Nanowrinkled Carbon Aerogels Embedded with FeN x Sites as Effective Oxygen Electrodes for Rechargeable Zinc-Air Battery.

Rational design of single-metal atom sites in carbon substrates by a flexible strategy is highly desired for the preparation of high-performance catalysts for metal-air batteries. In this study, biomass hydrogel reactors are utilized as structural templates to prepare carbon aerogels embedded with single iron atoms by controlled pyrolysis. The tortuous and interlaced hydrogel chains lead to the formation of abundant nanowrinkles in the porous carbon aerogels, and single iron atoms are dispersed and stabilized within the defective carbon skeletons. X-ray absorption spectroscopy measurements indicate that the iron centers are mostly involved in the coordination structure of FeN4, with a minor fraction (ca. 1/5) in the form of FeN3C. First-principles calculations show that the FeN x sites in the Stone-Wales configurations induced by the nanowrinkles of the hierarchically porous carbon aerogels show a much lower free energy than the normal counterparts. The resulting iron and nitrogen-codoped carbon aerogels exhibit excellent and reversible oxygen electrocatalytic activity, and can be used as bifunctional cathode catalysts in rechargeable Zn-air batteries, with a performance even better than that based on commercial Pt/C and RuO2 catalysts. Results from this study highlight the significance of structural distortions of the metal sites in carbon matrices in the design and engineering of highly active single-atom catalysts

Ruthenium atomically dispersed in carbon outperforms platinum toward hydrogen evolution in alkaline media.

Hydrogen evolution reaction is an important process in electrochemical energy technologies. Herein, ruthenium and nitrogen codoped carbon nanowires are prepared as effective hydrogen evolution catalysts. The catalytic performance is markedly better than that of commercial platinum catalyst, with an overpotential of only -12 mV to reach the current density of 10 mV cm-2 in 1 M KOH and -47 mV in 0.1 M KOH. Comparisons with control experiments suggest that the remarkable activity is mainly ascribed to individual ruthenium atoms embedded within the carbon matrix, with minimal contributions from ruthenium nanoparticles. Consistent results are obtained in first-principles calculations, where RuCxNy moieties are found to show a much lower hydrogen binding energy than ruthenium nanoparticles, and a lower kinetic barrier for water dissociation than platinum. Among these, RuC2N2 stands out as the most active catalytic center, where both ruthenium and adjacent carbon atoms are the possible active sites

Traffic Performance GPT (TP-GPT): Real-Time Data Informed Intelligent ChatBot for Transportation Surveillance and Management

The digitization of traffic sensing infrastructure has significantly accumulated an extensive traffic data warehouse, which presents unprecedented challenges for transportation analytics. The complexities associated with querying large-scale multi-table databases require specialized programming expertise and labor-intensive development. Additionally, traditional analysis methods have focused mainly on numerical data, often neglecting the semantic aspects that could enhance interpretability and understanding. Furthermore, real-time traffic data access is typically limited due to privacy concerns. To bridge this gap, the integration of Large Language Models (LLMs) into the domain of traffic management presents a transformative approach to addressing the complexities and challenges inherent in modern transportation systems. This paper proposes an intelligent online chatbot, TP-GPT, for efficient customized transportation surveillance and management empowered by a large real-time traffic database. The innovative framework leverages contextual and generative intelligence of language models to generate accurate SQL queries and natural language interpretations by employing transportation-specialized prompts, Chain-of-Thought prompting, few-shot learning, multi-agent collaboration strategy, and chat memory. Experimental study demonstrates that our approach outperforms state-of-the-art baselines such as GPT-4 and PaLM 2 on a challenging traffic-analysis benchmark TransQuery. TP-GPT would aid researchers and practitioners in real-time transportation surveillance and management in a privacy-preserving, equitable, and customizable manner.Comment: 8 pages, 5 figures, submitted to 27th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2024

Query Twice: Dual Mixture Attention Meta Learning for Video Summarization

Video summarization aims to select representative frames to retain high-level information, which is usually solved by predicting the segment-wise importance score via a softmax function. However, softmax function suffers in retaining high-rank representations for complex visual or sequential information, which is known as the Softmax Bottleneck problem. In this paper, we propose a novel framework named Dual Mixture Attention (DMASum) model with Meta Learning for video summarization that tackles the softmax bottleneck problem, where the Mixture of Attention layer (MoA) effectively increases the model capacity by employing twice self-query attention that can capture the second-order changes in addition to the initial query-key attention, and a novel Single Frame Meta Learning rule is then introduced to achieve more generalization to small datasets with limited training sources. Furthermore, the DMASum significantly exploits both visual and sequential attention that connects local key-frame and global attention in an accumulative way. We adopt the new evaluation protocol on two public datasets, SumMe, and TVSum. Both qualitative and quantitative experiments manifest significant improvements over the state-of-the-art methods.Comment: This manuscript has been accepted at ACM MM 202

Advanced Feedback Experiment Methods With Hiher Education Theory

Advanced reserved experiment method focused cognitive law, constructivism and assimilation theory in a way that science teaching method has a profound theoretical foundation and many years of teaching practice. It is a product of deepening the reform of higher education, it is a method of quality education, innovative ability indispensable.The advanced feedback experimental method, that is, to arrange the experimental activity ahead of teaching the theory, so that students can find problems in the course of experiment and solve them in the follow-up theory teaching, it is able to fully mobilize the enthusiasm of students and let them be full of “suspense” before the class. The biggest advantage of the advanced feedback experimental method is to provide more supports to the heuristic and interactive teaching. It enables students to get the maximum amount of information of physics, chemistry, biology and other natural phenomena within limited time and space and in turn to co-operate the classroom teaching strongly. The “hide” of experimental class and the “show” of theory teaching echo each other to make the experiment and theory class linked organically. That can stir up students’ interests and passion in learning. So that they have “Suspense” before class, after-class sense of accomplishment. After more than ten years of practice, it proves that the advanced feedback experimental method is indeed a good way to reform the professional of natural science for higher education sectionand. It is worthy for recommendation

Knowing the Past to Predict the Future: Reinforcement Virtual Learning

Reinforcement Learning (RL)-based control system has received considerable attention in recent decades. However, in many real-world problems, such as Batch Process Control, the environment is uncertain, which requires expensive interaction to acquire the state and reward values. In this paper, we present a cost-efficient framework, such that the RL model can evolve for itself in a Virtual Space using the predictive models with only historical data. The proposed framework enables a step-by-step RL model to predict the future state and select optimal actions for long-sight decisions. The main focuses are summarized as: 1) how to balance the long-sight and short-sight rewards with an optimal strategy; 2) how to make the virtual model interacting with real environment to converge to a final learning policy. Under the experimental settings of Fed-Batch Process, our method consistently outperforms the existing state-of-the-art methods

A machine-learning approach to modeling picophytoplankton abundances in the South China Sea

Picophytoplankton, the smallest phytoplankton (<3 micron), contribute significantly to primary production in the oligotrophic South China Sea. To improve our ability to predict picophytoplankton abundances in the South China Sea and infer the underlying mechanisms, we compared four machine learning algorithms to estimate the horizontal and vertical distributions of picophytoplankton abundances. The inputs of the algorithms include spatiotemporal (longitude, latitude, sampling depth and date) and environmental variables (sea surface temperature, chlorophyll, and light). The algorithms were fit to a dataset of 2442 samples collected from 2006 to 2012. We find that the Boosted Regression Trees (BRT) gives the best prediction performance with R2 ranging from 77% to 85% for Chl a concentration and abundances of three picophytoplankton groups. The model outputs confirm that temperature and light play important roles in affecting picophytoplankton distribution. Prochlorococcus, Synechococcus, and picoeukaryotes show decreasing preference to oligotrophy. These insights are reflected in the vertical patterns of Chl a and picoeukaryotes that form subsurface maximal layers in summer and spring, contrasting with those of Prochlorococcus and Synechococcus that are most abundant at surface. Our forecasts suggest that, under the “business-as-usual” scenario, total Chl a will decrease but Prochlorococcus abundances will increase significantly to the end of this century. Synechococcus abundances will also increase, but the trend is only significant in coastal waters. Our study has advanced the ability of predicting picophytoplankton abundances in the South China Sea and suggests that BRT is a useful machine learning technique for modelling plankton distribution

Mesoscale eddies drive phytoplankton-mediated biogeochemistry in the South China Sea

AbstractOcean mesoscale eddies are important drivers of upper ocean physical and biological processes. However, owing to their ephemeral nature and limited observational data, the impact of eddies on three‐dimensional biogeochemical cycles and hence related phytoplankton phenology remains unclear. Here, from ship‐based surveys, we assessed the impact of two eddies of opposite polarity on phytoplankton biomass and community structure, in the upper 200 m of the northwest South China Sea (SCS), as well as their effect on the diapycnal nutrient fluxes and oxygen concentration. These observations revealed that pico‐phytoplankton dominated phytoplankton community, whereas the fraction of micro‐ and nano‐ phytoplankton (Fmicro and Fnano) increased with depth, reaching a maximum near the SCM layer (located between 50 and 100 m). The magnitude of SCM and total phytoplankton Chl were greater within the cyclonic eddy (CE) compared to those influenced by the anticyclonic eddy due to the enhanced vertical diapycnal fluxes of nutrients within the CE. The elevated diapycnal nutrient flux in the CE resulted from an increase in turbulent kinetic energy dissipation coefficient and steeper vertical gradients in inorganic nutrients. Pigment‐based chemotaxonomy further indicated that eukaryotes increased significantly in the SCM layer with concentrations reaching 0.16 ± 0.08 mg m−3; the enhancement of Fmicro in the CE was mainly attributed to the increased contribution of diatoms. The vertical biogeochemical dynamics revealed by this research may showcase fundamental characteristics of oligotrophic ecosystems, where mesoscale perturbations are vertically heterogeneous, improving our understanding of the complex biophysical interactions within mesoscale eddies

Content and Occurrence State of Niobium and Rare Earth Elements in Hornblendite of Dagele, East Kunlun by the Electron Probe Technique

BACKGROUNDThe demand for rare metals and rare earth elements has been steadily rising as they play an important role in the high-tech industry. In response, there is an urgent need to study the exploration, development, and utilization of them. The formation of deposits containing rare metals and rare earth elements is intricately linked with igneous rocks, and it has been found that numerous large-scale rare metal and rare earth mines, both domestically and internationally, are associated with alkaline rock complex and carbonatite. In Dagele, East Kunlun, a groundbreaking discovery of the first occurrence of rare and rare-earth mineralized carbonatite-alkaline rock complex predominantly containing niobium was made. This finding represents a significant advancement in the understanding of mineralization in East Kunlun. Currently, the existing research has been primarily focused on surface rock assemblages and mineral characterization investigations. Hornblendite, being the predominant lithology in this complex, exhibits varying degrees of niobium and rare earth mineralization. However, the occurrence state of niobium and rare earth elements in hornblendite remains unclear. Studying the occurrence characteristics of niobium and rare earth elements is crucial for identifying the types of minerals present in the ores, summarizing distribution in their patterns, and exploring the enrichment mechanisms. Comprehensive knowledge of the mineralization laws within the alkaline complex and breakthroughs in mineral exploration should subsequently follow. Due to the small particles and complex dissemination characteristics of niobium minerals and rare earth minerals, precise identification and mineralogical and occurrence analysis under polarized light microscope pose significant challenges[18-19]. Fortunately, electron probe microanalyzers are well-suited for identifying minerals containing key metallic elements like niobium and rare earth minerals. They also enable analysis of the mineral forms in which these elements are present. Recent reports have highlighted their successful applications in related studies.OBJECTIVESIn order to find out the existing forms of niobium and rare earth elements in hornblende rocks and the host minerals of niobium and rare earth elements.METHODSIn this study, the hornblendite was analyzed by electron probe on the basis of petrographic observations under a microscope. The primary focus is on investigating the characteristics of niobium minerals and rare earth minerals, such as their species, dissemination relationships, and chemical composition. Additionally, the aim is to accurately analyze the occurrence state of niobium and rare earth elements. The polished thin section of the electron probe was polished and prepared at the Shougang Geological Exploration Laboratory, and subsequently examined and identified using a polarized light microscope (Leica DM4500p) at the Rock and Mineral Identification Center of the Qinghai Geological and Mineral Research Institute. A JEOL JXA-iHP200F electron probe was adopted, and its analysis and test were conducted at the Electron Probe Laboratory of the Institute of Mineral Resources, the Chinese Academy of Geological Sciences. To facilitate the analysis, a conductive carbon film was sprayed onto the surface of the electron probe sheet in a high vacuum environment. Subsequently, JED-2300 X-ray energy spectrum analysis and quantitative electron probe spectrum analysis were performed using an electron probe analyzer. For the quantitative analysis of oxides, the acceleration voltage was 15kV, acceleration current 20nA, and beam spot diameter 3μm. For the quantitative analysis of rare metals and rare earth minerals, the acceleration voltage was 15kV, acceleration current 20nA, and beam spot diameter less than 3μm. During the analysis, the elemental peak measurements were conducted for a duration of 10s, followed by 5s of pre-background measurement and 5s of post-background measurement time. To ensure accurate results, all the collected data were processed using the ZAF matrix correction method. The detection limits for different elements range from 50×10−6 to 300×10−6.RESULTS(1) The analysis results with the polarized light microscope suggest that hornblendite primarily consists of hornblende, pyroxene, phlogopite/biotite, apatite, and other minerals, with a minor presence of aeschynite. The electron probe study indicates that ① In hornblendite, niobium elements are primarily present in niobium aeschynite and niobium-bearing ilmenite, while rare earth elements are predominantly found in allanite and niobium aeschynite. Notably, these elements exhibit significant enrichment in light rare earth elements; ② Niobium aeschynite typically contains an average of 42.98% to 51.96% Nb2O5, 4.63% La2O3, and 12.16% Ce2O3. The mineral grains range in diameter from 15 to 90μm and are located within hornblende crystals or between hornblende and phlogopite crystals. In certain areas, they exhibit intergrowth with hornblende and are closely associated with allanite and apatite; ③ The average content of Nb2O5 in niobium-bearing ilmenite is 2.01%; ④ allanite inclusions exhibit an average Ce2O3 content of 10.73% and an average La2O3 content of 9.89%. These mineral particles have diameters ranging from 10 to 40μm and are primarily found in apatite marginal pores and fissures. They demonstrate a close association with apatite, displaying characteristic mutual intergrowth.　　(2) The analysis of chemical samples of rocks shows that the highest grade of Nb2O5 reaches 0.1% in hornblendite in the middle of complex (close to carbonatite and peridotite), and about 0.02% in hornblende at the edge of the complex. The analyzed sample (21DGb11) of apatite-bearing phlogopite hornblendite is situated in the central region of the complex. This specific sample exhibits a closer spatial relationship with the overall mineralization of carbonatite, peridotite, and pyroxene within the area. Notably, valuable ore minerals such as niobium aeschynite, allanite, and niobium-bearing ilmenite have been identified within this sample.　　(3) The presence of niobium minerals and rare earth minerals in hornblendite in Dagele is likely attributed to late-stage hydrothermal processes. Furthermore, the hornblendite that is in closer proximity to the whole-rock mineralized carbonatite and peridotite exhibits a higher degree of influence from late-stage hydrothermal processes, resulting in more significant mineralization.CONCLUSIONSThe formation of niobium minerals and rare earth minerals in rocks is primarily attributed to late-stage hydrothermal processes. Moreover, the hornblendite that is in closer proximity to the whole-rock mineralized carbonatite and peridotite exhibits a higher susceptibility to late-stage hydrothermal processes, resulting in enhanced mineralization. In the East Kunlun orogenic belt, rare and rare-earth mineralized alkaline complex predominantly enriched with niobium elements has been discovered for the first time. These findings highlight the exceptional concentration of niobium in this region. The Late Silurian—Devonian period is believed to be a highly significant timeframe for rare metal mineralization, particularly dominated by niobium elements, in the East Kunlun region