Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases. / åŒºåˆ†èƒ¶è´¨æ¯ç»†èƒžç˜¤å’Œå•å‘æ€§è„‘è½¬ç§»ç˜¤çš„å¤šæ¨¡æ€èžåˆæ·±åº¦å­¦ä¹ æ¨¡åž‹çš„å¼€å‘å’ŒéªŒè¯.

OBJECTIVES: Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system. This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases (SBMs), and to further explore the impact of multimodality data fusion on classification tasks. METHODS: Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed. First, structural T1-weight, T1C-weight, and T2-weight were selected as 3 inputs to the entire model, regions of interest (ROIs) were manually delineated on the registered three modal MR images, and multimodality radiomics features were obtained, dimensions were reduced using a random forest (RF)-based feature selection method, and the importance of each feature was further analyzed. Secondly, we used the method of contrast disentangled to find the shared features and complementary features between different modal features. Finally, the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. RESULTS: The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs. Furthermore, compared with single-modal data, the multimodal fusion models using machine learning algorithms such as support vector machine (SVM), Logistic regression, RF, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) achieved significant improvements, with area under the curve (AUC) values of 0.974, 0.978, 0.943, 0.938, and 0.947, respectively; our comparative disentangled multi-modal MR fusion method performs well, and the results of AUC, accuracy (ACC), sensitivity (SEN) and specificity(SPE) in the test set were 0.985, 0.984, 0.900, and 0.990, respectively. Compared with other multi-modal fusion methods, AUC, ACC, and SEN in this study all achieved the best performance. In the ablation experiment to verify the effects of each module component in this study, AUC, ACC, and SEN increased by 1.6%, 10.9% and 15.0%, respectively after 3 loss functions were used simultaneously. CONCLUSIONS: A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.

Titanium Nitride Nanorods Array-Decorated Graphite Felt as Highly Efficient Negative Electrode for Iron-Chromium Redox Flow Battery.

Iron-chromium redox flow batteries have attracted widespread attention because of their low cost. However, the performance of these batteries is still lower than that of vanadium redox flow batteries due to the poor electrochemical activity of Cr3+ /Cr2+ redox couples on graphite felt electrodes. Herein, binder-free TiN nanorods array-decorated 3D graphite felt composite electrode-is demonstrated. The dendrite-like TiN nanorods array increases the specific surface area of the electrode. The nitrogen and oxygen elements on the surface provide more adsorption sites and electrochemically active sites for Cr3+ /Cr2+ . The contact resistance of the composite electrode is effectively reduced and its homogeneity and stability are improved by avoiding the use of a binder and mixing process. A battery prepared using the TiN nanorods array-decorated 3D graphite felt electrode has enabled the maximum power density to be 427 mW·cm-2 , which is 74.0% higher than a battery assembled with TiN nanoparticles bonded to graphite felt. At a current density of 80 mA·cm-2 , the TiN nanorods battery exhibits the highest coulombic efficiency of 93.0%, voltage efficiency of 90.4%, and energy efficiency of 84.1%. Moreover, the battery efficiency and composite electrode structure remains stable during a redox flow battery cycle test.

Durable High-Temperature Proton Exchange Membrane Fuel Cells Enabled by the Working-Temperature-Matching Palladium-Hydrogen Buffer Layer.

The durability degradation during stack-operating conditions seriously deteriorates the lifetime and performance of the fuel cell. To alleviate the rapid potential rise and performance degradation, an anode design is proposed to match the working temperature of high-temperature proton exchange membrane fuel cells (HT-PEMFCs) with the release temperature of hydrogen from palladium. The result is significantly enhanced hydrogen oxidation reaction (HOR) activity of Pd and superior performance of the Pd anode. Furthermore, Pd as hydrogen buffer and oxygen absorbent layer in the anode can provide additional in situ hydrogen and absorb infiltrated oxygen during local fuel starvation to maintain HOR and suppress reverse-current degradation. Compared with the traditional Pt/C anode, the Pd/C also greatly improved HT-PEMFCs durability during start-up/shut-down and current mutation. The storage/release of hydrogen provides innovative guidance for improving the durability of PEMFCs.

Automatic delineation of organ at risk in cervical cancer radiotherapy based on ensemble learning. / åŸºäºŽé›†æˆå­¦ä¹ çš„å®«é¢ˆç™Œæ”¾å°„æ²»ç–—å±åŠå™¨å®˜çš„è‡ªåŠ¨å‹¾ç”».

OBJECTIVES: The automatic delineation of organs at risk (OARs) can help doctors make radiotherapy plans efficiently and accurately, and effectively improve the accuracy of radiotherapy and the therapeutic effect. Therefore, this study aims to propose an automatic delineation method for OARs in cervical cancer scenarios of both after-loading and external irradiation. At the same time, the similarity of OARs structure between different scenes is used to improve the segmentation accuracy of OARs in difficult segmentations. METHODS: Our ensemble model adopted the strategy of ensemble learning. The model obtained from the pre-training based on the after-loading and external irradiation was introduced into the integrated model as a feature extraction module. The data in different scenes were trained alternately, and the personalized features of the OARs within the model and the common features of the OARs between scenes were introduced. Computer tomography (CT) images for 84 cases of after-loading and 46 cases of external irradiation were collected as the train data set. Five-fold cross-validation was adopted to split training sets and test sets. The five-fold average dice similarity coefficient (DSC) served as the figure-of-merit in evaluating the segmentation model. RESULTS: The DSCs of the OARs (the rectum and bladder in the after-loading images and the bladder in the external irradiation images) were higher than 0.7. Compared with using an independent residual U-net (convolutional networks for biomedical image segmentation) model [residual U-net (Res-Unet)] delineate OARs, the proposed model can effectively improve the segmentation performance of difficult OARs (the sigmoid in the after-loading CT images and the rectum in the external irradiation images), and the DSCs were increased by more than 3%. CONCLUSIONS: Comparing to the dedicated models, our ensemble model achieves the comparable result in segmentation of OARs for different treatment options in cervical cancer radiotherapy, which may be shorten time for doctors to sketch OARs and improve doctor's work efficiency.

Unconventional p-d Hybridization Interaction in PtGa Ultrathin Nanowires Boosts Oxygen Reduction Electrocatalysis.

Alloying 3d transition metals with Pt has been discovered as an effective strategy to boost the catalytic activity in oxygen reduction reaction (ORR), which, however, often raises the insufficient catalyst durability issue due to rapid leaching of the 3d metal elements. To overcome this issue and realize enhancements in both the activity and the durability properties, here we report a new catalytic structure based on PtGa ultrathin alloy nanowires (NWs), which feature an unconventional strong p-d hybridization interaction. Relative to commercial Pt catalyst, the optimum Pt4.31Ga NWs catalyst exhibited 10.5- and 12.1-fold enhancement in the ORR mass activity and specific activity, respectively. Particularly, the Pt4.31Ga NWs catalyst showed only 15.8% loss in the mass activity after 30 000 cycles of durability test, as compared to a big decrease of 79.6% for the commercial Pt catalyst. Our mechanistic studies find a strong p-d hybridization interaction between Ga and Pt that accounts for the improved ORR performance via synergistically optimizing the surface electronic structure, enhancing the oxidation resistance of Pt, and suppressing the leaching of lattice Ga. We believe this work provides new perspectives to design active and durable electrocatalysts toward ORR.

Nickel Promoted Palladium Nanoparticles for Electrocatalysis of Carbohydrazide Oxidation Reaction.

Carbohydrazide is a potential alternative to toxic hydrazine for fuel cell applications to overcome the challenges of storage and transportation of hydrogen. In this work, Ni-alloyed Pd nanoparticles (NPs) with varied Pd-Ni ratios supported on carbon black (PdNix /C) are prepared and their catalytic performance for the carbohydrazide electro-oxidation reaction is investigated. The catalytic performance of PdNix /C NPs is significantly improved in comparison to Pd/C NPs. The current density of PdNix /C NPs with optimized Pd-Ni atom ratio can reach 3.26 A mg-1 metal at a potential of 0.4 V (vs reversible hydrogen electrode), which is an increase of 2.4 times compared to that of Pd/C. The density functional theory calculation indicates the enhanced catalytic activity is caused by the change of adsorption energy of carbohydrazide molecules on the metal surface. It exhibits a volcano relationship between the adsorption energy and the catalytic current density of PdNix /C with varied Pd-Ni atom ratios.

Inducing microstructural changes in Nafion by incorporating graphitic carbon nitride to enhance the vanadium-blocking effect.

Two-dimensional graphitic carbon nitride (g-C3N4) nanosheets are introduced into a Nafion matrix to prepare a 'vanadium-blocking' recast Nafion membrane for vanadium redox flow battery (VRFB) applications. After 0.2 wt% g-C3N4 nanosheets are incorporated, the vanadium ion permeability of the composite membrane decreases from 3.3 × 10-7 cm2 min-1 to 3.8 × 10-9 cm2 min-1, which is a reduction of two orders of magnitude in comparison to the pristine recast Nafion membrane. This satisfactory result contributes to the physical blocking effect as well as the Donnan effect from the 2D morphology and functional amino groups of g-C3N4 nanosheets. Notably, this work reveals that the g-C3N4 nanosheets can help reinforce the vanadium-blocking effect by changing the microstructure of Nafion in addition to the well-known effects mentioned above. The g-C3N4 nanosheets induce shrinkage in the original spherical structure of the ion cluster and generate a new lamellar structure. Correspondingly, the amorphous phase of Nafion is interrupted, and the membrane crystallinity is reduced. The VRFB with an optimized composite membrane achieves a high coulombic efficiency of 97% and an energy efficiency of 83% at a current density of 160 mA cm-2. Meanwhile, the battery exhibited excellent lifetime stability during a 100 charge-discharge cycling test.

An efficient cluster model to describe the oxygen reduction reaction activity of metal catalysts: a combined theoretical and experimental study.

A simple and efficient cluster model containing only seven metal atoms was proposed to investigate the oxygen reduction reaction (ORR) activity of various metal catalysts by density functional theory (DFT) calculation. The model was validated by comparing ORR volcano plots obtained from the cluster model in this work and the slab model in the literature. We then used this model to investigate the influence of the support of Ag nanoparticles on ORR activity, which is hard to describe by the slab model. The calculations revealed the binding energy of oxygen atoms on Ag/COOH-CNTs or Ag/OH-CNTs changed to 2.04 and 2.09 eV respectively, in comparison to that of Ag/CNTs (2.13 eV). As a result, the ORR current density improved to 2.24 and 1.88 mA cm-2 at the potential of 0.7 V (vs. RHE) for Ag/COOH-CNTs and Ag/OH-CNTs respectively, in comparison to that of Ag/CNTs (1.66 mA cm-2). The cluster model could simultaneously reduce the computing time and make it possible to consider the influence of catalyst supports, which would provide new insight to design more effective ORR metal catalysts.

The electrocatalytic characterization and mechanism of carbon nanotubes with different numbers of walls for the VO2+/VO2+ redox couple.

Carbon nanotubes (CNTs) have been applied as catalysts in the VO2+/VO2+ redox, whereas the mechanism of CNTs for the redox reaction is still unclear. In this work, the mechanism of the VO2+/VO2+ redox is investigated by comparing the electrocatalytic performance of CNTs with different distributions. For different CNTs, the peak current density of the VO2+/VO2+ redox increases with increasing content of oxygen-functional groups on the surface of CNTs, especially the carboxyl group which is proved as active sites for the redox reaction. Moreover, the reversibility of the VO2+/VO2+ redox decreases with increasing defects of CNTs, as the defects affect the charge transfer of the catalytic reaction. Nevertheless, when a multi-walled CNT sample is oxidized to achieve a high content of oxygen functional groups and defects, the peak current density of the redox reaction increases from 38.5 mA mg-1 to 45.4 mA mg-1 whilst the peak potential separation (ΔEp) also increases from 0.176 V to 0.209 V. Overall, a balance between the oxygen functional groups and the defects of CNTs affects the peak current and the reversibility for the VO2+/VO2+ redox.

A Bunch-Like Tertiary Amine Grafted Polysulfone Membrane for VRFBs with Simultaneously High Proton Conductivity and Low Vanadium Ion Permeability.

Novel polysulfone membranes with bunch-like tertiary amine groups are synthesized with high ion selectivity and outstanding chemical stability for vanadium redox flow batteries (VRFBs). The bunch-like tertiary amine groups simultaneously act as an ionic conductor for proton hopping and vanadium ion transport obstacles. The performance of the membrane is tuned via controlling the grafting degree of the chloromethylated polysulfone. The results show that membranes show increasing proton over vanadium ion (σ/p) selectivity with increasing functional tertiary groups. VRFBs assembled with the prepared membranes demonstrate an impressive Coulombic efficiency of 98.9% and energy efficiency of 90.9% at a current density of 50 mA cm-2 . Furthermore, the prepared membrane reported in this work shows excellent stability in 1 m VO2+ solution at 35 °C over 240 h. Overall, the synthesized polymers provide a new insight into the design of high-performance membranes toward VRFB applications.

Dual consistency regularization with subjective logic for semi-supervised medical image segmentation.

Semi-supervised learning plays a vital role in computer vision tasks, particularly in medical image analysis. It significantly reduces the time and cost involved in labeling data. Current methods primarily focus on consistency regularization and the generation of pseudo labels. However, due to the model's poor awareness of unlabeled data, aforementioned methods may misguide the model. To alleviate this problem, we propose a dual consistency regularization with subjective logic for semi-supervised medical image segmentation. Specifically, we introduce subjective logic into our semi-supervised medical image segmentation task to estimate uncertainty, and based on the consistency hypothesis, we construct dual consistency regularization under weak and strong perturbations to guide the model's learning from unlabeled data. To evaluate the performance of the proposed method, we performed experiments on three widely used datasets: ACDC, LA, and Pancreas. Experiments show that the proposed method achieved improvement compared with other state-of-the-art (SOTA) methods.

Dual-Proton Conductor for Fuel Cells with Flexible Operational Temperature.

The properties of proton conductors determine the operating temperature range of fuel cells. Typically, phosphoric acid (PA) proton conductors exhibit excellent proton conductivity owing to their high proton dissociation and self-diffusion abilities. However, at low temperatures or high current densities, water-induced PA loss causes rapid degradation of cell performance. Maintaining efficient and stable proton conductivity within a flexible temperature range can significantly reduce the start-up temperature of PA-doped proton exchange membrane fuel cells. In this study, a dual-proton conductor composed of an organic phosphonic acid (ethylenediamine tetramethylene phosphonic acid, EDTMPA) and an inorganic PA is developed for proton exchange membranes. The proposed dual-proton conductor can operate within a flexible temperature range of 80-160 °C, benefiting from the strong interaction between EDTMPA and PA, and the enhanced proton dissociation. Fuel cells with the EDTMPA-PA dual-proton conductor showed excellent cell stability at 80 °C. In particular, under the high current density of 1.5 A cm-2 at 160 °C, the voltage decay rate of the fuel cell with the dual-proton conductor is one-thousandth of that of the fuel cell with PA-only proton conductor, indicating excellent stability.

Linkage conversions in single-crystalline covalent organic frameworks.

Single-crystal X-ray diffraction is a powerful characterization technique that enables the determination of atomic arrangements in crystalline materials. Growing or retaining large single crystals amenable to it has, however, remained challenging with covalent organic frameworks (COFs), especially suffering from post-synthetic modifications. Here we show the synthesis of a flexible COF with interpenetrated qtz topology by polymerization of tetra(phenyl)bimesityl-based tetraaldehyde and tetraamine building blocks. The material is shown to be flexible through its large, anisotropic positive thermal expansion along the c axis (αc = +491 × 10-6 K-1), as well as through a structural transformation on the removal of solvent molecules from its pores. The as-synthesized and desolvated materials undergo single-crystal-to-single-crystal transformation by reduction and oxidation of its imine linkages to amine and amide ones, respectively. These redox-induced linkage conversions endow the resulting COFs with improved stability towards strong acid; loading of phosphoric acid leads to anhydrous proton conductivity up to ca. 6.0 × 10-2 S cm-1.

Evaluating the interfacial reaction kinetics of the bipolar membrane interface in the bipolar membrane fuel cell.

A reaction kinetic model of the bipolar membrane interface in the bipolar membrane fuel cell (BPMFC) was proposed based on the p-n junction theory and chemical reaction kinetics. It verified the self-humidification feasibility of the BPMFC successfully.

Layer-by-layer self-assembly in the development of electrochemical energy conversion and storage devices from fuel cells to supercapacitors.

As one of the most effective synthesis tools, layer-by-layer (LbL) self-assembly technology can provide a strong non-covalent integration and accurate assembly between homo- or hetero-phase compounds or oppositely charged polyelectrolytes, resulting in highly-ordered nanoscale structures or patterns with excellent functionalities and activities. It has been widely used in the developments of novel materials and nanostructures or patterns from nanotechnologies to medical fields. However, the application of LbL self-assembly in the development of highly efficient electrocatalysts, specific functionalized membranes for proton exchange membrane fuel cells (PEMFCs) and electrode materials for supercapacitors is a relatively new phenomenon. In this review, the application of LbL self-assembly in the development and synthesis of key materials of PEMFCs including polyelectrolyte multilayered proton-exchange membranes, methanol-blocking Nafion membranes, highly uniform and efficient Pt-based electrocatalysts, self-assembled polyelectrolyte functionalized carbon nanotubes (CNTs) and graphenes will be reviewed. The application of LbL self-assembly for the development of multilayer nanostructured materials for use in electrochemical supercapacitors will also be reviewed and discussed (250 references).

Machine Learning Study on Microwave-Assisted Batch Preparation and Oxygen Reduction Performance of Fe-N-C Catalysts.

The Fe-N-C catalyst represents one of the most promising candidates for replacing platinum-based catalysts toward the oxygen reduction reaction. The pivotal factor in the successful integration of Fe-N-C catalysts within applications is the attainment of a large-scale production capability. Microwave-assisted pyrolysis offers various advantages, including enhanced energy and time efficiency, uniform heating, and high yield in single-batch processes. These characteristics render it exceptionally suitable for the mass production of catalysts. Through a synergistic approach involving machine learning techniques and microscopic characterization, we discerned performance trends and underlying mechanisms within batch-synthesized Fe-N-C catalysts under microwave-assisted preparation conditions. Machine learning analysis revealed that the precursor mass exerts the most substantial influence on product performance. Furthermore, microscopic characterization unveiled that these influencing factors impact catalyst performance by modulating the degree of agglomeration. Our research introduces an efficacious machine learning model for prognosticating performance and dissecting the influencing factors pertinent to Fe-N-C catalyst synthesis within a microwave system.

Prognosis Forecast of Re-Irradiation for Recurrent Nasopharyngeal Carcinoma Based on Deep Learning Multi-Modal Information Fusion.

Radiation therapy is the primary treatment for recurrent nasopharyngeal carcinoma. However, it may induce necrosis of the nasopharynx, leading to severe complications such as bleeding and headache. Therefore, forecasting necrosis of the nasopharynx and initiating timely clinical intervention has important implications for reducing complications caused by re-irradiation. This research informs clinical decision-making by making predictions on re-irradiation of recurrent nasopharyngeal carcinoma using deep learning multi-modal information fusion between multi-sequence nuclear magnetic resonance imaging and plan dose. Specifically, we assume that the hidden variables of model data can be divided into two categories: task-consistency and task-inconsistency. The task-consistency variables are characteristic variables contributing to target tasks, while the task-inconsistency variables are not apparently helpful. These modal characteristics are adaptively fused when the relevant tasks are expressed through the construction of supervised classification loss and self-supervised reconstruction loss. The cooperation of supervised classification loss and self-supervised reconstruction loss simultaneously reserves the information of characteristic space and controls potential interference simultaneously. Finally, multi-modal fusion effectively fuses information through an adaptive linking module. We evaluated this method on a multi-center dataset. and found the prediction based on multi-modal features fusion outperformed predictions based on single-modal, partial modal fusion or traditional machine learning methods.

CT-based radiomics for predicting radio-chemotherapy response and overall survival in nonsurgical esophageal carcinoma.

Background: To predict treatment response and 2 years overall survival (OS) of radio-chemotherapy in patients with esophageal cancer (EC) by radiomics based on the computed tomography (CT) images. Methods: This study retrospectively collected 171 nonsurgical EC patients treated with radio-chemotherapy from Jan 2010 to Jan 2019. 80 patients were randomly divided into training (n=64) and validation (n=16) cohorts to predict the radiochemotherapy response. The models predicting treatment response were established by Lasso and logistic regression. A total of 156 patients were allocated into the training cohort (n=110), validation cohort (n=23) and test set (n=23) to predict 2-year OS. The Lasso Cox model and Cox proportional hazards model established the models predicting 2-year OS. Results: To predict the radiochemotherapy response, WFK as a radiomics feature, and clinical stages and clinical M stages (cM) as clinical features were selected to construct the clinical-radiomics model, achieving 0.78 and 0.75 AUC (area under the curve) in the training and validation sets, respectively. Furthermore, radiomics features called WFI and WGI combined with clinical features (smoking index, pathological types, cM) were the optimal predictors to predict 2-year OS. The AUC values of the clinical-radiomics model were 0.71 and 0.70 in the training set and validation set, respectively. Conclusions: This study demonstrated that planning CT-based radiomics showed the predictability of the radiochemotherapy response and 2-year OS in nonsurgical esophageal carcinoma. The predictive results prior to treatment have the potential to assist physicians in choosing the optimal therapeutic strategy to prolong overall survival.

Advancements of Polyvinylpyrrolidone-Based Polymer Electrolyte Membranes for Electrochemical Energy Conversion and Storage Devices.

Polymer electrolyte membranes (PEMs) play vital roles in electrochemical energy conversion and storage devices, such as polymer electrolyte membrane fuel cell (PEMFC), redox flow battery, and water electrolysis. As the crucial component of these devices, PEMs need to possess high ion conductivity and electronic insulation, remarkable mechanical and chemical stability, and outstanding isolation function for the materials on both sides of the cathode and anode. Polyvinylpyrrolidone has received widespread attention in the research of PEMs owing to its tertiary amine basic groups and exceptional hydrophilic properties. This review focuses on the application status of polyvinylpyrrolidone-based PEMs in PEMFC, vanadium redox flow battery, and alkaline water electrolysis, and describes in detail the key scientific problems in these fields, providing constructive suggestions and guidance for the application of polyvinylpyrrolidone-based PEMs in electrochemical energy conversion and storage devices.

Enhanced oxygen reduction at Pd catalytic nanoparticles dispersed onto heteropolytungstate-assembled poly(diallyldimethylammonium)-functionalized carbon nanotubes.

Both Keggin-type phosphotungstic acid (HPW) and Pd are not prominent catalysts towards the oxygen reduction (ORR), but their composite Pd-HPW catalyst produces a significantly higher electrochemical activity for the ORR in acidic media. The novel composite catalyst was synthesized by self-assembly of HPW on multi-walled carbon nanotubes (MWCNTs) via the electrostatic attraction between negatively charged HPW and positively charged poly(diallyldimethylammonium (PDDA)-wrapped MWCNTs, followed by dispersion of Pd nanoparticles onto the HPW-PDDA-MWCNT assembly. The as-prepared catalyst was characterized by transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectroscopy, thermal gravimetric analysis (TGA), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). TEM images show that Pd nanoparticles were uniformly dispersed on the surface of MWCNTs even when the Pd loading was increased to 60 wt%. Electrochemical activity of the catalysts for the ORR was evaluated by steady state polarization measurements using a rotating disk electrode. Compared with the acid treated MWCNTs, Pd nanoparticles supported on the HPW-assembled MWCNTs show a much higher ORR activity that is comparable to conventional Pt/C catalysts. The high electrocatalytic activities could be related to high dispersion of Pd nanoparticles as well as synergistic effects originating from the high proton conductivity of HPW. The Pd/HPW-PDDA-MWCNTs system as the cathode catalyst in proton exchange membrane fuel cells is demonstrated.