Automated primary particle sizing of nanoparticle aggregates by TEM image analysis

Dastanpour, Ramin; Boone, Jocelyne M.; Rogak, Steven N.

doi:10.1016/j.powtec.2016.03.027

Cited by 48 publications

(29 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The TEM intensity images were then processed in MATLAB (R2018b, MathWorks Inc., Natick, USA) to determine the morphological parameters of individual imaged soot aggregates, as described below, using the code from Dastanpour et al. (), which has been modified and extended for the purposes of our analysis. Similar to China et al.…”

Section: Methodsmentioning

confidence: 99%

“…Images of the collected unprocessed soot particles and hydrometeor residuals were taken with a JEOL TEM-1400+ (JEOL Ltd., Tokyo, Japan), equipped with a LaB6 filament and operating at 120 kV, at different magnifications (x20k, x50k, x80, and x100k). The TEM intensity images were then processed in MATLAB (R2018b, MathWorks Inc., Natick, USA) to determine the morphological parameters of individual imaged soot aggregates, as described below, using the code from Dastanpour et al (2016), which has been modified and extended for the purposes of our analysis. Similar to China et al (2014), we used different 2-D morphological descriptors to characterize the soot particle morphology, including aspect ratio, roundness, and particle circularity (see SI Figure S1).…”

Section: Morphological Characterization Of Soot Particlesmentioning

confidence: 99%

See 1 more Smart Citation

The Impact of Cloud Processing on the Ice Nucleation Abilities of Soot Particles at Cirrus Temperatures

et al. 2020

View full text Add to dashboard Cite

Soot particles are generally considered to be poor ice nucleating particles. Involvement of soot in clouds and their release back into the atmosphere can form residual particles with altered cloud forming potential. The impact and extent of such different cloud processing scenarios on ice nucleation is, however, not well understood. In this work, we present the impact of cloud processing of soot aerosols on subsequent ice nucleation cycles at T≤233 K. Coupling of two continuous flow diffusion chambers allows the simulation of different cloud processing scenarios and investigation of subsequent ice nucleation activity of the processed particles. The processing scenarios presented here encompass contrail, cirrus, and mixed‐phase cloud processing, mimicking typical pathways that soot particles can be exposed to in the atmosphere. For all scenarios tested, the processed particles showed an enhanced ice active fraction for T<233 K. The relative humidity with respect to water for the ice nucleation onset was observed to be on average approximately 10% (relative humidity with respect to ice, RHi≈16%) lower for the cloud‐processed particles compared to the unprocessed soot for which ice nucleation was observed close to or at homogeneous freezing conditions of solution droplets. We attribute the enhanced ice nucleation abilities of the cloud‐processed soot to a pore condensation and freezing mechanism and have identified key parameters governing these changes. Enhanced ice nucleation abilities of soot in cirrus clouds can have significant impacts, given the importance of the atmospheric ice phase for precipitation formation and global climate.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Morphological Characterization Of Soot Particlesmentioning

confidence: 99%

The Impact of Cloud Processing on the Ice Nucleation Abilities of Soot Particles at Cirrus Temperatures

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Köylü et al (1995Köylü et al ( , 1995 and Sorensen et al (1992). More recently, automated algorithms have emerged that can measure morphological parameters such as the primary particle diameter from TEM images of soot (Anderson et al, 2017;Bescond et al, 2014;Dastanpour et al, 2016;Grishin et al, 2012). Given the large availability of TEM-based information on primary particle size and overlapping coefficients, we make these parameters a central part of our soot-PCF framework presented here.…”

Section: Soot Propertiesmentioning

confidence: 99%

Soot-PCF: Pore condensation and freezing framework for soot aggregates

Marcolli¹,

Mahrt²,

Kärcher³

2020

Preprint

View full text Add to dashboard Cite

Abstract. How ice crystals form in the troposphere strongly affects cirrus cloud properties. Atmospheric ice formation is often initiated by aerosol particles that act as ice nucleating particles. The aerosol-cloud interactions of soot and associated feedbacks remain uncertain, in part because a coherent understanding of the ice nucleation mechanism and activity of soot has not yet emerged. Here, we provide a new framework that predicts ice formation on soot particles via pore condensation and freezing (PCF) that, unlike previous approaches, considers soot particle properties capturing their vastly different pore properties compared to other aerosol species such as mineral dust. During PCF, water is taken up below water saturation into pores on soot aggregates by capillary condensation. At cirrus temperatures, pore water can freeze homogeneously and subsequently grow into a macroscopic ice crystal. In the soot-PCF framework presented here, the relative humidity conditions required for these steps are derived for different pore types as a function of temperature. The pore types considered here evolve from idealized stacking of equally sized primary particles, either in tetrahedral or cubic packing arrangements. Specifically, we encompass n-membered ring pores that form between n individual spheres within the same layer of primary particles as well as pores in the form of inner cavities that form between two layers of primary particles. We treat soot primary particles as perfect spheres and use the contact angle between soot and water (θsw), the primary particle diameter (Dpp) and the degree of primary particle overlap (overlap coefficient, Cov) to characterize soot pore properties. We find that n-membered ring pores are the dominant pore structures for soot-PCF, as they are common features of soot aggregates and have a suitable geometry for both, filling with water and growing ice below water saturation. We focus our analysis on three-membered and four-membered ring pores as they are of the right size for PCF assuming primary particle sizes typical for atmospheric soot particles. For these pore types, we derive equations that describe the conditions for all three steps of soot-PCF, namely capillary condensation, ice nucleation, and ice growth. Since at typical cirrus conditions homogeneous ice nucleation can be considered immediate as soon as the water volume within the pore is large enough to host a critical ice embryo, soot-PCF becomes either limited by capillary condensation or ice crystal growth. For instance, our results show that at typical cirrus temperatures of T = 220 K, three-membered ring pores formed between primary particles with θsw = 60°, Dpp = 20 nm, and Cov = 0.05 are ice growth limited, as the ice requires a relative humidity with respect to ice of RHi = 137 % to grow out of the pore, while a sufficient volume of pore water for ice nucleation has condensed already at RHi = 86 %. Conversely, four-membered ring pores with the same primary particle size and an overlap coefficient of Cov = 0.1 are capillary condensation limited as they require RHi = 129 % to gather enough water for ice nucleation, compared with only 124 % RHi, required for ice growth. We use the soot-PCF framework to derive a new equation to parameterize of ice formation on soot particles via PCF. This equation is based on soot properties that are routinely measured, including the primary particle size and overlap, and the fractal dimension. These properties, along with the number of primary particles making up an aggregate and the contact angle between water and soot, constrain the parameterization. Applying the new parameterization to previously reported laboratory data of ice formation on soot particles provides direct evidence that ice nucleation on soot aggregates takes place via PCF. We conclude that this new framework clarifies the ice formation mechanism on soot particles at cirrus conditions and provides a new perspective to represent ice formation on soot in climate models.

show abstract

“…2). Several methods have been recently proposed to measure the primary particle diameter (Kook et al, 2015;Mirzaei & Rafsanjani, 2017), the geometric standard deviation of their size distribution (Bescond et al, 2014;Dastanpour et al, 2016), the aggregates/agglomerates fractal dimension (Wang et al, 2016) and even the overlap coefficient of primary particles (De Temmerman et al, 2014). It is worth noting that none of those methods allows the measurement of the specific surface area.…”

Section: -(I)mentioning

confidence: 99%

“…Numerical image processing is a good compromise to improve the quality of those measurements and avoid the uncertainty related to subjectivity of the operator. If recent developments have mainly focused on the determination of the size of primary particles and the fractal dimension of the agglomerates/aggregates (Bescond et al, 2014;Dastanpour et al 2016;Mirzaei & Rafsanjani, 2017), limited work has been performed regarding the overlap coefficient between primary particles (De Temmerman et al, 2014). To our best knowledge, there is no recent study dealing specifically with the issue of the automated determination of the specific surface area based on TEM image analysis.…”

Section: Introductionmentioning

confidence: 99%

A semi-automatic analysis tool for the determination of primary particle size, overlap coefficient and specific surface area of nanoparticles aggregates

Bourrous

Ribeyre

Lintis

et al. 2018

Journal of Aerosol Science

View full text Add to dashboard Cite

The high reactivity of nanostructured materials makes their use very attractive for various industrial applications. However, these materials may also have an important impact on health / environment / climate and on the performances of protective devices (i.e. high efficiency particulate air filters, electrostatic precipitators). Those properties are mainly due to their high specific surface area, which is directly related to the size of the non-porous primary nanoparticles and to the nature of the bridging between them (from point contact for agglomerates to partial fusion for aggregates). In this paper, a straightforward image processing has been developed to measure, assuming a log-normal size distribution, the primary particle diameter (D pp), the geometric standard deviation GSD (or σ g), the projected overlap coefficient (C ov, p) and the specific surface area (SS) directly from TEM images according to the approach introduced by Bau et al. (2010). Measurements have been performed from TEM images obtained for 22 different kinds of nanoparticles, from simple spheres to soot particles and virtual aggregates. The results show a good agreement (within +/-20 %) between automatic and manual analysis of D pp , σ g and SS while the overlap coefficient has been compared to the manual analysis showing a reasonable agreement (within +/-40 %).

show abstract

Automated primary particle sizing of nanoparticle aggregates by TEM image analysis

Cited by 48 publications

References 42 publications

The Impact of Cloud Processing on the Ice Nucleation Abilities of Soot Particles at Cirrus Temperatures

The Impact of Cloud Processing on the Ice Nucleation Abilities of Soot Particles at Cirrus Temperatures

Soot-PCF: Pore condensation and freezing framework for soot aggregates

A semi-automatic analysis tool for the determination of primary particle size, overlap coefficient and specific surface area of nanoparticles aggregates

Contact Info

Product

Resources

About