Species transport in a high-pressure oxygen-generating proton-exchange membrane electrolyzer

Proton-exchange membrane (PEM) electrolyzers have been a source of interest due to the ability of these electrolyzers to produce product gases at high differential pressures across the membrane electrode assembly (MEA) without the need for external compression. This work studies species transport within the membrane of a high-pressure oxygen-generating water electrolyzer using the dusty-fluid model (DFM) for the case involving liquid water being supplied to the cathode. The model was calibrated against experimental polarization data from Hamilton Sundstrand's high-pressure oxygen generating assembly (HPOGA) at varying differential oxygen pressures. The governing equations were cast in a non-dimensional form to examine the dehydration of the cell with increasing current density and differential pressure expressed in terms of a Damkohler number and the ratio of the membrane diffusion coefficient to the species diffusion coefficient. It was determined that the dehydration of the cell occurred at an approximately constant Damkohler number of 0.196 ± 0.004 regardless of the ratio of the pressure difference. It was also observed that electro-osmotic drag had a strong influence on the dehydration of the cell since the drag coefficient directly and substantially elevated the required water flux for a given current density operation above what was needed based only on the stoichiometry of the chemical reactions.     

An investigation of the cost and performance of a solar-powered LED light designed as an alternative to candles in Zambia: A project case study

The economic, health, and environmental costs of kerosene, candles, and other fuel-based lighting are well-documented. As a result of efforts by the World Bank and other organizations, numerous lighting products incorporating solar photovoltaic and light-emitting diodes (LEDs) have been introduced in Sub-Saharan Africa. The category of solar portable lights is increasingly popular, in part because the lower retail price of these lights can make them more affordable to lower-income households. The UC Davis Lighting the Way Zambia project sought to explore the minimum costs and performance requirements for a solar portable light targeting candle and kerosene users in Zambia. This paper discusses the product design process, including the establishment of performance requirements and metrics, as well as a cost-optimization exercise used to evaluate key electronic components. The cost structure of the final design is presented with end-user costs and actual manufacturing costs. The results suggest that an 18-lumen solar portable light with a 4-h run time would meet many users’ needs and can be manufactured for less than US$9 per unit, with a cost of $0.34 per 1000 l m-h and a payback period of around 6 months.     

On the Use of Surfactant-Complexed Chitosan for Toughening 3D Printed Polymethacrylate Composites

This work reports a simple approach to prepare toughened 3D-printed polymethacrylate (PMA) composites using surfactant-modified chitosan (SMCS) particles at loadings between 2–10 wt%. Chitosan (CS) is modified with anionic surfactant, sodium dodecyl sulfate, via ionic complexation to facilitate compatibility and dispersion of CS to PMA matrix by non-covalent interactions between the components. The study successfully demonstrates high-accuracy 3D printing of composites with significant improvements in the overall mechanical properties. The composite with the best loading of 8 wt% SMCS shows a tensile modulus of 1.23 ± 0.05 GPa, a tensile strength at 49.8 ± 0.96 MPa, a yield stress at 33.3 ± 1.48 MPa, and a strain-at-failure 10.3 ± 0.61%, which are 45%, 40%, 32%, and 68% higher than neat PMA, respectively. This provides a significant improvement in toughness at 4.92 ± 0.55 MJ m⁻³ for the composite, 184% higher than that of neat PMA. The marked increase in toughness is due to enhanced filler-matrix interactions which improve the ability of the 3D printed composite to absorb energy under tensile load. The results from this work provide new understandings into the strategies for design and preparation of stereolithography 3D printed materials reinforced with toughening fillers from renewable resources.     

Targeted Degradation of PARP14 Using a Heterobifunctional Small Molecule

PARP14 is an interferon-stimulated gene that is overexpressed in multiple tumor types, influencing pro-tumor macrophage polarization as well as suppressing the antitumor inflammation response by modulating IFN-γ and IL-4 signaling. PARP14 is a 203 kDa protein that possesses a catalytic domain responsible for the transfer of mono-ADP-ribose to its substrates. PARP14 also contains three macrodomains and a WWE domain which are binding modules for mono-ADP-ribose and poly-ADP-ribose, respectively, in addition to two RNA recognition motifs. Catalytic inhibitors of PARP14 have been shown to reverse IL-4 driven pro-tumor gene expression in macrophages, however it is not clear what roles the non-enzymatic biomolecular recognition motifs play in PARP14-driven immunology and inflammation. To further understand this, we have discovered a heterobifunctional small molecule designed based on a catalytic inhibitor of PARP14 that binds in the enzyme's NAD⁺-binding site and recruits cereblon to ubiquitinate it and selectively target it for degradation.     

Common cause failure analysis of cyber–physical systems situated in constructed environments

While cyber–physical system sciences are developing methods for studying reliability that span domains such as mechanics, electronics and control, there remains a lack of methods for investigating the impact of the environment on the system. External conditions such as flooding, fire or toxic gas may damage equipment and failing to foresee such possibilities will result in invalid worst-case estimates of the safety and reliability of the system. Even if single component failures are anticipated, abnormal environmental conditions may result in common cause failures that cripple the system. This paper proposes a framework for modeling interactions between a cyber–physical system and its environment. The framework is limited to environments consisting of spaces with clear physical boundaries, such as power plants, buildings, mines and urban underground infrastructures. The purpose of the framework is to support simulation-based risk analysis of an initiating event such as an equipment failure or flooding. The functional failure identification and propagation (FFIP) framework is extended for this purpose, so that the simulation is able to detect component failures arising from abnormal environmental conditions and vice versa: Flooding could be caused by a failure in a pipe or valve component. As abnormal flow states propagate through the system and its environment, the goal of the simulation is to identify the system-wide cumulative effect of the initiating event and any related common cause failure scenario. FFIP determines this effect in terms of degradation or loss of the functionality of the system. The method is demonstrated with a nuclear reactor’s redundant coolant supply system.     

Sample size determination strategies for normal tolerance intervals using historical data

Statistical tolerance intervals are often used during design verification or process validation in diverse applications, such as the manufacturing of medical devices, the construction of nuclear reactors, and the development of protective armor for the military. Like other statistical problems, the determination of a minimum required sample size when using tolerance intervals commonly arises. Under the Faulkenberry-Weeks approach for sample size determination of parametric tolerance intervals, the user must specify two quantities—typically set to rule-of-thumb values—that characterize the desired precision of the tolerance interval. Practical applications of sample size determination for tolerance intervals often have historical data that one expects to closely follow the distribution of the future data to be collected. Moreover, such data are typically required to meet specification limits. We provide a strategy for specifying the precision quantities in the Faulkenberry-Weeks approach that utilizes both historical data and the required specification limits. Our strategy is motivated by a sampling plan problem for the manufacturing of a certain medical device that requires calculation of normal tolerance intervals. Both classical and Bayesian normal tolerance intervals are considered. Additional numerical studies are provided to demonstrate the general applicability of our strategy for setting the precision quantities.