Electrophoretically deposited copper manganese spinel protective coatings on metallic interconnects for prevention of Cr-poisoning in solid oxide fuel cells

Metallic interconnects in intermediate temperature solid oxide fuel cells (IT-SOFC) stacks form Cr2O3 scales on their surface. Such oxide scales can be further oxidized to Cr6+ containing gaseous species that migrate and deposit at the cathode triple phase boundaries, causing significant degradation in the performance of the SOFCs. This phenomenon is termed as ‘Cr-poisoning’. A solution to this problem is the application of coatings on the interconnects that act as a diffusion barrier to Cr migration. Two different Cu/Mn spinel compositions, Cu1.3Mn1.7O4 and CuMn1.8O4, were studied as coating materials. Dense coatings were deposited on both flat plates and meshes by electrophoretic deposition (EPD) followed by subsequent thermo-mechanical or thermal densification steps. At room temperature, Cu1.3Mn1.7O4 coatings were found to have a mixture of CuO and spinel phases, while CuMn1.8O4 coatings were found to have a mixture of Mn3O4 and spinel phases. However, CuMn1.8O4 is a pure spinel phase between 750 °C and 850 °C. After densification processing and high temperature oxidation, a Cr2O3 layer was formed at the coating/alloy interface, which partially reacted with the spinel coatings to form a dense cubic spinel layer of the general composition (Cu,Mn,Cr)3-xO4. In addition, Cr-rich precipitates, formed in the dense layer close to coating/alloy interface. It is believed that these are Cr2O3 precipitates, formed when the solubility of Cr in the spinel phase is reached. Solubility experiments using powders showed that 1 mole of CuMn1.8O4 can effectively getter 1.83 moles of Cr2O3 at 800°C. Electrical conductivity of (Cu,Mn,Cr)3-xO4 was found to be at least two orders of magnitude higher than that of Cr2O3. The coatings acted as an effective Cr getter whose lifetime depends on the oxidation temperature, coating thickness, and the overall porosity in the coating. In-cell electrochemical testing showed that the CuMn1.8O4 coatings on Crofer 22 APU meshes performed significantly better than commercial Cu/Mn spinel coatings. The CuMn1.8O4 coatings gettered Cr effectively for 12 days at 800 ºC, leading to no performance loss of the cell due to Cr-poisoning. Significantly longer lifetime can be achieved at 750 ºC or lower, which is the target operational temperature regime of IT-SOFCs

Online Algorithms for Weighted Paging with Predictions

In this paper, we initiate the study of the weighted paging problem with predictions. This continues the recent line of work in online algorithms with predictions, particularly that of Lykouris and Vassilvitski (ICML 2018) and Rohatgi (SODA 2020) on unweighted paging with predictions. We show that unlike unweighted paging, neither a fixed lookahead nor knowledge of the next request for every page is sufficient information for an algorithm to overcome existing lower bounds in weighted paging. However, a combination of the two, which we call the strong per request prediction (SPRP) model, suffices to give a 2-competitive algorithm. We also explore the question of gracefully degrading algorithms with increasing prediction error, and give both upper and lower bounds for a set of natural measures of prediction error

Substrate Aggregation and Ubiquitination Dictate the Fate of Misfolded Proteins for Endoplasmic Reticulum Associated Degradation over Post Endoplasmic Reticulum Degradation

Protein folding is inherently dynamic and error prone. Even with an elaborate network of cellular factors, protein misfolding occurs frequently. To maintain protein homeostasis, eukaryotes have evolved a hierarchy of protein quality control checkpoints along the secretory pathway, including endoplasmic reticulum associated degradation (ERAD) and post-ER quality control (QC). Although most aberrant proteins are eliminated by ERAD, some misfolded proteins do exit the ER and are turned over by lysosomal proteases. To date, it remains elusive how misfolded membrane proteins are selected for different fates: ERAD versus post-ERQC. To address this question, a novel model substrate, SZ*, was designed and utilized in this study. SZ* is a single-pass membrane chimeric protein bearing cytosolic folding lesion. I first investigated its degradation fate in yeast and found that SZ* is eliminated by both the proteasome via the ERAD pathway and vacuolar proteases via the Golgi-QC pathway. The post-ER degradation of SZ* occurs after ER exit and requires the multivesicular body pathway. I then interrogated cells with different stress treatment to test how various conditions affect the fate of SZ*. My results showed that both heat-shock and substrate overexpression increase ERAD targeting, which both lead to substrate aggregation. Therefore, a misfolded membrane protein with a higher aggregation propensity is preferentially retained in the ER and targeted for ERAD. Next, I sequentially inhibited different steps in the ERAD pathway and tested SZ* ER export efficiency both in vivo and in vitro. I discovered that inhibiting steps required for ubiquitination, including substrate recognition and ubiquitination, facilitate ER exit of SZ* through coat protein complex (COP) II transport. These studies suggest that ERAD substrates can be rescued for ER export by eliminating ubiquitination. In line with this evidence, I fused SZ* to four tandem ubiquitin moieties that are not efficiently degraded by ERAD and found that this substrate could not exit the ER even when ERAD was inhibited. Together, these data provide evidence that substrate aggregation and ubiquitination can be sufficient for ER retention

The Cardinal Complexity of Comparison-based Online Algorithms

We consider ordinal online problems, i.e., those tasks that only depend on the pairwise comparisons between elements in the input. E.g., the secretary problem and the game of googol. The natural approach to these tasks is to use ordinal online algorithms that at each step only consider relative ranking among the arrived elements, without looking at the numerical values of the input. We formally study the question of how cardinal algorithms (that can use numerical values of the input) can improve upon ordinal algorithms. We give a universal construction of the input distribution for any ordinal online problem, such that the advantage of the cardinal algorithms over the ordinal algorithms is at most $1+\varepsilon$ for arbitrary small $\varepsilon> 0$. However, the value range of the input elements in this construction is huge: $O\left(\frac{n^3\cdot n!}{\varepsilon}\right)\uparrow\uparrow (n-1)$ for an input sequence of length $n$. Surprisingly, we also identify a natural family of hardcore problems that achieve a matching advantage of $1+ \Omega \left(\frac{1}{\log^{(c)}N}\right),$ where $\log^{(c)}N=\log\log\ldots\log N$ with $c$ iterative logs and $c$ is an arbitrary constant $c\le n-2$. We also consider a simpler variant of the hardcore problem, which we call maximum guessing and is closely related to the game of googol. We provide a much more efficient construction with cardinal complexity $O\left(\frac{1}{\varepsilon}\right)^{n-1}$ for this easier task. Finally, we study the dependency on $n$ of the hardcore problem. We provide an efficient construction of size $O(n)$, if we allow cardinal algorithms to have constant factor advantage against ordinal algorithms

Influence of green financing, technology innovation, and trade openness on consumptionbased carbon emissions in BRICS countries

The study explores the dynamic effects of renewable energy investment (green financing), green technology, and trade openness on consumption-based (trade-adjusted) carbon emissions in BRICS economies from 2000 to 2020. The study employs the cross-section autoregressive distributed lag method for empirical estimation to address slope heterogeneity and cross-sectional dependency issues in panel data. The findings exhibit that green financing and sustainable technologies mitigate consumption-based carbon emissions in the long-run, while trade openness contributes to emissions in BRICS countries. The short-run outcomes are compatible with long-run; however, the magnitude of long-run estimates is larger than the short-run. Moreover, the error correction term reveals a significant negative coefficient value, endorsing the conversion towards steady-state equilibrium with a 37% yearly adjustment rate in case of any deviation from equilibrium. The robustness of results is confirmed through augmented mean group and common correlated effect mean group. These findings imply that BRICS countries should encourage financing in renewable energy projects and allocate R&D investment to promote the adaptation of sustainable technologies. In addition, sustainable and green trade policies would help to curb trade-adjusted pollution

Rethinking Image Editing Detection in the Era of Generative AI Revolution

The accelerated advancement of generative AI significantly enhance the viability and effectiveness of generative regional editing methods. This evolution render the image manipulation more accessible, thereby intensifying the risk of altering the conveyed information within original images and even propagating misinformation. Consequently, there exists a critical demand for robust capable of detecting the edited images. However, the lack of comprehensive dataset containing images edited with abundant and advanced generative regional editing methods poses a substantial obstacle to the advancement of corresponding detection methods. We endeavor to fill the vacancy by constructing the GRE dataset, a large-scale generative regional editing dataset with the following advantages: 1) Collection of real-world original images, focusing on two frequently edited scenarios. 2) Integration of a logical and simulated editing pipeline, leveraging multiple large models in various modalities. 3) Inclusion of various editing approaches with distinct architectures. 4) Provision of comprehensive analysis tasks. We perform comprehensive experiments with proposed three tasks: edited image classification, edited method attribution and edited region localization, providing analysis of distinct editing methods and evaluation of detection methods in related fields. We expect that the GRE dataset can promote further research and exploration in the field of generative region editing detection