Efficient Utilization Research Articles

Interlayer synergistic reaction of radical precursors for ultraefficient 1O2 generation via quinone-based covalent organic framework

Singlet oxygen (1O2) is important in the environmental remediation field, however, its efficient production has been severely hindered by the ultrafast self-quenching of the as-generated radical precursors in the Fenton-like reactions. Herein, we elaborately designed lamellar anthraquinone-based covalent organic frameworks (DAQ-COF) with sequential localization of the active sites (C═O) at molecular levels for visible-light-assisted peroxymonosulfate (PMS) activation. Theoretical and experimental results revealed that the radical precursors (SO5·-) were formed in the nearby layers with the migration distance less than 0.34 nm, via PMS donating electrons to the photogenerated holes. This interlayer synergistic effect eventually led to ultraefficient 1O2 production (14.8 μM s-1), which is 12 times that of the highest reported catalyst. As an outcome, DAQ-COF enabled the complete degradation of bisphenol A in 5 min with PMS under natural sunlight irradiation. This interlayer synergistic concept represents an innovative and effective strategy to increase the utilization efficiency of ultrashort-lived radical precursors, providing inspirations for subtle structural construction of Fenton-like catalysts.

Biofortification of Cucumbers with Iron Using Bio-Chelates Derived from Spent Coffee Grounds: A Greenhouse Trial

The transformation of spent coffee grounds (SCGs) into hydrochars has been extensively studied in recent years to explore their potential in biofortifying foods and mitigating the plant toxicity associated with SCGs. This study aimed to evaluate the effects of adding activated (ASCG and AH160) and functionalized SCGs, as well as SCG-derived hydrochars (ASCG-Fe and AH160-Fe), on cucumber production and plant iron content. To achieve this, SCGs and SCG-derived hydrochars activated and functionalized with Fe were incorporated into cucumber crops grown in a greenhouse over multiple harvests. Among the treatments, SCG-Fe proved to be the most promising for cucumber production, yielding an average of 25 kg of cumulative production per treatment across three harvests. Regarding iron content, the average results across all harvests showed that SCGs and functionalized SCG-hydrochars matched the performance of the commercial chelate (0.108 vs. 0.11 mg Fe/100 g fresh weight). However, in subsequent harvests, iron appeared to leach out, with the activated bio-products (ASCG and AH160) leaving the highest iron reserves in the soil. Additionally, the hydrochar activated at 160 °C demonstrated the highest utilization efficiency. In conclusion, the incorporation of SCG residues and second-generation residues (hydrochars) shows promise as agents for biofortifying cucumbers.

A Review on the Optimization of Irrigation Schedules for Farmlands Based on a Simulation–Optimization Model

Agriculture is the most important sector that is consuming water resources. In the context of global water scarcity, how to use limited water resources to improve water use efficiency in agriculture or achieve maximum crop yield and fruit quality is of great significance for ensuring food and water security. Optimizing irrigation schedules is an effective measure to improve water use efficiency, where crop models also play an important role. However, there is little research summarizing the optimization of irrigation schedules based on crop models. This study provides a systematic review on how to optimize irrigation schedules based on crop models and simulation–optimization models. When optimizing irrigation schedules based on crop models, the selected models are usually mechanistic agro-hydrological models. Irrigation scenarios and optimization objectives are mainly focused on both crop and water aspects, such as maximizing crop yield, fruit quality, water productivity, and irrigation water productivity. Minimizing crop water consumption and total irrigation amounts serve as optimization objectives, and irrigation quantity, irrigation frequency, and irrigation interval serve as decision variables. In saline areas or low fertilizer utilization areas, the optimization objectives and decision variables also involve some indicators related to salt and nitrogen, such as the maximum desalination rate, minimum salt content, fertilizer utilization efficiency, nitrogen fertilizer productivity, nitrogen fertilizer utilization efficiency, nitrogen leaching rate, which serve as the optimization objectives, and the irrigation water salinity, or fertilization schedules serve as the decision variables. When optimizing irrigation schedules based on simulation–optimization models, the models have mainly been upgraded from water-production function to crop mechanism models. In addition, optimization algorithms have been upgraded from traditional optimization techniques to intelligent optimization algorithms. Decision-making techniques are used to make decisions on optimization results. In addition, the spatial scale for the optimization problem of irrigation schedules was developed from fields to regions, and the time scale was developed from the growth stage, beginning with months, and shortening to ten days, then to a day, and then to an hour. This study also provides a detailed introduction to widely used optimization algorithms, such as genetic algorithms, as well as decision techniques. At the same time, it is proposed that the future should focus on improving crop models and analyzing uncertainty in research on irrigation schedule optimization, which is of great significance for the precise regulation of irrigation schedules.

Effective hydrogen production and coupling degradation of organics (4-nitrophenol, methylene blue, and Rhodamine B) in wastewater by electrospun PAN@Fe0 composite nanofibers

The pursuit of clean energy generation and environmental preservation is of utmost importance for societal progress. However, couple clean energy production and pollution control is still a difficulty. In this study, a novel electrospun composite mat, with nano zero valent iron (Fe0, nZVI) encapsulated into polyacrylonitrile (PAN) nanofibers was acted as a catalyst. The activation energy (Ea) for the hydrolysis of NaBH4 to produce hydrogen is 22 kJ·mol−1, hydrogen atom utilization efficiency (HGE) of NaBH4 is 60.80%. Three kinds of organic dyes 4-nitrophenol (4-NP), methylene blue (MB), and Rhodamine B (RhB) were served as the model pollutant, to construct NaBH4-organic system for energy production and pollution reduction. The NaBH4-organic system demonstrates a collaborative capability to simultaneously remove organic pollutants and generate hydrogen, with a coupling equilibrium point between the two processes. The hydrogen generation rate (HGR) and HGE increased with the concentration of pollutants increased. The degradation of 4-NP, MB, and RhB followed pseudo-first-order kinetics, with RhB degrading dosage 20 and 44 times higher than 4-NP and MB, respectively. H2 and H· contributed to the organic degradation. nZVI directly participated in the formation H2 and H·. PAN@Fe0 possessed good recyclability and moderate cost. The degradation process adhered to the classical surface reaction controlled Langmuir-Hinshelwood (L-H) model. The integration of synergistic production capacity and pollutant degradation aligns well with the principles of green chemistry and sustainable development. This study demonstrates a novel approach to no precursor loss catalysts preparation, efficient production clean H2, advanced treatment of dye-containing wastewater, carbon neutral operation of sewage treatment plant.

Influence of Printing Interval on the Imbibition Behavior of 3D-Printed Foam Concrete for Sustainable and Green Building Applications

Foam concrete is highly valued as a sustainable cement-based material, but the development of 3D-printed foam concrete (3DPFC) has remained constrained. This study investigated the influence of printing interval on the microstructure and imbibition behavior of 3DPFC. The results revealed that horizontal interlayers are broader compared to vertical interlayers, leading to more significant imbibition. For X-oriented 3DPFC, the vertical interlayer was rapidly occupied by water after imbibition, forming an elliptical moisture profile. For Y-oriented 3DPFC, the moisture profile appeared more convoluted, mainly surrounding the horizontal interlayers but shifting at intersections with the vertical interlayers. In Z-oriented 3DPFC, where only tight horizontal interlayers were present, interlayer imbibition was almost negligible. Additionally, when the printing interval was less than 15 min, imbibition was primarily restricted to the top filament since the bottom filament was compacted by the filament above. Conversely, with a printing interval longer than 15 min, the bottom filament hardened before the setting of the top filament. This allowed the surface of the bottom filament to be compacted by the top filament, resulting in a dense interlayer that offers better resistance against imbibition compared to the matrix of 3DPFC. This work contributes to the advancement of green building technologies by providing insights into optimizing the 3D printing process for foam concrete, thereby enhancing its structural performance without compromising the designated air content and consistency of the foam concrete, facilitating a more efficient utilization of materials and a reduction in overall material consumption.

Synthetic redesign of Escherichia coli W for faster metabolism of sugarcane molasses

BackgroundSugarcane molasses, rich in sucrose, glucose, and fructose, offers a promising carbon source for industrial fermentation due to its abundance and low cost. However, challenges arise from the simultaneous utilization of multiple sugars and carbon catabolite repression (CCR). Despite its nutritional content, sucrose metabolism in Escherichia coli, except for W strain, remains poorly understood, hindering its use in microbial fermentation. In this study, E. coli W was engineered to enhance sugar consumption rates and overcome CCR. This was achieved through the integration of a synthetically designed csc operon and the optimization of glucose and fructose co-utilization pathways. These advancements facilitate efficient utilization of sugarcane molasses for the production of 3-hydroxypropionic acid (3-HP), contributing to sustainable biochemical production processes.ResultsIn this study, we addressed challenges associated with sugar metabolism in E. coli W, focusing on enhancing sucrose consumption and improving glucose-fructose co-utilization. Through targeted engineering of the sucrose utilization system, we achieved accelerated sucrose consumption rates by modulating the expression of the csc operon components, cscB, cscK, cscA, and cscR. Our findings revealed that monocistronic expression of the csc genes with the deletion of cscR, led to optimal sucrose utilization without significant growth burden. Furthermore, we successfully alleviated fructose catabolite repression by modulating the binding dynamics of FruR with the fructose PTS regulon, enabling near-equivalent co-utilization of glucose and fructose. To validate the industrial applicability of our engineered strain, we pursued 3-HP production from sugarcane molasses. By integrating heterologous genes and optimizing metabolic pathways, we achieved improvements in 3-HP titers compared to previous studies. Additionally, glyceraldehyde-3-phosphate dehydrogenase (gapA) repression aids in carbon flux redistribution, enhancing molasses conversion to 3-HP.ConclusionsDespite limitations in sucrose metabolism, the redesigned E. coli W strain, adept at utilizing sugarcane molasses, is a valuable asset for industrial fermentation. Its synthetic csc operon enhances sucrose consumption, while mitigating CCR improves glucose-fructose co-utilization. These enhancements, coupled with repression of gapA, aim to efficiently convert sugarcane molasses into 3-HP, addressing limitations in sucrose and fructose metabolism for industrial applications.

An efficient intrusion detection system for IoT security using CNN decision forest

The adoption and integration of the Internet of Things (IoT) have become essential for the advancement of many industries, unlocking purposeful connections between objects. However, the surge in IoT adoption and integration has also made it a prime target for malicious attacks. Consequently, ensuring the security of IoT systems and ecosystems has emerged as a crucial research area. Notably, advancements in addressing these security threats include the implementation of intrusion detection systems (IDS), garnering considerable attention within the research community. In this study, and in aim to enhance network anomaly detection, we present a novel intrusion detection approach: the Deep Neural Decision Forest-based IDS (DNDF-IDS). The DNDF-IDS incorporates an improved decision forest model coupled with neural networks to achieve heightened accuracy (ACC). Employing four distinct feature selection methods separately, namely principal component analysis (PCA), LASSO regression (LR), SelectKBest, and Random Forest Feature Importance (RFFI), our objective is to streamline training and prediction processes, enhance overall performance, and identify the most correlated features. Evaluation of our model on three diverse datasets (NSL-KDD, CICIDS2017, and UNSW-NB15) reveals impressive ACC values ranging from 94.09% to 98.84%, depending on the dataset and the feature selection method. Notably, our model achieves a remarkable prediction time of 0.1 ms per record. Comparative analyses with other recent random forest and Convolutional Neural Networks (CNN) based models indicate that our DNDF-IDS performs similarly or even outperforms them in certain instances, particularly when utilizing the top 10 features. One key advantage of our novel model lies in its ability to make accurate predictions with only a few features, showcasing an efficient utilization of computational resources.

Enhancing Smart Grid Efficiency through Machine Learning-Based Renewable Energy Optimization

Managing renewable energy in smart grids poses a significant challenge due to the inherent uncertainty and variability of energy sources like solar and wind power. To address this issue, we propose a novel approach that leverages the strengths of both Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) algorithms. Our method utilizes ELM to model and predict renewable energy generation, enabling more accurate forecasting and planning. Meanwhile, PSO optimizes the parameters of the ELM algorithm, ensuring optimal performance and efficiency. We evaluated our approach using a dataset of solar energy production and compared its performance to existing optimization techniques. The results show that our ELM-PSO approach significantly improves the accuracy of renewable energy predictions and reduces energy costs in smart grids. The implications of our research are far-reaching, as our approach can be applied to various renewable energy systems, including wind turbines, solar panels, and hydroelectric power plants. By enhancing the efficiency and reliability of renewable energy utilization, we can create a more sustainable and resilient energy future.

Effects of Industrial Structure on the Green Utilization Efficiency of Urban Land: A Case Study of the Bohai Rim Region, China

The green utilization of urban land is a critical component of regional high-quality development. Enhancing the green utilization efficiency of urban land (GUEUL) is of great significance to improving the quality of ecological environment and achieving a green lifestyle and low-carbon production. With the process of urbanization, the industrial structure is constantly adjusted, which has an impact on the structure and efficiency of urban land utilization. Taking 43 cities in the Bohai Rim region as an example, the super-efficiency slack-based measure model with undesirable outputs (super-SBM-undesirable model) was employed to evaluate the GUEUL from 2006 to 2021, and the panel quantile model was used to explore the impact of industrial structure on GUEUL. The results showed that the GUEUL in the Bohai Rim region appeared a fluctuating upward trend from 0.664 in 2006 to 0.837 in 2021. High-value zones shifted from western Liaoning province and southern Shandong province to a “C”-shaped belt around the coastline and expanded continuously, while low-value zones decreased significantly. Furthermore, the influence of industrial structure on GUEUL was significantly positive, but there were significant differences among different efficiency levels. Industrial structure upgrading exerted the most significant effect on GUEUL improvement in low-value zones, while industrial structure rationalization was dominant in high-value zones, and the influence of industrial structure technologization was more significant in medium-low-value zones. Therefore, differentiated industrial structure adjustment policies should be formulated based on the actual condition of each city to improve GUEUL.

Carbon-intercalated halloysite-based aerogel efficiently encapsulating phase change materials with excellent photothermal conversion and energy storage

Latent heat storage systems based on organic phase change materials (PCMs) are considered to be an efficient solar energy utilization strategy, but leakage vulnerability and insensitivity to sunlight of PCMs limit their further application in energy storage. In this work, a new hierarchical porous aerogel was constructed with the carbon intercalated halloysite nanotubes (CHNTs) as the assembly units for PCMs encapsulation to improve the load weight, stability and sunlight absorption. The CHNTs were first successfully synthesized through intercalation and carbonization, with the aim of increasing their specific surface area and sunlight absorption.Subsequently, the CHNTs/polyvinyl alcohol (PVA) aerogels with 3D honeycomb structure were prepared by self-assembly using a freeze-drying method, which can significantly promote the encapsulation ratio of lauric acid (LA) in the pores of both aerogels and CHNTs. The CHNTs can not only improve compressive strength of the aerogels and shape stability of the composite PCMs, but also enhance light absorption ability of the composites compared to pristine HNTs. As a support material, CHNTs aerogel (CHNP) loaded more than 92 wt% PCM (LA@CHNP) and exhibited an extremely high latent heat capacity (170.9 J/g). The LA@CHNP had good structural and thermal stability in 300 cycles without visible leakage. Besides, solar energy absorption increased from 43 % to 98 %, and the solar-to-thermal energy conversion efficiency increased to 95.88 %, which could be attributed to 3D porousness and carbon nanolayers of the CHNTs. We have successfully synthesized an eco-friendly and facile composite LA@CHNP, which will contribute to the design of advanced composites with excellent solar energy storage properties, and provide a new direction for the application of carbonized materials.

Nanoarchitectonics of MIL-101(Fe)/g-C3N4 S-Scheme heterojunction for photocatalytic nitrogen fixation: Mechanisms and performance

Recently, there has been a growing interest in the fundamental understanding of the mechanism of how MIL-101(Fe)/g-C3N4 heterojunctions facilitate the occurrence of photocatalytic nitrogen fixation reactions, especially the electron transfer mechanism has attained increasing attention in photocatalysis. Herein, we implemented a "triple win scenario" strategy to fabricate the S-scheme MIL-101(Fe)/g-C3N4 heterojunction through a straightforward solvothermal process. The MC-6 heterojunction minimizes the recombination of photogenerated carriers, enhances light utilization efficiency, and activates the N≡N bond, thus boosting photocatalytic nitrogen fixation efficiency. The transition metal iron activates the N≡N bond, while S-scheme heterojunctions reduce the photogenerated carrier recombination. In addition, the decrease in band gap (Eg) leads to an increase in visible light utilization efficiency. ISIXPS proved the mechanism of interelectron transfer of MIL-101(Fe)/g-C3N4 heterojunction under illumination. Upon the creation of the heterojunction, electrons migrate from g-C3N4 to MIL-101(Fe), establishing an inherent electric field due to the disparate Fermi levels between the two materials. The electrons (e-) on the g-C3N4 CB with a more negative reduction potential and the holes (h+) on the MIL-101(Fe) VB are retained, which increased the redox capacity to a great extent required for the reduction of N2 to NH3. The ammonia production efficiency of MC-6 photocatalyst was 160 µmol gcat-1 h-1, representing an 8-fold and 2.8-fold improvement over pristine g-C3N4 (20 µmol gcat-1 h-1) and MIL-101(Fe) (57 µmol gcat-1 h-1), respectively.

Bi-Level Optimal Configuration of Electric Thermal Storage Boilers in Thermal–Electrical Integrated Energy System

Electric thermal storage boilers (ETSBs) are important devices in enhancing the electric–thermal decoupling ability and spatiotemporal transfer of integrated energy system (IES), which is beneficial for improving system flexibility and energy utilization efficiency. In order to obtain more accurate and comprehensive results, a bi-level optimal model is proposed to study the site selection and capacity configuration of ETSB in IES based on the established mathematical model of ETSB. The objective of upper-level optimization of the model is obtaining the lowest energy supply cost when configuring the location and capacity of ETSB, while the lower-level model optimizes the operation scheduling with the goal of obtaining the lowest operational cost. The mixed-integer linear programming method and the genetic algorithm method are selected to obtain the optimal model. To illustrate the effectiveness and advantages of the proposed method, case studies are carried out. The optimal configuration scheme for an ETSB is obtained by comparing the lowest energy supply cost under different configuration parameters. Furthermore, the impact of an ETSB on the system is also analyzed based on the variations in energy balance, abandoned energy, and energy allocation before and after configuring the ETSB.

Employee attrition prediction with convolutional neural network and synthetic minority over-sampling technique

ABSTRACT Employee attrition negatively affects organizational efficiency, customer service quality, and brand reputation. In this study, we explore the important issue of predicting employee attrition, which aids managers in taking effective retention strategies and mitigating the significant costs associated with recruiting and training new personnel. Different from existing research, we introduce a novel deep learning model featuring an intermediary layer that automatically generates a hidden image representation from tabular data. This intermediary step facilitates the efficient utilization of Convolutional Neural Networks, specifically designed for image data, thereby enhancing predictive accuracy. Furthermore, we employ the widely used Synthetic Minority Over-sampling Technique for handling imbalanced data to further improve our model’s performance. Our new CNN-based deep learning model demonstrated the best performance for predicting employee attrition, which can assist in reducing costs related to turnover and facilitate the implementation of a succession plan to ensure seamless transitions.

Deployment options of AI components for network resource management in 5G‐enabled agile industrial production cell

SummaryOn‐demand manufacturing in Industry 4.0 requires flexibility of the networks which can be provided with the fifth generation (5G) of mobile communications wireless connectivity. A key component in the efficient utilization of the radio resources in a manufacturing scenario is network resource management (NRM). We show how NRM can be automated with artificial intelligence (AI). We introduce several futuristic industrial use cases that require AI in various parts of the process. We analyze the AI components' benefits and disadvantages in several deployment scenarios. The findings can be used by business stakeholders interested in deploying the 5G cellular wireless network to choose the best NRM and AI implementation strategy for a particular use case. We show that there are many viable options for the AI component in the process automation, but the cost of AI has to be considered in all cases. Also, we point out that an essential component, the standardized information flow on the status of the productivity key performance indicators (KPIs), is needed for the successful deployment and application of the 5G AI.

Does Nitrogen Fertilization Improve Nitrogen-Use Efficiency in Spring Wheat?

To investigate the effects and mechanism of prolonged inorganic nitrogen (N) fertilization on the N-use efficiency of spring wheat (Triticum aestivum L.), a long-term study initiated in 2003 was conducted. The study analyzed how N fertilization affects dry matter translocation, N translocation, soil NO3-N, and N-use efficiency. Five different N-fertilizer rate treatments were tested: N0, N52.5, N105, N157.5, and N210, corresponding to annual N fertilizer doses of 0, 52.5, 105.0, 157.5, and 210.0 kg N ha−1, respectively. Results showed that increasing N-fertilizer rates significantly enhanced the two-year average dry matter accumulation amount (DMA) at maturity by 22.97–56.25% and pre-flowering crop growth rate (CGR) by 17.11–92.85%, with no significant increase beyond 105 kg N ha−1. However, no significant correlation was observed between the dry matter translocation efficiency (DTE) and wheat grain yield. Both insufficient and excessive N applications resulted in an imbalanced N distribution favoring vegetative growth over reproductive growth, thus negatively impacting N-use efficiency. At maturity, the N-fertilized treatments significantly increased the two-year average N accumulation amount (NAA) by 52.04–129.98%, with no further increase beyond 105 kg N ha−1. N fertilization also improved the two-year average N translocation efficiency (NTE) by 56.89–63.80% and the N contribution proportion (NCP) of wheat vegetative organs by 27.79–57.83%, peaking in the lower-N treatment (N52.5). However, high-N treatment (N210) led to an increase in NO3-N accumulation in the 0–100 cm soil layer, with an increase of 26.27% in 2018 and 122.44% in 2019. This higher soil NO3-N accumulation in the 0–100 cm layer decreased NHI, NUE, NAE, NPFP, and NMB. Additionally, N fertilization significantly reduced the two-year average N harvest index (NHI) by 9.89–12.85% and N utilization efficiency (NUE) by 11.14–20.79%, both decreasing with higher N application rates. The NAA followed the trend of anthesis > maturity > jointing. At the 105 kg N ha−1 rate, the highest N agronomic efficiency (NAE) (9.31 kg kg−1), N recovery efficiency (NRE) (38.32%), and N marginal benefit (NMB) (10.67 kg kg−1) were observed. Higher dry matter translocation amount (DTA) and N translocation amount (NTA) reduced NHI and NUE, whereas higher NTE improved NHI, NUE, and N partial factor productivity (NPFP). Overall, N fertilization enhanced N-use efficiency in spring wheat by improving N translocation rather than dry matter translocation.

Ambient-pressure alkoxycarbonylation for sustainable synthesis of ester

Alkoxycarbonylation reactions are common in the chemical industry, yet process sustainability is limited by the inefficient utilization of CO. In this study, we address this issue and demonstrate that significant improvements can be achieved by adopting a heterogeneously catalyzed process, using a Ru/NbOx catalyst. The Ru/NbOx catalyst enables the direct synthesis of methyl propionate, a key industrial commodity, with over 98% selectivity from CO, ethylene and methanol, without any ligands or acid/base promoters. Under ambient CO pressure, a high CO utilization efficiency (336 mmolestermolCO−1h−1) is achieved. Mechanistic investigations reveal that CO undergoes a methoxycarbonyl (COOCH3) intermediate pathway, attacking the terminal carbon atom of alkene and yielding linear esters. The origins of prevailing linear regioselectivity in esters are revealed. The infrared spectroscopic feature of the key COOCH3 species is observed at 1750 cm−1 (C=O vibration) both experimentally and computationally. The broad substrate applicability of Ru/NbOx catalyst for ester production is demonstrated. This process offers a sustainable and efficient approach with high CO utilization and atom economy for the synthesis of esters.

Tandem Pt/TiO2 and Fe3C catalysts for direct transformation of CO2 to light hydrocarbons under high space velocity

CO2 hydrogenation to hydrocarbons under high space velocity is crucial for industrial applications, but traditional Fe-based catalysts often suffer from the low activity and poor stability. Herein, we report a new tandem catalyst system combining Pt/TiO2 catalysts with Fe3C catalysts for the direct conversion of CO2 into C2-C4 hydrocarbons under high space velocity. The Pt/TiO2 component promotes *CO intermediate production with an enhanced Reverse Water-Gas Shift (RWGS) reaction efficiency, providing a highly reactive species for the Fe3C catalyst to achieve Fischer-Tropsch synthesis (FTS). By maximizing the contact interface between the Pt/TiO2 and Fe-based components through a granule mixing configuration, we achieve significant enhancements in both CO2 conversion rate (24.0 %) and C2-C4 hydrocarbons selectivity (51.1 %) under the gaseous hourly space velocity (GHSV) of 100000 mL gcat−1h−1. Besides, excellent stability is achieved by the tandem catalysts with continuous catalysis for up to 80 h without significant decrease in activity. Through modulation of the reduction states of iron oxide, we effectively tune the composition of Fe-based catalyst, thereby tailoring the product distribution. Through this work, we not only offer a promising avenue for reducing CO2 for efficient CO2 utilization but also highlight the importance of catalyst design in advancing sustainable chemical synthesis.

A preference adjustable capacity configuration optimization method for hydrogen-containing integrated energy system considering dynamic energy efficiency improvement and load fast tracking

The new hydrogen-containing integrated energy system proposed in this paper has complementary coupling of multiple energy forms such as electricity, gas, cold, heat, and hydrogen. For the same type of load demand, equipment with different energy input forms can be dispatched for joint supply. However, different equipment differ in dynamic response speed and energy utilization efficiency, and the reasonable configuration of equipment capacity will directly affect the overall dynamic characteristics and energy efficiency level of the system. Therefore, this paper first proposes an evaluation index that can comprehensively characterize the dynamic energy efficiency and load tracking ability of energy equipment. In addition, a preference adjustable capacity configuration optimization method based on utopian point tracking is proposed with the two optimization objectives of this indicator and the system's equivalent annualized investment cost, and the solution complexity is reduced through segmented linearization of the objective. Simulations presented here show that the capacity configuration optimization method proposed in this paper has a positive effect on improving the dynamic energy efficiency and load tracking ability of the new hydrogen-containing integrated energy system, and can also meet the preference setting needs of different investment entities for capacity configuration optimization goals.

Orbital coupling of Pt single crystal via decoration of hetero-diatomic nickel-cobalt for efficient hydrogen evolution

Loading active metals in forms of single atoms or nanocluster is considered as an effective approach for designing catalysts with high atomic utilization efficiency. However, synthesis single atoms or clusters precisely in large quantities is a challenge. In this work, the isolated diatomic sites (Ni, Co) decorated single crystal Pt cluster were synthesized as efficient catalyst for hydrogen evolution. Adjacent Ni and Co atoms optimize the electronic structure of Pt single crystal, effectively lowering the water dissociation barrier and ensuring optimal binding strength of intermediates throughout the reaction process. As results, the mass activity of 2.087 A mg−1 was achieved under 200 mA cm−2, approximately 3 times than that of Pt/C. Theoretical calculations reveal that diatomic Ni, Co decorated Pt cluster reduces the energy barrier for breaking the OH-H bond, as well as facilitating the preferential adsorption and dissociation of H*. This work provides an opportunity for regulation electronic structure of catalytic via decoration single crystal cluster with diatomic sites and provides guidance for designing high efficiency electrocatalysts for promising applications.

Optimizing ‘red Fuji’ apple quality: Auxin-mediated calcium distribution via fruit-stalk in bagging practices

In apples, a bottleneck effect in calcium (Ca) transport within fruit stalk has been observed. To elucidate that how auxin affects Ca forms and distribution in the apple fruit stalk, we investigated the effects of different concentrations of auxin treatment (0, 10, 20, and 30 mg·L−1) on Ca content, forms, distribution, and fruit quality during later stages of fruit expansion. The results showed that auxin treatment led to a dramatic reduction in total Ca content in stalk, while an approximately 30 % increase in fruit. Furthermore, auxin treatment effectively enhanced the functionality of xylem vessels in vascular bundles of the stalk in bagged apples. Finally, TOPSIS method was used to assess fruit quality, with treatments ranked as follows: IAA20 > NAA20 > IAA30 > IAA10 > CK > NPA. The findings lay a foundation for further studies on the bottleneck in Ca transport within stalk, uneven distribution of Ca in fruit, and provide insights into Ca utilization efficiency in bagged apples.