Heart rate variability as a preictal marker for determining the laterality of seizure onset zone in frontal lobe epilepsy.

Determining the laterality of the seizure onset zone is challenging in frontal lobe epilepsy (FLE) due to the rapid propagation of epileptic discharges to the contralateral hemisphere. There is hemispheric lateralization of autonomic control, and heart rate is modulated by interactions between the sympathetic and parasympathetic nervous systems. Based on this notion, the laterality of seizure foci in FLE might be determined using heart rate variability (HRV) parameters. We explored preictal markers for differentiating the laterality of seizure foci in FLE using HRV parameters. Twelve patients with FLE (6 right FLE and 6 left FLE) were included in the analyzes. A total of 551 (460 left FLE and 91 right FLE) 1-min epoch electrocardiography data were used for HRV analysis. We found that most HRV parameters differed between the left and right FLE groups. Among the machine learning algorithms applied in this study, the light gradient boosting machine was the most accurate, with an AUC value of 0.983 and a classification accuracy of 0.961. Our findings suggest that HRV parameter-based laterality determination models can be convenient and effective tools in clinical settings. Considering that heart rate can be easily measured in real time with a wearable device, our proposed method can be applied to a closed-loop device as a real-time monitoring tool for determining the side of stimulation.

Design of dissimilar material joint for defect-free multi-material additive manufacturing via laser-directed energy deposition.

Additive manufacturing technology has advanced beyond creating optimized features, from strengthening materials to make them lightweight to fabricating multi-material combinations that offer functionalities beyond the capabilities of individual materials. In this study, a lamination method for laser-directed energy deposition (LDED) is developed to achieve dense multi-material features, and a design that combines different and dissimilar materials is developed. To evaluate these novel developments, two materials-AISI 316L stainless steel and Inconel 625-are introduced. Tensile specimens, fabricated via multi-material additive manufacturing using LDED, are subjected to tensile tests that are recorded on video for digital image correlation. After the tests, fracture surface analyses of the fractured specimens are performed via scanning electron microscopy, and optical monitoring analyses are performed on the specimens that are not subjected to the tensile tests. The results indicate that the specimens demonstrate varied mechanical properties due to the influence of lamination direction and order, which affect the formation of critical cracks and pores.

Effect of α″-Ti Martensitic Phase Formation on Plasticity in Ti-Fe-Sn Ultrafine Eutectic Composites.

Extensive research has been conducted on Ti-Fe-Sn ultrafine eutectic composites due to their high yield strength, compared to conventional microcrystalline alloys. The unique microstructure of ultrafine eutectic composites, which consists of the ultrafine-grained lamella matrix with the formation of primary dendrites, leads to high strength and desirable plasticity. A lamellar structure is known for its high strength with limited plasticity, owing to its interface-strengthening effect. Thus, extensive efforts have been conducted to induce the lamellar structure and control the volume fraction of primary dendrites to enhance plasticity by tailoring the compositions. In this study, however, it was found that not only the volume fraction of primary dendrites but also the morphology of dendrites constitute key factors in inducing excellent ductility. We selected three compositions of Ti-Fe-Sn ultrafine eutectic composites, considering the distinct volume fractions and morphologies of ß-Ti dendrites based on the Ti-Fe-Sn ternary phase diagram. As these compositions approach quasi-peritectic reaction points, the α″-Ti martensitic phase forms within the primary ß-Ti dendrites due to under-cooling effects. This pre-formation of the α″-Ti martensitic phase effectively governs the growth direction of ß-Ti dendrites, resulting in the development of round-shaped primary dendrites during the quenching process. These microstructural evolutions of ß-Ti dendrites, in turn, lead to an improvement in ductility without a significant compromise in strength. Hence, we propose that fine-tuning the composition to control the primary dendrite morphology can be a highly effective alloy design strategy, enabling the attainment of greater macroscopic plasticity without the typical ductility and strength trade-off.

Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.

In neurointensive care units (NICU), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of pre-admission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

Differentiating changes in movement-related EEG response induced by transcranial direct current stimulation using convolutional neural network.

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique that can modulate neuronal excitability and induce brain plasticity. Although tDCS has been studied with various methods, more research is needed on the movement-related electroencephalography (EEG) changes induced by tDCS. Moreover, it is necessary to investigate whether these changes can be distinguished through a convolutional neural network (CNN)-based classifier. In this study, we measured the EEG during the voluntary foot-tapping task of participants who received tDCS or sham stimulation and evaluated the classification performance. As a result, significantly higher classification accuracy was shown using the ß band (88.7±9.4%), which is more related to motor function, than in the other bands (71.4±10.6% for δ band, 64.1±13.4% for θ band, and 65.7±10.9% for α band). Consequently, EEG changes during the voluntary foot-tapping task induced by tDCS appeared large in the ß band, implying that it is effective in classifying whether tDCS was given or not, and plays an important role in identifying the effect of tDCS.

Design of High-Performance Organic Nonlinear Optical and Terahertz Crystals by Controlling the van der Waals Volume.

In the development of new organic crystals for nonlinear optical and terahertz (THz) applications, it is very challenging to achieve the essentially required non-centrosymmetric molecular arrangement. Moreover, the resulting crystal structure is mostly unpredictable due to highly dipolar molecular components with complex functional substituents. In this work, new organic salt crystals with top-level macroscopic optical nonlinearity by controlling the van der Waals volume (VvdW ), rather than by trial and error, are logically designed. When the VvdW of molecular ionic components varies, the corresponding crystal symmetry shows an observable trend: change from centrosymmetric to non-centrosymmetric and back to centrosymmetric. All non-centrosymmetric crystals exhibit an isomorphic P1 crystal structure with an excellent macroscopic second-order nonlinear optical response. Apart from the top-level macroscopic optical nonlinearity, new organic crystals introducing highly electronegative fluorinated substituents with strong secondary bonding ability show excellent performance in efficient and broadband THz wave generation, high crystal density, high thermal stability, and good bulk crystal growth ability.

Effects of altered functional connectivity on motor imagery brain-computer interfaces based on the laterality of paralysis in hemiplegia patients.

Motor imagery (MI)-based brain-computer interfaces are widely employed for improving the rehabilitation of paralyzed people and their quality of life. It has been well documented that brain activity patterns in the primary motor cortex and sensorimotor cortex during MI are similar to those of motor execution/imagery. However, individuals paralyzed owing to various neurological disorders have debilitated activation of the motor control region. Therefore, the differences in brain activation based on the paralysis location should be considered. We analyzed brain activation patterns using the electroencephalogram (EEG) acquired while performing MI on the right upper limb to investigate hemiplegia-related brain activation patterns. Participants with hemiplegia of the right upper limb (n=7) and left upper limb (n=4) performed the MI task within the right upper limb. EEG signals were acquired using 14 channels based on a 10-20 global system, and analyzed for event-related desynchronization (ERD) based on event-related spectral perturbation and functional connectivity, using the weighted phase-lag index of both hemispheres at the location of hemiplegia. Enhanced ERD was found in the ipsilateral region, compared to the contralateral region, after MI of the affected limb. The reduced difference in the centrality of the channels was observed in all subjects, likely reflecting an altered brain network from increased interhemispheric connections. Furthermore, the tendency of distinct network-based features depending on the MI task on the affected limb was diluted between the inter-hemispheres. Analysis of interaction between inter-region using functional connectivity could provide avenues for further investigation of BCI strategy through the brain state of individuals with hemiplegia.

Commonly Associated Disorders with Complete Scalp Alopecia in Early Childhood: A Review.

Complete scalp hair loss can be a source of distress for affected children and their families. In addition to infectious and trauma-related causes of hair loss, infants and children may present with total scalp alopecia arising from a range of genetic predispositions. Our objective with this review was to identify the common genetic conditions in children with complete scalp alopecia. The PubMed Database was reviewed for all articles from 1962 to 2019 containing the search terms related to genetic alopecia. The conditions with at least five reported cases in the literature were considered for the inclusion. All clinical trials, retrospective studies, and cases on human subjects and written in English were included. Six genetic conditions related to complete scalp alopecia were included in this review. The most common genetic conditions associated with total scalp hair loss include: alopecia totalis/Alopecia universalis (AU), atrichia with papular lesions, AU congenita, hereditary Vitamin D-resistant rickets type IIA, alopecia with mental retardation, and pure hair and nail ectodermal dysplasia. In children presenting with total scalp hair loss, a myriad of genetic and environmental factors may be the underlying cause. Increased awareness of potential genetic conditions associated with total scalp hair loss may assist in diagnosis, with improved the prognosis for the children.

Inherent seizure susceptibility in patients with antihistamine-induced acute symptomatic seizure: a resting-state EEG analysis.

We compared neural activities and network properties between the antihistamine-induced seizures (AIS) and seizure-free groups, with the hypothesis that patients with AIS might have inherently increased neural activities and network properties that are easily synchronized. Resting-state electroencephalography (EEG) data were collected from 27 AIS patients and 30 healthy adults who had never had a seizure. Power spectral density analysis was used to compare neural activities in each localized region. Functional connectivity (FC) was measured using coherence, and graph theoretical analyses were performed to compare network properties between the groups. Machine learning algorithms were applied using measurements found to be different between the groups in the EEG analyses as input features. Compared with the seizure-free group, the AIS group showed a higher spectral power in the entire regions of the delta, theta, and beta bands, as well as in the frontal areas of the alpha band. The AIS group had a higher overall FC strength, as well as a shorter characteristic path length in the theta band and higher global efficiency, local efficiency, and clustering coefficient in the beta band than the seizure-free group. The Support Vector Machine, k-Nearest Neighbor, and Random Forest models distinguished the AIS group from the seizure-free group with a high accuracy of more than 99%. The AIS group had seizure susceptibility considering both regional neural activities and functional network properties. Our findings provide insights into the underlying pathophysiological mechanisms of AIS and may be useful for the differential diagnosis of new-onset seizures in the clinical setting.

A Case for Doxycycline as an Effective Treatment for Confluent and Reticulated Papillomatosis (CARP).

Confluent and reticulated papillomatosis (CARP) is a rare keratinization disorder that presents with asymptomatic, reticulated papules coalescing into plaques, which adversely affect, most often, young black persons. Minocycline is considered the drug of choice, but it is not without its host of potential side effects, including drug hypersensitivity, drug-induced lupus/vasculitis/hepatitis, blue-gray skin hyperpigmentation, acute eosinophilic pneumonia, pseudotumor cerebri, and vestibular instability, among others. Alternatively, doxycycline might be considered as another first-line agent for CARP as it can effectively clear lesions while offering a more favorable side effect profile in select patients. Herein, we present a case of CARP successfully resolved with doxycycline after a protracted treatment history of topical and oral antifungal medications for suspected tinea versicolor (TV).

Molecularly Imprinted Chemiresistive Sensor for Specific Recognition of Furaneol as a Biomarker of Strawberry Flavor Conditions.

This work introduces the concept of a molecularly imprinted gas sensor to monitor the condition of naturally ripened strawberries. Furaneol, 2,5-dimethyl-4-hydroxy-3(2H)-furanone, is considered as an important biomarker related to the strawberry flavor. Identification of furaneol concentration is still a challenge because of its weak adsorption, nonpolar, and unreactive properties. Therefore, no study has been reported yet to measure furaneol gases via a simple chemiresistive mechanism. Herein, we demonstrate the sensor based on molecularly imprinted polymer (MIP)-based polyaniline (PANI). The sensitive and selective detection of furaneol gas with a MIP-PANI gas sensor was observed at room temperature and under different humidity conditions. The comparison between MIP and the nonimprinted (NIP)-based PANI shows a strong interaction between furaneol and the molecularly imprinted polymer. The furaneol gas sensing mechanism is explained based on the interaction between the gas molecules and the charge carriers of MIP-PANI, which results in the functional group change in the carboxylic group. Furthermore, the developed MIP-chemiresistive sensor for real strawberries was compared with a commercial e-nose system. The results show the potential to offer a rapid and cost-effective platform for specific recognition of furaneol.

Prediction of Serious Intracranial Hypertension from Low-Resolution Neuromonitoring in Traumatic Brain Injury: An Explainable Machine Learning Approach.

There is a strong association between intracranial hypertension (IH) that occurs following the acute phase of traumatic brain injury (TBI) and negative outcomes. This study proposes a pressure-time dose (PTD)-based parameter that may specify a possible serious IH (SIH) event and develops a model to predict SIH. The minute-by-minute signals of arterial blood pressure (ABP) and intracranial pressure (ICP) of 117 TBI patients were utilized as the internal validation dataset. The SIH event was explored through the prognostic power of the IH event variables for the outcome after 6 months, and an IH event with thresholds that included an ICP of 20 mmHg and PTD > 130 mmHg * minutes was considered an SIH event. The physiological characteristics of normal, IH and SIH events were investigated. LightGBM was employed to forecast an SIH event from various time intervals using physiological parameters derived from the ABP and ICP. Training and validation were conducted on 1,921 SIH events. External validation was performed on two multi-center datasets containing 26 and 382 SIH events. The SIH parameters could be used to predict mortality (AUROC = 0.893, p < 0.001) and favorability (AUROC = 0.858, p < 0.001). The trained model robustly forecasted SIH after 5 and 480 minutes with an accuracy of 86.95% and 72.18% in internal validation. External validation also revealed a similar performance. This study demonstrated that the proposed SIH prediction model has reasonable predictive capacities. A future intervention study is required to investigate whether the definition of SIH is maintained in multi-center data and to ensure the effects of the predictive system on TBI patient outcomes at the bedside.

Polyaniline/Graphene-Functionalized Flexible Waste Mask Sensors for Ammonia and Volatile Sulfur Compound Monitoring.

A flexible resistive-type polyaniline-based gas sensor was fabricated by simple dip-coating of graphene combined with in situ polymerization of aniline on a flexible waste mask substrate. The prepared polypropylene/graphene/polyaniline (PP/G/PANI) hybrid sensor demonstrated a fast response (114 s) and recovery time (23 s), ppb-level detection limit (100 ppb), high response value (250% toward 50 ppm NH3, which is over four times greater than that of the pristine PANI sensor), acceptable flexibility, excellent selectivity, and long-term stability at room temperature. The morphological and structural properties of the composite sensor materials were characterized by scanning electron microscopy and energy-dispersive spectroscopy characterization, and the surface chemistry of the hybrid sensors was analyzed by Fourier transform infrared spectroscopy. The excellent sensing performance was mainly ascribed to the larger specific surface area and efficient conducting paths of the porous PP/G/PANI network. Moreover, the PP/G/PANI hybrid gas sensor exhibited excellent sensing capability on volatile sulfur compounds contained in human breath, indicating that the hybrid sensor can be applied to breath analysis and kidney disease diagnosis.

Subject-Transfer Decoding using the Convolutional Neural Network for Motor Imagery-based Brain-Computer Interface.

Various pattern-recognition or machine learning-based methods have recently been developed to improve the accuracy of the motor imagery (MI)-based brain-computer interface (BCI). However, more research is needed to reduce the training time to apply it to the real-world environment. In this study, we propose a subject-transfer decoding method based on a convolutional neural network (CNN) which is robust even with a small number of training trials. The proposed CNN was pre-trained with other subjects' MI data and then fine-tuned to the target subject's training MI data. We evaluated the proposed method using the BCI competition IV data2a, which had the 4-class MIs. Consequently, on the same test dataset, with changing the number of training trials, the proposed method showed better accuracy than the self-training method, which used only the target subject's data for training, as averaged 86.54±7.78% (288 trials), 85.76 ±8.00% (240 trials), 84.65±8.11% (192 trials), and 83.29 ±8.25% (144 trials), respectively, which was 4.94% (288 trials), 6.10% (240 trials), 9.03% (192 trials), and 12.31% (144 trials)-point higher than the self-training method. Consequently, the proposed method was shown to be effective in maintaining classification accuracy even with the reduced training trials.

Large-area vertically aligned 2D MoS2layers on TEMPO-cellulose nanofibers for biodegradable transient gas sensors.

Crystallographically anisotropic two-dimensional (2D) molybdenum disulfide (MoS2) with vertically aligned (VA) layers is attractive for electrochemical sensing owing to its surface-enriched dangling bonds coupled with extremely large mechanical deformability. In this study, we explored VA-2D MoS2layers integrated on cellulose nanofibers (CNFs) for detecting various volatile organic compound gases. Sensor devices employing VA-2D MoS2/CNFs exhibited excellent sensitivities for the tested gases of ethanol, methanol, ammonia, and acetone; e.g. a high response rate up to 83.39% for 100 ppm ethanol, significantly outperforming previously reported sensors employing horizontally aligned 2D MoS2layers. Furthermore, VA-2D MoS2/CNFs were identified to be completely dissolvable in buffer solutions such as phosphate-buffered saline solution and baking soda buffer solution without releasing toxic chemicals. This unusual combination of high sensitivity and excellent biodegradability inherent to VA-2D MoS2/CNFs offers unprecedented opportunities for exploring mechanically reconfigurable sensor technologies with bio-compatible transient characteristics.

Electrically Tunable Solution-Processed Transparent Conductive Thin Films Based on Colloidally Dispersed ITO@Ag Composite Ink.

Silver (Ag) introduced colloidal Sn-doped In2O3 (ITO) ink for transparent conductive electrodes (TCEs) was prepared to overcome the limitation of colloidally prepared thin film; low density thin film, high resistance. ITO@Ag colloid ink was made by controlling the weight ratio of ITO and Ag nanoparticles through ball-milling and fabricated using spin coating. These films were dried at 220 °C and heat-treated at 450−750 °C in an air atmosphere to pyrolyze the organic ligand attached to the nanoparticles. All thin films showed high crystallinity. As the thermal treatment temperature increased, films showed a cracked surface, but as the weight percentage of silver increased, a flattened and smooth surface appeared, caused by the metallic silver filling the gap between the nano-particles. This worked as a bridge to allow electrical conduction, which decreases the resistivity over an order of magnitude, from 309 to 0.396, and 0.107 Ω·cm for the ITO-220 °C, ITO-750 °C, and ITO@Ag (7.5 wt.%)-750 °C, respectively. These films also exhibited >90% optical transparency. Lowered resistivity is caused due to the inclusion of silver, providing a sufficient number of charge carriers. Furthermore, the work function difference between ITO and silver builds an ohmic junction, allowing fluent electrical flow without any barrier.

Temperature Characteristics of Traditional Indirect Moxibustion and Electronic Moxibustion.

Background: Electronic moxibustion (EM) was developed to minimize the side effects of traditional moxibustion, such as burns, and to overcome therapeutic compliances such as smoke or smell. Objectives: To investigate distributions and thermal stimulation of EM at various depths using silicon phantom and to compare this methodology to traditional indirect moxibustion (TIM). Methods: A silicon phantom composed of polydimethylsiloxane was heated and immersed in a hot plate containing warm water to set the phantom's temperature to that of biological tissue. K-type thermocouples were inserted into the phantom at depths of 0, 2, 5, 7, and 10 mm to measure temperature changes with thermal stimulation of EM or TIM placed on top of the phantom. Results: At the surface of the phantom, the peak temperature after applying TIM (55.04 ± 0.92℃ [Δ23.79 ± 0.96℃]) was significantly higher than after EM (43.25 ± 1.95℃ [Δ13.00 ± 2.23℃]), with both interventions reaching the highest temperature after 2 minutes. The temperature increase for TIM was also statistically significant compared to EM when measured at a depth of 2 mm. For the experimental setting with TIM, after reaching peak surface temperature, a rapid decrease was observed at the surface and 2 mm while EM showed a much more gradual decline. There was no significant difference in temperature change between the groups at depths of 5, 7, and 10 mm. Conclusion: TIM resulted in a higher temperature rise compared to EM at the surface and at a 2 mm depth reaching over 50℃, which creates risk of burns. Thermal stimulation with EM had a lower risk of burns with temperature increment not being statistically different from TIM below the depth of 5 mm.

DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography.

Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovascular disease (CVD); however, existing methods for measuring ABP outside the clinic use inconvenient cuff sphygmomanometry, or do not estimate continuous ABP waveforms. This study proposes a novel deep learning model DeepCNAP for estimating continuous BP waveform from a noninvasively measured photoplethysmography (PPG) signal in real-time. DeepCNAP was designed through the combination of deep convolutional networks and self-attention. The proposed method was constructed via 10-fold cross-validation based on the MIMIC database (the number of subjects = 942, recording time = 374.43 hours). The performance of DeepCNAP was evaluated from two perspectives: estimating ABP from PPG and classifying hemodynamically unstable events (i.e., hypertension, prehypertension, hypotension, and the normal state). The mean absolute errors of the BP estimates were 3.40 ± 4.36 mmHg for systolic BP, 1.75 ± 2.25 mmHg for diastolic BP, and 3.23 ± 2.21 mmHg for the BP waveform, indicating that DeepCNAP satisfies the standards of both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). From the estimated BP, hypertension, prehypertension, hypotension, and the normal state were classified with 99.44, 97.58, 92.23, and 94.64% accuracy, respectively. DeepCNAP enables feasible real-time estimation of invasively measured ABP from noninvasive PPG. With its noninvasive nature, high accuracy, and clinical relevance, the proposed model could be valuable in general wards at hospitals and for wearable devices in daily life.

Autonomic Dysfunction in Sleep Disorders: From Neurobiological Basis to Potential Therapeutic Approaches.

Sleep disorder has been portrayed as merely a common dissatisfaction with sleep quality and quantity. However, sleep disorder is actually a medical condition characterized by inconsistent sleep patterns that interfere with emotional dynamics, cognitive functioning, and even physical performance. This is consistent with sleep abnormalities being more common in patients with autonomic dysfunction than in the general population. The autonomic nervous system coordinates various visceral functions ranging from respiration to neuroendocrine secretion in order to maintain homeostasis of the body. Because the cell population and efferent signals involved in autonomic regulation are spatially adjacent to those that regulate the sleep-wake system, sleep architecture and autonomic coordination exert effects on each other, suggesting the presence of a bidirectional relationship in addition to shared pathology. The primary goal of this review is to highlight the bidirectional and shared relationship between sleep and autonomic regulation. It also introduces the effects of autonomic dysfunction on insomnia, breathing disorders, central disorders of hypersomnolence, parasomnias, and movement disorders. This information will assist clinicians in determining how neuromodulation can have the greatest therapeutic effects in patients with sleep disorders.

Anion Exchange Membranes for Fuel Cell Application: A Review.

The fuel cell industry is the most promising industry in terms of the advancement of clean and safe technologies for sustainable energy generation. The polymer electrolyte membrane fuel cell is divided into two parts: anion exchange membrane fuel cells (AEMFCs) and proton exchange membrane fuel cells (PEMFCs). In the case of PEMFCs, high-power density was secured and research and development for commercialization have made significant progress. However, there are technical limitations and high-cost issues for the use of precious metal catalysts including Pt, the durability of catalysts, bipolar plates, and membranes, and the use of hydrogen to ensure system stability. On the contrary, AEMFCs have been used as low-platinum or non-platinum catalysts and have a low activation energy of oxygen reduction reaction, so many studies have been conducted to find alternatives to overcome the problems of PEMFCs in the last decade. At the core of ensuring the power density of AEMFCs is the anion exchange membrane (AEM) which is less durable and less conductive than the cation exchange membrane. AEMFCs are a promising technology that can solve the high-cost problem of PEMFCs that have reached technological saturation and overcome technical limitations. This review focuses on the various aspects of AEMs for AEMFCs application.