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Data from all spatial locations of nine turbulent flames in the Cambridge/Sandia swirl database are combined to study how the choice of scalar variables in conditional moment closure (CMC) type approaches affect the conditional spatial fluctuations of reactive scalars. In order to investigate the influence of swirl and stratification, two additional data-sets have been constructed. Principal component analysis (PCA) is applied to help identify the number of scalar variables and the most appropriate choices to describe the composition space. Two PCA scaling methods have been adopted, namely Pareto and Auto-scaling. Regardless of the data-set investigated and the scaling method used, the results suggest that a single principal component correlated with temperature accounted for the largest variance. For the first moment hypothesis, four progress variable, c, definitions identified by PCA are selected as conditioning variables to investigate the conditional fluctuations and normalised RMS of various species and temperature from all three databases at all axial locations. The results indicate that two control variables based on mixture fraction, Z, and progress variable significantly reduce the conditional fluctuations of scalars compared to a single variable. The selection of progress variables had minimal effects on the RMS of conditional fluctuations for all tested conditions, although a slight reduction of conditional fluctuations was found for the temperature-based progress variable, which can potentially help the further extension of CMC-based models in different flame configurations. The present study also shows that using Z and c (regardless of its definition) as two conditioning scalars enables the detachment of the thermo-chemical state from space, swirl and stratification effects. This suggests that adopting a doubly conditioned source term estimation (DCSE) approach might successfully predict the considered set of flames, assuming that ensembles are divided along the axial direction.

In this study, one-dimensional detonations in ammonia/hydrogen-air mixtures are numerically investigated by solving the fully compressible Navier-Stokes equations with detailed chemistry. Pulsating instabilities with single-mode are observed during the detonation wave propagation, accompanied by periodic coupling and decoupling of the lead shock wave and the reactive front. The ratio between driver pressure and initial pressure determines the overdrive degree and thus the oscillatory mode of detonation for a premixture with certain composition. The effects of hydrogen dilution and mixture equivalence ratio on pulsating detonations are also examined under a constant driver pressure. The growing hydrogen fraction in fuel blends significantly increases the oscillation frequency. In addition, the pulsating detonation frequency rises with increasing equivalence ratio under fuel-lean conditions, peaks under stoichiometric conditions, and falls under fuel-rich conditions as the equivalence ratio increases further. Evolutions of reactants, main intermediate radicals, and products are analysed in both fuel-lean and fuel-rich conditions. A chemical explosive mode analysis further confirms the highly-autoignitive nature of the mixture in the induction zone between reaction front and shock front where thermal diffusion plays a negligible role.

Laminar burning velocity (LBV) is a crucial parameter in many practical combustion simulations, and the acquisition of LBVs is a critical step for evaluating potential alternative fuels and/or suitable surrogates. Ethanol is regarded as one of the most promising alternative fuels, particularly for spark ignition engines, as it may be produced from renewable biomass. While there exists data showing the pressure dependence on ethanol’s LBV compared to iso-octane and n-heptane (primary reference fuels (PRFs)), the relative magnitude of the pressure dependence of LBV between ethanol and toluene has not been established. The inclusion of toluene is important because commercial gasoline has significant levels of aromatics. In this work, common gasoline surrogate components of iso-octane, n-heptane, toluene, and ethanol were studied as pure compounds and in equal volume binary, ternary, and quaternary mixtures. Experiments are conducted in a spherical combustion bomb at elevated temperatures (380 K and 450 K) and pressures (1 to 4 bar). Under these conditions, ethanol and n-heptane present faster LBVs than iso-octane and toluene, particularly at 450 K. Ethanol and iso-octane present strong pressure dependence, with this effect accentuated at higher temperature conditions. As equal volume mixtures were studied, the effect of these distinct behaviours was examined in comparison to chemical kinetic predictions and widely used mixing rules.

This study presents the first demonstration of Marshall and Hinton’s empirical correlation for laminar burning velocity (LBV) applied to iso-octane/ethanol blends. The “Oxford” correlation is first validated for neat iso-octane and ethanol combustion against the outputs of three different chemical kinetics mechanisms and experimental data from the literature. The effects of three different mixing rules, simple energy fraction, modified Le Châtelier, and modified version of the more complex mixing law of Hirasawa, on the predicted LBVs of iso-octane/ethanol blends are evaluated. The modified Le Châtelier mixing law is used with the Oxford correlation to predict blend LBVs at various temperature and pressure conditions. The results are validated against previously unpublished data for iso-octane/ethanol binary fuels at elevated temperature and pressure (up to 10 bar) both from this group, and from the earlier experimental studies of Varea et al. (CORIA) and Broustail et al. (PRISME). The Oxford correlation is shown to perform well across a wide range of temperature and pressure conditions (respectively 298–640 K and 1–10 bar) for the single component fuels. Used in conjunction with a modified Le Châtelier mixing law, the LBVs predicted by the correlation provide an excellent match to the available experimental data across the full range of ethanol concentrations examined (25, 50, and 75% v/v).

Conditional Source-term Estimation (CSE) is a combustion model based on the conditional moment hypothesis where transport equations for reactive species are conditionally averaged on conserved scalars. Major advantages of this strategy are the reduced spatial dependence of the conditional averages and negligible fluctuations around the conditional averages, which considerably simplify the reaction rate closure. Historically, simulations using CSE are limited to low carbon fuels (i.e. methane and hydrogen) where the reduced chemistry manifold can be constructed through techniques including intrinsic low dimensional manifolds and trajectory generated manifolds. However, the use of such strategies to create manifolds for diesel surrogates has proven problematic. In this study, the potential of a combination of an unsteady Flamelet Generated Manifold (FGM) and the Conditional Source-term Estimation approach to predict the ignition and flame propagation on an autoigniting n-dodecane spray flame is assessed. Simulations are performed on a single-hole injection of n-dodecane under a wide range of Engine Combustion Network’s “Spray A” conditions within a Reynolds-averaged Navier-Stokes (RANS) framework. Results from parametric sweeps of ambient temperature and oxygen concentration are qualitatively validated against experimental data from the literature and compared against predictions from an industry standard well-stirred reactor model. The efficacy of the CSE-FGM RANS approach in predicting flame characteristics is evaluated and further compared with high fidelity CSE-FGM simulations using the Large Eddy Simulation (LES) turbulence model. Overall, it was found that the CSE-FGM RANS model was able to capture global flame properties — showing particular strength in predicting auto-ignition events in the low temperature region. The model was also able to satisfactorily capture details of the two-stage ignition process. The results were shown to be consistent with those of the CSE-FGM LES model, demonstrating the adaptability of the CSE-FGM approach to different turbulence modelling paradigms.

In this article, different manifold reduction techniques are implemented for the post-processing of Particle Image Velocimetry (PIV) images from a Spark Ignition Direct Injection (SIDI) engine. The methods are proposed to help make a more objective comparison between Reynolds-averaged Navier-Stokes (RANS) simulations and PIV experiments when Cycle-to-Cycle Variations (CCV) are present in the flow field. The two different methods used here are based on Singular Value Decomposition (SVD) principles where Proper Orthogonal Decomposition (POD) and Kernel Principal Component Analysis (KPCA) are used for representing linear and non-linear manifold reduction techniques. To the authors’ best knowledge, this is the first time a non-linear manifold reduction technique, such as KPCA, has ever been used in the study of in-cylinder flow fields. Both qualitative and quantitative studies are given to show the capability of each method in validating the simulation and incorporating CCV for each engine cycle. Traditional Relevance Index (RI) and two other previously developed novel indexes: the Weighted Relevance Index (WRI) and the Weighted Magnitude Index (WMI), are used for the quantitative study. The results indicate that both POD and KPCA show improvements in capturing the main flow field features compared to ensemble-averaged PIV experimental data and single cycle experimental flow fields while capturing CCV. Both methods present similar quantitative accuracy when using the three indexes. However, challenges were highlighted in the POD method for the selection of the number of POD modes needed for a representative reconstruction. When the flow field region presents a Gaussian distribution, the KPCA method is seen to provide a more objective numerical process as the reconstructed flow field will see convergence with an increasing number of modes due to its usage of Gaussian properties. No additional criterion is needed to determine how to reconstruct the main flow field feature. Using KPCA can, therefore, reduce the amount of analysis needed in the process of extracting the main flow field while incorporating CCV.

Accurate modeling of the initial transient period of spray development is critical within diesel engines, as it impacts on the amount of vapor penetration and hence the combustion characteristics of the spray. In addition, in multiple injection schemes shorter injections will be mostly, if not totally, within the initial transient period. This paper investigates how two different commercially available Computational Fluid Dynamics (CFD) codes (hereafter noted as Code 1 and Code 2) simulate transient diesel spray atomization, in a non-combusting environment. The case considered for comparison is a single-hole injection of n-dodecane representing the Engine Combustion Network’s ‘Spray A’ condition. It was identified that the different spray break-up models used by the codes (Reitz-Diwakar for Code 1, Kelvin-Helmholtz/Rayleigh-Taylor (KH-RT) for Code 2) had a significant impact on the transient liquid penetration. From differing initial base setups, Code 1’s case was then matched as closely as possible to Code 2’s case, applying the KH-RT break-up model in Code 1 with the same constants for the break-up and turbulence models as in Code 2. Despite the nominal equivalence between the two simulations, there existed a discrepancy in liquid length prediction throughout injection between codes. This was thought to be caused by differing implementations of the KH-RT model in both codes. Therefore, a new implementation of the KH-RT model was input into Code 1 in order to allow correct matching of the liquid length to experimental data throughout the injection period. This was achieved utilizing a numerical switch based on the Ohnesorge number. Results from the new model are shown and compared to the previous implementation, showing an improved ability to match to experimental data. Differences between the results from the modified KH-RT model in Code 1 and the standard implementation in Code 2 are also discussed.

The predictive ability of artificial neural networks where a large number of experimental data are available, has been studied extensively. Studies have shown that ANN models are capable of accurately predicting NOx emissions from engines under various operating conditions and different fuel types when trained well. One of the major advantages of an ANN model is its ability to relearn when new experimental data is available, thus continuously improving its accuracy. The present work explored the potential of an ANN model to predict NOx emissions for various engine configurations outside its training envelop. This work also looked into quantifying the amount of new data required to improve the accuracy of the model when exposed to unknown conditions. The chosen ANN model was constructed using data from a high-speed direct injection diesel engine and is capable of accurate NOx emissions over a wide range of operating conditions. The optimized network utilized 14 input parameters and is using 6 neurons in a single hidden layer feed-forward neural network. Experimental data from the various engine configurations tested, were then used to predict NOx from the existing ANN model. The results indicate that when the new data are within the baseline training envelop, the ANN model is capable of accurate NOx prediction even when there are substantial changes in engine configuration such as piston material. Similar results were also observed when the injector nozzle is changed. However, the model’s performance drops significantly when new data, outside the baseline training envelop, were employed indicating that additional training is required. As such, various methods for retraining the ANN model were explored with the selected method showing the best compromise between new-data accuracy and old-data accuracy retention. The retrained ANN model developed was found to be an effective tool in predicting NOx emissions for different engine configurations and operating conditions.

A novel combustion modelling approach is proposed here to study the transient effects of diesel spray. Conditional Source-term Estimation (CSE) is a combustion model which invokes the Conditional Moment Closure (CMC) hypothesis to provide an approximation of the mean chemical source term in an averaged transport equation. Unlike CMC, where transport equations are solved for conditional moments, CSE recovers these conditional moments through the solution of an inverse problem. Integral equations are inverted for the conditional moments, by assuming spatial homogeneity in the conditional averages where Tikhonov regularisation is applied. Previous CSE studies have shown that the model is able to predict the flame characteristics successfully for both premixed and non-premixed combustion modes. However, most of these investigations were based on methane flames. This study will be the first successful application of CSE to a complex hydrocarbon fuel, n-dodecane, under the Engine Combustion Network's (ECN) ‘Spray A’ conditions. Detailed chemistry is included in tabulated form using the Flamelet Generated Manifold (FGM) methodology. The predictions of this study include both the Favre averaged conditional mass fraction of reactive species and temperature. The results are compared with available experimental data and previous numerical results. Both RANS and LES simulations are performed under the same condition. The objectives of the paper are (i) assessment of the application of CSE on igniting diesel spray (ii) comparison of the CSE numerical results with available experimental results and previous numerical simulations. Overall, the combination of a chemical mechanism that has been tuned to predict ‘Spray A’ conditions with the CSE-FGM model is able to successfully predict autoignition delay time and lift-off length of n-dodecane spray within the scatter of the experimental data. CSE-FGM offers a feasible tool for detailed combustion analysis of diesel spray flames. Both RANS and LES can give reasonably good global predictions of the flame.

This article considers the application and refinement of artificial neural network methods for the prediction of NO x emissions from a high-speed direct injection diesel engine over a wide range of engine operating conditions. The relative computational cost and performance of two backpropagation algorithms, Levenberg–Marquardt and Bayesian regularization, for this application are compared, with the Levenberg–Marquardt algorithm demonstrating a significant cost advantage. This work also assesses the performance of two alternative filtering approaches, a p-value test and the Pearson correlation coefficient, for reducing the required number of input variables to the model. The p-value test identified 32 input parameters of significance, whereas the Pearson correlation test highlighted 14 significant parameters while additionally providing a ranking of their relative importance. Finally, the article compares the predictive performance of the models generated by the two filtering methods. Overall, both models show good agreement to the experimental data with the model created using the Pearson correlation test showing improved performance in the low-NO x region.

In this study, the role of turbulence-chemistry interaction in diesel spray auto-ignition, flame stabilization and end of injection phenomena is investigated under engine relevant “Spray A” conditions. A recently developed diesel spray combustion modeling approach, Conditional Source-term Estimation (CSE-FGM), is coupled with Reynolds-averaged Navier-Stokes simulation (RANS) framework to study the details of spray combustion. The detailed chemistry mechanism is included through the Flamelet Generated Manifold (FGM) method. Both unsteady and steady flamelet solutions are included in the manifold to account for the auto-ignition process and the subsequent flame propagation in a diesel spray. Conditionally averaged chemical source terms are closed by the conditional scalars obtained in the CSE routine. Both non-reacting and reacting spray jets are computed over a wide range of Engine Combustion Network (ECN) diesel. “Spray A” conditions. The reacting spray results are compared with simulations using a homogeneous reactor combustion model and a flamelet combustion model with the same chemical mechanism. The present study represents the first application of CSE for a diesel spray. The non-reacting liquid/vapour penetration, the mean and RMS mixture fraction, the reactive region, the flame lift-off and the ignition delay show a good agreement with literature data from an optically accessible combustion vessel over a wide range of tested conditions. The CSE-FGM model also shows a better capability in predicting the end-of-injection events in diesel spray combustion. Overall, the CSE-FGM model is shown to capture the experimental trends well, both quantitatively and qualitatively.

Combustion recession, an end of injection (EOI) diesel spray phenomenon, has been found to be a robust correlation parameter for UHC in diesel LTC strategies. Previous studies have shown that the likelihood of capturing combustion recession in numerical simulations is highly dependent on the details of the low-temperature chemistry reaction mechanisms employed. This study aims to further the understanding of the effects of different chemical mechanisms in the prediction of a reactive diesel spray and its EOI process: combustion recession. Studies were performed under the Engine Combustion Network’s (ECN) “Spray A” conditions using the Reynolds-Averaged Navier-Stokes simulation (RANS) and the Flamelet Generated Manifold (FGM) combustion model with four different chemical mechanisms for n-dodecane that are commonly used in the engine simulation communities - including recently developed reduced chemistry mechanisms. The flamelet database for each of the chemical mechanism is generated using two methods: 0D homogeneous reactor (HR) ignition flamelets and 1D igniting counterflow diffusion (ICDF) flamelets. The effect of different tabulation approaches is investigated first following by the discussion of the impact of chemical mechanisms on the prediction of combustion recession. Further discussions include an evaluation of the performance of chemical mechanisms in predicting the most relevant reacting spray characteristics compared to the ECN experimental database: ignition delay time (IDT), flame lift-off length (LOL) and flame reactive region. Results show that the choice of both tabulation method and chemical mechanism play a significant role in initial flame stabilization and end of injection (EOI) transient processes. In general, both tabulation techniques were able to qualitatively capture the flame characteristics before EOI, however ICDF tabulation is better suited for the FGM approach in order to capture combustion recession. Furthermore, the chemical mechanisms studied indicate that mechanisms with stronger low temperature chemistry predictions are more likely to promote combustion recession under an FGM framework.

It is known that low-temperature combustion (LTC) strategies can help simultaneously reduce nitrogen oxides (NOx) and particulate matter (PM) emissions from diesel engines to very low levels. However, it is also known that LTC may cause emissions of unburned hydrocarbons (UHC) to rise — especially in low load operating conditions. Recent studies indicate that end-of-injection (EOI) processes may support ignition recession back to injector nozzle thereby helping to reduce these emissions. This paper contributes to the physical understanding of this EOIphe-nomenon, combustion recession, using computational fluid dynamics studies at LTC conditions. Simulations are performed on a single-hole injection of n-dodecane under a range of Engine Combustion Network’s “Spray A” conditions. The primary objective of this paper is to assess the ability of a Flamelet Generated Manifold (FGM) combustion model to predict and characterize combustion recession. First, a baseline condition FGM simulation is compared with two other combustion models namely the Well Stirred model (WSR), the Representative Interactive Flamelet model (RIF) using the commercially-available CFD solver, CONVERGE. Further studies were carried out for FGM model alone including: varying ambient temperature conditions and chemical mechanisms. Two chemical kinetics mechanisms with low temperature chemistry for n-dodecane are employed to help to predict the occurrence of combustion recession. All simulations are performed under the Reynolds-Averaged Navier-Stokes (RANS) framework in a grid-converged Lagrangian spray scenario. The simulation of combustion recession is qualitatively validated against experimental data from literature and the efficacy of each model in predicting combustion recession is evaluated. Overall, it was found that the FGM model was able to capture the combustion recession phenomenon well — showing particular strength in predicting distinct auto-ignition events in the near nozzle region.

A new reflected shock tube facility, the Cold Driven Shock Tube (CDST), has been designed, built and commissioned at the University of Oxford for investigating IC engine fuel spray physics and chemistry. Fuel spray and chemical kinetics research requires its test gas to be at engine representative pressures and temperatures. A reflected shock tube generates these extreme conditions in the test gas for short durations (order milliseconds) by transiently compressing it through a reflected shock process. The CDST has been designed for a nominal test condition of 6 MPa, 900 K slug of air (300 mm long) for a steady test duration of 3 ms. The facility is capable of studying reacting mixtures at higher pressures (up to 150 bar) than other current facilities, whilst still having comparable size (100 mm diameter) and optical access to interrogate the fuel spray with high speed imaging and laser diagnostics. Future data gathered will support fundamental research for IC engine and fuel technologies leading to even higher thermal efficiency along with a reduction in emissions, and provide high quality, repeatable validation data for advanced model development. This paper describes the scope of the facility's capabilities, aspects of its design, details of the instrumentation, and the axially mounted single hole diesel injector.

Sulfated BiWO (SBiWO) was synthesized by an impregnation method to enhance the visible-light-driven photoactivities of BiWO (BiWO). The characterization results verified that sulfate anion mainly anchored on the catalyst surface greatly extended the visible-light-responsive range without destroying the crystal lattice. Moreover, the SBiWO-based photoactivities were evaluated with the removal of Malachite Green (MG) under UV-Vis irradiation emitted from two microwave-powered electrodeless discharge lamps (MPEDL −2 ) and under visible light (λ > 420 nm). The results demonstrated that the kinetic constant was increased 2.25 times, varying from 0.1478 (BiWO) to 0.3328 min −1 (SBiWO-1). Similar results were also obtained for the visible light-driven reaction. Furthermore, radical scavengers such as t -butanol restricted the visible-light induced degradation of MG over BiWO and SBiWO-1. This indicated that the sulfating process increased the generation of reactive oxygen species, which was further verified by molecular probe with salicylic acid. Thus, more blue-shifting at λ = 618 nm was observed over SBiWO. On the basis of the above results, the photocatalytic mechanism over the sulfated catalyst was also discussed.

Background and aims Silicon (Si) was suggested to enhance plant resistance to toxic elements, and its beneficial role was mainly based on external and internal plant mechanisms. This work aimed at investigating the internal effect of Si on zinc (Zn) detoxification to rice (Oryza sativa L., cv. Tian You 116) seedlings. Methods In a hydroponic experiment, we examined the uptake, xylem loading and localization of Zn in rice seedlings under the condition of 200 μ MZ n contamination with the additional silicate supply at three levels ( 0, 0.5 and 1.8 mM). Results The silicate addition significantly increased the seedling biomass, and decreased Zn concentration in both root and shoot of seedlings and in xylem sap flow. Zinpyr-1 fluorescence test and Energy-dispersive X-ray spectroscopy analysis showed the concentration of biologically active Zn 2+ decreased, and Zn and Si co-localized in the cell wall of metabolically less active tissues, especially in sclerenchyma of root. The fractionation analysis further supported silicate supply increased about 10% the cell wall bound fraction of Zn. Conclusions This study suggests the Si-assisted Zn tolerance of rice is mainly due to the reduction of uptake and translocation of excess Zn, and a stronger binding of Zn in the cell wall of less bioactive tissues might also contribute to some degree.

Through pot experiment, effects of phytoremediation on microbial communities in soils at different nickel treatment levels were studied. Two Ni hyperaccumulating and one Ni tolerant species were planted in paddy soils different in Ni concentration, ranging from 100 to 1 600 mg/kg. After 110 days of incubation, soil microbial activities were analyzed. Results showed that populations of bacteria, fungus, and actinomycetes and biomass of the microorganisms were stimulated when nickel was added at a rate of 100 mg/kg in non-rhizospheric soil. When the rate was over 100 mg/kg in the soil, adverse effects on the soil microbial communities were observed. The plantation of Ni hyperaccumulating species could increase both the population and biomass of soil microorganisms, because, by absorbing nickel from the soil and excreting root exudates, the plants reduced nickel toxicity and improved the living environment of the microbes. However, different plant species had different effects on microorganisms in soil. Randomly Amplified Polymorphic DNA (RAPD) with five primers was used in this study in 25 soil samples of four types of soils. A total of 947 amplified bands were obtained, including 888 polymorphic bands and 59 non-polymorphic bands. The results indicated that the composition of microbial DNA sequences had changed because of the addition of nickel to the treated soils. Shannon-Weaver index of soil microbial DNA sequences reduced in the nickel contaminated soils with increasing nickel concentration. The changes in Shannon-Weaver index in the four types of soils ranged from 1.65 to 2.32 for Alyssum corsicum, 1.37 to 2.27 for Alyssum murale, 1.37 to 1.96 for Brassica juncea, and 1.19 to 1.85 for nonrhizospheric soil. With the same amount of nickel added to soils, the Shannon-Weaver index in rhizospheric soil with plants was higher than that in non-rhizospheric soil.

The ability of Potentilla griffithii Hook var. velutina Cardot to hypaeraccumulate zinc (Zn) was identified through field survey and hydroponic experiments. Our results showed that P. griffithii could be classified as a new Zn hyperaccumulator. Zn concentrations in the shoots of P. griffithii averaged 6250 mg kg(-1) (3870-8530 mg kg(-1)) growing in Zn-rich soils. The highest Zn concentration was observed in the leaves of P. griffithii at 22,990 mg kg(-1). The fact that P. griffithii was able to grow in a mining soil with a Zn concentration of 193,000 mg kg(-1) without showing a major sign of phytotoxicity demonstrated its high tolerance to Zn. When growing in hydroponic systems, P. griffithii accumulated a maximum 26700 mg kg(-1) zinc concentration in the shoots, indicating the ability of this species to effectively take up and translocate Zn. Translocation factors (the ratio of Zn concentration in shoot to root) of 1.1 to 1.6 were obtained. Compared to the control, dry biomass of P. griffithii in 160 mg L(-1) Zn treatment increased 66.6% (P < 0.05). The time-course experiment showed that the maximum Zn concentration at 100 mg L(-1) Zn treatment was found at 16 d, much later than that of the 10 mg L(-1) Zn treatment, which might be an attribution of a accumulating mechanism or detoxification of a plant. The report of a new Zn hyperaccumulator provides a new plant species for the phytoremediation of contaminated soil and for the research on mechanisms of Zn hyperaccumulation in plants.

A field investigation and a pot experiment were conducted to study the Zn and Cd accumulation in Picris divaricata Vant. Under natural conditions, the average Zn and Cd concentrations in shoot of P. divaricata were 5911 and 246 µg g -1 (DW), with the maxima of 18,339 and 585 µg g -1 , respectively. Grown on a mixed soil for three months, P. divaricata accumulated up to 15,183 µg g -1 Zn and 364 µg g -1 Cd in shoot with translocation factors of Zn and Cd all larger than one. Thus, P. divaricata could be identified as a new Zn and Cd hyperaccumulator found in China.

The effects of exogenous citric acid and malic acid on the uptake of nickel (Ni) by the Ni hyperaccumulator Alyssum corsicum and the non-accumulator leaf mustard were studied. Citric acid and malic acid addition reduced Ni uptake by the roots of leaf mustard. Malic acid addition yielded no significant effect on Ni translocation in leaf mustard. Citric acid addition reduced Ni concentration of roots, without significant effect on Ni translocation in A. corsicum. Malic acid at 1.0 mmolL−1 increased Ni concentration in shoots of A. corsicum and also, stimulated the translocation of Ni from root to shoot.