Reliability-Based Design Optimization for Polymer Electrolyte Membrane Fuel Cells: Tackling Dimensional Uncertainties in Manufacturing and Their Effects on Costs of Cathode Gas Diffusion Layer and Bipolar Plates.

Polymer Electrolyte Membrane Fuel Cells (PEMFCs) have emerged as a pivotal technology in the automotive industry, significantly contributing to the reduction of greenhouse gas emissions. However, the high material costs of the gas diffusion layer (GDL) and bipolar plate (BP) create a barrier for large scale commercial application. This study aims to address this challenge by optimizing the material and design of the cathode, GDL and BP. While deterministic design optimization (DDO) methods have been extensively studied, they often fall short when manufacturing uncertainties are introduced. This issue is addressed by introducing reliability-based design optimization (RBDO) to optimize four key PEMFC design variables, i.e., gas diffusion layer thickness, channel depth, channel width and land width. The objective is to maximize cell voltage considering the material cost of the cathode gas diffusion layer and cathode bipolar plate as reliability constraints. The results of the DDO show an increment in cell voltage of 31 mV, with a reliability of around 50% in material cost for both the cathode GDL and cathode BP. In contrast, the RBDO method provides a reliability of 95% for both components. Additionally, under a high level of uncertainty, the RBDO approach reduces the material cost of the cathode GDL by up to 12.25 $/stack, while the material cost for the cathode BP increases by up to 11.18 $/stack Under lower levels of manufacturing uncertainties, the RBDO method predicts a reduction in the material cost of the cathode GDL by up to 4.09 $/stack, with an increase in the material cost for the cathode BP by up to 6.71 $/stack, while maintaining a reliability of 95% for both components. These results demonstrate the effectiveness of the RBDO approach in achieving a reliable design under varying levels of manufacturing uncertainties.

DFT-Machine Learning Approach for Accurate Prediction of pKa.

In this study, we propose a novel method of pKa prediction in a diverse set of acids, which combines density functional theory (DFT) method with machine learning (ML) methods. First, the DFT method with B3LYP/6-31++G**/SM8 is used to predict pKa, yielding a mean absolute error of 1.85 pKa units. Subsequently, such pKa values predicted from the DFT method are employed as one of 10 molecular descriptors for developing ML models trained on experimental data. Kernel Ridge Regression (KRR), Gaussian Process Regression, and Artificial Neural Network are optimized using three Pipelines: Pipeline 1 involving only hyperparameter optimization (HPO), Pipeline 2 involving HPO followed by a relative contribution analysis (RCA) and recursive feature elimination (RFE), and Pipeline 3 involving HPO followed by RCA and RFE on an expanded set of composite features. Finally, it is demonstrated that KRR with Pipeline 3 yields optimal pKa prediction at an MAE of 0.60 log units. This algorithm was then utilized to predict the pKa of 37 novel acids. The two most important features were determined to be the number of hydrogen atoms in the molecule and the degree of oxidation of the acid. The predicted pKa values were documented for future reference.

Transforaminal Epidural Steroid Injection for Zoster-Related Pain: The Golden Period for the Best Outcome.

BACKGROUND: Zoster-related pain (ZRP) has many negative effects on a patient's quality of life. The transforaminal steroid injection (TFESI), which reduces neural inflammation and pain, has been advocated by pain physicians. Many reports demonstrated that early administration of TFESI showed better efficacy; however, the golden period during which TFESI is most effective remains unclear. OBJECTIVES: This multicentre retrospective cohort study aimed to identify the golden period by which TFESI yields the best outcome in patients with ZRP. STUDY DESIGN: Multicenter, retrospective cohort study. SETTING: University-affiliated hospitals. METHODS: After performing the TFESI in patients with ZRP, the patients were classified into two groups: the effective group (E) and the not effective group (N) based on the changes in the pain intensity 3 months after the TFESI. The receiver operating characteristic (ROC) curve analysis was used to assess the cut-off time point for predicting TFESI effectiveness. Furthermore, a logistic regression analysis was performed to identify patients' factors associated with a successful treatment outcome. RESULT: Of the 302 patients, 186 and 116 patients were classified into the E and N group, respectively. ROC curve analysis showed that the best cut-off time point for TFESI was 12 weeks (95% confidence interval [CI]; 10-14 weeks) after the onset of HZ. The only variable associated with a favorable outcome was a symptom duration of ? 12 weeks compared with > 12 weeks (Odd ratio, 0.107; 95% CI, 0.055-0.205; P < 0.001). Other patient variables were not significantly associated with the effectiveness of TFESI. TFESI was most effective when administered within 12 weeks of the onset of herpes zoster. LIMITATION: This study was not a prospective randomized controlled trial (RCT) and the follow-up period was only 3 months after TFESI. CONCLUSION: TFESI is more effective when administered within 12 weeks of onset of herpes zoster.

Conformation-modulated three-dimensional electrocatalysts for high-performance fuel cell electrodes.

Unsupported Pt electrocatalysts demonstrate excellent electrochemical stability when used in polymer electrolyte membrane fuel cells; however, their extreme thinness and low porosity result in insufficient surface area and high mass transfer resistance. Here, we introduce three-dimensionally (3D) customized, multiscale Pt nanoarchitectures (PtNAs) composed of dense and narrow (for sufficient active sites) and sparse (for improved mass transfer) nanoscale building blocks. The 3D-multiscale PtNA fabricated by ultrahigh-resolution nanotransfer printing exhibited excellent performance (45% enhanced maximum power density) and high durability (only 5% loss of surface area for 5000 cycles) compared to commercial Pt/C. We also theoretically elucidate the relationship between the 3D structures and cell performance using computational fluid dynamics. We expect that the structure-controlled 3D electrocatalysts will introduce a new pathway to design and fabricate high-performance electrocatalysts for fuel cells, as well as various electrochemical devices that require the precision engineering of reaction surfaces and mass transfer.

Effect of the Side-Chain Length in Perfluorinated Sulfonic and Phosphoric Acid-Based Membranes on Nanophase Segregation and Transport: A Molecular Dynamics Simulation Approach.

The effect of side-chain length on the nanophase-segregated structure and transport in perfluorinated sulfonic acid (PFSA)-based and perfluorinated phosphoric acid (PFPA)-based membranes is investigated at 20 and 5 wt % water content conditions using a molecular dynamics simulation method. It is found using the pair correlation analysis that the longer side chain leads to more developed local water structures in the water phase at 20 wt % water content, observable in both membrane chemistries albeit more distinct in PFPA-based membranes. It is also confirmed from the structure factor analysis that large-scale nanophase segregation is enhanced with increasing side-chain length for PFPA membranes, whereas no significant change is observed at these scales for PFSA membranes. Next, it is revealed that the proton transport is increased by 0.004 S/cm in PFSA-based membranes with increasing side-chain length due to the enhanced vehicular and hopping mechanisms, whereas the proton transport in PFPA-based membranes is decreased by 0.002 S/cm despite improved nanophase segregation. As confirmed by the pair correlation function analysis, the diminished proton transport in PFPA-based membranes is attributed to the molecular association of phosphate groups with hydronium ions via hydrogen bond in the longer side-chain case, which is namely a hydronium-mediated bridge configuration. Such bridge configurations and correspondingly similar trends in proton transport are also observed at 5 wt % water content condition to a lesser extent. Our simulation study demonstrates that the proton transport is affected by the hydrogen-bonding network as well as by the nanophase segregation.

Synergetic Structural Transformation of Pt Electrocatalyst into Advanced 3D Architectures for Hydrogen Fuel Cells.

A new direction for developing electrocatalysts for hydrogen fuel cell systems has emerged, based on the fabrication of 3D architectures. These new architectures include extended Pt surface building blocks, the strategic use of void spaces, and deliberate network connectivity along with tortuosity, as design components. Various strategies for synthesis now enable the functional and structural engineering of these electrocatalysts with appropriate electronic, ionic, and electrochemical features. The new architectures provide efficient mass transport and large electrochemically active areas. To date, although there are few examples of fully functioning hydrogen fuel cell devices, these 3D electrocatalysts have the potential to achieve optimal cell performance and durability, exceeding conventional Pt powder (i.e., Pt/C) electrocatalysts. This progress report highlights the various 3D architectures proposed for Pt electrocatalysts, advances made in the fabrication of these structures, and the remaining technical challenges. Attempts to develop design rules for 3D architectures and modeling, provide insights into their achievable and potential performance. Perspectives on future developments of new multiscale designs are also discussed along with future study directions.

Effects of porous properties on cold-start behavior of polymer electrolyte fuel cells from sub-zero to normal operating temperatures.

In this investigation, a parametric study was performed using the transient cold-start model presented in our previous paper, in which the ice melting process and additional constitutive relations were newly included for transient cold-start simulations of polymer electrolyte fuel cells (PEFCs) from a sub-zero temperature (-20°C) to a normal operating temperature (80°C). The focus is placed on exploring the transient cold-start behavior of a PEFC for different porous properties of the catalyst layer (CL) and gas diffusion layer (GDL). This work elucidates the detailed effects of these properties on key cold-start phenomena such as ice freezing/melting and membrane hydration/dehydration processes. In particular, the simulation results highlight that designing a cathode CL with a high ionomer fraction helps to retard the rate of ice growth whereas a high ionomer fraction in the anode CL is not effective to mitigate the anode dry-out and membrane dehydration issues during PEFC cold-start.