Relative prevalence of top-down versus bottom-up control in planktonic ecosystem under eutrophication and climate change: A comparative study of typical bay and estuary.

Eutrophication and climate change may affect the top-down versus bottom-up controls in aquatic ecosystems. However, the relative prevalence of the two controls in planktonic ecosystems along the eutrophication and climate gradients has rarely been addressed. Here, using the field surveys of 17 years in a typical bay and estuary, we test two opposite patterns of trophic control dominance and their response to regional temporal eutrophication and climate fluctuations. It was found that trophic control of planktonic ecosystems fluctuated between the dominance of top-down and bottom-up controls on time scales in both the bay and estuary studied. The relative prevalence of these two controls in both ecosystems was significantly driven directly by regional dissolved inorganic nitrogen but, for the estuary, also by the nonlinear effects of regional sea surface temperature. In terms of indirect pathways, community relationships (synchrony and grazing pressure) in the bay are driven by both regional dissolved inorganic nitrogen - soluble reactive phosphorus ratio and sea surface temperature, but this drive did not continue to be transmitted to the trophic control. Conversely, trophic control in estuary was directly related to grazing pressure and indirectly related to synchrony. These findings support the view that eutrophication and climate drive the relative prevalence of top-down versus bottom-up controls at ecosystem and temporal scales in planktonic ecosystems, which has important implications for predicting the potential impacts of anthropogenic and environmental perturbations on the structure and function of marine ecosystems.

Temperature and nutrients alter the relative importance of stochastic and deterministic processes in the coastal macroinvertebrates biodiversity assembly on long-time scales.

Macroinvertebrates play a vital role in coastal ecosystems and are an important indicator of ecosystem quality. Both anthropogenic activity and environmental changes may lead to significant changes in the marine macroinvertebrate community. However, the assembly process of benthic biodiversity and its mechanism driven by environmental factors at large scales remains unclear. Here, using the benthic field survey data of 15 years at large spatial and temporal scales from the Yellow Sea Large Marine Ecosystem, we investigated the relative importance of environmental selection, dispersal processes, random-deterministic processes of macroinvertebrates community diversity assembly, and the responses of this relative importance driven by temperature and nutrients. Results showed that the macroinvertebrates community diversity is mainly affected by dispersal. Nitrogen and phosphorus are the most important negative factors among environmental variables, while geographical distance is the main limiting factor of ß diversity. Within the range of 0.35-0.70 mg/L of nutrients, increasing nutrient concentration can significantly facilitate the contribution of the decay effect to ß diversity. Within the temperature range studied (15.0-18.0°C), both warming and cooling can lead to a greater tendency for species diversity assembly processes to be dominated by deterministic processes. The analysis contributes to a better understanding of the assembly process of the diversity of coastal marine macroinvertebrates communities and how they adapt to global biogeochemical processes.

Active Learning the High-Dimensional Transferable Hubbard U and V Parameters in the DFT + U + V Scheme.

Density functional theory (DFT) is a powerful quantum mechanical computational tool to perform electronic structure calculations for materials. Few DFT methods can ensure accuracy and efficiency simultaneously. DFT + U + V is an alternative effective approach to overcome this drawback. However, the accuracy sensitively depends on the self-consistent estimation of the high-dimensional onsite and intersite Hubbard interaction U and V terms. We propose Bayesian optimization using a dropout (BOD) algorithm, one type of active learning method, to optimize U and V terms. The DFT + U + V with U/V obtained by BOD can produce improved electronic properties for diverse bulk materials of comparable quality to the hybrid functionals with lower computational cost compared to the linear response approach. Note that the band gaps calculated by BOD are somewhat different from that of hybrid functionals by simply applying the same U/V parameters as in the case of surface slabs and interfaces, which suggests that the transferability of U/V from the bulk models to slabs and interfaces is not as well as expected. BOD is extended to calculate the U/V parameters for slabs and interfaces and reach similar results as bulk solids. Moreover, we find that the U/V are reasonably transferable between surface slabs and interfaces with different thicknesses under various effects of quantum confinement, which contributes to fast access to the electronic properties of large-scale systems with higher accuracy.

Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook.

Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration.

MTA1, a Novel ATP Synthase Complex Modulator, Enhances Colon Cancer Liver Metastasis by Driving Mitochondrial Metabolism Reprogramming.

Liver metastasis is the most fatal event of colon cancer patients. Warburg effect has been long challenged by the fact of upregulated oxidative phosphorylation (OXPHOS), while its mechanism remains unclear. Here, metastasis-associated antigen 1 (MTA1) is identified as a newly identified adenosine triphosphate (ATP) synthase modulator by interacting with ATP synthase F1 subunit alpha (ATP5A), facilitates colon cancer liver metastasis by driving mitochondrial bioenergetic metabolism reprogramming, enhancing OXPHOS; therefore, modulating ATP synthase activity and downstream mTOR pathways. High-throughput screening of an anticancer drug shows MTA1 knockout increases the sensitivity of colon cancer to mitochondrial bioenergetic metabolism-targeted drugs and mTOR inhibitors. Inhibiting ATP5A enhances the sensitivity of liver-metastasized colon cancer to sirolimus in an MTA1-dependent manner. The therapeutic effects are verified in xenograft models and clinical cases. This research identifies a new modulator of mitochondrial bioenergetic reprogramming in cancer metastasis and reveals a new mechanism on upregulating mitochondrial OXPHOS as the reversal of Warburg effect in cancer metastasis is orchestrated.

Deep-learning-assisted theoretical insights into the compatibility of environment friendly insulation medium with metal surface of power equipment.

Exploring a new generation of eco-friendly gas insulation medium to replace greenhouse gas sulphur hexafluoride (SF6) in power industry is significant for reducing the greenhouse effect and building a low-carbon environment. The gas-solid compatibility of insulation gas with various electrical equipment is also of significance before practical applications. Herein, take a promising SF6 replacing gas trifluoromethyl sulfonyl fluoride (CF3SO2F) for example, one strategy to theoretically evaluate the gas-solid compatibility between insulation gas and the typical solid surfaces of common equipment was raised. Firstly, the active site where the CF3SO2F molecule is prone to interact with other compounds was identified. Secondly, the interaction strength and charge transfer between CF3SO2F and four typical solid surfaces of equipment were studied by first-principles calculations and further analysis was conducted, with SF6 as the control group. Then, the dynamic compatibility of CF3SO2F with solid surfaces was investigated by large-scale molecular dynamics simulations with the aid of deep learning. The results indicate that CF3SO2F has excellent compatibility similar to SF6, especially in the equipment whose contact surface is Cu, CuO, and Al2O3 due to their similar outermost orbital electronic structures. Besides, the dynamic compatibility with pure Al surfaces is poor. Finally, preliminary experimental verifications indicate the validity of the strategy.

Liquid metal for high-entropy alloy nanoparticles synthesis.

High-entropy alloy nanoparticles (HEA-NPs) show great potential as functional materials1-3. However, thus far, the realized high-entropy alloys have been restricted to palettes of similar elements, which greatly hinders the material design, property optimization and mechanistic exploration for different applications4,5. Herein, we discovered that liquid metal endowing negative mixing enthalpy with other elements could provide a stable thermodynamic condition and act as a desirable dynamic mixing reservoir, thus realizing the synthesis of HEA-NPs with a diverse range of metal elements in mild reaction conditions. The involved elements have a wide range of atomic radii (1.24-1.97 Å) and melting points (303-3,683 K). We also realized the precisely fabricated structures of nanoparticles via mixing enthalpy tuning. Moreover, the real-time conversion process (that is, from liquid metal to crystalline HEA-NPs) is captured in situ, which confirmed a dynamic fission-fusion behaviour during the alloying process.

High-Throughput Screening of Gas Sensor Materials for Decomposition Products of Eco-Friendly Insulation Medium by Machine Learning.

Nowadays, trifluoromethyl sulfonyl fluoride (CF3SO2F) has shown great potential to replace SF6 as an eco-friendly insulation medium in the power industry. In this work, an effective and low-cost design strategy toward ideal gas sensors for the decomposed gas products of CF3SO2F was proposed. The strategy achieved high-throughput screening from a large candidate space based on first-principle calculation and machine learning (ML). The candidate space is made up of different transition metal-embedded graphic carbon nitrides (TM/g-C3N4) owing to their high surface area and subtle electronic structure. Four main noteworthy decomposition gases of CF3SO2F, namely, CF4, SO2, SO2F2, and HF, as well as their initial stable structure on TM/g-C3N4 were determined. The best-performing ML model was established and implemented to predict the interaction strength between gas products and TM/g-C3N4, thus determining the promising gas-sensing materials for target gases with the requirements of interaction strength, recovery time, sensitivity, and selectivity. Further analysis guarantees their stability and reveals the origin of excellent properties as a gas sensor. The high-throughput strategy opens a new avenue of rational and low-cost design principles of desirable gas-sensing materials in an interdisciplinary view.

Quantifying hydrological-ecological response relationships based on zooplankton index of biotic integrity and comprehensive habitat quality index - A case study of typical rivers in Xi'an, China.

With the accelerated development of urbanization, rivers in urban areas have become the most closely synergized water ecosystem between human activities and natural processes. To achieve the restoration goal of using hydrological regime change-ecological response relationship to advance the sustainable development of regulated river ecosystems, this study collected ecohydrological data at four tributaries of the Wei River system (Ba, Chan, Feng, and Hei Rivers) at a total of 24 stations in October 2020 and June 2021. Taking ecological flow as hydrological parameter and zooplankton as indicator organism, combined with habitat data scored on-site, the indicator system of zooplankton index of biological integrity and comprehensive habitat quality index was established to explore the hydrological-ecological response relationship in a multi-dimensional way. The results showed that during the ebb stage, the ecological health of the Feng River was better overall, with an average ecological flow value of 267.09 ± 348.62. The ecological health of the Hei River was the worst, with an average ecological flow value of 37.80 ± 38.80. During the abundant water period, the ecological health of the Chan River was optimal with an average ecological flow value of 189.25 ± 190.10, while the ecological health of the Hei River remained unimproved, but the average ecological flow value increased by 283.12 ± 197.76. There was a clear negative correlation relationship between the comprehensive habitat quality index and ecological flow. The correlation between zooplankton index of biological integrity and ecological flows is extremely strong and threshold values exist, but there is strong heterogeneity in the interaction of disturbance factors across water systems, which may not provide a predictable response to flow changes. This study aims to provide a case reference for flow management in watersheds that also lack long-time series hydrological data and to contribute new thinking to the wide application of the hydrological-ecological response relationship.

Machine-learning-assisted discovery of highly efficient high-entropy alloy catalysts for the oxygen reduction reaction.

High-entropy alloys (HEAs) have recently been applied in the field of heterogeneous catalysis benefiting from vast chemical space. However, huge chemical space also brings extreme challenges for the comprehensive study of HEAs by traditional trial-and-error experiments. Therefore, the machine learning (ML) method is presented to investigate the oxygen reduction reaction (ORR) catalytic activity of millions of reactive sites on HEA surfaces. The well-performed ML model is constructed based on the gradient boosting regression (GBR) algorithm with high accuracy, generalizability, and simplicity. In-depth analysis of the results demonstrates that adsorption energy is a mixture of the individual contributions of coordinated metal atoms near the reactive site. An efficient strategy is proposed to further boost the ORR catalytic activity of promising HEA catalysts by optimizing the HEA surface structure, which recommends a highly efficient HEA catalyst of Ir48Pt74Ru30Rh30Ag74. Our work offers a guide to the rational design and nanostructure synthesis of HEA catalysts.

Strain-promoted conductive metal-benzenhexathiolate frameworks for overall water splitting.

Designing efficient catalysts for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is a desirable strategy for overall water splitting and the generation of clean and renewable energies. Herein, the electrocatalytic HER and OER activity of the conductive metal-benzenhexathiolate (M-BHT) frameworks has been evaluated utilizing first-principles calculations. The in-plane π-d conjugation of M-BHT guarantees fast electron transfer during electrocatalytic reactions. Notably, Rh-BHT holds the promise of bifunctional HER/OER activity with the overpotentials of 0.07/0.36 V. Furthermore, the application of strain engineering tailors the adsorption of intermediates and promotes the overall water splitting performance. Rh-BHT with the +1% tensile strain shows the HER/OER overpotential of 0.02/0.37 V. This work not only demonstrates the prospects of conductive metal-organic frameworks in electrocatalysis but also offers new insights into designing efficient catalysts by strain engineering.

A Feasible Strategy for Identifying Single-Atom Catalysts Toward Electrochemical NO-to-NH3 Conversion.

Combining NO removal and NH3 synthesis, electrochemical NO reduction reaction (NORR) toward NH3 is considered as a novel and attractive approach. However, exploring suitable catalysts for NO-to-NH3 conversion is still a formidable task due to the lack of a feasible method. Herein, utilizing systematic first-principles calculations, a rational strategy for screening efficient single-atom catalysts (SACs) for NO-to-NH3 conversion is reported. This strategy runs the gamut of stability, NO adsorbability, NORR activity, and NH3 selectivity. Taking transition metal atom embedded in C2 N (TM-C2 N) as an example, its validity is demonstrated and Zr-C2 N is selected as a stable NO-adsorbable NORR catalyst with high NH3 selectivity. Therefore, this work has established a theoretical landscape for screening SACs toward NO-to-NH3 conversion, which will contribute to the application of SACs for NORR and other electrochemical reactions.

Machine-Learning-Accelerated Catalytic Activity Predictions of Transition Metal Phthalocyanine Dual-Metal-Site Catalysts for CO2 Reduction.

The highly active and selective carbon dioxide reduction reaction (CO2RR) can generate valuable products such as fuels and chemicals and reduce the emission of greenhouse gases. Single-atom catalysts (SACs) and dual-metal-sites catalysts (DMSCs) with high activity and selectivity are superior electrocatalysts for the CO2RR as they have higher active site utilization and lower cost than traditional noble metals. Herein, we explore a rational and creative density-functional-theory-based, machine-learning-accelerated (DFT-ML) method to investigate the CO2RR catalytic activity of hundreds of transition metal phthalocyanine (Pc) DMSCs. The gradient boosting regression (GBR) algorithm is verified to be the most desirable ML model and is used to construct catalytic activity prediction, with a root-mean-square error of only 0.08 eV. The results of ML prediction demonstrate Ag-MoPc as a promising CO2RR electrocatalyst with the limiting potential of only -0.33 V. The DFT-ML hybrid scheme accelerates the efficiency 6.87 times, while the prediction error is only 0.02 V, and it sheds light on the path to accelerate the rational design of efficient catalysts for energy conversion and conservation.

The Electrophilicity of Surface Carbon Species in the Redox Reactions of CuO-CeO2 Catalysts.

Electronic metal-support interactions (EMSI) describe the electron flow between metal sites and a metal oxide support. It is generally used to follow the mechanism of redox reactions. In this study of CuO-CeO2 redox, an additional flow of electrons from metallic Cu to surface carbon species is observed via a combination of operando X-ray absorption spectroscopy, synchrotron X-ray powder diffraction, near ambient pressure near edge X-ray absorption fine structure spectroscopy, and diffuse reflectance infrared Fourier transform spectroscopy. An electronic metal-support-carbon interaction (EMSCI) is proposed to explain the reaction pathway of CO oxidation. The EMSCI provides a complete picture of the mass and electron flow, which will help predict and improve the catalytic performance in the selective activation of CO2 , carbonate, or carbonyl species in C1 chemistry.

A novel comprehensive model of set pair analysis with extenics for river health evaluation and prediction of semi-arid basin - A case study of Wei River Basin, China.

The accelerated development of urbanization in semi-arid areas is easy to cause varying degrees of disturbance to its fragile aquatic ecosystem. To find a general method for assessing the health status in semi-arid basins in China, and to provide theoretical basis for river health management and sustainable development, this study is that health evaluation index system is established in the Wei River Basin, covering a huge semi-arid area, and analyzed the biological structure of plankton obtained by field sampling and identified in laboratory, chemical conditions including dissolved oxygen, water temperature, ammonia nitrogen, pH, chlorophyll and other water physical and chemical factors determined by field instruments and sediment heavy metals analyzed in laboratory, physical habitat scored on site and social factors including water resource utilization rate and water consumption per 10,000 yuan of GDP collected in local water resource bulletin. Based on the idea of game theory, an improved coupling model of set pair analysis with extenics is established to assess and predict health. The results show that Wei River System and Jing River System are healthy, Beiluo River System is sub-healthy, and the whole Wei River Basin is also healthy; most of the cross-sections have a tendency to change to a bad level. Compared with simple weighting, set pair analysis and variable fuzzy set methods based on the corresponding sampling data and the weight in this paper, the coupling model can explain the transition and its trend between levels, reflect the certainty and uncertainty, and get more accurate results. It is suggested that daily monitoring and management should be strengthened in most sections to improve their health. And improve the development level of ecological function and social service function, give priority to the development of downstream water resources economy.

Tellurium Nanowire Gate-All-Around MOSFETs for Sub-5 nm Applications.

The nanowire (NW) and gate-all-around (GAA) technologies are regarded as the ultimate solutions to sustain Moore's law benefitting from the exceptional gate control ability. Herein, we conduct a comprehensive ab initio quantum transportation calculation at different diameters (single trigonal-tellurium NW (1Te) and three trigonal-tellrium NW (3Te)) sub-5 nm tellurium (Te) GAA NW metal-oxide-semiconductor field-effect transistors (MOSFETs). The results claim that the performance of 1Te FETs is superior to that of 3Te FETs. Encouragingly, the single Te (1Te) n-type MOSFET with 5 nm gate length achieves International Technology Roadmap for Semiconductors (ITRS) high-performance (HP) and low-dissipation (LP) goals simultaneously. Especially, the HP on-state current reaches 6479 µA/µm, 7 times higher than the goal (900 µA/µm). Moreover, the subthreshold swing of the n-type 1Te FETs even hits a thermionic limit of 60 mV/dec. In terms of the spin-orbit coupling effect, the drain currents of devices are further improved, particularly the p-type Te FETs can also achieve the ITRS HP goal. Hence, the GAA Te MOSFETs provide a feasible approach for state-of-the-art sub-5 nm device applications.

Comprehensive ecological risk assessment for semi-arid basin based on conceptual model of risk response and improved TOPSIS model-a case study of Wei River Basin, China.

Comprehensive ecological risk assessment is of great significance for the restoration of watershed ecosystem health, and the appropriate and effective assessment method is the premise of ecological risk assessment. In this study, the conceptual model of risk response was developed by identification of ecological risk sources, stressors, endpoints and the corresponding response mechanism as well as the improved TOPSIS model based on Canberra distance and the combinatorial weighting method based on AHP and Critic were combined for the assessment. According to the three aspects of agriculture, industrial and urbanization, the occurrence mechanism of comprehensive ecological risk of rivers in semi-arid areas was analyzed. Furthermore, twenty-four indexes were selected to establish the index system and the Wei River Basin was taken as an example to verify the model. The results of comprehensive ecological risk assessment and stressor analysis showed that the deterioration of water quality (enrichment of heavy metals) and the decrease of benthos integrity were the two main risk factors for the increase of comprehensive ecological risk in Wei River Basin. And the regulation of ecological risk for the Wei River Basin is improvement of water quality and biotic integrity.