Emission Performance Research Articles

Effects of alcohol fuels on SACI engine and analyze of prediction of SACI engine performance by artificial neural networks

The combustion characteristics of spark assistant compression ignition mode under different loads and fuels were explored, as well as by constructing artificial neural networks(ANN) model, the prediction ability of different algorithms for engine performance was discussed. Burning methanol and n-butanol can help to improve IMEP and induce earlier spontaneous combustion. The knock intensity(KI) of engine fueled with methanol was highest followed by n-butanol and gasoline, but maximum amplitude of filtered pressure oscillation(MAPO) shows the opposite trend. KI showed great positive liner correlation with ignition timing. Methanol showed the most outstanding tolerance on the compression ignition state. Fueled with methanol can decreased equivalent brake specific fuel consumption(ESFC) up to 52.9 % compared with the initial SI gasoline engine. N-butanol can improve BSNOx, BSTHC and BSCO concurrently, however fueled with methanol will worsen BSNOx. ANN model for engine combustion, economy and emissions performance was built. The prediction accuracy of Bayesian regularization algorithm for engine performance predicting was highest, but it have no advantage in the calculating times when the amount of data was large while the Levenberg-Marquardt algorithm would be the ideal efficient. The mean square error of ESFC and emissions parameters under scaled conjugate gradients algorithm always poorest.

Environmental Regulation, Factor Marketisation Allocation and Carbon Emissions Performance: Empirical Evidence from Resource-Based Cities in China

Resource-based cities had an irreplaceable role in the process of the economic miracle in China. Advancing such cities’ carbon emissions reduction is a crucial aspect of the country’s steady realisation of the dual carbon peak and neutrality strategy. The reasonable implementation of environmental regulation and the efficiency of factor marketisation allocation are the key links for resource-based cities to improve carbon emissions performance, break the resource curse and reduce carbon emissions. Based on this, this study centres on the driving relationship between environmental regulation, the efficiency of factor marketisation allocation and carbon emissions performance as the core research problem. This study takes the panel data of 116 resource-based cities in China from 2006 to 2020 as the research sample; the non-radial meta-frontier total factor carbon emissions performance index is selected as the measurement index of carbon emission performance of resource-based cities based on the applicability analysis of the model. This study explores the characteristics of regional heterogeneity and type heterogeneity of carbon emissions performance driven by environmental regulation under the moderating effect of the efficiency of factor marketisation allocation and further explores the threshold effect, aiming to clarify the driving relationship between the three. The findings reveal that the driving effect of environmental regulation intensity on carbon emissions performance exhibits a fluctuating upward trend, the effect transformed by compliance cost and innovation compensation. The efficiency of factor marketisation allocation has a double threshold superposition effect on carbon emissions performance fluctuation that is driven by environmental regulation, indicating that market and government effectiveness can operate together to improve the carbon emissions performance. Based on these results, this study proposes countermeasures and suggestions for improving carbon emissions performance using environmental regulation and the efficiency of factor marketisation allocation.

Oil price uncertainty and corporate carbon performance: An international investigation

Drawing on legitimacy theory and stakeholder theory, this study examines whether oil price uncertainty (OPU) affects corporate carbon performance (CCP) in the international context. Based on data extracted from CDP (previously known as the Carbon Disclosure Project), World Bank, and Thomson Reuters Eikon databases, the study's sample consists of 9074 firm-year observations over the period 2011–2018 for all non-financial multinational companies invited to take part in the CDP questionnaire. Using an ordinary least squares regression model, we identify a strong relationship between OPU and carbon emissions performance. Our findings are robust to a battery of sensitivity tests, all of which support our original results. This study contributes new knowledge regarding the influence of OPU on CCP. The results will be of interest to investors and policymakers as they provide a useful basis for understanding OPU and its impact on CCP to promote better decision-making.

Influence of AM Generated Burner Surface Roughness on NOx Emissions and Operability of Hydrogen-Rich Fuels

ABSTRACT The additive manufacturing (AM) technique enables the fabrication of advanced burner components to enhance the hydrogen capability of the existing gas turbines (GTs) and reduce the carbon footprints of the power generation sector. This technique produces rough surfaces that may require post-processing to maintain the desired functionality of a burner, particularly for hydrogen fuel with unique thermo-physical properties. This study, therefore, compared the stability of three swirlers of variable surface roughness manufactured using AM and traditional machining methods, with one of the AM swirler post-processed by grit-blasting. The comparison included a conventional benchmark (100% CH4), low carbon (23%volCH4/77%volH2) and zero carbon (100% H2) fuels across a range of equivalence ratios. Additionally, the study quantified the flame topology and emissions performance of the fuel blends for each swirler using high-speed OH* chemiluminescence and exhaust gas emissions measurements, respectively. The experimental investigation concluded that the AM-generated surface roughness within the considered range does not detrimentally impact NOX emissions and the stability of the fuel mix. However, the flame location was observed to be influenced by surface roughness and shifted more toward the vertical centerline of the burner with increased roughness. From the practical perspective, the results showed that post-manufacturing surface finishing offers negligible performance advantages, indicating potential cost reductions. It is recommended that further studies should investigate the influence of increased surface roughness on burner performance, as well as numerical modeling techniques which could provide an insight into when AM surfaces are likely to be more influential.

Wide-Range Excitation-Dependent Phosphorescence of Coordination Polymers Exhibiting Dynamic Anticounterfeiting, White-Light Emission, and Antibacterial Performance.

Multicolor-tunable room-temperature phosphorescence (RTP) is attracting wide attention in optoelectronic applications. Here, we propose a coordination-oriented assembly approach to achieve wide-range RTP with a benzimidazole derivative (2,7-diazabenzimidazole, DZBIM) as a luminogen. These two compounds exhibit unexpected excitation-responsive RTP emission, and the phosphorescence emission nearly covers the entire visible region with the change of the excitation wavelength from 360 to 620 nm. To the best of our knowledge, this is the first report of coordination polymers with such a full-color-tunable RTP. Compound 1 also shows white-light emission upon excitation at 280 nm. Experimental and theoretical results demonstrate that multiple intermolecular interactions and emission centers from different aggregates are responsible for the generation of multicolor emission. The white-light emission and multiple anticounterfeiting are explored. Besides, compound 1 exhibits high antibacterial activity benefiting from efficient 1O2 generation. This work provides an efficient way to prepare a color-tunable RTP.

Dual-Mode Immunoassay Constructed by Water-Induced Perylene Diimide Supramolecular Self-Assembly and Enzymatic Biocatalytic Precipitation Strategy.

A water-induced electron-deficient dye, the supramolecule perylene diimide (PDI), has been identified recently. PDI possesses advantages such as easy reduction, nontoxicity, low cost, and simple preparation, making it a promising candidate for electrochemiluminescence (ECL) sensing platforms. In this study, a series of PDI supramolecular systems with morphological changes were prepared by utilizing water molecules to induce PDI self-assembly. This method improves the π-π stacking interactions between PDI molecules and effectively mitigates the aggregation-caused quenching (ACQ) effect on the luminous efficiency of the coplanar polycyclic aromatic hydrocarbon PDI. It is noteworthy that excellent ECL emission performance of the PDI supramolecular system was observed at -0.4 V. This low excitation potential aids in preserving antigen-antibody bioactivity and ensures accurate identification of the immune response. As a proof of concept, a dual-mode immunosensing platform for carcinoembryonic antigen (CEA) detection was constructed using an enzymatic biocatalytic precipitation (EBCP) strategy. The dual-mode immunosensor exhibited good detection performance in the concentration range of 0.001-80 ng·mL-1, presenting an advanced bioprotective analytical method for CEA detection.

Corporate culture and carbon emission performance

Using a large sample of U.S. firms from 2002 to 2020, we investigate the relationship between corporate culture and the extent of carbon emissions. We provide evidence that the quantum of carbon emissions is negatively associated with corporate cultural attributes manifested by integrity, teamwork, innovation, and respect. These results hold after controlling for potential endogeneity issues using several identification techniques. We also document that the negative culture–emissions relationship is magnified in firms with weak corporate governance and in those operating in environmentally sensitive industries. Additionally, this relationship is less salient in the presence of social capital. Finally, we demonstrate that in firms with a stronger culture, elevated carbon emissions result in a lower firm value. Our findings may be of interest to environmental regulators and management in their pursuit of firm-level carbon emission targets.

Spatial network analysis and driving forces of urban carbon emission performance: Insights from Guangdong Province

As the primary contributor to carbon emissions, how cities enhance their carbon emission performance and mitigate emissions is crucial for achieving low-carbon urban environments in China. However, existing research often overlooks the spatial interconnectedness of carbon emission performance, neglecting reciprocal influences among cities. This study examines the network structure of carbon emission performance among Guangdong's cities from 1997 to 2019, using a super-efficient SBM model and social network analysis, and measures spatial impacts of network factors with the spatial Durbin model. Findings reveal that: (1) The overall network of carbon emission performance is relatively loose with minimal changes in connectivity and efficiency but shows significant local clustering. (2) Shenzhen, Guangzhou, and Zhuhai have high centrality, dominating carbon emission performance resources and acting as key transmission nodes, while most other regions have low centrality, indicating network polarization and potential vulnerabilities. (3) Enhancing a region's centrality, economic development, industrial structure, openness, and attraction of talent and technology can boost local carbon emission performance, but may also lead to the displacement of emissions to neighboring areas and outflow of low-carbon and innovative elements, negatively affecting surrounding regions through spatial spillover effects. This research advances regional carbon emission reduction strategies by highlighting the interplay between spatial networks and carbon emission performance, fostering synergies in reduction efforts.

Fabrication of controllable porous tungsten tips for indium FEEP by dynamic reciprocating electrochemical etching combined with ultrasonic cleaning method

The porous tungsten emitter is a core component of field emission electric propulsion (FEEP). It combines the advantages of sharp tip and internal propellant transport. The emission performance of a FEEP thruster is highly depended on the emitter's tip apex radius. Traditional processing methods for solid tungsten tips are unsuitable for porous tungsten due to its unique structure and electrochemical properties. This paper introduces a novel approach combining dynamic reciprocating electrochemical etching with ultrasonic cleaning to fabricate cone-shaped porous tungsten tips with controlled apex radii. By alternating between 5 cycles of etching and an ultrasonic cleaning, a 1.9 μm apex radius is achieved after 90 repetitions. The etching current waveform reveals the material dissolution mechanism. The electrochemical characteristics of porous tungsten are investigated, with an optimal processing voltage of 2 V identified. To refine the apex radius and prevent tip collapse, a 1.9 V etching voltage is used in the final stages, maintaining the apex within 2 μm. The effectiveness of the fabricated tip is confirmed through wetting in indium and ignition tests.

Enhanced field emission performance of holey expanded graphite by heat treatment

Expanded graphite (EG) is a promising material in field emission due to its high conductivity, thermal stability, and low cost. In this paper, the field emission performance of EG is enhanced by introducing hole defects through partial air oxidation. First, EG was prepared using electrochemical intercalation and microwave-assisted expansion methods. Subsequently, holey expanded graphite (HEG) was obtained by air-heating treatment of EG at different temperatures. Finally, bulk HEG cathodes were prepared by the cold pressing method, and the holes' effect on field emission performance was investigated. The results showed that EG's conductivity and tensile properties significantly improved after heat treatment. Optimal conditions of heat treatment were established at 400 °C providing material with the best field emission performance. The turn-on field (at 1 mA/cm2) and threshold field (at 10 mA/cm2) are 3.01 V/μm and 3.42 V/μm, respectively, with a maximum current density of 1359.17 mA/cm2 (at 5.83 V/μm) and a maximum field enhancement factor of 2407. The voltage fluctuation is only 1.1 % over 24 h at 3 mA @ 440.33 mA/cm2, indicating good emission stability and operation life.

Highly efficient AlGaN-based deep-ultraviolet light-emitting diodes: from bandgap engineering to device craft

AlGaN-based light-emitting diodes (LEDs) operating in the deep-ultraviolet (DUV) spectral range (210–280 nm) have demonstrated potential applications in physical sterilization. However, the poor external quantum efficiency (EQE) hinders further advances in the emission performance of AlGaN-based DUV LEDs. Here, we demonstrate the performance of 270-nm AlGaN-based DUV LEDs beyond the state-of-the-art by exploiting the innovative combination of bandgap engineering and device craft. By adopting tailored multiple quantum wells (MQWs), a reflective Al reflector, a low-optical-loss tunneling junction (TJ) and a dielectric SiO2 insertion structure (IS-SiO2), outstanding light output powers (LOPs) of 140.1 mW are achieved in our DUV LEDs at 850 mA. The EQEs of our DUV LEDs are 4.5 times greater than those of their conventional counterparts. This comprehensive approach overcomes the major difficulties commonly faced in the pursuit of high-performance AlGaN-based DUV LEDs, such as strong quantum-confined Stark effect (QCSE), severe optical absorption in the p-electrode/ohmic contact layer and poor transverse magnetic (TM)-polarized light extraction. Furthermore, the on-wafer electroluminescence characterization validated the scalability of our DUV LEDs to larger production scales. Our work is promising for the development of highly efficient AlGaN-based DUV LEDs.

Study on precise fuel injection under multiple injections of high pressure common rail system based on deep learning

The fluctuation of fuel injection quantity under multiple injections of high pressure common rail system leads to a decline in engine economy and emission performance, the precise injection control is the key problem in current research. In this study, a deep learning data-driven model of injection quantity under multiple injections was established based on generalized regression neural network (GRNN), and the particle swarm optimization (PSO) was introduced to optimize the smoothing factor in the model, thereby improving its prediction accuracy. An injection quantity correction method based on the data-driven model was proposed and applied to actual common rail fuel injection system for experimental verification. The results demonstrate that the proposed data-driven model has excellent prediction performance and generalization ability for the predicted main injection quantity with mean absolute percentage error of 1.10 % and determination coefficient of 0.997. Through the experimental verification of the correction method under 144 groups of operating conditions, it was observed that the maximum deviation of main injection quantity decreased from −25.83～17.91 mm3 to −9.81～7.64 mm3, and the maximum absolute deviation reduced by 62.02 %. The precise of injection control under multiple injections is improved.

A Reactor-Network Framework to Model Performance and Emissions of a Longitudinally-Staged Combustion System for Carbon-Free Fuels

Abstract Hydrogen and ammonia are considered crucial carbon-free energy carriers optimally suited for seasonal chemical storage and energy system balance. In this context, longitudinally-staged combustion systems (LSC) represent an attractive technology for achieving low emissions while conserving high load and fuel flexibility at high efficiency. While these two-stage combustion systems have been successfully implemented for natural gas and hydrogen firing of gas turbines, optimal operation with ammonia-based fuel mixtures not yet established. Recent works have shown that ammonia can be combusted with low emissions in gas turbines by employing a Rich-Quench-Lean operational concept where a slightly rich fuel-air mixture is burnt in the first-stage followed by dilution air addition, which completely oxidizes the remaining unburnt fuel. However, an optimal operational strategy to efficiently combust all three fuels within the same system is yet to be established. In this work, we exploit a reactors-network framework to efficiently investigate the emissions performance of a LSC system fired with natural gas, hydrogen and ammonia. Firstly, the framework is validated with experimental and computational emission results in the literature. Secondly, the optimal operational strategy (in terms of fuel- and air-split between the stages) for clean and efficient ammonia-firing operation is found. Thirdly, the consequences of such ammonia-optimized operational strategy on flame stabilization and emissions with natural gas- and hydrogen are considered. Finally, the air and fuel distribution for optimal performance and minimal emissions for all three fuels is found for the LSC system.

Revealing the Properties of Electrically Driven Optical Antennas via Conductive Atomic Force Microscope.

Light emission from ultracompact electrically driven optical antennas (EDOAs) has garnered significant attention due to its terahertz modulation bandwidth. Typically, the EDOAs are fixed and nonadjustable once fabricated, thus hindering the attempts to investigate the influence of structural geometry on light emission properties. Here, we propose and demonstrate that the EDOAs can be constructed by carefully manipulating the gold-coated tips of atomic force microscopy operated in conductive mode into contact with the optical antennas covered by insulating film, where the position of the tunnel junction on the antenna surface can be controlled with high accuracy and flexibility. Taking gold nanorod antennas covered by HfO2 film as an example, we found that the highest light generation efficiency is obtained when the tunnel junction is located at the shoulder edge of the nanorod antenna, where the bonding dipolar surface plasmon mode in the junction is spectrally and spatially coupled with the longitudinal radiation mode of the EDOAs. Besides, position variation of the tunnel junction on the nanorod surface also strongly influences the far-field radiation angular distribution and emission spectrum. Numerical simulations are in good agreement with the experimental results. Our findings offer fundamental insights into the influence of structural parameters on the light emission performance of EDOAs, thus leading to better design of EDOAs with high light generation efficiency and powerful functionality.

Enhancing diesel engine efficiency and emission performance through oxygenated and non-oxygenated additives: A comparative study of alcohol and cycloalkane impacts on diesel-biodiesel blends

This study evaluates the impact of renewable fuel additives on diesel engine performance, combustion, and emissions. Safflower seed oil was converted into biodiesel (BD) through transesterification, achieving a 94.12 % conversion yield, verified by spectroscopic analysis. Test fuels were prepared by adding 5 %, 15 %, and 25 % n-pentanol (PE, oxygenated) and cyclohexane (CHx, non-oxygenated) to diesel fuel (DF) and BD. These blends were tested in a single-cylinder diesel engine under varying loads. At maximum load, BSFC values were 0.251 for DF, 0.333 for 25 % PE, and 0.269 kg/kWh for 25 % CHx. BTE values were 32.184 % for DF, 27.028 % for 25 % PE, and 31.147 % for 25 % CHx. The peak in-cylinder pressure and net heat release for the 25 % PE blend, which were the highest among the test fuels, were 58.148 bar and 37.010 J/°CA, respectively. Average NOx emissions were 171 for DF, 135.75 for 25 % PE, and 170.50 ppm for 25 % CHx. CO emissions were 171 for DF, 149.25 for 25 % PE, and 170.25 ppm for 25 % CHx. Smoke opacity values were 0.75 for DF, 0.308 for 25 % PE, and 0.675 m-1 for 25 % CHx. Despite higher costs, CHx offers reduced environmental impact without significantly compromising engine performance.

Government Environmental Expenditure, Budget Management, and Regional Carbon Emissions: Provincial Panel Data from China

To explore the impact of government fiscal intervention on regional carbon emissions, this paper employs a two-way fixed-effects model to analyze data from 30 provinces in China, spanning the period from 2008 to 2017. This study investigates the effects of local government environmental expenditure and government budget on the per capita volume, intensity, and performance of regional carbon emissions. The results show that government environmental expenditure is beneficial to reducing regional carbon emissions and improving regional carbon emission performance. Second, the smaller the deviation between the government budget and final accounts, the more conducive it is to reducing carbon emissions. Third, we found that government environmental expenditure has the strongest inhibitory effect on regional carbon emissions in the eastern region, followed by the central region, and the weakest in the western region. Finally, government financial transparency positively moderates the inhibitory effect of government budget management on regional carbon emissions, that is, when fiscal transparency is high, the amplification effect of budget deviation on regional carbon emissions is weakened.

Historical volatile organic compound emission performance, drivers, and mitigation potential for automobile manufacturing in China

The rising emission trend of volatile organic compound (VOC) from automobile manufacturing (AMVOC) in China has been effectively curbed. However, the effect and driving force of this reduction remains unclear, posing a challenge of further deep emission mitigation for better air quality. Herein, we analyzed the spatiotemporal emission characteristics of AMVOC, identified the key drivers, and predicted the mitigation potential under different scenarios. The results indicated that China's AMVOC emissions followed an increasing-then-decreasing trend from 2001 to 2020, from 44.7 Gg in 2001, peaking at 211.8 Gg in 2012 and declining to 81.6 Gg in 2020. The most notable factor that positively drove AMVOC emissions was economic development, contributing to 88.3% of the increase. Conversely, the reduction in AMVOC emissions was primarily attributed to the internal structure of the automotive industry and the emission intensity, contributing to 44.7% and 43.8% of the emission decrease, respectively. Furthermore, scenario analysis results show that China's AMVOC emissions in 2035 were reduced by 55.5% through a combination of the whole process control measures. Overall, our study confirms the effectiveness of China's AMVOC control measures and offers valuable insights for future policymakers in China and other countries facing similar challenges.

Nano-fuels of Al2O3/SiO2/MgO/tamarind seed oil biodiesel for CI engines: An evaluation of combustion consumption and emission performance

This piece of research investigates the performance of biodiesel in terms of combustion, consumption, and emission produced from tamarind seeds through the transesterification process. The tamarind seeds are a sustainable source. To enhance engine performance, the three different oxide nanoparticles of Alumina oxide (Al2O3), silicon oxide (SiO2), and magnesium oxide (MgO) were blended as additives with the base fuel. The experiments were done over four blends, namely Diesel (D), tamarind seed oil 20 % diesel 80 % (D80TSOB20), TSO20 with Al2O3 nanoparticles (D80TSOB20A), TSO20 with SiO2 nanoparticles (D80TSOB20S), and TSO25 with MgO nanoparticles (D80TSOB20M). The nanoparticles were blended with fuel by ultrasonication process. and the stability was ensured through measure of Zeta-potential values of nano-fuels (Nanofluid). The fitness of fuel was ensured through characterization of them (including standard fuel of pure diesel). The different fuel blends were tested in a single-cylinder, direct-injection, 4-stroke diesel engine at a constant speed of 1600 rpm with different load conditions like 0 %, 25 %, 50 %, 75 %, and 100 %. The combustion performance, fuel consumption, and emission levels of the different fuel blends were derived from the experimental observations. Experimental results show that a higher HRR and cylinder pressure were reported as 75 J/deg and 77 bar for the D80TSOB20A nanofuel than other fuels tested. Similarly, a higher BTE of 32 % was recorded at full load conditions for the same D80TSOB20A nano-fuel blend, which is a better BTE value than Diesel. Increases the BTE value (all fuels) due to higher fuel rates at higher loads for obtaining higher power. Furthermore, the engine's CO, UHC, NOX, and smoke capacity using tamarind seed oil biodiesel with Al2O3 nanoparticles gives a lesser value than other fuel blends.

Electric field emission properties of ZnO/microcrystalline diamond composite structures modified by Cu nanoparticles decoration

ZnO nanorods hold promise as cathode materials for field electron emission (FEE) applications, yet their high work function and wide bandgap can hinder performance. Recent research has focused on utilizing Cu nanoparticles (NPs) to decorate and modify the FEE properties of these nanorods, leveraging Cu's low work function and excellent conductivity. In this study, ZnO nanorods were synthesized on diamond substrates via the sol method to create ZnO/microcrystalline diamond (MCD) composite structures. Subsequently, Cu NPs were decorated onto the nanorods using a gel method. The influence of Cu NP concentration on the organization and FEE performance of the composites was investigated. The decorated of Cu NPs can improve the Fermi energy level of the nanorods, replenish electrons lost on the surface due to oxygen adsorption, and improve the field emission performance. With the increase of doping concentration, Cu NPs increase, and when the doping concentration is 0.5 M, NPs appear agglomeration phenomenon, resulting in that the surface of ZnO nanorods is almost completely covered by bulk Cu NPs, and the field emission performance decreases. The minimum value of the turn-on electric field is 0.48 V/μm and the maximum value of the current density is 1.60 mA/cm2, when the decorated concentration is 0.25 M.

The effects of aluminate compounds on the free Ba generation and electron emission performance of dispenser cathode

Free barium (Ba) is critical to the formation of the active layer on the surface of dispenser cathode, which is contributed to the electron emission properties of the cathode. The Ba in the active layer of the dispenser cathode is generated by the chemical reaction between aluminate compounds and tungsten (W) under vacuum and high temperature. Nowadays, the effect of aluminate compounds on the generation of free Ba is not clear. In this work, aluminate impregnants called 411, 532 and 612 impregnants were prepared by coprecipitation method. The structure of the dominate compounds in 411, 532 and 612 impregnants were determined with density functional theory (DFT) calculation and Rietveld structure refinement. It was found that the dominate compounds of 411, 532 and 612 impregnants are Ba3.5Ca0.5Al2O7, Ba5CaAl4O12 and Ba3CaAl2O7, respectively. Then, the temperature dependence of the thermodynamic properties, such as Debye temperature, entropy, heat capacity and enthalpy, of the aluminate compounds were calculated. Based on the thermodynamic properties, the potential chemical reaction between aluminate compounds and W are energetically determined by deviation Gibbs energy. The effects of different aluminate compounds on the free Ba generation were evaluated with the equilibrium Ba(g) amounts that generated by the chemical reactions between aluminate compounds and W. It is shown that the equilibrium Ba(g) amounts generated by Ba3.5Ca0.5Al2O7 is higher than that by Ba5CaAl4O12 and Ba3CaAl2O7, implying the superiority of electron emission performance of Ba3.5Ca0.5Al2O7. The electron emission densities of the dispenser cathodes that impregnated with the 411, 612 and 532 impregnants are 5.06, 4.31 and 4.05 A/cm2, respectively. It is revealed that the Ba3.5Ca0.5Al2O7 has superiority in generating free Ba, which agreed well with thermodynamic simulation results.