Current Step Research Articles

Demonstration of a Validated Direct Current Wearable Device for Monitoring Sweat Rate in Sports

Sweat rate magnitude is a desired outcome for any wearable sensing patch dedicated to sweat analysis. Indeed, sweat rate values can be used two-fold: self-diagnosis of dehydration and correction/normalization of other physiological metrics, such as Borg scale, VO2, and different chemical species concentrations. Herein, a reliable sweat rate belt device for sweat rate monitoring was developed. The device measures sweat rates in the range from 1.0 to 5.0 µL min−1 (2 to 10 µL min−1 cm−2), which covers typical values for humans. The working mechanism is based on a new direct current (DC) step protocol activating a series of differential resistance measurements (spatially separated by 800 µm) that is gradually initiated by the action of sweat, which flows along a customized microfluidic track (~600 µm in width, 10 mm in length, and 235 µm in thickness). The device has a volumetric capacity of ~16 µL and an acquisition frequency between 0.010 and 0.043 Hz within the measured sweat rate range. Importantly, instead of using a typical and rather complex AC signal interrogation and acquisition, we put forward the DC approach, offering several benefits, such as simplified circuit design for easier fabrication and lower costs, as well as reduced power consumption and suitability for wearable applications. For the validation, either the commercial sweat collector (colorimetric) or the developed device was performed. In five on-body tests, an acceptable variation of ca. 10% was obtained. Overall, this study demonstrates the potential of the DC-based device for the monitoring of sweat rate and also its potential for implementation in any wearable sweat platform.

Quantized current steps due to the synchronization of microwaves with Bloch oscillations in small Josephson junctions.

Synchronization of Bloch oscillations in small Josephson junctions (JJs) under microwave radiation, which leads to current quantization, has been proposed as an effect that is dual to the appearance of Shapiro steps. This current quantization was recently demonstrated in superconducting nanowires in a compact high-impedance environment. Direct observation of current quantization in JJs would confirm the synchronization of Bloch oscillations with microwaves and help with the realisation of the metrological current standard. Here, we place JJs in a high-impedance environment and demonstrate dual Shapiro steps for frequencies up to 24 GHz (I = 7.7 nA). Current quantization exists, however, only in a narrow range of JJ parameters. We carry out a systematic study to explain this by invoking the model of a JJ in the presence of thermal noise. The findings are important for fundamental physics and application in quantum metrology.

The effect of positive and reverse current cycles on the wettability, morphology, and corrosion behaviour of electrodeposited nickel matrix layer

ABSTRACT In this study, Ni-TiO2 coatings were formed using a multi-step pulse method with positive and, in some cases, reverse current steps. All coatings were produced at a current density of 4 A dm−2 and a frequency and duty cycle of 10 Hz and 10%, respectively with a thickness of 30 µm. The results showed that by applying the reverse current cycle, the codeposition of TiO2 particles in the coating decreased and the morphology of the coating became irregular. It was found that the effect of the wetting angle of the coating is greater than the incorporation of TiO2 particles on the corrosion behaviour of the coating. Thus, a coating was created with a positive 4-step pulse current with the lowest amount of TiO2 particles (7.7 vol.%) having the highest resistance against corrosion. The sample had compact morphology and high wetting angle (82.61o֯).

Diagnosis of myelodysplastic syndromes: the classic and the novel.

The Myelodysplastic syndromes (MDS) are a heterogenous group of clonal bone marrow (BM) stem cell myeloid neoplasms, characterized by bone marrow (BM) dysplasia, macrocytic anemia or cytopenia with a tendency for leukemic transformation. The suspicion of MDS is raised by a typical but not specific clinical picture and routine labs, but the gold standard for MDS diagnosis is still BM examination with the presence of uni-or multi-lineage dysplasia and blast percentage, together with exclusion of other reasons. Cytogenetics is also a part of the diagnostic process. Flow cytometry and genetics are helpful but are not always mandatory for MDS diagnosis. This review summarizes the current steps in the diagnostic approach for a patient suspected of having MDS. We also describe new concepts that use non-invasive diagnostic technologies, especially digital methods as well as peripheral blood genetics. The hope is that one day these will mature, be introduced into clinical practice, and perhaps in many cases even replace the invasive BM biopsy.

Effects of limiting current density and current step rate on microstructure and hardness of flash sintered MgAl2O4 ceramics

This study investigates the effects of limiting current density and current step rate on the microstructure and hardness of current-step flash-sintered (CS-FS) disc-shaped MgAl2O4 ceramics. The results indicated that, unlike conventional flash sintering, the temperature of the sample during the current-step flash started at a lower initial value and subsequently increased gradually. Furthermore, the differences in oxygen vacancies between the positive and negative sides of the CS-FS-ed MgAl2O4 ceramics were significantly reduced. This led to the CS-FS samples exhibiting higher relative density, finer grain size, a more uniform microstructure, and increased hardness. The homogeneous microstructure resulted in consistent hardness across both sides of each CS-FS-ed sample. Additionally, as the limiting current density increased, the grain sizes near the positive and negative sides gradually enlarged, while the relative density initially increased before decreasing. A similar trend was observed when the current step rate decreased.

A Robust Trajectory Multi-Bernoulli Filter for Superpositional Sensors

This paper proposes a trajectory multi-Bernoulli filter applied to the superpositional sensor model for multi-target tracking in the presence of unknown measurement noise. This filter can provide a Multi-Bernoulli approximation of the posterior density on a set of alive trajectories at the current time step. We also provide a Gaussian mixture (GM) implementation of this filter, employing a mixture of Gaussian and inverse Wishart distributions to represent the combined state of measurement noise and target information. Subsequently, the variational Bayesian (VB) method is employed to approximate the posterior distribution, ensuring its form remains consistent with the prior distribution. This method is capable of directly generating trajectory estimates and can jointly estimate both multi-object tracking and measurement noise covariance. The performance of this algorithm is verified through simulation. Finally, a computationally more efficient L-scan approximation is provided. The simulation results indicate that the filter can achieve robust tracking performance, adapting to unknown measurement noise.

Demonstration of dual Shapiro steps in small Josephson junctions.

Bloch oscillations in small Josephson junctions were predicted theoretically as the quantum dual to Josephson oscillations. A significant consequence of this prediction is the emergence of quantized current steps, so-called dual Shapiro steps, when synchronizing Bloch oscillations to an external microwave signal. These steps potentially enable a fundamental standard of current I, defined via the frequency f of the external signal and the elementary charge e, I=±n×2ef, where n is a natural number. Here, we realize this fundamental relation by synchronizing the Bloch oscillations in small Al/AlOx/Al Josephson junctions to sinusoidal drives with frequencies from 1 to 6 GHz and observe dual Shapiro steps up to I≈3 nA. Inspired by today's voltage standards and to further confirm the duality relation, we investigate a pulsed drive regime and observe an asymmetric pattern of dual Shapiro steps. This work confirms quantum duality effects in Josephson junctions and paves the way towards a range of applications in quantum metrology based on well-established fabrication techniques and straightforward circuit design.

Vehicle trajectory prediction method integrating spatiotemporal relationships with hybrid time-step scene interaction

In vehicle trajectory prediction, constructing the interactive relationships among vehicles within the traffic environment poses a significant challenge. Existing models predominantly focus on temporal dependencies within vehicle histories and spatial correlations among neighboring vehicles, overlooking the continuous influence of historical vehicle states on the current time step and the interplay of multiple sequences over time. To address these limitations, we propose a method for multimodal vehicle trajectory prediction that integrates Hybrid Time-step Scene Interaction (HTSI) into the spatiotemporal relationships. Firstly, we introduce the HTSI module, comprising Multi-step Temporal Information Aggregation (MTIA) and Single-step Temporal Information Aggregation (STIA) methods. MTIA utilizes multi-head attention mechanisms to capture temporal dependencies between consecutive frames, thereby generating new time series amalgamating the ongoing influence of historical time states on the current timestamp. Simultaneously, STIA employs multi-head attention mechanisms to capture the spatial dimension weights of multiple time series and, by aggregating spatial interaction features at each timestamp, generates new time series fused with spatial interaction influences. Subsequently, feature extraction is performed through LSTM layers. Moreover, we propose an improved DIPM pooling module, improving the model’s long-term prediction capability by selectively reusing historical hidden states. Ultimately, based on training results from the HighD and NGSIM datasets, our model demonstrates significant advantages in long-term prediction compared to other state-of-the-art trajectory prediction models. Specifically, within the 5 s prediction window, the model achieved a root mean square error (RMSE) of 2.79 m on the NGSIM dataset, representing a 33.62% improvement over the baseline model’s average accuracy. Additionally, on the HighD dataset, the model attained an RMSE of 2.16 m, reflecting a 33.43% enhancement. The crucial code can be obtained from the provided link: https://github.com/gyhhq/Prediction-trajectory .

Computer-Assisted Processing of Current Step Signals in Single Blocking Impact Electrochemistry.

Current step signals related to single-entity collisions in blocking impact electrochemistry were analyzed by computer-assisted processing for estimating the size distributions of various particles. In this work, three different types of entities were studied by single blocking impact electrochemistry: polystyrene nanospheres (350 nm diameter) and microspheres (1 μm diameter), phospholipid liposomes (300 nm diameter) and two different strains of Gram-negative bacillus bacteria (Escherichia coli and Shewanella oneidensis). The size estimations of these different entities from the current step signal analysis were compared and discussed according to the shape and size of each entity. From the magnitude of the current step transient, the size distribution of each entity was calculated by a new computer program assisting in the detection and analysis of single impact events in chronoamperometry measurements. The data processing showed that the size distributions obtained from the electrochemical data agreed with the dynamic light scattering and atomic force microscopy data for nanospheres and liposomes. In contrast, the size estimation calculated from the electrochemical data was underestimated for microspheres and bacteria. We demonstrated that our computer program was efficient for detecting and analyzing the collision events in single blocking impact electrochemistry for various entities from spherical hard nanoparticles to micrometer-sized rod-shaped living bacteria.

Abstract P383: Increased renal afferent nerve activity and peak response to pro-inflammatory cytokine interleukin 6 in isolated dorsal root ganglion neurons from hypertensive rats

Renal afferent nerves are reported to mediate the progression of hypertension in preclinical models, putatively via an aberrant increase in activation. The underlying source of this nerve activation is unknown but we hypothesized renal inflammatory cytokines are a primary driver. We previously reported interleukin 6 (IL-6) is increased in the kidneys of hypertensive rats, and intrarenal administration of IL-6 can activate renal afferent nerves. Though these data are promising, the direct effects of IL-6 on afferent renal nerves remain unclear. We hypothesized that isolated renal afferent neurons from hypertensive rats will have increased activity and responsiveness to inflammatory cytokine IL-6 compared to normotensive controls. To test our hypothesis, we tested the direct effects of interleukin-6 (IL-6) on renal afferent nerve action potentials (AP) generated in freshly isolated dorsal root ganglion (DRG) neurons. To model salt-sensitive hypertension, uninephrectomized rats (n=6) were administered deoxycorticosterone acetate (DOCA; 100mg, sc) and 0.9% saline for drinking water for 21 days. Control rats (n=6) were uninephrectomized. To label renal afferent nerves, neuronal tracer DiI was introduced by intrarenal injection 6 days prior to DRG collection. DRGs from T10-L1 were isolated, enzymatically digested and dissociated, and cultured overnight for whole-cell patch-clamp the following day. DiI-positive neurons from both control and DOCA rats generated AP during the delivery of 100, 200, and 300 pA current steps. Data were analyzed by two-way, repeat-measures ANOVA (α=.05). At baseline, DOCA neurons had a higher (p<0.05) mean AP frequency compared to controls (no difference in AP mean peak amplitude or AP duration). In both groups, increasing concentrations of IL-6 (0. 4, 4, and 40 ng/ml) elicited a dose-dependent increase in AP peak amplitude compared to baseline. No difference was detected in IL-6 responses between DOCA and controls. Overall, these data supported our original hypothesis that pro-inflammatory cytokine IL-6 would increase renal afferent nerve activity, and that said activity is increased in hypertensive DOCA-salt rats. IL-6 accumulation triggered by end organ damage due to hypertension might lead to increased afferent renal nerve activity, which exacerbates the hypertensive phenotype through a sympatho-excitatory response. Additional measurements of isolated DRG responses to pro-inflammatory mediators are underway for direct comparison.

The IGD-based prediction strategy for dynamic multi-objective optimization

In recent years, an increasing number of prediction-based strategies have shown promising results in handling dynamic multi-objective optimization problems (DMOPs), and prediction models are also considered to be very favorable. Nevertheless, some linear prediction models may not always be effective. In particular, when the motion direction trends of different individuals are not aligned, these models can yield inaccurate prediction results. Inverted generational distance (IGD) is a commonly used metric for evaluating the performance of algorithms. This paper proposes a prediction model based on the IGD metric. Specifically, we assume that the pareto optimal front (POF) of the population at the previous time step is the true POF, and the POF at the current time step is the approximate POF. We cluster the current population with reference to the euclidean distances from uniform points on the true POF to the current POF points, with slight overlap between adjacent clusters, enables a better tradeoff between convergence and diversity in the prediction process. We consider the movement directions of individuals within each cluster separately through different cluster distributions, while balancing the individual movement directions and the overall population movement direction by overlaying cluster coverage areas, thereby helping to avoid the clustered prediction population from getting trapped in local optima. Experimental results and comparisons with other algorithms demonstrate that this strategy exhibits strong competitiveness in handling DMOPs.

Hydrophobic modification enhances the microstructure stability of the catalyst layer in alkaline polymer electrolyte fuel cells.

Alkaline polymer electrolyte fuel cells (APEFCs) have achieved notable advancements in peak power density, yet their durability during long-term operation remains a significant challenge. It has been recognized that increasing the hydrophobicity of the catalyst layer can effectively alleviate the performance degradation. However, a microscopic view of how hydrophobicity contributes to the stability of the catalyst layer microstructure is not clear. Here, we construct a membrane electrode assembly (MEA) with enhanced structural stability and durability by incorporating polytetrafluoroethylene (PTFE) particles into the catalyst layer. MEAs modified by this approach exhibit stabilized voltage platforms in current step tests and reduced hysteresis in current-voltage polarization curves during operation, indicating the critical role of PTFE in the removal of the excess water within the catalyst layer. Fuel cells with PTFE modification show more than 45% increase in electrochemical durability. By characterizing with field-emission scanning electron microscopy (FE-SEM) the surface and the internal microstructures of MEAs after durability tests, we find that the catalyst layers modified by PTFE experience much less reduction in porosity and less agglomeration of the solid components. These findings elucidate the microscopic mechanisms by which hydrophobicity promotes a more stable catalyst layer structure, thereby enhancing the durability of APEFCs. This research advances our understanding of hydrophobicity's impact on catalyst layer stability and offers a practical method to enhance the durability of APEFCs.

Experimental Investigation of Heat Dissipation of Lithium–Ion Cells and Its Correlation with Internal Resistance

Power loss is a limiting factor for batteries and individual cells. The resulting heat generation due to the power loss leads to reduced battery performance and, thus, lower efficiency. These losses are largely due to the internal resistance of the cells. Therefore, it is important to accurately determine the value of the internal resistance of lithium–ion cells. From the literature, it was found that there are three widely used internal resistance-measurement methods (current step method, direct-energy-loss method, and calorimeter measurement), with negligible research on their comparison demonstrating the most efficient method. Henceforth, to find the most optimal method, this research adopts all three methods on a variety of cell chemistries, including Lithium-ion Manganese Oxide (LMO), Lithium Iron Phosphate (LFP), Nickel Manganese Cobalt (NMC), and Lithium Titanium-Oxide (LTO) for different c-rates (1 C, 2 C, and 3 C), with a wide temperature range (from 0 °C to 40 °C).

Nanoimpact Electrochemistry for Unraveling the Reactivity of Diffusion Limited Enzymes

The ultimate challenge of modern electroanalytical chemistry is to reach a single-molecule level of detection. In this framework, nanoimpact electrochemistry (NIE) has been recently developed. It allows studying at high throughput the reactivity of small entities such as nanoparticles, cells, or proteins, that collide on a biased ultramicroelectrode (UME) inside a solution or suspension.1 The collisions cause disturbances in the recorded chronoamperogram such as current spikes or current steps.2 Single enzyme electrochemistry (SEE) can contribute to uncovering effects of dynamics and non-equilibrium behavior of individual enzyme molecules on catalysis, which are hidden in measurements on ensembles due to averaging of the measured parameters over the entire molecular population. However, NIE of smaller entities such as enzymes, is very challenging due to the low sensitivity of the state-of-the-art electrochemical equipment, and most single enzyme techniques are currently based on fluorescence.3 One strategy to overcome this issue is the amplification of the current produced by a single enzyme molecule by fast catalysis: the single enzyme converts several substrate molecules per second consuming several electrons leading measurable current. Some of us reported the implementation of this strategy for the first time, designing a single-entity electrochemical experiment that allowed the detection of catalytic currents from single laccase molecules.4 Soon after the initial SEE report in 2016, a similar approach was applied for investigations of catalase,5 an enzyme that breaks down H2O2 to O2 and H2O with diffusion limited kinetics. However, the unequivocal attribution of current disturbances to the reduction of O2 (produced locally by enzyme) on the electrode during enzyme-electrode collisions remains a challenge. We will address this challenge based on the analysis of SEE experimental data under various conditions, on protein film voltammetry (PFV) studies on rotating disk electrode (RDE), and on simulations. The collected data from the SEE, that is the current spike characteristics in each set of conditions, are statistically analyzed and discussed. PFV studies are used to evaluate basic thermodynamic and kinetic parameters of the system. Finally, we have built a model using Comsol Multiphysics. To the best of our knowledge, this is the first model reported so far that can satisfactorily reproduce the experimental SEE data, using experimentally evaluated parameters as input. The developed methodology is the succesfully implemented to the study of superoxide dismutase.6

Influence of Potentiostat Hardware on Electrochemical Measurements.

We describe two operating modes for the same potentiostat, where the redox processes of hydroquinone in a hydrochloric acid medium are contrasted for cyclic voltammetry (CV) as functions of a digital/staircase scan and an analogue/linear scan. Although superficially there is not much to separate the two modes of operation as an end user, differences can be seen in the voltammograms while switching between the digital and analogue modes. The effects of quantization clearly have some impact on the measurements, with the outputs between the two modes being a function of the equivalent-circuit model of the electrochemical system under investigation. Increasing scan rates when using both modes produces higher peak redox currents, with the differences between the analogue and digital modes of operation being consistent as a function of the scan rate. Differences between the CV loops between the analogue and digital modes show key differences at certain points along the scans, which can be attributed to the nature of the electrolyte affecting the charging and discharging processes and consequently changing the peak currents of the redox processes. The faradaic processes were shown to be independent of the scan rates. Simulations of the equivalent-circuit behaviour show differences in the responses to different input signals, i.e., the step and ramp responses of the system. Both the voltage and current steps and ramp responses showed the time-domain behaviour of distinct elements of the equivalent electrochemical circuit model as an approximation of the applied digital and analogue CV input signals. Ultimately, it was concluded that similar parameters between the two modes of operation available with the potentiostat would lead to different output voltammograms and, despite advances in technology, digital systems can never fully emulate a true analogue system for electrochemical applications. These observations showcase the value of having hardware capable of true analogue characteristics over digital systems.

Effects of transient, persistent, and resurgent sodium currents on excitability and spike regularity in vestibular ganglion neurons.

Vestibular afferent neurons occur as two populations with differences in spike timing regularity that are independent of rate. The more excitable regular afferents have lower current thresholds and sustained spiking responses to injected currents, while irregular afferent neurons have higher thresholds and transient responses. Differences in expression of low-voltage-activated potassium (K LV ) channels are emphasized in models of spiking regularity and excitability in these neurons, leaving open the potential contributions of the voltage-gated sodium (Na V ) channels responsible for the spike upstroke. We investigated the impact of different Na V current modes (transient, persistent, and resurgent) with whole-cell patch clamp experiments in mouse vestibular ganglion neurons (VGNs), the cultured and dissociated cell bodies of afferents. All VGNs had transient Na V current, many had a small persistent (non-inactivating) Na V current, and a few had resurgent current, which flows after the spike peak when Na V channels that were blocked are unblocked. Na V 1.6 channels conducted most or all of each Na V current mode, and a Na V 1.6-selective blocker decreased spike rate and altered spike waveforms in both sustained and transient VGNs. A Na V channel agonist enhanced persistent current and increased spike rate and regularity. We hypothesized that persistent and resurgent currents have different effects on sustained (regular) VGNs vs. transient (irregular) VGNs. Lacking blockers specific for the different current modes, we used modeling to isolate their effects on spiking of simulated transient and sustained VGNs, driven by simulated current steps and noisy trains of simulated EPSCs. In all simulated neurons, increasing transient Na V current increased spike rate and rate-independent regularity. In simulated sustained VGNs, adding persistent current increased both rate and rate-independent regularity, while adding resurgent current had limited impact. In transient VGNs, adding persistent current had little impact, while adding resurgent current increased both rate and rate-independent irregularity by enhancing sensitivity to synaptic noise. These experiments show that the small Na V current modes may enhance the differentiation of afferent populations, with persistent currents selectively making regular afferents more regular and resurgent currents selectively making irregular afferents less regular.

A power-efficient fast-transient OCL-LDO with adaptive super source follower and active capacitor compensation management

This paper presents a flipped voltage follower (FVF) based output-capacitor-less low-dropout regulator (OCL-LDO) with fast transient response, high power supply rejection (PSR), and low quiescent current for noise-sensitive circuits in internet-of-things (IoTs). An adaptive super source follower (ASSF) is proposed to effectively reduce the output impedance of the voltage buffer under heavy-loading conditions while keeping a low quiescent current under light-loading conditions. The active capacitor compensation management (ACCM) is proposed to solve the charge-sharing problem caused by the floating capacitors in the dynamic capacitor compensation circuit. The proposed OCL-LDO has been designed and fabricated in 22-nm CMOS technology. It can stabilize with load current ranging from 0 to 12 mA while consuming only 4.8-μA quiescent current. when the load current steps from 0.1 to 10 mA within 3.8 ns, the measured voltage undershoot is 55 mV and the recovery time is about 60 ns.

The implicit inversion method for calculating the forward dynamics input Jacobian

AbstractThis paper presents the implicit inversion method (IIM), a recursive method to evaluate the Jacobian of the forward dynamics w.r.t. the system inputs, using intermediate results obtained from an O(n) forward dynamics algorithm. The resulting coefficient matrix, called the inertia-weighted input matrix (IWIM), can be used to significantly improve the performance of solving optimal control problems that take into account system dynamics for only the current time step. As the relationship between inputs and accelerations appears fixed within a time step, this matrix can be evaluated in the initialization step of the optimization. This means that the forward dynamics only needs to be solved once at the initialization of the optimization, rather than having to solve the equations in every iteration of the optimization. The method presented in this paper especially targets a case where the forward dynamics are calculated using an O(n) method and takes advantage of variables that are already known through the evaluation of that method. These quantities allow us to obtain the inertia-weighted input matrix without having to convert the system to its generalized coordinate form. Exploiting the shape of the resulting equation, it is even possible to avoid an explicit inversion of any matrices in the process. Finally, runtime comparisons between three different methods to calculate the IWIM are made for several examples.

Eighth-Order Numerov-Type Methods Using Varying Step Length

This work explores a well-established eighth-algebraic-order numerical method belonging to the explicit Numerov-type family. To enhance its efficiency, we integrated a cost-effective algorithm for adjusting the step size. After each step, the algorithm either maintains the current step length, halves it, or doubles it. Any off-step points required by this technique are calculated using a local interpolation function. Numerical tests involving diverse problems demonstrate the significant efficiency improvements achieved through this approach. The method is particularly effective for solving differential equations with oscillatory behavior, showcasing its ability to maintain high accuracy with fewer function evaluations. This advancement is crucial for applications requiring precise solutions over long intervals, such as in physics and engineering. Additionally, the paper provides a comprehensive MATLAB-R2018a implementation, facilitating ease of use and further research in the field. By addressing both computational efficiency and accuracy, this study contributes a valuable tool for the numerical analysis community.

One-dimensional dynamic model of a PEM fuel cell for analyzing through-plane species distribution and irreversible losses under various operating conditions

This study develops a one-dimensional dynamic model of a proton-exchange membrane fuel cell (PEMFC), using species mole fractions as shared variables to examine through-plane water and oxygen distribution. During dynamic operation, the species mole fraction difference between the cathode channel/cathode gas distribution media (cCH/cGDM) interface and the cGDM/cathode catalyst layer (cGDM/cCL) interface varies with current density. When relative humidity (RH) changes from 20 % to 90 % at 1 A/cm2, the water mole fraction difference between cCH/cGDM and cGDM/cCL increases by 33.21 %, while the oxygen mole fraction difference increases by only 3.29 %. Current density step changes cause overshoot and undershoot behavior in the water mole fraction, affecting membrane water content, especially at low RH, where it remains low and sensitive to current and temperature changes. Beyond 50 % RH, membrane water content stabilizes, with fluctuations mainly observed on the anode side. The impact of species distribution on irreversible losses was then examined.Furthermore, oxygen transport resistance increases with operating pressure, while oxygen molecular diffusion remains stable. Conversely, rising temperatures substantially improve oxygen molecular diffusion. An analysis of FC loss profiles under various temperatures (333.15K–363.15K) and RH (0%–100 %) at constant loads (0.2&1 A/cm2) shows the model's capability in determining optimized operating conditions for PEMFCs.