Fault diagnosis of PEMFC based on the AC voltage response and 1D convolutional neural network

Real-time diagnosis is required to ensure the safety, reliability, and durability of the polymer electrolyte membrane fuel cell (PEMFC) system. Two categories of methods are (1) intrusive, time consuming, or require alterations to the cell architecture but provide detailed information about the system or (2) rapid and benign but low-information-yielding. A strategy based on alternating current (AC) voltage response and one-dimensional (1D) convolutional neural network (CNN) is proposed as a methodology for detailed and rapid fuel cell diagnosis. AC voltage response signals contain within them the convoluted information that is also available via electrochemical impedance spectroscopy (EIS), such as capacitive, inductive, and diffusion processes, and direct use of time-domain signals can avoid time-frequency conversion. It also overcomes the disadvantage that EIS can only be measured under steady-state conditions. The utilization of multi-frequency excitation can make the proposed approach an ideal real-time diagnostic/characterization tool for fuel cells and other electrochemical power systems

Dual network extraction algorithm to investigate multiple transport processes in porous materials: Image-based modeling of pore and grain scale processes

The final publication is available at Elsevier via https://doi.org/10.1016/j.compchemeng.2018.12.025 © 2018 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Image processing of 3D tomographic images to extract structural information of porous materials has become extremely important in porous media research with the commoditization of x-ray tomography equipment to the lab scale. Extracted pore networks from images using image analysis techniques enable transport properties calculation for bigger domains at a low computational cost, allowing pore-scale investigation of porous media over meaningful macroscopic length scales. The present study reports a pore network extraction algorithm to simultaneously extract void and solid networks from tomographic images of porous materials using simple image analysis techniques. Crucially, it includes connectivity and geometrical information of both void and solid phases as well as the interlinking of these phases with each other. Validation was obtained on networks extracted from simple cubic and random sphere packings over a range of porosities. The effective diffusivity in the void phase and thermal conductivity in the solid phase was then calculated and found to agree well with direct numerical simulation results on the images, as well as a range of experimental data. One important outcome of this work was a novel and accurate means of calculating interfacial areas between grains and voids directly from digital images, which is critical to many phenomena where phase interactions occur. The efficient ‘dual network’ algorithm is written in PYTHON using open source tools and provides a new way to study critical processes that depend on transport in both void and solid phase such as catalytic reactors and electrochemical systems.University of Engineering and Technology Lahore, PakistanNatural Sciences and Engineering Research Council of Canad

Current imbalance in parallel battery strings measured using a hall‐effect sensor array

Herein, individual cell currents in parallel connected battery strings are measured using micro‐Hall‐effect sensors. Cells are routinely connected in electrical series and parallel to meet the power and energy requirements of automotive and consumer electronics applications. Cells connected in series have been extensively studied; however, cells in parallel are often assumed to be a “black box” in battery management systems. Poor pack design can result in positive feedback between current and temperature differentials along the parallel string, driving greater levels of heterogeneous behavior and uneven degradation. Herein, a noninvasive multisensor array board using Hall‐effect sensors is used to individually record the current passing through eight parallel connected cells in two different electrical configurations, showing highly heterogeneous current distribution. Characteristic “waves” of current and temperature are found to propagate along the parallel battery string and cell rebalancing is found to occur over hundreds of seconds with individual cell currents of up to 1 C rate

NEOSCOPE: A randomised Phase II study of induction chemotherapy followed by either oxaliplatin/capecitabine or paclitaxel/carboplatin based chemoradiation as pre-operative regimen for resectable oesophageal adenocarcinoma

Background: Both oxaliplatin/capecitabine-based chemoradiation (OXCAP-RT) and carboplatin-paclitaxel based radiation (CarPac-RT) are active regimens in oesophageal adenocarcinoma, but no randomised study has compared their efficacy and toxicity. This randomised phase II "pick a winner" trial will identify the optimum regimen to take forward to a future phase III trial against neo-adjuvant chemotherapy, the current standard in the UK. Methods/Design: Patients with resectable adenocarcinoma of the oesophagus or Siewert Type 1-2 gastro-oesophageal junction (GOJ), ≥T3 and/or ≥ N1 are eligible for the study. Following two cycles of induction OXCAP chemotherapy (oxaliplatin 130 mg/m2 D1, Cape 625 mg/m2 D1-21, q 3 wk), patients are randomised 1:1 to OXCAP-RT (oxaliplatin 85 mg/m2 Day 1,15,29; capecitabine 625 mg/m2 twice daily on days of RT; RT-45 Gy/25 fractions/5 weeks) or CarPac-RT (Carboplatin AUC2 and paclitaxel 50 mg/m2 Day 1,8,15,22,29; RT-45 Gy/25 fractions/5 weeks). Restaging CT/PET-CT is performed 4-6 weeks after CRT, and a two-phase oesophagectomy with two-field lymphadenectomy is performed six to eight weeks after CRT. The primary end-point is pathological complete response rate (pCR) at resection and will include central review. Secondary endpoints include: recruitment rate, toxicity, 30-day surgical morbidity/mortality, resection margin positivity rate and overall survival (median, 3- and 5-yr OS. 76 patients (38/arm) gives 90% power and one-sided type 1 error of 10% if patients on one novel treatment have a response rate of 35% while the second treatment has a response rate of 15%. A detailed RT Quality Assurance (RTQA) programme includes a detailed RT protocol and guidance document, pre-accrual RT workshop, outlining exercise, and central evaluation of contouring and planning. This trial has been funded by Cancer Research UK (C44694/A14614), sponsored by Velindre NHS Trust and conducted through the Wales Cancer Trials Unit at Cardiff University on behalf of the NCRI Upper GI CSG. Discussion: Following encouraging results from previous trials, there is an interest in neo-adjuvant chemotherapy and CRT containing regimens for treatment of oesophageal adenocarcinoma. NEOSCOPE will first establish the efficacy, safety and feasibility of two different neo-adjuvant CRT regimens prior to a potential phase III trial

Identification of fungi isolated from commercial bumblebee colonies

Fungi can have important beneficial and detrimental effects on animals, yet our understanding of the diversity and function of most bee-associated fungi is poor. Over 2 million bumblebee colonies are traded globally every year, but the presence and transport of viable fungi within them is unknown. Here, we explored whether any culturable fungi could be isolated from commercial bumblebee nests. We collected samples of various substrates from within 14 bumblebee colonies, including the honey, honey cup wall, egg cup wall, and frass then placed them on agar and recorded any growth. Fungal morphotypes were then subcultured and their ITS region sequenced for identification. Overall, we cultured 11 fungal species from the various nest substrates. These included both pathogenic and non-pathogenic fungi, such as Aspergillus sp., Penicillium sp., and Candida sp. Our results provide the first insights into the diversity of viable fungal communities in commercial bumblebee nests. Further research is needed to determine if these fungi are unique to commercial colonies or prevalent in wild bumblebee nests, and crucially to determine the ecological and evolutionary implications of these fungi in host colonies.</p

A continuum of physics-based lithium-ion battery models reviewed

Physics-based electrochemical battery models derived from porous electrode theory are a very powerful tool for understanding lithium-ion batteries, as well as for improving their design and management. Different model fidelity, and thus model complexity, is needed for different applications. For example, in battery design we can afford longer computational times and the use of powerful computers, while for real-time battery control (e.g. in electric vehicles) we need to perform very fast calculations using simple devices. For this reason, simplified models that retain most of the features at a lower computational cost are widely used. Even though in the literature we often find these simplified models posed independently, leading to inconsistencies between models, they can actually be derived from more complicated models using a unified and systematic framework. In this review, we showcase this reductive framework, starting from a high-fidelity microscale model and reducing it all the way down to the single particle model, deriving in the process other common models, such as the Doyle–Fuller–Newman model. We also provide a critical discussion on the advantages and shortcomings of each of the models, which can aid model selection for a particular application. Finally, we provide an overview of possible extensions to the models, with a special focus on thermal models. Any of these extensions could be incorporated into the microscale model and the reductive framework re-applied to lead to a new generation of simplified, multi-physics models

Temporal Variability of Surface Reflectance Supersedes Spatial Resolution in Defining Greenland’s Bare-Ice Albedo

Ice surface albedo is a primary modulator of melt and runoff, yet our understanding of how reflectance varies over time across the Greenland Ice Sheet remains poor. This is due to a disconnect between point or transect scale albedo sampling and the coarser spatial, spectral and/or temporal resolutions of available satellite products. Here, we present time-series of bare-ice surface reflectance data that span a range of length scales, from the 500 m for Moderate Resolution Imaging Spectrometer’s MOD10A1 product, to 10 m for Sentinel-2 imagery, 0.1 m spot measurements from ground-based field spectrometry, and 2.5 cm from uncrewed aerial drone imagery. Our results reveal broad similarities in seasonal patterns in bare-ice reflectance, but further analysis identifies short-term dynamics in reflectance distribution that are unique to each dataset. Using these distributions, we demonstrate that areal mean reflectance is the primary control on local ablation rates, and that the spatial distribution of specific ice types and impurities is secondary. Given the rapid changes in mean reflectance observed in the datasets presented, we propose that albedo parameterizations can be improved by (i) quantitative assessment of the representativeness of time-averaged reflectance data products, and, (ii) using temporally-resolved functions to describe the variability in impurity distribution at daily time-scales. We conclude that the regional melt model performance may not be optimally improved by increased spatial resolution and the incorporation of sub-pixel heterogeneity, but instead, should focus on the temporal dynamics of bare-ice albedo

Temporal variability of surface reflectance supersedes spatial resolution in defining Greenland’s bare-ice albedo

Ice surface albedo is a primary modulator of melt and runoff, yet our understanding of how reflectance varies over time across the Greenland Ice Sheet remains poor. This is due to a disconnect between point or transect scale albedo sampling and the coarser spatial, spectral and/or temporal resolutions of available satellite products. Here, we present time-series of bare-ice surface reflectance data that span a range of length scales, from the 500 m for Moderate Resolution Imaging Spectrometer’s MOD10A1 product, to 10 m for Sentinel-2 imagery, 0.1 m spot measurements from ground-based field spectrometry, and 2.5 cm from uncrewed aerial drone imagery. Our results reveal broad similarities in seasonal patterns in bare-ice reflectance, but further analysis identifies short-term dynamics in reflectance distribution that are unique to each dataset. Using these distributions, we demonstrate that areal mean reflectance is the primary control on local ablation rates, and that the spatial distribution of specific ice types and impurities is secondary. Given the rapid changes in mean reflectance observed in the datasets presented, we propose that albedo parameterizations can be improved by (i) quantitative assessment of the representativeness of time-averaged reflectance data products, and, (ii) using temporally-resolved functions to describe the variability in impurity distribution at daily time-scales. We conclude that the regional melt model performance may not be optimally improved by increased spatial resolution and the incorporation of sub-pixel heterogeneity, but instead, should focus on the temporal dynamics of bare-ice albedo