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Endosperm as the storage organ of starch and protein in cereal crops largely determines grain yield and quality. Despite the fact that several pentatricopeptide repeat (PPR) proteins required for endosperm development have been identified in rice, the molecular mechanisms of many P-type PPR proteins in endosperm development remains unclear. Here, we isolated a rice floury endosperm mutant ppr5 that developed small starch grains and an abnormal aleurone layer, accompanied by decreased starch, protein, and amylose contents. Map-based cloning combined with a complementation test demonstrated that PPR5 encodes a P-type PPR protein that is localized to the mitochondria. The mutation in PPR5 caused reduced splicing efficiency of mitochondrial NADH dehydrogenase 4 (nad4) gene intron 3 and reduced complex I assembly and activity. Loss of PPR5 function greatly up-regulated expression of alternative oxidases (AOXs), reduced ATP production, and affected mitochondrial morphology. We demonstrate that PPR5, as a P-type PPR protein, is required for mitochondrial function and endosperm development by controlling the cis-splicing of mitochondrial nad4 intron 3.

Abstract High yield is the primary criterion for the development of new cultivars at the University of Guelph common bean breeding program. As a complex trait, yield is encoded by a number of genes contributing minor effects while also being significantly affected by environmental factors. Genes that increase yield with fixed resources have their effects by increasing input use efficiency. When suppressed, the BnMicEmUP gene has a positive effect on seed production in Arabidopsis. Preliminary work has shown that ortholog of this gene (Phvul.009G190100) exists in common bean, and its expression was negatively correlated with yield in a field test of 10 navy bean cultivars with different yield potentials. The aim of this research was to characterize the Phvul.009G190100 gene and to develop gene‐based marker(s) to test for alleles associated with high yield in common bean. A database search identified a second yield‐related gene (Phvul.009G202100) on the same chromosome (Pv09), which is a homolog to Phvul.009G190100. Both genes contain a DUF1118 protein domain, which has the molecular characteristics of a basic leucine zipper (bZIP) transcription factor, based on in silico analysis. Temperature switch polymerase chain reaction (PCR) markers, which were developed for both genes, were significantly associated with yield and maturity in 42 bean genotypes belonging to different market classes. The work will benefit bean breeding programs by making them more efficient in selecting high yielding cultivars, and it will directly benefit bean producers through accelerated access to new, high yielding cultivars.

A fuel cell is an energy conversion device that utilizes hydrogen energy through an electrochemical reaction. Despite their many advantages, such as high efficiency, zero emissions, and fast startup, fuel cells have not yet been fully commercialized due to deficiencies in service life, cost, and performance. Efficient evaluation methods for performance and service life are critical for the design and optimization of fuel cells. The purpose of this paper was to review the application of common machine learning algorithms in fuel cells. The significance and status of machine learning applications in fuel cells are briefly described. Common machine learning algorithms, such as artificial neural networks, support vector machines, and random forests are introduced, and their applications in fuel cell performance prediction and optimization are comprehensively elaborated. The review revealed that machine learning algorithms can be successfully used for performance prediction, service life prediction, and fault diagnosis in fuel cells, with good accuracy in solving nonlinear problems. Combined with optimization algorithms, machine learning models can further carry out the optimization of design and operating parameters to achieve multiple optimization goals with good accuracy and efficiency. It is expected that this review paper could help the reader comprehend the state of the art of machine learning applications in fuel fuels and shed light on further development directions in fuel cell research.

The performance of a polymer electrolyte membrane fuel cell (PEMFC) closely depends on internal reactant diffusion and liquid water removal. As one of the key components of PEMFCs, bipolar plates (BPs) provide paths for reactant diffusion and product transport. Therefore, to achieve high fuel cell performance, one key issue is designing BPs with a reasonable flow field. This paper provides a comprehensive review of various modifications of the conventional parallel flow field, interdigitated flow field, and serpentine flow field to improve fuel cells’ overall performance. The main focuses for modifications of conventional flow fields are flow field shape, length, aspect ratio, baffle, trap, auxiliary inlet, and channels, as well as channel numbers. These modifications can partly enhance reactant diffusion and product transport while maintaining an acceptable flow pressure drop. This review also covers the detailed structural description of the newly developed flow fields, including the 3D flow field, metal flow field, and bionic flow field. Moreover, the effects of these flow field designs on the internal physical quantity transport and distribution, as well as the fuel cells’ overall performance, are investigated. This review describes state-of-the-art flow field design, identifies the key research gaps, and provides references and guidance for the design of high-performance flow fields for PEMFCs in the future.

The convection thermal coupling between adjacent power devices in power converters is dependent on the ambient temperature. When the ambient temperature changes, the convection thermal coupling also changes. This results in an inaccurate thermal model that causes errors in the prediction of the thermal distribution and junction temperature based on a fixed ambient temperature for power devices in converters application. To solve this variable-ambient-temperature-related issue, a thermal coupling experiment for semiconductor power devices (the MOSFET and diode) was performed to discuss the influence of the thermal coupling effect between adjacent devices and the FEM (Finite Element Method) thermal models for the power devices considering the convection thermal coupling are established. Through these simulations, the junction temperatures of devices under different ambient temperatures were obtained, and the relationships between the junction temperature and ambient temperatures were established. Moreover, the junction temperatures of power devices under different ambient temperatures were calculated and temperature distributions are analyzed in this paper. This method shows a strong significance and has potential applications for high-efficiency and high-power density converter designs.

Abstract The pore size distribution of the catalyst layer (CL) significantly influences the effective diffusion coefficient (EDC) of the reactant gas due to the Knudsen diffusion effect in the proton exchange membrane fuel cell (PEMFC). In this study, a novel fractal method is proposed to evaluate the Knudsen diffusion based on the pore size distribution of the CL, which is then incorporated in the calculation of the EDC of oxygen in the CL. It suggests that the EDC of oxygen obtained from the proposed method considering the pore size distribution is greatly smaller than that obtained from the conventional Bruggeman correlation, hence resulting in much smaller maximum current density of PEMFC.

Proton exchange membranes with short-pathway through-plane proton conductivity are attractive for proton exchange membrane fuel cells. Here the authors align proton conducting channels orthogonal to the plane of composite proton exchange membranes using a magnetic field for improved fuel cell performance.

Based on a coupled finite element method (FEM) and computational fluid dynamics (CFD) model, the structural deformation and performance of a proton exchange membrane fuel cell (PEMFC) under different membrane water contents are studied. The water absorption behavior of the membrane is investigated experimentally to obtain its expansion coefficient with water content, and the Young’s modulus of the membrane and catalyst (CL) are obtained through a tensile experiment. The simulation results show that the deformation of the membrane increases with water content, and membrane swelling under the channel is larger than that under the rib, forming a surface bump under the channel. The structural changes caused by the membrane water content have little effect on the performance of PEMFC in the low-current density range; while its influence is significant in the medium- and high-current density range. A medium membrane water content value of 12 achieves the best fuel cell performance due to the balance of membrane resistance and mass transport.

The degradation of the membrane electrode assembly, originating from microstructural changes in the catalyst layer, inhibits the commercialization of polymer electrolyte membrane fuel cell. In particular, changes in relative humidity during starting/working conditions cause crack growth and propagation within the catalyst layer, but the reason is still not clear. Here, accelerated stress tests are designed from starting conditions (25 °C, 45% RH) to working conditions with different RHs (20%, 45% and 99%) at 85 °C for different cycles. For low working RH of 20%, no obvious change can be observed, while the accelerated stress test to high working RH induces apparent changes in the catalyst layer structure, with ionomer aggregation and migration leading to crack generation. The obtained results indicated that ionomer migration plays an important role in the structure changes of catalyst layer, suggesting that the design of ionomer with high stability and reliable water retention is necessary to improve the structural stability of the catalyst layer. Keywords: Catalyst layer degradation, Ionomer migration, Polymer electrolyte membrane fuel cell, Membrane electrode assembly

Water transport and removal in the proton exchange membrane fuel cell (PEMFC) is critically important to fuel cell performance, stability, and durability. Water emerging locations on the membrane-electrode assembly (MEA) surface and the channel surface wettability significantly influence the water transport and removal in PEMFC. In most simulations of water transport and removal in the PEMFC flow channel, liquid water is usually introduced at the center of the MEA surface, which is fortuitous, since water droplet can emerge randomly on the MEA surface in PEMFC. In addition, the commonly used no-slip wall boundary condition greatly confines the water sliding features on hydrophobic MEA/channel surfaces, degrading the simulation accuracy. In this study, water droplet is introduced with various locations along the channel width direction on the MEA surface, and water transport and removal is investigated numerically using an improved model incorporating the sliding flow property by using the shear wall boundary condition. It is found that the water droplet can be driven to the channel sidewall by aerodynamics when the initial water location deviates from the MEA center to a certain amount, forming the water corner flow in the flow channel. The channel surface wettability on the water transport is also studied and is shown to have a significant impact on the water corner flow in the flow channel.