Optimal Structure Research Articles

Quantum annealing-aided design of an ultrathin-metamaterial optical diode

Thin-film optical diodes are important elements for miniaturizing photonic systems. However, the design of optical diodes relies on empirical and heuristic approaches. This poses a significant challenge for identifying optimal structural models of optical diodes at given wavelengths. Here, we leverage a quantum annealing-enhanced active learning scheme to automatically identify optimal designs of 130 nm-thick optical diodes. An optical diode is a stratified volume diffractive film discretized into rectangular pixels, where each pixel is assigned to either a metal or dielectric. The proposed scheme identifies the optimal material states of each pixel, maximizing the quality of optical isolation at given wavelengths. Consequently, we successfully identify optimal structures at three specific wavelengths (600, 800, and 1000 nm). In the best-case scenario, when the forward transmissivity is 85%, the backward transmissivity is 0.1%. Electromagnetic field profiles reveal that the designed diode strongly supports surface plasmons coupled across counterintuitive metal–dielectric pixel arrays. Thereby, it yields the transmission of first-order diffracted light with a high amplitude. In contrast, backward transmission has decoupled surface plasmons that redirect Poynting vectors back to the incident medium, resulting in near attenuation of its transmission. In addition, we experimentally verify the optical isolation function of the optical diode.

A Review on Underwater Collection and Transportation Equipment of Polymetallic Nodules in Deep-Sea Mining

In response to the anticipated scarcity of terrestrial land resources in the coming years, the acquisition of marine mineral resources is imperative. This paper mainly summarizes the development of underwater collection and transportation equipment of polymetallic nodules in deep-sea mining. Firstly, the collection equipment is reviewed. The deep-sea mining vehicle (DSMV), as the key equipment of the collection equipment, mainly includes the collecting device and the walking device. The micro and macro properties of sediments have a great influence on the collection efficiency of mining vehicles. For the collecting device, the optimization of the jet head structure and the solid–liquid two-phase flow transport of the hose are discussed. The structure of the walking device restricts mining efficiency. The optimization of the geometric structure is studied, and the geometric passability and lightweight design of the walking device are discussed. Secondly, the core of transportation equipment is the lifting device composed of a riser and lifting pump. In order to explore the key factors affecting mineral transport, the lifting device is summarized, and the design optimization of the lifting pump and the factors affecting the stability of the riser are discussed. Then, the relationship between each device is discussed, and the overall coupling of the device is summarized. Finally, the existing problems and future research focus are summarized.

Measuring the gap between optimal and observed energy structure: Evidence from 36 OECD countries

Sustainable development has always been given priority by economic authorities, therefore pursuing both environmental emissions reduction and economic growth at the same time is required. However, energy structure is often disregarded in existing performance evaluation methodologies, despite its essential function in determining environmental efficiency or productivity. This study shows how ignoring the energy structure can lead to distorted policy recommendations as well as evaluation results. The present study provides a non-parametric estimating approach to quantify the difference between the optimal (theoretical) energy structure required to achieve sustainable development and the actual (observed) energy structure. This method may assess the energy transition process and evaluate the observed and optimal energy structure of a country or region. We demonstrate that using the countries of the Organization for Economic Co-operation and Development as an example, that only a limited number of countries are able to meet their objectives for carbon reduction and economic development simultaneously without transforming their energy structure. The relationship between energy structure and green total factor productivity (GTFP) was further studied using a two-stage least squares regression on the observed energy structure and GTFP. The findings indicate that most countries need to increase their share of renewable energy in order to achieve green growth.

Integrated Heterogeneous Engineering with the Vacancy Defect of Porous CoPv-MoxPv Nanosheets for an Accelerated Hydrogen Evolution Reaction.

Electrochemical water splitting is a possible way of realizing sustainable and clean hydrogen production but is challenging, because a highly active and durable electrocatalyst is essential. In this work, we integrated heterogeneous engineering and vacancy defect strategies to design and fabricate a heterostructure electrocatalyst (CoPv-MoxPv/CNT) with abundant phosphorus vacancies attached to carbon nanotubes (CNTs). The vacancy defects enabled the optimization of the electronic structure; thereby, the electron-rich low-valent metal sites enhanced the ability of nonmetallic P to capture proton H. Meanwhile, the heterogeneous interface between bimetallic phosphides and CNTs realized rapid electron transfer. In addition, the Co, Mo, and P active species in the electrocatalytic process exposed increased amounts of active sites featuring porous nanosheet structures, which facilitated the adsorption of reaction intermediates and thus enhanced the hydrogen evolution reaction performance. In particular, the optimized CoPv-MoxPv/CNT catalyst possesses an overpotential of 138 mV at a current density of 10 mA cm-2 and long-term stability for 24 h. This work offers insights and possibilities for the engineering and exploration of transition metal-based electrocatalysts through combining multiple synergistic strategies.

Developing Polymeric Carbon Nitrides for Photocatalytic H2O2 Production.

Hydrogen peroxide (H2O2) plays a crucial role in various applications, such as green oxidation processes and the production of high-quality fuels. Currently, H2O2 is primarily manufactured using the indirect anthraquinone method, known for its significant energy consumption and the generation of intensive by-products. Extensive research has been conducted on the photocatalytic production of H2O2via oxygen reduction reaction (ORR), with polymeric carbon nitride (PCN) emerging as a promising catalyst for this conversion. This review article is organized around two approaches. The first part main consists of the chemical optimization of the PCN structure, while the second focuses on the physical integration of PCN with other functional materials. The objective is to clarify the correlation between the physicochemical properties of PCN photocatalysts and their effectiveness in H2O2 production. Through a thorough review and analysis of the findings, this article seeks to stimulate new insights and achievements, not only in the domain of H2O2 production via ORR but also in other redox reactions.

Heavy steers and heifers run at low stocking rates enhance drought resilience in a pastoral region of Australia

CONTEXTThe semi-arid mulga lands of the Southern Rangelands of Western Australia experience frequent droughts. Maintaining a profitable animal grazing enterprise whilst preserving rangeland condition requires selecting an appropriate stocking rate and herd structure. OBJECTIVEWe construct and apply bioeconomic models of beef enterprises for the semi-arid Southern Rangelands of Western Australia, to assess the profitability and relative riskiness of three different livestock production systems and three different stocking rates when exposed to different frequencies of drought. METHODSWe construct bio-economic models of herd structures that separately focus on producing either: (i) 200 kg heifers and 210 kg steers (ii) live export of 340 kg heifers and 380 kg steers, or (iii) 500 kg heifers and 560 kg steers for local slaughter. The models are applied to assess the financial and production resilience of each herd structure for three different stocking rates, given the incidence of drought and various price scenarios. RESULTS AND CONCLUSIONSComparing the three herd structures, option (iii) produces more kilograms of beef at an equivalent grazing pressure and recovers fastest from drought. Considering cattle prices over the 7-year period ending in 2021, live export and slaughter production systems generate comparable financial returns to graziers' actual reported results. Under two different scenarios of future prices, option (iii) provides the most profitable outcome. Applying a stocking rate that is 66% of a recommended stocking rate allows use of residual rangeland feed (known as ‘haystack’) during drought, generates greater profit, and produces a more stable herd size and income. Not matching the haystack with the longest modelled drought reduces the grazier's average annual profit by $172,000. The optimum herd structure identified in this study targets the production of 500 kg heifers and 560 kgs steers for local slaughter, and relies on a conservative stocking rate that leaves enough haystack to increase resilience if ever consecutive droughts occur. SIGNIFICANCEIn the Southern Rangelands of Western Australia, there are financial and environmental merits in a herd structure that produces heavy steers and heifers for local slaughter yet is underpinned by conservative stocking rates.

EFFECT OF DEBT STRUCTURE ON PROFITABILITY TOOLS (ANALYTICAL STUDY: A SAMPLE OF IRAQI INDUSTRIAL FIRMS)

This study aims to know the impact of the corporate debt structure on its profitability tools. The industrial sector firms operating in the Iraq Stock Exchange were selected as one of the important sectors as a community for the study. A sample of 10 industrial firms was chosen for the period (2014-2018), and the study problem focused That most firms suffer from high cost of capital as a result of the lack of prior planning to develop an optimal financial structure commensurate with its financial capabilities, by which the firm can reach safety through the use of a structure that includes the ratio of debt and equity from which the firm can achieve a higher rate of return From the cost of funding sources and with less risks, and then provide a theoretical and cognitive framework for the variables of the study, and use the descriptive analytical method, as the statistical programs (SPSS-26) and (Excel-21) were used to analyze the effect of independent variables (debt structure) on the dependent variables ( profitability tools) to reach a set of results by testing the hypotheses of the study whether they were accepted or not, Multiple regression was used to test the effect between the variables, and a number of conclusions were reached, the most important of which is that the debt structure has a positive effect if the rate of return for profitability tools(return on investment, return on equity and revenue power) is higher than the cost of debt for investments that were financed with debt. And vice versa, as she referred to a number of recommendations, the most important of which is paying attention to raising the debt ratio in the financial structure for the purpose of benefiting from tax savings and achieving higher returns than the cost of debt to achieve the optimal financial structure.

Managing the quality of primary health care in urban China: the impact of organizational and physician features.

Global health care quality improvement efforts have focussed on management practices. However, knowledge in primary care settings, especially in developing countries, such as China, is lacking. To examine the organizational and physician features associated with health care quality in China's community health centres (CHCs). We conducted a cross-sectional survey of 224 primary care physicians (PCPs) in 38 CHCs in Jinan, Tianjin, Shenzhen, and Shanghai. Clinical and prevention care quality with a 5-level scale (1 = never, 5 = always) reported by the PCPs were used to measure the quality of care. Two-level hierarchical linear models were estimated to examine the organization and physician-level variables associated with primary care quality. The average clinical care quality score was 4.08 and 3.59 for preventative care out of 5. At the organizational level, organizational culture and organizational support were the strongest predictors of physician-reported quality of care. At the physician level, professional fulfilment, psychological safety, and organizational citizenship behaviour were positively associated with care quality. Chinese CHCs clinical quality ranked high by PCPs, but the quality of preventative care provision required improvement. To improve primary care quality, managers of CHCs should implement optimal organizational structures, supportive organizational cultures, and strong organizational support at the organization level and cultivate high professional fulfilment, safe, and trustful relationships with colleagues at the physician level.

A waste extended input-output-based Transformer-LSTM method for analyzing hazardous waste reduction patterns: A case study of shanghai

Population growth, economic development, and industrialization have resulted in a notable surge in the hazardous waste (HW) generation, consequently leading to adverse effects on human health and environmental safety. In this work, a Waste Extended Input-Output-based Transformer-Long Short-Term Memory (WEIO-TL) method was developed to simulate the HW generation patterns of various economic industries in the supply chain and generate the desired strategies for reducing HW generation. The WEIO-TL method was applied to Shanghai to identify its HW reduction path and potential by 2035. Multiple scenarios were designed to investigate the impacts of different degrees of economic and technological development on HW generation. The primary results indicated that the economic proportion of each key industry and the technological innovation capability were the main factors influencing the HW generation in Shanghai. The six key industries identified by the WEIO were the chemical industry (CI), other services (OS), transportation equipment (MTE), computers, communication, and other electronic equipment (MCCOEE), wholesale and retail trades (WRT), and construction (C). HW generation would peak at 1570.9 kt in 2025 and subsequently decrease to 949.0 kt by 2035 under the optimal scenario based on the optimal TL model (R2 and RMSE values of 0.976 and 5.968), associated with industry structure optimization (i.e., part of high-polluting industries would be shifted to service) and technological innovation capability improvement. The results of the study will serve as a theoretical foundation to guide other cities toward achieving "zero waste."

Research on ship propeller noise reduction: Advanced materials and innovative geometric design

The noise reduction technology of ship propellers is currently one of the challenging directions, despite some progress having been made, continuous research and development are still underway. This paper elucidates commonly used noise reduction methods, namely geometric structure optimization and material optimization. Geometric structure optimization involves aspects such as the number of propeller blades, disk loading ratio, skew angle, and blade shape. Material optimization encompasses material selection, surface coating optimization, and propeller duct design. Corresponding optimization methods are provided for both approaches. A novel and innovative research direction is proposed, leveraging machine learning and neural networks to optimize propeller parameters. Additionally, employing a circumferential friction nanogenerator to sense propeller bearings and identify the coupling relationship between electrical signals and factors such as roughness and rotational speed. This optimization aims to reduce noise by optimizing propeller parameters. The paper concludes by offering insights for current research on ship propeller noise reduction technology in China.

Knowledge-shareable adaptive deep dynamic programming for hierarchical generation control of distributed high-percentage renewable energy systems

The increased proportion of distributed renewable energy sources connected to systems leads to frequency regulation difficulty, such as uncoordinated algorithms, inconsistent optimization and control objectives, and long training time. This work proposes a knowledge-shareable adaptive deep dynamic programming (KSADDP) to improve the active control accuracy and generation economy of the proposed hierarchical generation control framework, which contains two-layer control structures. In the first layer, droop control is employed to optimize the outputs of controllable generators, follow the frequency changes, reduce frequency deviation, and improve the operation economy of battery energy storage systems. In the second layer, a KSADDP is proposed to mitigate local optimum and accelerate the hybrid cooperative structure optimization by sharing the parameters of neural networks. These two layers synergistically optimize control performance and reduce generation costs. The simulation results of a three-high-percentage renewable energy system show that: with two layers of cooperation and sharing training information, the KSADDP outperforms the comparison algorithms in seven evaluation indices and reduces the generation cost by 4.85%–7.41 % compared to the comparison algorithms. The proposed KSADDP cooperates the time scales between multiple layers, cooperates the objectives between multiple layers, increases the parameter-sharing process, and reduces training time.

Chemical bonding and electronic properties along Group 13 metal oxides

ContextThe present work provides a systematic theoretical analysis of the nature of the chemical bond in Al2O3, Ga2O3, and In2O3 group 13 cubic crystal structure metal oxides. The influence of the functional in the resulting band gap is assessed. The topological analysis of the electron density provides unambiguous information about the degree of ionicity along the group which is linearly correlated with the band gap values and with the cost of forming a single oxygen vacancy. Overall, this study offers a comprehensive insight into the electronic structure of metal oxides and their interrelations. This will help researchers to harness information effectively, boosting the development of novel metal oxide catalysts or innovative methodologies for their preparation.MethodsPeriodic density functional theory was used to predict the atomic structure of the materials of interest. Structure optimization was carried out using the PBE functional, using a plane wave basis set and the PAW representation of the atomic cores, using the VASP code. Next, the electronic properties were computed by carrying out single point calculations employing PBE, PBE + U functionals using VASP and also with PBE and the hybrid HSE06 functionals using the FHI-AIMS software. For the hybrid HSE06, the impact of the screening parameter, ω, and mixing parameter, α, on the calculated band gap has also been assessed.

Synthesis and antitumor activity of dolutegravir derivatives bearing 1,2,3-triazole moieties

Modification of marketed drugs is an important way to develop drugs because its safety and clinical applicability. Oxygen-nitrogen heterocycles are a class of important active substances discovered in the process of new drug development. Dolutegravir, an HIV drug with a nitrogen-oxygen heterocycle structure, has the potential ability to inhibit cell survival. In order to find and explore novel anti-tumor drugs, new dolutegravir derivatives bearing different 1,2,3-triazole moieties were prepared via click reactions. In vitro biological experiments performed in several lung cancer cell lines suggested that these novel compounds displayed potent anti-tumor ability. Especially, the compound 9e with a substituent of 2-methyl-3-nitrophenyl and the compound 9p with a substituent of 3-trifluoromethylphenyl were effective against PC-9 cell line with IC50 values of 3.83 and 3.17 µM, respectively. Moreover, compounds 9e and 9p were effective against H460 and A549 cells. Further studies suggested that compounds 9e and 9p could induce cancer cell apoptosis in PC-9 and H460, inhibit cancer cell proliferation, change the cell cycle, and increase the level of reactive oxygen species (ROS) which further induce tumor cell apoptosis. In addition, compounds 9e and 9p increased LC3 protein expression which was the key regulator in autophagy signaling pathway in PC-9 cells. Compound 9e also showed low toxicity against normal cells, and could be regarded as an interesting lead compound for further structure optimization.

Tailoring hole density and nitriding in graphene membranes for enhanced gas separation and selectivity

Sour gas, containing acidic gases like hydrogen sulfide and carbon dioxide, has a characteristic odor, and toxic effects, and reduces the overall energy content of the gas. Moreover, the presence of these acid gases in water leads to the formation of highly corrosive acid compounds, increasing transportation costs. Membrane technology is widely used for gas separation due to its low energy consumption, ability to operate at ambient temperatures, lightweight and compact equipment, ease of access to all separated phases, and high process flexibility. Computer simulation bridges the gap between microscopic temporal and spatial scales and the macroscopic laboratory environment. Molecular dynamics is a technique for analyzing the physical motion of atoms and molecules. By simulating the interactions between atoms and molecules over time, insights into the dynamic evolution of the system can be obtained. This research investigates the CO2/CH4 separation performance of modified graphene membranes (C3N) using molecular dynamics simulations under various operating conditions. First, cavities with different geometrical shapes and edge atoms were created, and gas separation was examined at 300 K. A cavity with a diameter of 4.9 Å exhibited the highest selectivity (infinity) and a carbon dioxide flux of 365.65 mol/m2·s, while methane molecules were completely blocked. This cavity was selected as the optimal structure. Next, the effects of pressure, and hole density on gas separation were studied. The results showed that the flux is directly proportional to the feed pressure, reaching 503.7845 mol/m2·s at a pressure of 328.0424 atm while maintaining infinite selectivity. Finally, examining the impact of cavity density on the separation process revealed that drilling four holes in the membrane area was the optimal density, achieving infinite selectivity, zero methane flux, and a carbon dioxide flux of 292.601 mol/m2·s.

Does Industrial Intelligence Promote Sustainable Employment?

One of the key driving factors for achieving the goal of sustainable economic development is to ensure decent employment opportunities. This article explores the relationship between industrial intelligence and sustainable development in China from the perspective of employment. Based on interprovincial panel data from 2006 to 2019, using the fixed-effect regression model and mediating-effect regression model, this study empirically tests the impact of industrial intelligence on sustainable employment in China. The following conclusions are drawn: (1) Industrial intelligence has a significant positive impact on the overall scale of employment. Industrial intelligence has promoted the optimization and upgrading of employment skill structure and industrial structure. Industrial intelligence will reduce the employment proportion of low-skilled labor and increase the employment proportion of medium-skilled labor and high-skilled labor. Industrial intelligence significantly reduces the employment share of the manufacturing sector and increases the employment share of the service sector. (2) Industrial intelligence reduces employment levels through capital deepening effects. Industrial intelligence has significantly improved regional labor productivity and significantly improved employment levels through productivity effects. (3) The results of regional heterogeneity show that industrial intelligence has promoted the improvement of employment level and the upgrading of employment structure in the eastern region but has not had a significant positive impact on other regions.

From pixels to production: the impact of digital finance on the industrial fabric of rural China

PurposeThe aim of this study is to discern the role of digital finance in driving rural industrial integration and revitalization. Specifically, it intends to shed light on how the deep development of digital finance can contribute to the optimization and transformation of the rural industrial structure. The research further explores the particular effects of this financial transformation in the central and western regions of China.Design/methodology/approachThis research studies the influence of digital finance on rural industrial integration across 30 Chinese provinces from 2011 to 2020. Utilizing the entropy weight method, a comprehensive evaluation index system is established to gauge the level of rural industrial integration. A two-way fixed effects model, intermediary effect model, and threshold effect model are employed to decipher the relationship between digital finance and rural industrial integration.FindingsFindings reveal a positive relationship between digital finance and rural industrial integration. A single threshold feature was identified: beyond a traditional finance development level, the marginal effect of digital finance on rural industrial integration increases. These effects are more noticeable in central and western regions.Originality/valueEmpirical outcomes contribute to policy discourse on rural digital finance, assisting policymakers in crafting effective strategies. Understanding the threshold of traditional finance development provides a new perspective on the potential of digital finance to drive rural industrial integration.

Development of Low-Carbon Finance Helps the Construction of China's Low-Carbon Financial Legal System

The massive consumption of coal, oil and other resources has led to a significant increase in greenhouse gas emissions and global warming, seriously threatening the ecological environment. Achieving low-carbon development has become a common goal pursued by all countries. On this basis, China has put forward the goals of carbon peaking by 2030 and carbon neutrality by 2060. The best way to solve this problem is to promote low-carbon finance. Low energy consumption and low pollution have become the core of green economic development, which reasonably improves the status quo of serious environmental pollution and promotes the realisation of high-quality development of the social economy. Since the 19th National Congress, China's economic development has shifted from high-speed growth to the development stage of high-quality growth, and this shift not only requires sustained and stable economic growth, but also emphasises the protection of the ecological environment and sustainable development. The development of low-carbon economy not only helps to reduce greenhouse gas emissions and protect the ecological environment, but also promotes the optimisation and upgrading of the economic structure, achieving a win-win situation for the economy and the environment. However, the healthy development of low-carbon economy needs strong institutional guarantee. In particular, the improvement of the low carbon financial legal system.

Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering

Semi-supervised clustering aims to guide the clustering by utilizing auxiliary information about the class labels. Among the semi-supervised clustering categories, the constraint-based approach uses the available pairwise constraints in some steps of the clustering procedure, usually by adding new terms to the objective function. Considering this category, Semi-supervised FCM (SSFCM) is a semi-supervised version of the fuzzy c-means algorithm, which takes advantage of fuzzy logic and auxiliary class distribution knowledge. Despite the performance enhancement caused by incorporating this extra knowledge in the clustering process, semi-supervised fuzzy approaches still suffer from some problems. All the data attributes in the feature space are assumed to have equal importance in the cluster formation, while some features may be more informative than others. Thus the feature importance issue is not addressed in the semi-supervised category. This paper proposes a novel Semi-Supervised Fuzzy c-means approach, which is designed based on Feature-Weight, and Cluster-Weight learning, named SSFCM-FWCW. Inspired by the SSFCM, a fuzzy objective function is presented, which is composed of (1) a semi-supervised term representing the external class knowledge; (2) a feature weighting; and (3) a cluster weighting. Both feature weights and cluster weights are determined adaptively during the clustering. Considering these two techniques leads to insensitivity to the initial center selection, insensitivity to noise, and consequently helps to form an optimal clustering structure. Experimental comparisons are carried out on several benchmark datasets to evaluate the proposed approach's performance, and promising results are achieved. The Matlab implementation source code of the proposed method is publicly accessible at https://github.com/Amin-Golzari-Oskouei/SSFCM-FWCW.

Industrial structure optimization and green growth in China based on a population heterogeneity perspective

AbstractPopulation and industrial structure, as foundational characteristics of economic and social systems, exhibit significant spatial heterogeneity and dynamic evolutionary trends in their impact on sustainable economic and social development. However, existing research often employs subjective spatial categorization of samples and overlooks the dynamic transitions of influencing patterns, potentially leading to biases in empirical results. To address this, the current study, based on the calculation of green total factor productivity (GTFP) for 30 provinces in China from 2000 to 2018, incorporates a finite mixture model. This model examines the objective heterogeneity and dynamic transition patterns of industrial structure's impact on GTFP, both from the perspectives of industrial structure advancement (ISA) and rationalization (ISR), and reveals the mechanisms of heterogeneity and dynamic changes from a population standpoint. The findings indicate that there are three patterns in the impact of industrial structure on GTFP, with nearly half of the provinces undergoing pattern transitions during the observation period. The key factors for these transitions are identified as the improvement in human capital levels and urbanization rates. In provinces like Beijing, Guangdong, and Shanghai, ISA and ISR significantly promote GTFP, with their effects further enhanced by increased urbanization and human capital levels. Conversely, in regions such as Shanxi and Hebei, ISA does not favor GTFP improvement, and while ISR can enhance GTFP, this positive effect diminishes with increasing urbanization and labor force numbers. This research not only enriches the literature on the positive interaction patterns between industrial and population structures but also provides a comprehensive analytical framework for governments to adopt differentiated policy measures for sustainable economic development.

Investigation on drawdown process of light-floating particles in a pilot scale cup-shaped blade agitator

The novel cup-shaped blade agitator with pilot scale was successfully applied in light-floating particles drawdown dispersion under high solid content (40 vt.%) for the first time. The drawdown dispersion effect of cup-shaped blade agitator was systematically investigated, by integrating the CFD simulation, the research unveils the impact mechanisms of flow conditions within the agitator on particle drawdown and dispersion, under various structural and operational parameters of the cup-shaped blade (CB) impeller. The optimum structure for CB was determined as CB2, with impeller deflection angle of -30°, and impeller diameter of 0.43 m. Under this structure, the CB agitator exhibits the best performance and can complete drawdown dispersion of light-floating particles at high solid concentration exceeding 40 vt.%. Besides, at lower solid particle volume concentration (Cv), both Njd and Pjd increase with increasing Cv. Interestingly, after Cv exceeds 10%, Njd and Pjd decrease as Cv continues to rise. Comparing with traditional impeller RT and PBT, the CB impeller is more competitive. Finally, dimensionless correlations of Njd and Pjd were established to provide practical guidance for application and optimization of CB agitator.