Identification of coke species on Fe/USY catalysts used for recycling polyethylene into fuels.

The Fe/USY catalyst used for converting plastic waste into fuels faces coking problems. A comprehensive understanding of coke distribution and structure is crucial for catalyst design, enabling resistance to coke deposition and facilitating regeneration. In this study, we analyze the coke deposition on Fe/USY catalysts after catalytic pyrolysis of polyethylene for fuel oil, and present insights into the coke distribution over the metal and acid sites, as well as its specific molecular structure. The coke distributes over both the metal and acid sites, exhibiting distinct TPO peaks corresponding to metal-site coke (370 °C) and acid-site coke (520 °C). The total coke yields range from 2.0% to 2.4%, with distribution on metal and acid sites dependent on Fe loading and acidity. Structurally, the coke is highly-condensed, containing more than four aromatic rings with limited alkyl groups. The acid-site coke is more condensed than the metal-site coke, showing lower H/C ratios (0.5-0.75) relative to the acid-site coke (0.75-0.9). Identified by MALDI-TOF mass analysis, the predominant molecular structures of the coke located on metal and acid sites are illustrated. The metal-site cokes typically exhibit 4-7 aromatic rings, while the acid-site cokes display even greater condensation with 5-12 aromatic rings.

Universal materials model of deep-learning density functional theory Hamiltonian.

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence, but how to achieve this fantastic and challenging objective remains elusive. Here, we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian (DeepH), enabling computational modeling of the complicated structure-property relationship of materials in general. By constructing a large materials database and substantially improving the DeepH method, we obtain a universal materials model of DeepH capable of handling diverse elemental compositions and material structures, achieving remarkable accuracy in predicting material properties. We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models. This work not only demonstrates the concept of DeepH's universal materials model but also lays the groundwork for developing large materials models, opening up significant opportunities for advancing artificial intelligence-driven materials discovery.

Roles and Research Trends of Neuroscience on Major Information Systems Journal: A Bibliometric and Content Analysis.

Over the past decade, neuroscience has been integrated into information systems as a new methodology and perspective to study and solve related problems. Therefore, NeuroIS has emerged as a new cutting-edge research field. This review aimed to identify, summarize, and classify existing NeuroIS publications through knowledge mapping and bibliometric analysis. To effectively understand the development trend of NeuroIS, this study referred to the journal selection index of the Association of Business Schools in 2021 and journals above three stars in the field of information management as the main selection basis. A total of 99 neuroscience papers and their citation data were included from 19 major information systems journals of SCI/SSCI. This study analyzed bibliometric data from 2010 to 2021 to identify the most productive countries, universities, authors, journals, and prolific publications in NeuroIS. To this end, VOSviewer was used to visualize mapping based on co-citation, bibliographic coupling, and co-occurrence. Keywords with strong citation bursts were also identified in this study. This signifies the evolution of this research field and may reveal potential research directions in the near future. In selecting research methods and analysis tools for NeuroIS, content analysis was used to further conclude and summarize the relevant trends. Moreover, a co-citation network analysis was conducted to help understand how the papers, journals, and authors in the field were connected and related, and to identify the seminal or pioneering major literature. For researchers, network maps visualized mainstream research and provided a structural understanding of NeuroIS. The review concludes by discussing potential research topics in this field.

Research Landscape of Artificial Intelligence and e-Learning: A Bibliometric Research.

While an increasing number of organizations have introduced artificial intelligence as an important facilitating tool for learning online, the application of artificial intelligence in e-learning has become a hot topic for research in recent years. Over the past few decades, the importance of online learning has also been a concern in many fields, such as technological education, STEAM, AR/VR apps, online learning, amongst others. To effectively explore research trends in this area, the current state of online learning should be understood. Systematic bibliometric analysis can address this problem by providing information on publishing trends and their relevance in various topics. In this study, the literary application of artificial intelligence combined with online learning from 2010 to 2021 was analyzed. In total, 64 articles were collected to analyze the most productive countries, universities, authors, journals and publications in the field of artificial intelligence combined with online learning using VOSviewer through WOS data collection. In addition, the mapping of co-citation and co-occurrence was explored by analyzing a knowledge map. The main objective of this study is to provide an overview of the trends and pathways in artificial intelligence and online learning to help researchers understand global trends and future research directions.

Valorization of fluid petroleum coke for efficient catalytic destruction of biomass gasification tar.

Large volumes of waste petroleum coke stockpiled in open yard not only represent a huge loss of valuable material but also pose a significant risk to the environment. This work proposed an innovative strategy for waste petroleum coke valorization by exploring its catalytic performance of biomass gasification tar destruction. Waste petroleum coke was firstly activated by potassium hydroxide (KOH) to obtain high specific surface area as well as low sulfur and ash contents. Petroleum coke derived catalyst showed superior performance than a commercial activated carbon derived catalyst for destruction of naphthalene as the tar model compound. The petroleum coke derived catalyst exhibited 99.1% naphthalene destruction efficiency at 800 °C but deactivated quickly under N2 atmosphere. Under H2 and steam atmospheres, the catalytic activities were 98.6% and 96.5% for 8 h, respectively. To study the correlation between catalytic performance and the structure of carbon catalyst, elemental analysis, scanning electron microscope (SEM) analysis, transmission electron microscope (TEM) analysis, X-ray powder diffraction (XRD) analysis, Brunauer-Emmett-Teller method (BET) analysis, Fourier transform infrared (FTIR) spectroscopy, temperature programmed oxidation (TPO) analysis and Raman spectroscopy were performed on both fresh and spent catalysts. Results demonstrated that the hydrogen-rich groups (small rings and amorphous carbon) and oxygen-containing groups may account for the good resistance to coke deposition under H2 and steam atmospheres.

Simulation and practice of particle inertial focusing in 3D-printed serpentine microfluidic chips via commercial 3D-printers.

Inertial focusing of particles in serpentine microfluidic chips has been studied over the past decade. Here, a study to investigate the particle inertial focusing in 3D-printed serpentine microfluidic chips was conducted by simulation and practice. A test model was designed and printed using four commercial 3D-printers. Commercial inkjet 3D-printers have shown the best printing channel resolution of up to 0.1 mm. The force analysis of particle inertial focusing in 3D-printed microfluidic chips with large cross-sectional channels was discussed. Important parameters such as the channel curvature and flow velocity were studied by simulation. The optimal channel curvature and flow velocity are 5.9 mm and 480 µL min-1 (Re: 29.8 and De: 4.49) in the 3D-printed microfluidic chips with 0.2 mm × 0.4 mm cross-sectional channels. Under these optimal conditions, particles were well focused in the middle of the channel. Furthermore, two kinds of cancer cells were focused in these 3D-printed serpentine microfluidic chips under the optimal conditions. We envision that this improved study would provide helpful insights into simulating particle inertial focusing in 3D-printed microfluidic chips and promoting 3D-printed microfluidic chips to commercial production.