection of specific microorganisms. No change was noticed in electrical properties of carbon-based nano material when negative experiments were conducted. Results The initial findings showed the presence of specific microorganisms within the sample in less than 15 minutes with carbon-based nano materials sensors. Comparative analysis of the electrical signal magnitude values obtained from carbon nanotube measurements against the corresponding CT values from quantitative PCR (qPCR) provides insights into the correlation between the electrical readouts and molecular quantification, offering a comprehensive evaluation of the nanotube-based detection system's performance in diagnostics procedure. Conclusions Unlike conventional blood culture and qPCR, our technology, delivers outcomes with heightened sensitivity, cost-effectiveness, and time efficiency. Our team's uncomplicated sample collection method notably reduces the intricacies of sample preparation, enabling the proper disposal of both the sample container and sensor part without requiring disassembly. Enhanced sensitivity, coupled with the economical and rapid detection capabilities of advanced technologies like carbon-based nano materials, is a promising alternative for the accurate and efficient diagnosis of fungal infections. Consequently, the implementation of such technology not only enhances diagnostic efficiency but also contributes to better patient outcomes through prompt and targeted therapeutic interventions.

Study Effect of rGO Addition on Photocatalytic Activity of TiO2 in Reducing Carbon Dioxide Using Electrochemical Method

Abstract Increasing the carbon dioxide (CO2) concentration every year can cause various environmental problems. One of the technologies to reduce CO2 is carbon capture. The semiconductor of TiO2 is widely used as a photocatalyst to reduce CO2. However, TiO2 has limitations in absorbing light in the visible region because it has a large band gap value. Besides, TiO2 has a fast recombination of electron-hole pairs so it can reduce photocatalytic activity. An addition of rGO is expected to cover the shortcomings of TiO2 because rGO has good conductivity and a small band gap. We synthesized rGO-TiO2 photocatalyst and measured its activity in capturing and converting CO2 using an electrochemical method. By using voltage difference as an electron driver in reacting with CO2 we observe the reaction between electrons and CO2. The measurements were carried out under irradiated and unirradiated conditions. The CV measurements show that the rGO-TiO2 electrode with 60% rGO content has reduction and oxidation peaks. Based on the electrochemical half-reaction table, the reduction peaks in unirradiated conditions indicate the conversion of CO2 to CH4 and (COOH)2, while under irradiated the reduction peak indicates the formation of HCOO− and CH2CH2. The results show that by using the rGO-TiO2 composite as an electrode not only reduces CO2 but also converts the CO2 into hydrocarbon.

Sex-associated Differences In Low QRS Voltage In Young Athletes

Sex-associated Differences In Low QRS Voltage In Young Athletes

Optimizing COP by RSM and MATLAB model of mini refrigerator based on thermoelectric units driven by solar photovoltaic

AbstractEnergy scarcity in the world and the pollutants resulting from excessive use of conventional energy aroused the need for sustainable alternatives that are environment friendly. A multi-use thermoelectric refrigerator powered by solar energy to obtain the lowest consumption with the highest efficiency. The designed refrigerator is based on the Peltier effect using Peltier units where a temperature difference is created between the junctions by applying a voltage difference across the junction. This study investigates the performance of a refrigerator cooling system powered by a photovoltaic (PV) system. The research aims to assess the efficiency, effectiveness, and feasibility of utilizing solar energy to drive refrigeration, particularly in off-grid or environmentally conscious applications. Through a comprehensive experimental setup and data analysis, the study examines energy consumption, cooling efficiency, and overall system performance under varying conditions. The findings contribute valuable insights into the potential of PV-powered refrigerators as sustainable cooling solutions. It relies on a control unit that measures the resulting temperature to determine the appropriate connection mode to give the highest cooling efficiency. The average solar radiation when operating for 8 h, for the different seasons of the year was 149.5, 67.5, 119.3, and 118.3 w/m2 in summer, winter, spring, and fall, respectively. The average cooling energy consumption was 107.25, 137.04, 107, and 138.08 w for temperatures (20 ± 1, 15 ± 1, 20 ± 2, and 15 ± 2) °C respectively that proof solar radiation is sufficient to produce energy for the summer of cooling temperatures up to 15 °C, while in the spring and fall it is sufficient to 20 °C. The Fast not Eco mode is the least energy consuming and the fastest cooling, it can be used for rapid cooling at a short time less than an hour. The best mode in the case of continuous operation is the case of as next Eco mode cooling temperature of 20 ± 0.1 °C. The MATLAB Simulink model was developed to reduce the design cycle and facilitate the integration of solar photovoltaic with the TEC. The optimal operating point is identified through simulation and validated through experimental analysis, the optimal COP was 71.089% by Response surface methodology (RSM).

Capacitor State Monitoring of MMC Photovoltaic Grid Connected Inverter Based on Capacitance Voltage Difference

Abstract Due to the high degree of modularity and high energy transmission efficiency, MMC is widely used in the field of photovoltaic grid connection. This paper focuses on the abnormal fluctuations of capacitor voltage caused by capacitor aging in MMC submodule, and comes up with a capacitance monitoring method based on the voltage fluctuation difference of submodule capacitors. The validity of the raised method is validated through simulation and experiments.

Model-free detection and quantitative assessment of micro short circuits in lithium-ion battery packs based on incremental capacity and unsupervised clustering

Timely diagnosis of micro short circuit (MSC) faults is crucial for ensuring the safe operation of lithium-ion battery energy storage systems. Existing diagnostic methods face limitations such as high dependency on battery models, difficulty in determining accurate diagnostic thresholds, or low computational efficiency. This work presents a model-free approach for the detection and quantitative assessment of MSCs in lithium-ion battery packs, with incremental capacity (IC) and unsupervised clustering. First, the IC is extracted from charging voltage data to effectively characterize MSC faults in lithium-ion batteries. Next, principal component analysis is used to map the high-dimensional feature space onto a two-dimensional plane to facilitate fault detection and result visualization. Then, an unsupervised clustering algorithm is employed for anomaly detection to identify MSC cells within the battery pack. For the detected MSC cells, a method based on the maximum charging voltage difference between adjacent cycles is designed to estimate the MSC resistance, quantitatively assessing the severity and evolution stage of the MSC. Experimental results show that the accuracy of MSC detection is 99.17 % and the minimum relative error of short-circuit resistance estimation is 1.20 %, which demonstrates the effectiveness and feasibility of the proposed method.

Negative Differential Resistance with Ultralow Peak-to-Valley Voltage Difference in Td-WTe2/2H-MoS2 Heterostructure.

Negative differential resistance (NDR) devices with a low peak-to-valley voltage difference (ΔV) exhibit a high cut off frequency and low power consumption efficiency, which is significant for fabricating high-performance oscillators. However, achieving an ultralow ΔV is challenging. In this work, we report the first construction of an NDR device utilizing a van der Waals heterostructure composed of semimetallic Td-WTe2 and semiconducting 2H-MoS2. Our findings reveal that the narrow energy region of the decreasing density of states (DOS) above the Fermi level of WTe2 acts as a narrow band gap, facilitating type-III band alignment with MoS2 and enabling band-to-band tunneling-based NDR transport. Notably, the NDR device exhibits an ultralow ΔV of approximately 0.01 V, which is at least an order of magnitude lower than previously reported values. This work not only introduces a new approach for NDR device fabrication but also provides new insights into the pivotal role of Td-WTe2 in NDR transport.

An emergent attractor network in a passive resistive switching circuit

Resistive memory devices feature drastic conductance change and fast switching dynamics. Particularly, nonvolatile bipolar switching events (set and reset) can be regarded as a unique nonlinear activation function characteristic of a hysteretic loop. Upon simultaneous activation of multiple rows in a crosspoint array, state change of one device may contribute to the conditional switching of others, suggesting an interactive network existing in the circuit. Here, we prove that a passive resistive switching circuit is essentially an attractor network, where the binary memory devices are artificial neurons while the pairwise voltage differences define an anti-symmetric weight matrix. An energy function is successfully constructed for this network, showing that every switching in the circuit would decrease the energy. Due to the nonvolatile hysteretic function, the energy change for bit flip in this network is thresholded, which is different from the classic Hopfield network. It allows more stable states stored in the circuit, thus representing a highly compact and efficient solution for associative memory. Network dynamics (towards stable states) and their modulations by external voltages have been demonstrated in experiment by 3-neuron and 4-neuron circuits.

High Spatiotemporal Resolution Biomimetic Thermoreceptors Realizing by Jointless p‐n Integration Thermoelectric Composites

AbstractHuman body temperature is a critical physiological indicator, reflecting metabolism and temperature regulation. Biomimetic temperature sensors with high temporal and spatial resolution are highly desired to emulate human skin's temperature perception capabilities. Here, a selective filtration method is proposed to fabricate a jointless p‐n thermoelectric module, integrating p‐type and n‐type materials with minimal interfacial barriers. The structure enables rapid response, minimal signal variation, and a robust linear relationship between open circuit voltage and temperature difference, making it suitable as a biomimetic thermoreceptor for enhancing humanoid robots with intelligent sensing capabilities. A smart thermoelectric sensing system with a high temporal and spatial resolution is developed using with jointless p‐n module as the sensing element. In temporal thermal sensing, the sensing system can timely and effectively identify the heat source, with a fast response time of <0.1 s. In addition, in spatial thermal sensing, the system reliably detects thermal stimuli within discrete regions of each measuring area of <1 × 1 cm2. This study provides an effective strategy for temperature sensors in thermoelectric sensing and biomimetic thermoreceptor applications.

A multistrategy differential evolution algorithm combined with Latin hypercube sampling applied to a brain–computer interface to improve the effect of node displacement

Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain–computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain–computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain–computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain–computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain–computer interface after node displacement optimization can be evaluated.

Steric effect induces enhancement of electroconvective flow near electrochemical interfaces.

Electroconvection, occurring near electrochemical interfaces, propels the movement of ions and water, leading to intricate phenomena rooted in the fine interplay between fluid, voltage, and ion. Here, neglecting ionic interactions, by incorporating the steric term into the Poisson-Nernst-Planck-Stokes coupling equation, direct numerical simulations of electroconvective vortex near nanoslot-bulk interfaces are conducted. For the steric effect, the steric number is introduced to discuss the factors and laws affecting the vortex. We illustrate the substantial enhancement of electroconvective vortex due to the steric effect of ions within the nanoslot. Upon increasing the steric number, the cation concentration in the nanoslot is enhanced, resulting in the expansion of the electric double layer (EDL). The EDLs on the walls inside the nanoslot come into contact with each other, causing the EDLs to overlap, consequently increasing the total charge within the EDLs inside the nanoslot. This EDL overlap enhances the charge density of the extended space charge layer, leading to the enhancement of the electroconvective vortex. Further, our scaling analysis, corroborated by direct numerical simulation and existing data, establishes the scaling of slip velocity, jointly regulated by the steric number and voltage difference. By modulating the membrane transport characteristics, the steric effect reduces flow structure size and flux fluctuations, which offers new perspectives for manipulating ion transport and flow instability.

Experimental investigation for the influence mechanism of air intake method and humidity level on performance of proton exchange membrane fuel cells

As pivotal component of hydrogen fuel cell vehicles (HFCVs), proton exchange membrane fuel cells (PEMFCs) play a crucial role in determining performance and energy efficiency of HFCVs. In this study, comprehensive bench tests of fuel cell stack and electrochemical impedance spectroscopy (EIS) tests of single cells were conducted, and influence mechanisms of air intake methods and humidity levels on performance of stack and single cells at different locations were analyzed in depth. The results indicate that counter-flow inlet method exhibits superior voltage output performance and stability across various relative humidity (RH) and current densities (CDs). A moderate increase in RH can mitigate performance degradation, but excessive humidity can lead to flooding. When RH is increased to 60%, the voltage difference between co-flow and counter-flow of fuel cell stack does not exceed 0.05 V at 1.6 A/cm2. Meanwhile, the performance of cells near inlet end plate is consistently worse than those farther away under different inlet conditions. In addition, the performance trend of single cells in serpentine channels aligns with that of fuel cell stack, underscoring strong adaptability of mechanisms influencing performance under different conditions. These findings provide theoretical guidance and data support for optimizing performance and preventing failures in PEMFCs.