Size Distribution Information Research Articles

Using a novel vector length stability index (VLSI) to evaluate soil aggregate stability: A preliminary study

Soil aggregate stability is an important soil characteristic which can represent the improvement of soil structure after revegetation. Due to the high heterogeneity of soil, large-scale sampling is usually necessary to estimate the spatial variation of aggregate stability across landscapes. Although there are many methods and corresponding indices for analysis of aggregate stability, many of them are not suitable for investigations with a large number of samples due to time-consuming experimental practices. To avoid unnecessary costs of time and labor in the long-term land degradation and restoration monitoring across large regional scales, simple indices of aggregate stability would help monitoring across large regional scales. For this reason, we introduced a new, simple, and normalized index, the vector length stability index (VLSI) and its weighted form (wVLSI) to describe soil aggregate stability. The VLSI shares key information of aggregate size distribution with mean weight diameter (MWD), but it is naturally normalized and dimensionless. This new index was tentatively tested to describe soil aggregate stability of the 0–30 cm soil layers under artificial plantations, natural grassland, abandoned farmland, and farmland in production in a small watershed located in the Loess Plateau of China. The results showed that the aggregate stability of the 0–20 cm layers in abandoned farmland could be recovered near to the condition under artificial plantations just after 2-year abandonment, but the aggregate stability hardly changed in the 20–30 cm layer. The aggregate stability of the 0–10 cm layer in natural recovered grassland is close to that in top soil layers under artificial plantations, but the stability in the 10–30 layers appeared as low as that in farmland. Because of its simple form and low data requirements, the VLSI could be an alternative efficient tool for soil aggregate stability evaluation when a large number of samples are needed.

Observed size distribution changes in American lobsters over a 12-year period in southwestern Nova Scotia, Canada.

Size distribution and size frequency information of American lobsters (Homarus americanus) are often used to help estimate the age distributions, and reproductive output for the species and to guide the determination of appropriate minimum legal sizes for the fishery. This study used truncated linear regression models to estimate the effects of sampling year, sampling month, lobster sex and water depth on the lobster size. A dataset of almost 130,000 trap-caught lobsters from the two most important lobster fishing areas of Atlantic Canada collected over a 12-year period (2004-2015) was analyzed. It was shown that truncated models can help to account for biases due to the trap sampling method from vessels and from wharf samplings. There were significant annual and seasonal changes in size distribution, and data collected outside the fishing season showed a significant increase in carapace length in 2014 and 2015, potentially reflecting a northward shift of the range of lobster populations due to more favourable settlement and recruitment habitats. Size also increased in late summer, likely due to moult. Our results demonstrated that landed lobsters, especially females, were smaller than the predicted size-at-maturity in the region (96.5 mm carapace length), which could have long-term repercussions for the stock's reproductive potential.

Deep‐learning based in situ image monitoring crystal polymorph and size distribution: Modeling and validation

AbstractIn situ monitoring and closed‐loop control of the solution crystallization process are the modern trends for pharmaceutical development, in which the critical process parameters (CPPs) as well as the product critical quality attributes (CQAs) can be regulated and guaranteed during the manufacturing process. In this study, an in situ image monitoring methodology based on a state‐of‐the‐art deep‐learning model was developed to track the CQAs such as polymorph ratio, two‐dimensional crystal size, and crystal shape in a solvent‐mediated polymorphic transformation (SMPT) process. Coupled with the multidimensional process information, a 2D population balance model (PBM) was developed and validated using the results of the in situ image‐based CQAs analysis. The 2D‐PBM was solved using a high‐resolution finite volume method (HR‐FVM) which could provide a high dimensional particle‐size distribution. Through the validation between the process image analysis and the 2D‐PBM, the accuracy of image analysis was discussed, and the potential and challenges of in situ image analysis were proposed. This work aims to integrate the crystal polymorphism and two‐dimensional crystal size distribution (2D‐CSD) information in the SMPT process using intelligent microscopic image analysis and then to validate the results of neural network processing by solving the numerical solution of the multidimensional PBM.

Modeling atmospheric microplastic cycle by GEOS-Chem: An optimized estimation by a global dataset suggests likely 50 times lower ocean emissions

Modeling atmospheric microplastic cycle by GEOS-Chem: An optimized estimation by a global dataset suggests likely 50 times lower ocean emissions

Evaluation of irreducible water saturation by electrical imaging logging based on capillary pressure approximation theory

One of the most important metrics for evaluating oil and gas reservoirs is irreducible water saturation (Swir). The completion of oil and gas reservoir development tasks, such as fluid identification, productivity prediction, and water-flooded reservoir discrimination, requires the accurate evaluation of Swir. Nuclear magnetic resonance (NMR) logging can reflect the intricate pore structure of reservoirs, so it offers a natural advantage in the evaluation of Swir, but its high measurement cost leads to infrequent use. In geophysical logging, electrical imaging logging can obtain pore size distribution information at a lower cost. Hence, a method based on electrical imaging logging data is proposed to predict Swir. This method not only has a high degree of accuracy but is also cost-effective. First, the pore characteristics of the bound fluid were analyzed with the mercury injection capillary pressure curve. Based on the capillary pressure approximation theory, the reverse cumulative curve of the porosity spectrum was derived. And the physical meaning of its “inflection point” was clarified. Then, the resistivity of the formation containing just irreducible water was computed using the conversion between the core displacement pressure and resistivity. Finally, based on Archie's formula, the Swir at each depth of the reservoir was predicted. By using ultradeep carbonate reservoir logging data from the Yuanba gas field, the model was validated. The results demonstrated that the calculated values using this method agreed with the measured values from the cores. Compared with the core fitting method, the average absolute error is reduced by 2.61%, and the average relative error decreases by 12.0%. If NMR logging information is not present, this method serves as an effective supplement to accurately predict Swir.

Numerical investigation on droplet collision phenomena in moisture separators using population balance model under Lagrangian framework

Droplet collision is an important phenomenon in the industry field. In this work, a new droplet collision model is proposed using moment method coupling with population balance model (PBM) under Lagrangian framework. First, by solving PBM equation with coalescence kernel, the droplet size distribution is obtained. Second, droplets are classified into represented droplets according to the moment parameters. Third, trajectories of represented droplets are simulated by a motion model under the Lagrangian framework. In the results part, three cases (e.g. two-dimensional wave-plate steam water separator, three-dimensional cyclone separator and three-dimensional swirl-vane separator) are performed to verify the accuracy of the new model. A variety of calculation conditions such as inlet velocity varies from 3 m/s to 11 m/s and inlet volume fraction varies from 0.018 % to 0.119 % are set under environment condition of normal temperature and pressure. The pressure distribution, velocity distribution and separation efficiency in the flow field are calculated. The comparison between the calculated results and the experimental values shows that the calculated model has high accuracy. This model shows good potential to describe the droplet collision behavior and the detail information of the particle size distribution for droplet-laden flows.

Particle size distribution recovery from non-Gaussian intensity autocorrelation functions obtained from dynamic light scattering at ultra-low particle concentrations

Dynamic light scattering (DLS) from ultra-low concentration suspensions (the average number of particles in the scattering volume is less than ~100) gives rise to autocorrelation functions (ACFs) containing a non-Gaussian term due to particle number fluctuations. This term is difficult to characterize and account for and makes recovery of particle size distribution (PSD) information unreliable. We show that an initial analysis of the intensity ACF to determine parameters describing the amplitude and relaxation rate of the non-Gaussian term and then using these parameters to create a better theoretical non-Gaussian ACF model allows a more accurate recovery of the PSD. The modified model is consistent with the measured ACF data, and a reconstructed kernel matrix matching the measured data is obtained. When compared with the usual kernel function reconstruction (KFR) method, the proposed method gives significantly improved PSD recovery accuracy with experimental data. Furthermore, the PSDs obtained have no obvious differences to those obtained from measurements at normal particle concentrations. • Fitting the measured ACF data with a non-Gaussian ACF model of lower concentration. • Reducing focused beam waist value to obtain non-Gaussian model at lower concentration. • Providing a solution for nanoparticle size measurement at ultra-low concentration.

An Investigation of Mechanical Properties of Recycled Carbon Fiber Reinforced Ultra-High-Performance Concrete.

Carbon fiber-reinforced concrete as a structural material is attractive for civil infrastructure because of its light weight, high strength, and resistance to corrosion. Ultra-high performance concrete, possessing excellent mechanical properties, utilizes randomly oriented one-inch long steel fibers that are 200 microns in diameter, increasing the concrete's strength and durability, where steel fibers carry the tensile stress within the concrete similar to traditional rebar reinforcement and provide ductility. Virgin carbon fiber remains a market entry barrier for the high-volume production of fiber-reinforced concrete mix designs. In this research, the use of recycled carbon fiber to produce ultra-high-performance concrete is demonstrated for the first time. Recycled carbon fibers are a promising solution to mitigate costs and increase sustainability while retaining attractive mechanical properties as a reinforcement for concrete. A comprehensive study of process structure-properties relationships is conducted in this study for the use of recycled carbon fibers in ultra-high performance concrete. Factors such as pore formation and poor fiber distribution that can significantly affect its mechanical properties are evaluated. A mix design consisting of recycled carbon fiber and ultra-high-performance concrete was evaluated for mechanical properties and compared to an aerospace-grade and low-cost commercial carbon fiber with the same mix design. Additionally, the microstructure of concrete samples is evaluated non-destructively using high-resolution micro X-ray computed tomography to obtain 3D quantitative spatial pore size distribution information and fiber clumping. This study examines the compression, tension, and flexural properties of recycled carbon fibers reinforced concrete considering the microstructure of the concrete resulting from fiber dispersion.

An assessment of rainfall kinetic energy functional relationships with GPM DPR

The kinetic energy (KE) of rainfall is an important parameter in estimating the rainfall erosivity factor, an essential parameter in the soil erosion equation. The global precipitation measurement dual-frequency precipitation radar (GPM DPR) can provide radar reflectivity at Ku- and Ka-band frequencies (Zku and Zka), rainfall rate (I, mm h−1), and drop size distribution parameters (mass-weighed mean diameter: Dm in mm and normalized intercept parameter: log10Nw, Nw in m−3 mm−1). The empirical relations between rainfallKEand GPM DPR parameters (KEtime–I, KEmm–Dm, KEtime–Zku, and KEtime–Zka relations) can allow us to estimate the KE at the locations where there are no ground-based measurements. In the present work, long-term measurements of impact type disdrometers located at three different locations (Zhongli, Feitsui, and Hsiayun) in north Taiwan are used to collect the raindrop size distribution (RSD) information of six seasons (spring, mei-yu, summer, autumn, winter, and typhoon). Measured RSDs in six seasons are used to estimate the rainfall intensity, mass-weighted mean diameter, rainfall KE flux (KEtime, J m–2 h−1), KE content (KEmm, J m−2 mm−1), radar reflectivity at Ku- and Ka-band frequencies. For the first time, present study establish and validate the rainfall KE functional relationships with GPM DPR data products (KEtime–I, KEmm–Dm, KEtime–Zku, and KEtime–Zka relations). The empirical relations between rainfall KE and GPM DPR parameters (I, Dm, Zku, and Zka) are established for six seasons of each disdrometer location. Derived KE relations did not show any spatial disparity for a given season, especially the KEmm–Dm, KEtime–Zku, and KEtime–Zka relations. On the other hand, a clear seasonal differences in the rainfall KE relations are noticed at each observational site. The established relations showed reasonable agreement in estimating the KE from the GPM DPR. However, in determining the KE, the KEmm–Dm relation showed outperformance over the remaining three relations (KEtime–I, KEtime–Zku, and KEtime–Zka relations).

Photo-catalytic and biomedical applications of one-step, plant extract-mediated green-synthesized cobalt oxide nanoparticles.

In the present work, for the first time, green chemically synthesized and stabilized Co3O4 nanoparticles were employed for catalytic conversion of isopropyl alcohol to acetone by dehydrogenation of IPA. Plant extract of Rosmarinus officinalis was used as a reducing and stabilizing agent for this synthesis. The biosynthesized Co3O4 nanoparticles were annealed at 450℃ followed by their physiochemical characterizations through XRD, SEM, AFM, and FTIR. Size distribution information collected through XRD and AFM back each other, and it was found to be 6.5nm, having the highest number of nanoparticles in this size range. While SEM confirms the self-arranging property of synthesized nanoparticles due to their magnetic nature, furthermore, the biogenic Co3O4 nanoparticles were studied for their catalytic potential to convert isopropyl alcohol to acetone with the help of a UV-Visible spectrophotometer. The highest photocatalytic conversion of 99% was obtained in time period of 48s. For the first time ever, nanoparticles were used for 5 cycles to evaluate their recyclable nature and conversion fell from 99 to 86% and the end of the 5th cycle. Later anti-bacterial activity against 3 Gram-positive and 3 Gram-negative strains gave the highest inhibition value of 99% against Streptococcus pneumoniae at 500µg/mL. Finally, a cytotoxicity study on synthesized nanomaterials was carried out by exposing freshly drawn human macrophages to them. It was found that even at the highest concentration of 500µg/mL, the nanoparticles showed only 28% lysis.

On the size distribution of truncation areas for fractal surfaces

The size distribution of contact spot areas is critical for the evaluation of contact behavior of rough surfaces. A size distribution law proposed by Mandelbrot has been used widely in multi-asperity fractal contact models to predict the rough contact behavior. However, the Mandelbrot law has a limitation on the representation of the truncation area distribution for rough surfaces. Therefore, the present study develops a new size distribution law of the truncation areas for fractal surfaces. Specifically, the three-dimensional Weierstrass-Mandelbrot (WM) function is used to characterize rough surfaces. A novel method is proposed to determine the fractal parameters of the WM function. Numerical investigation of rough surface truncation is conducted to obtain size distribution information of the truncation regions for various rough surfaces. Based on the numerical results, the new size distribution law of the truncation areas is proposed and validated by fractal surface and realistic rough surface. It is found that the proposed law can accurately reproduce the size distribution of the truncation areas for rough surfaces with a wide range of fractal parameters.

The Application of maximum entropy technique to determine number concentration and size distribution in pneumatic transport

Based on laser-diffraction technique, the maximum entropy technique is applied to measure particle number concentration and size distribution in pneumatic transport. The information of particle number concentration and size distribution can be obtained simultaneously without distort the fluid and the particle trajectories. Detailed information about the distribution of the particle size and concentration in gas-solid two-phase flows near sand-bed surface are obtained in a large-scale wind tunnel test system. The experimental result shows that particle number concentration increases linearly with the increase of the wind speed. The mean particle size decreases exponentially with height (H > 3 cm), but almost unchanged for H < 3 cm. The weakly relationship of fluctuating velocity and particle-size dispersion is deduced from the underlying mechanism of equilibrium transport. This illustrates that transportation close to the mobile bed has selectivity for particle size.

Effect of pore size distribution on sorption-induced deformation of porous materials: A theoretical study

Porous materials exhibit complex deformation when interacting with adsorbates in the environment. Depending on the size and shape of individual pores, interactions between solid and adsorbates can give rise to several pore-scale forces that impact the overall adsorption and strain isotherms. Accurate prediction of the adsorption-deformation behavior of a porous material would benefit from the consideration of its pore network characteristics described by the pore size distribution (PSD). This paper presents a theoretical investigation on how the pore size distribution can affect the sorption-induced deformation of micro/meso/macro porous solids. The recently developed surface poromechanics theory is generalized to account for the information of arbitrary pore size distributions. The adsorptive energetics of a generic pore network is statistically upscaled from the solid-adsorbate interaction in a single slit pore geometry, which is then infused into the thermodynamics of a deformable body. The theory is first validated against the experimental data on microporous carbon interacting with nitrogen gas. Then the same parameter set is used to study the effects of pore size on the adsorption and strain isotherms of porous materials. By only varying the input PSDs, a variety of adsorption-deformation behaviors that are commonly observed from experiments is reproduced by the model. Conclusions are drawn from this analysis regarding the relative dominance of different pore-scale forces at varying pore size ranges, and the circumstances where simplified representation of the pore network can be acceptable.

Simulation and 3-D Visualization of Crystallization Process Based on Cellular Automata

Crystallization is widely used for separation and purification. During this process, the different growth conditions, such as temperature and supersaturation, would result in different size and morphology, which not only affect the product quality, but also affect the subsequent process operations. Through the simulation of the crystallization process, a better understanding of crystallization process dynamics can be extracted. The population balance (PB) model has been used to simulate industrial crystallization processes, while the multi-dimensional population balance (MPB) model can be used to obtain the information of size and morphology distribution. However, the morphological distribution information provided by numerical solution of MPB is macroscopic, and the specific morphology of a crystal particle cannot be displayed at the microscopic level, so the difference among crystals morphology cannot be clearly displayed. Cellular automata (CA) is a method that simulates a process by considering simple cell changes and cell-to-cell interactions. It can be employed to conduct cross-scale simulation, i.e., both macro and micro. In this work, a CA method is introduced into an MPB model to simulate the batch cooling crystallization process from solution. The rule of crystal growth comes from classical diffusion theory, and whether the solution is transformed into a crystal is realized by the Monte Carlo method. The crystallization of potassium dihydrogen phosphate (KDP) is taken as a case-study and the results is verified with the results in the literature. Through CA simulation, not only the macroscopic crystal size and morphology distribution information can be obtained, but also the crystal morphology in microscopic can be exhibited. At the same time, the crystallization process can be visualized in 3-D environment, with size and morphology distribution being presented intuitively. In addition, the calculation time is reduced under certain accurate conditions. This result provides a theoretical reference for modeling, analysis and controlling of the crystallization process in the future.

Characteristics of Rain-Induced Attenuation over Signal Links at Frequency Ranges of 25 and 38 GHz Observed in Beijing

Wireless communication has become a very important part of our lives, and it is well known that meteorological factors affect the quality of communication links, especially at higher frequencies because the physical dimensions of raindrops, hail stones, and snowflakes are on a similar wavelength to the propagating radio frequency. Millimeter-waves are an important technology for fifth-generation cellular networks which are currently being deployed all over the world. Since atmospheric effects are challenging in millimeter-wave transmissions, in this paper, we conducted line-of-sight field measurements at 25 GHz and 38 GHz. We monitored the received signal during rainfall events and compared the theoretical attenuation and the recorded rain-induced attenuation. We also derived the rain-induced attenuation (A) and rainfall rate (R) relation for stratiform and convective rain, respectively, using local rain drop size distribution (DSD) information at our measurement site collected during the period of two years. Furthermore, opportunistic sensing of atmospheric phenomena using microwave or millimeter-wave communication links in commercial cellular networks has recently attracted more attention in meteorological research worldwide. The accuracy of calculating rainfall rates from microwave links highly depends on the retrieval model and values of coefficients in the model, i.e., a and b of the A-R relation model. Here, the coefficients a and b are estimated based on local DSD measurement, and the performance of the improved A-R model is evaluated using propagated signal power based on measurement data. Compared to the (a, b) coefficients in the International Telecommunication Union Recommendation (ITU-R) P.838 document, the derived coefficients achieved an improved rainfall rate estimation.

Grain size characterization in metallic alloys using different microscopy and post-processing techniques

In this work, an image analysis procedure which enables an estimation of grain size distribution from optical and scanning electron micrographs is detailed. Two applicative cases (a face centered cubic austenitic stainless steel and a hexagonal close packed zirconium alloy, both with fully recrystallized equiaxed microstructure) are considered. Grain size definition is discussed both in terms of mean grain size and of grain size distribution. Classical methodologies as well as standardized definitions are recalled and compared to each other. Results obtained by electron backscattered diffraction analysis are compared to the ones obtained after image analysis based on backscattered electron or optical micrographs. The effect of data processing parameters on grain size results is also examined. Image analysis based on optical or scanning electron micrographs proves to be suitable to obtain accurate grain size distribution information with shorter acquisition and processing times compared to EBSD orientation mapping of the same analyzed area.

A new type of electron microscope rapid collection device foratmospheric particles and its application cases.

A device for collecting atmospheric particulates matter for on-line electron microscopy was designed and manufactured by using 3D modeling and printing technology. The device was used to collect atmospheric particulates matter in Xining city of Qinghai province for seven consecutive days and used to collect atmospheric particulates matter in Jinan city of Shandong province for 30 consecutive days. And the samples were analyzed by electron microscopy. Through it can obtain ultra structure and size distribution information of atmospheric particulates matter. The experimental results reflected the distribution of atmospheric particulate matter in real time. The on-line acquisition device for electron microscopy is practical, fast, and convenient, which greatly shortens the sampling period and can be carried to any place outdoors for real-time sampling. The morphological structure and particle size distribution of atmospheric particles in sampling area was observed by electron microscope. The device has reference value to the study of PM2.5 and PM10 in atmosphere and air pollution. This acquisition device is convenient for the detection of sample by electron microscopy. It can further expand and simplify the sampling technology of large-scale instruments and equipment. This new method of real-time monitoring of air pollution is worth popularizing and applying.

Constraining the Twomey effect from satellite observations: issues and perspectives

Abstract. The Twomey effect describes the radiative forcing associated with a change in cloud albedo due to an increase in anthropogenic aerosol emissions. It is driven by the perturbation in cloud droplet number concentration (ΔNd, ant) in liquid-water clouds and is currently understood to exert a cooling effect on climate. The Twomey effect is the key driver in the effective radiative forcing due to aerosol–cloud interactions, but rapid adjustments also contribute. These adjustments are essentially the responses of cloud fraction and liquid water path to ΔNd, ant and thus scale approximately with it. While the fundamental physics of the influence of added aerosol particles on the droplet concentration (Nd) is well described by established theory at the particle scale (micrometres), how this relationship is expressed at the large-scale (hundreds of kilometres) perturbation, ΔNd, ant, remains uncertain. The discrepancy between process understanding at particle scale and insufficient quantification at the climate-relevant large scale is caused by co-variability of aerosol particles and updraught velocity and by droplet sink processes. These operate at scales on the order of tens of metres at which only localised observations are available and at which no approach yet exists to quantify the anthropogenic perturbation. Different atmospheric models suggest diverse magnitudes of the Twomey effect even when applying the same anthropogenic aerosol emission perturbation. Thus, observational data are needed to quantify and constrain the Twomey effect. At the global scale, this means satellite data. There are four key uncertainties in determining ΔNd, ant, namely the quantification of (i) the cloud-active aerosol – the cloud condensation nuclei (CCN) concentrations at or above cloud base, (ii) Nd, (iii) the statistical approach for inferring the sensitivity of Nd to aerosol particles from the satellite data and (iv) uncertainty in the anthropogenic perturbation to CCN concentrations, which is not easily accessible from observational data. This review discusses deficiencies of current approaches for the different aspects of the problem and proposes several ways forward: in terms of CCN, retrievals of optical quantities such as aerosol optical depth suffer from a lack of vertical resolution, size and hygroscopicity information, non-direct relation to the concentration of aerosols, difficulty to quantify it within or below clouds, and the problem of insufficient sensitivity at low concentrations, in addition to retrieval errors. A future path forward can include utilising co-located polarimeter and lidar instruments, ideally including high-spectral-resolution lidar capability at two wavelengths to maximise vertically resolved size distribution information content. In terms of Nd, a key problem is the lack of operational retrievals of this quantity and the inaccuracy of the retrieval especially in broken-cloud regimes. As for the Nd-to-CCN sensitivity, key issues are the updraught distributions and the role of Nd sink processes, for which empirical assessments for specific cloud regimes are currently the best solutions. These considerations point to the conclusion that past studies using existing approaches have likely underestimated the true sensitivity and, thus, the radiative forcing due to the Twomey effect.

Asymmetric-flow field-flow fractionation for measuring particle size, drug loading and (in)stability of nanopharmaceuticals. The joint view of European Union Nanomedicine Characterization Laboratory and National Cancer Institute - Nanotechnology Characterization Laboratory

Asymmetric-flow field-flow fractionation (AF4) has been recognized as an invaluable tool for the characterisation of particle size, polydispersity, drug loading and stability of nanopharmaceuticals. However, the application of robust and high quality standard operating procedures (SOPs) is critical for accurate measurements, especially as these complex drug nanoformulations are most often inherently polydisperse. In this review we describe a unique international collaboration that lead to the development of a robust SOP for the measurement of physical-chemical properties of nanopharmaceuticals by multi-detector AF4 (MD-AF4) involving two state of the art infrastructures in the field of nanomedicine, the European Union Nanomedicine Characterization Laboratory (EUNCL) and the National Cancer Institute-Nanotechnology Characterisation Laboratory (NCI-NCL). We present examples of how MD-AF4 has been used for the analysis of key quality attributes, such as particle size, shape, drug loading and stability of complex nanomedicine formulations. The results highlight that MD-AF4 is a very versatile analytical technique to obtain critical information on a material particle size distribution, polydispersity and qualitative information on drug loading. The ability to conduct analysis in complex physiological matrices is an additional very important advantage of MD-AF4 over many other analytical techniques used in the field for stability studies. Overall, the joint NCI-NCL/EUNCL experience demonstrates the ability to implement a powerful and highly complex analytical technique such as MD-AF4 to the demanding quality standards set by the regulatory authorities for the pre-clinical safety characterization of nanomedicines.

Soot formation and growth with palladium acetylacetonate-toluene injection in ethylene base flames investigated by in situ synchrotron small-angle X-ray scattering

Organo-Pd compounds as fuel additives are proven to have distinct catalytic effects on enhancing ignition and combustion performance. However, their effects on soot emission and the mechanism are not well-established. In this study, the effect of palladium acetylacetonate (Pd(C5H7O2)2, Pd(acac)2) on soot formation and growth was investigated experimentally. Different concentrations of Pd(acac)2 were added in the ethylene flames. Synchrotron small-angle X-ray scattering (SAXS) and transmission electron microscopy (TEM) were used to detect the soot particles to obtain the size, shape and morphology information. The results show that the Pd(acac)2 addition with low concentration (100 ppm) suppresses the formation and growth of the primary soot particles at each height above the burner. As the addition concentration increases to 200 and 300 ppm, the size and volume fraction of the soot particles are decreased at the low flame region, and increased at the high flame region. Detailed size distribution, morphology and nanostructure information of the soot particles at different flame heights are discussed.