Catalyst failure analysis of a direct methanol fuel cell membrane electrode assembly

Lifetime testing of a single cell direct methanol fuel cell (DMFC) was carried out at 100 mA cm⁻², ambient pressure and 60 °C. X-ray diffraction (XRD) and X-ray photoelectron spectra (XPS) were used to characterize the anode and cathode catalysts before and after lifetime testing. The XRD results showed that the particle sizes and lattice parameters of anode catalyst increased from 2.8 to 3.2 nm and from 3.8761 to 3.8871 Å; the cathode catalyst increased from 7.3 to 8.9 nm and from 3.9188 to 3.9204 Å before and after the lifetime test, respectively. The XPS results indicated that during the lifetime period, the extent of oxidation of the anode Pt and Ru components increased, and Ru appears in the XPS of the cathode. Polarization curves, power density curves, and in situ cyclic voltammetry were employed to test the performance of fuel cell and electrochemically active specific surface areas (S_EAS) of the anode and cathode catalysts before and after the lifetime test. The overall findings are that the cathode suffers the greatest degradation over the test period and that subtle changes at the anode can have substantial adverse effects on the cathode.     

Modelling of the Am(III) – Cm(III) kinetic separation effect observed during metal ion extraction by bis-(1,2,4)-triazine ligands

The kinetic separation effect was observed leading to a separation factor for Am(III) over Cm(III) as high as 7.9 by using 2,9-bis-(1,2,4-triazin-3-yl)-1,10-phenantroline (BTPhen) ligands in our recent study. In an attempt to explain the observed tendencies, several kinetic models were tested. A model based on mass transfer as the rate-controlling process was found to best describe the kinetic data and allowed to simulate the dependence of Am/Cm separation factor on time. The calculated values of the overall mass-transfer coefficients confirmed that the observed kinetic effect was caused by the different rates of Am(III) and Cm(III) extraction. This kinetic separation phenomenon and its explanation paves the way for potential new approaches to separation of metal ions with very similar properties, such as the adjacent minor actinides Am(III) and Cm(III).     

Output‐feedback H∞ quadratic tracking control of linear systems using reinforcement learning

This paper presents an online learning algorithm based on integral reinforcement learning (IRL) to design an output‐feedback (OPFB) H_∞ tracking controller for partially unknown linear continuous‐time systems. Although reinforcement learning techniques have been successfully applied to find optimal state‐feedback controllers, in most control applications, it is not practical to measure the full system states. Therefore, it is desired to design OPFB controllers. To this end, a general bounded L₂ ‐gain tracking problem with a discounted performance function is used for the OPFB H_∞ tracking. A tracking game algebraic Riccati equation is then developed that gives a Nash equilibrium solution to the associated min‐max optimization problem. An IRL algorithm is then developed to solve the game algebraic Riccati equation online without requiring complete knowledge of the system dynamics. The proposed IRL‐based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an OPFB policy and updates the OPFB gain using the information given by the evaluated policy. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. A simulation example is provided to verify the convergence of the proposed algorithm to a suboptimal OPFB solution and the performance of the proposed method.     

Indoor fine particulate matter and demographic,household, and wood stove characteristics among rural US homes heated with wood fuel

Household heating using wood stoves is common practice in many rural areas of the United States (US) and can lead to elevated concentrations of indoor fine particulate matter (PM_2.5). We collected 6-day measures of indoor PM_2.5 during the winter and evaluated household and stove-use characteristics in homes at three rural and diverse study sites. The median indoor PM_2.5 concentration across all homes was 19 µg/m³, with higher concentrations in Alaska (median = 30, minimum = 4, maximum = 200, n = 10) and Navajo Nation homes (median = 29, minimum = 3, maximum = 105, n = 23) compared with Montana homes (median = 16, minimum = 2, maximum = 139, n = 59). Households that had not cleaned the chimney within the past year had 65% higher geometric mean PM_2.5 compared to those with chimney cleaned within 6 months (95% confidence interval [CI]: −1, 170). Based on a novel wood stove grading method, homes with low-quality and medium-quality stoves had substantially higher PM_2.5 compared to homes with higher-quality stoves (186% higher [95% CI: 32, 519] and 161% higher; [95% CI:27, 434], respectively). Our findings highlight the need for, and complex nature of, regionally appropriate interventions to reduce indoor air pollution in rural wood-burning regions. Higher-quality stoves and behavioral practices such as regular chimney cleaning may help improve indoor air quality in such homes.     

Dilution Processes of Rainfall-Enhanced Acid Mine Drainage Discharges from Historic Underground Coal Mines,New Zealand

Mine Water and the Environment - In much of the world, seasonal weather patterns cause a flush of dissolved contaminants from mined areas at the beginning of wet seasons. However, on parts of New...     

Current Progress and Future Directions for Tau-Based Fluid Biomarker Diagnostics in Alzheimer’s Disease

Despite continued efforts, there remain no disease-modifying drugs approved by the United States Food and Drug Administration (FDA) or European Medicines Agency (EMA) to combat the global epidemic of Alzheimer’s disease. Currently approved medicines are unable to delay disease progression and are limited to symptomatic treatment. It is well established that the pathophysiology of this disease remains clinically silent for decades prior to symptomatic clinical decline. Identifying those at risk of disease progression could allow for effective treatment whilst the therapeutic window remains open for preservation of quality of life. This review aims to evaluate critically the current advances in the interpretation of tau-based biomarkers and their use to provide insights into the onset and progression of Alzheimer’s disease, whilst highlighting important future directions for the field. This review emphasises the need for a more comprehensive analysis and interrogation of tau within biological fluids, to aid in obtaining a disease specific molecular signature for each stage of Alzheimer’s disease. Success in achieving this could provide essential utility for presymptomatic patient selection for clinical trials, monitoring disease progression, and evaluating disease modifying therapies.