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Coking is the leading cause of catalyst deactivation in many important hydrocarbon conversion technologies. Understanding regeneration mechanisms is critical for developing effective carbon removal strategies that improve catalyst longevity and reduce operational costs. Here, we present a spatially resolved operando investigation of the regeneration of a spent Ni/CeO2 catalyst under industrially relevant air-like conditions, using in-situ environmental transmission electron microscopy (ETEM) combined with semantic segmentation. By deconvoluting competing gasification events for filamentous carbon removal, we found three distinct gasification modes─while fast catalytic gasification was expected, less steady noncatalytic combustion and cooperative gasification were also present and even more prevalent. Microstructure-informed kinetics directly linked the maximum gasification rates to axial filament consumption through either Ni/carbon contact or filament breakage, emphasizing the pivotal role of edge-plane carbon sites across all gasification pathways. Moreover, our operando characterization uncovered a Ni(−Cx)-limited carbon diffusion mechanism, which challenges the conventional carbon bulk diffusion model typically assumed for catalytic gasification. Furthermore, adverse processes such as Ni/carbon contact disruption and gasification-induced catalyst sintering were also identified. Collectively, these findings provide mechanistic insights into carbon gasification processes, highlighting critical pathways and potential pitfalls that can guide the optimization of catalyst regeneration strategies. 

MultiTaskDeltaNet (MTDN) reframes semantic segmentation as change detection, enabling data-efficient, automated operando ETEM analysis for spatially-resolved carbon gasification kinetics with superior performance on small, ambiguous features. 

The present study tested the learning avoidance model by examining the degree to which learning avoidance in various afterschool settings mediated the negative association between math anxiety and math achievement. Participants consisted of 207 third to sixth graders. Using a path model, findings showed that students’ math anxiety was negatively associated with both standardized math achievement test scores and parent-reported math school grades. Additionally, higher math anxiety was associated with more negative homework behaviors and less frequent participation in math-related extracurricular activities. Finally, the association between math anxiety and math achievement was partially mediated by negative math homework behaviors and participation in math extracurricular activities. Effort in math exam preparation did not contribute to explaining the association between math anxiety and math achievement. Overall, these findings support the learning avoidance model and suggest that avoidance behaviors in everyday learning in the afterschool setting may contribute to explaining the undesired math achievement among highly math anxious students. 

Abstract Poly(ethylene terephthalate) (PET) is a highly recyclable plastic that has been extensively used and manufactured. Like other plastics, PET resists natural degradation, thus accumulating in the environment. Several recycling strategies have been applied to PET, but these tend to result in downcycled products that eventually end up in landfills. This accumulation of landfilled PET waste contributes to the formation of microplastics, which pose a serious threat to marine life and ecosystems, and potentially to human health. To address this issue, our project leveraged synthetic biology to develop a whole‐cell biocatalyst capable of depolymerizing PET in seawater environments by using the fast‐growing, nonpathogenic, moderate halophileVibrio natriegens. By leveraging a two‐enzyme system—comprising a chimera ofIsPETase andIsMHETase fromIdeonella sakaiensis—displayed onV. natriegens, we constructed whole‐cell catalysts that depolymerize PET and convert it into its monomers in salt‐containing media and at a temperature of 30°C. 

Math anxiety (MA) and math performance are generally negatively correlated (Barroso et al., 2020; Namkung et al., 2019). However, the mechanisms underlying this negative association remain unclear. According to the Attentional Control Theory (ACT; Eysenck, et al., 2007), anxious individuals experience impaired attentional control during problem solving, which compromises their performance on cognitive tasks. In a sample of 168 elementary and middle school students, the current study used an eye-tracking approach to investigate whether math-anxious students exhibit deficits in their attentional control during a math problem solving task, and whether such attentional control deficits account for the negative association between MA and performance on this math task. Consistent with the ACT, we found that students with higher MA were more likely to engage attention to both task-relevant and task-irrelevant distractors during problem solving, and their enhanced attention to these distractors was associated with their impaired performance on the math task. These findings suggest that the MA-related math performance deficit is partly mediated by impaired attentional control, which is indicated by the maladaptive attentional bias toward distracting information during math problem solving. 

The proliferation of modern data processing tools has given rise to open-source columnar data formats. These formats help organizations avoid repeated conversion of data to a new format for each application. However, these formats are read-only, and organizations must use a heavy-weight transformation process to load data from on-line transactional processing (OLTP) systems. As a result, DBMSs often fail to take advantage of full network bandwidth when transferring data. We aim to reduce or even eliminate this overhead by developing a storage architecture for in-memory database management systems (DBMSs) that is aware of the eventual usage of its data and emits columnar storage blocks in a universal open-source format. We introduce relaxations to common analytical data formats to efficiently update records and rely on a lightweight transformation process to convert blocks to a read-optimized layout when they are cold. We also describe how to access data from third-party analytical tools with minimal serialization overhead. We implemented our storage engine based on the Apache Arrow format and integrated it into the NoisePage DBMS to evaluate our work. Our experiments show that our approach achieves comparable performance with dedicated OLTP DBMSs while enabling orders-of-magnitude faster data exports to external data science and machine learning tools than existing methods.