Log-normal Size Distribution Research Articles

Dry powder inhaler deposition in the larynx and the risk of steroid inhaler laryngitis: A computational fluid dynamics study

Dry Powder Inhalers (DPIs) are a mainstay in the treatment of obstructive respiratory diseases, including asthma and chronic obstructive pulmonary disease (COPD). Deposition of inhaled corticosteroids in the larynx elicits local side effects, potentially leading to steroid inhaler laryngitis. The objective of this study was to estimate the dose of DPIs that are deposited in the larynx relative to other regions of the respiratory tract using computational fluid dynamics (CFD). An anatomically accurate model of the airways (mouth to main bronchi) was constructed based on medical imaging of a healthy adult. Respiratory airflow and particle transport were simulated for constant inhalation rates of 30, 45, and 60 L/min. Two turbulence models were compared, namely the large eddy simulation (LES) and the k−ωSST models. DPIs were assumed to generate an aerosol cloud with a log-normal particle size distribution characterized by the mass median aerodynamic diameter (d50) and geometric standard deviation (σg). We compared two commercial DPIs, namely DPI 1 had a large particle size (d50 = 50 μm, σg=2.55) and DPI 2 had a small particle size (d50 = 2 μm, σg=1.99). The laryngeal dose was 1.6-to-3.8-fold higher than the bronchial dose for DPI 1, while the laryngeal and bronchial doses (units of mass per unit surface area) were similar for DPI 2 for both turbulence models and all inhalation rates. The choice of turbulence model had little impact on the total extrathoracic deposition, but a significant impact on regional doses, with the LES model predicting higher larynx-to-bronchi relative doses than the k−ω model. Our prediction that the larynx is a hotspot for DPI deposition is consistent with the observation of laryngeal side effects in DPI users. Importantly, our simulations suggest that DPIs with larger particles (d50 = 50 μm) may increase the risk of steroid inhaler laryngitis.

Fluid flow in three-dimensional porous systems shows power law scaling with Minkowski functionals

Integral geometry uses four geometric invariants—the Minkowski functionals—to characterize certain subsets of three-dimensional (3D) space. The question was, how is the fluid flow in a 3D porous system related to these invariants? In this work, we systematically study the dependency of permeability on the geometrical characteristics of two categories of 3D porous systems generated: (i) stochastic and (ii) deterministic. For the stochastic systems, we investigated both normal and lognormal size distribution of grains. For the deterministic porous systems, we checked for a cubic arrangement and a hexagonal arrangement of grains of equal size. Our studies reveal that for any three-dimensional porous system, ordered or disordered, permeability k follows a unique scaling relation with the Minkowski functionals: (a) volume of the pore space, (b) integral mean curvature, (c) Euler characteristic, and (d) critical cross-sectional area of the pore space. The cubic and the hexagonal symmetrical systems formed the upper and lower bounds of the scaling relations, respectively. The disordered systems lay between these bounds. Moreover, we propose a combinatoric F that weaves together the four Minkowski functionals and follows a power-law scaling with permeability. The scaling exponent is independent of particle size and distribution and has a universal value of 0.428 for 3D porous systems built of spherical grains.

An Analytical Study for Explosive Grain Initiation

The most common form of solidification of metals is heterogeneous nucleation, in which the particles, regardless of whether they are endogenous or exogenous, nucleate the primary crystal phase, becoming solid crystal particles and, subsequently, initiating into grains during solidification. Explosive grain initiation has been proposed recently for these particles, which have significant nucleation undercooling, in which once nucleation happens, a certain number of solid particles can initiate into grains simultaneously, resulting in recalescence. This is a different form of grain initiation and has high potential for more significant grain refinement in casting alloys. In this work, an analytical model is designed to describe explosive grain initiation, based on which the criteria for the three different grain initiation forms, explosive grain initiation (EGI), hybrid grain initiation (HGI), and progressive grain initiation (PGI), are derived. These criteria are employed to develop a grain initiation map for the Mg-Al alloy system inoculated with nucleant particles having a log-normal size distribution. This work can not only help us to understand the effect of each condition, such as the cooling rate and the solute concentration, on grain initiation behaviors, but also predict the grain size for alloy systems with relatively impotent nucleant particles during solidification.

Machine learning-enhanced stochastic uncertainty and sensitivity analysis of the ICRP human respiratory tract model for an inhaled radionuclide

The International Commission on Radiological Protection (ICRP) has developed the reference Human Respiratory Tract Model (HRTM), detailed in ICRP Publications 66 and 130, to estimate the deposition and clearance of inhaled radionuclides. These models utilize reference anatomical and physiological parameters for particle deposition (PD). Biokinetic models further estimate retention and excretion of internalized particulates, aiding the derivation of inhalation dose coefficients (DC). This study aimed to assess variability in deterministic131I biokinetic and dosimetry models through stochastic analysis using the updated HRTM from ICRP Publication 130. The complexities of the ICRP PD model were reconstructed into a new, independent computational model. Comparison with reference data for total PD fractions for reference worker, solely a nose breather, covering activity median aerodynamic diameters from 0.3μm to 20μm, showed a 1.04% relative and 0.7% absolute difference, demonstrating good agreement with ICRP deposition fractions. The deterministic DC module was reconstructed in Python and expanded for stochastic analysis, systematically expanding deposition components from HRTM and assigning probability distribution functions to uncertain parameters. These were integrated into an in-house stochastic radiological exposure dose calculator, utilizing latin hypercube sampling. A case of an occupational radionuclide intake was explored, in which biodistribution and committed effective DC (CEDC) were computed for131I type F, considering a lognormal particle size distribution with a median of 5μm. Results showed the published ICRP reference CEDC marginally exceeds the 75th percentile of observed samples, with log-gamma distribution as the best-fit probability distribution. A Random Forest regression model with SHapley Additive exPlanations was employed for sensitivity analysis to predict feature importance. The analysis identified the HRTM particle transport rates scaling factor, followed by the aerodynamic deposition efficiency in the alveolar interstitial region as the most impactful parameters. This study offers a unique stochastic approach on inhaled particulate metabolism, enhancing radiation consequence management, medical countermeasures, and dose reconstruction for epidemiological studies.

MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models

Aerosols influence weather and climate by interacting with radiation through absorption and scattering. These effects heavily rely on the optical properties of aerosols, which are mainly governed by attributes such as morphology, size distribution, and chemical composition. These attributes undergo continuous changes due to chemical reactions and aerosol micro-physics, resulting in significant spatio-temporal variations. Most atmospheric models struggle to incorporate this variability because they use pre-calculated tables to handle aerosol optics. This offline approach often leads to substantial errors in estimating the radiative impacts of aerosols along with posing significant computational burdens. To address this challenge, we introduce a computationally efficient and robust machine learning approach called MieAI. It allows for relatively inexpensive calculation of the optical properties of internally mixed aerosols with a log-normal size distribution. Importantly, MieAI fully incorporates the variability in aerosol chemistry and microphysics. Our evaluation of MieAI against traditional Mie calculations, using number concentrations from the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART) simulations, demonstrates that MieAI exhibits excellent predictive accuracy for aerosol optical properties. MieAI achieves this with errors well within 10%, and it operates more than 1000 times faster than the benchmark approach of Mie calculations. Due to its generalized nature, the MieAI approach can be implemented in any chemistry transport model which represents aerosol size distribution in the form of log-normally distributed internally mixed modes. This advancement has the potential to replace frequently employed look-up tables and plays a substantial role in the ongoing attempts to reduce uncertainties in estimating aerosol radiative forcing.

Time-Dependent Multi-Particle Model Describing the Hydrogen Absorption of Nanocrystalline Magnesium Powders: A Case Study

Classical kinetic models describing the hydrogen absorption of nanocrystalline metallic hydrides generally do not involve any parameter related to the change in the crystallite size during the hydrogenation at constant temperature. In the present investigation, ball-milled nanocrystalline Mg powders exhibiting lognormal crystallite size distribution have been subjected to hydrogen absorption in a Sievert-type apparatus. Partially absorbed states were achieved by interrupting the hydrogenation cycle at different hydrogen content, i.e., when 15%, 50%, and 90% of Mg powder transformed to MgH2. The evolution of the characteristic size of the nucleating MgH2 phase was determined from X-ray diffraction analysis. Considering the crystallite size distribution of the as-milled powder agglomerate as well as the growth during the isothermal hydrogenation process, a time-dependent multi-particle reaction function ∝CV¯t;R(t) was developed. It was shown unambiguously for this case study that the measured hydrogen absorption curve of the ball-milled Mg powder shows the best correlation with this model when it is compared to classical kinetic functions or the previously developed multi-particle reaction function excluding the change in the average crystallite size during hydrogenation.

Acoustic measurements of photosynthetically formed gas bubble distributions in Posidonia oceanica seagrass meadows

The formation of gas bubbles on seagrass leaves commonly occurs during periods of high productivity in many aquatic ecosystems. Gas bubbles produced by seagrass meadows and subsequently released to the atmosphere can be a major component of primary production that is not quantified by measurements of dissolved oxygen. Acoustic propagation measurements have been explored as a possible tool for estimating photosynthetically relevant properties in situ. However, a lack of knowledge of bubble densities and size distributions has hindered acoustic attenuation as a quantitative measurement technique. Here we explore using measurements of acoustic backscattering from gas bubbles in and above Posidonia oceanica seagrass meadows as a more direct method for estimating bubble densities and size distributions. Acoustic backscattering data for this study was collected in September 2023 in Biodola Bay, Elba Island, Italy, using 1i–16 kHz linear frequency modulated pulses. Inversions of the broadband acoustic data yielded log-normal bubble size distributions with mean sizes between 0.4 and 0.5 mm in agreement with previously reported values. Estimated bubble densities yielded predicted attenuations in line with those found by propagation experiments. Results demonstrate the possible use of direct acoustic backscattering measurements as a supplement to dissolved oxygen measurements to better estimate rates of gas production which in turn can be used to better understand seagrass ecosystem properties, such as productivity and health.

The investigation of diesel soot emission using instrument combination of multi-wavelength photoacoustic spectroscopy and scanning mobility particle sizer

The parallel measurements of wavelength dependent optical absorption, particle number size distribution have made by a multi wavelength photoacoustic spectrometer (4λ-PAS) and scanning mobility particle sizer (SMPS) respectively at different modes of a diesel engine using two different types of fuel. The thermal evolution of the emission was also investigated using posterior temperature treatment of emission. The bimodal size distribution of emitted particles at a set reference temperature has been observed regardless of the applied fuel at idle. However, the emitted particulate assembly had lognormal size distribution falls into the accumulation mode at all other defined engine modes and both fuel types. The total number- and volume concentration (TNC and TVC) showed retrograde tendency with the increasing torque and rpm independently of the applied fuel types. The TNC values decreased up to 50% for both fuels with engine operation changes from idle engine mode(em#1) to low engine mode(em#2). With further increase in torque and rpm of engine, the change in TNC is negligible. On the other hand, the TVC remains more or less the same for idle to low engine mode transition and increased more than 60% for high mode (em#3) transition. The Optical Absorption Coefficient (OAC) values measured at the operational wavelengths of the 4λ-PAS instrument decreased at all wavelengths with increasing rpm and torque. The wavelength dependency quantified by Aerosol Ängström Exponent (AAE) was applied here for qualitative analysis of the carbonaceous emission and showed decreased values towards the higher engine speed and torque output of the engine. The proposed technique can be used as real-time, precise and accurate measurement of light absorption by DPM aerosols, which opens up novel possibilities for the volatility and thermal evolution investigation of diesel emissions.

Nature of Oxides in Al–Mg Alloys

Acting as substrates for heterogeneous nucleation, native oxides in Al–Mg alloys have shown their potential for grain refinement. However, the limited knowledge about the nature of the oxides in Al–Mg alloys impedes the widespread application as native grain refiners. The aim of this work is to comprehensively investigate the native oxides in Al–Mg alloys through electron microscopy. Our results show that the predominant inclusions in Al–Mg alloys are oxides in three types of oxide films at the micrometer scales: young films, old films and oxide skins. All oxide films consist of discrete oxide particles of three types in nanometer scale depending on the Mg contents: γAl2O3 (< 0.4 wt.%), MgAl2O4 (0.08–3.5 wt.%) and MgO (> 2 wt.%). Specifically, MgAl2O4 particles have sizes ranging from a few tens to a few hundreds nanometer and possess an elementary shape of octahedron faceted by {111} planes. In Al–Mg alloys, the native oxides have a lognormal size distribution, with the average mean size fluctuating in accordance with the oxide configurations as Mg content varies. The agglomerating feature causes inhomogeneous sampling, and dual-peak lognormal curves are found for low-Mg-content alloys (0.08/0.4%), which could be eliminated by increasing the Mg content (2.0/3.5%) or by using the high-shear melt conditioning (HSMC) technology. Understanding the native oxides in Al–Mg alloys shall provide instructions on their application in grain refinement.

Steady-State Dynamics of Ganglia Populations During Immiscible Two-Phase Flows in Porous Micromodels: Effects of the Capillary Number and Flow Ratio on Effective Rheology and Size Distributions

We study experimentally the flow of non-wetting ganglia during the co-injection of n-heptane and water in a predominantly 2D PMMA micromodel, which is constructed based on a stochastic digital algorithm. The dynamics of the phase distribution patterns are recorded optically and post-processed using cluster identification and motion tracking algorithms in order to study the characteristics and the interactions between the mobile and stranded ganglia populations. We focus primarily on the effects of the capillary number (Ca) and the ratio of the injection flow rates (Q) on the observed ganglia size distributions and the effective two-phase rheology. Our experimental setup allows for the study of ganglia fragmentation and coalescence dynamics over five orders of magnitude (in terms ganglia sizes), thus offering novel physical insight on the pore-scale characteristics of different ganglia populations and on how their interactions determine the relative permeability of the non-wetting phase. We demonstrate that the rates of ganglia fragmentation and coalescence intensify at higher Ca values, as viscous forces become dominant over capillary ones, leading to a log-normal size distribution that shifts toward smaller mean values. This effect is directly correlated with the emergence of new flow paths that develop progressively through narrower pores-throats, where the continuous wetting phase sweeps ganglia with sizes smaller than the mean pore-throat diameter. These flow paths further contribute to the Darcy scale velocity of the non-wetting phase, thus leading to a power-law Darcian regime at intermediate Ca values with a scaling exponent that is found to be a function of Q.

Unveiling the deformability of mussel plaque core: the role of pore distribution and hierarchical structure.

The mussel thread-plaque system exhibits strong adhesion and high deformability, allowing it to adhere to various surfaces. While the microstructure of plaques has been thoroughly studied, the effect of their unique porous structure on the high deformability remains unclear. This study first investigated the porous structure of mussel plaque cores using scanning electron microscopy (SEM). Two-dimensional (2D) porous representative volume elements (RVEs) with scaled distribution parameters were generated, and the calibrated phase-field modelling method was applied to analyse the effect of the pore distribution and multi-scale porous structure on the failure mechanism of porous RVEs. The SEM analysis revealed that large-scale pores exhibited a lognormal size distribution and a uniform spatial distribution. Simulations showed that increasing the normalised mean radius value (ū) of the large-scale pore distribution can statistically lead to a decreasing trend in final failure strain, strength and strain energy density but cannot solely determine their values. The interaction between pores can lead to two different failure modes under the same pore distribution: progressive failure mode and sudden failure mode. Additionally, the hierarchical structure of multi-scale porous RVEs can further increase the final failure strain by 40-60% compared to single-scale porous RVEs by reducing stiffness, highlighting the hierarchical structure could be another key factor contributing to the high deformability. These findings deepen our understanding of how the pore distribution and multi-scale porous structure in mussel plaques contribute to their high deformability and affect other mechanical properties, providing valuable insights for the future design of highly deformable biomimetic materials.

Multistability in neural systems with random cross-connections.

Neural circuits with multiple discrete attractor states could support a variety of cognitive tasks according to both empirical data and model simulations. We assess the conditions for such multistability in neural systems using a firing rate model framework, in which clusters of similarly responsive neurons are represented as single units, which interact with each other through independent random connections. We explore the range of conditions in which multistability arises via recurrent input from other units while individual units, typically with some degree of self-excitation, lack sufficient self-excitation to become bistable on their own. We find many cases of multistability-defined as the system possessing more than one stable fixed point-in which stable states arise via a network effect, allowing subsets of units to maintain each others' activity because their net input to each other when active is sufficiently positive. In terms of the strength of within-unit self-excitation and standard deviation of random cross-connections, the region of multistability depends on the response function of units. Indeed, multistability can arise with zero self-excitation, purely through zero-mean random cross-connections, if the response function rises supralinearly at low inputs from a value near zero at zero input. We simulate and analyze finite systems, showing that the probability of multistability can peak at intermediate system size, and connect with other literature analyzing similar systems in the infinite-size limit. We find regions of multistability with a bimodal distribution for the number of active units in a stable state. Finally, we find evidence for a log-normal distribution of sizes of attractor basins, which produces Zipf's Law when enumerating the proportion of trials within which random initial conditions lead to a particular stable state of the system.

A phenomenological model for predicting the size of soot primary particles emitted from an aero-engine combustor

Soot particles emitted from aero-engines are harmful to airport local air quality, and the adverse impact from soot particles is closely related to particle morphology, microstructure and chemical properties. To understand those properties of aviation soot particles, combustion experiments using conventional aviation fuel RP-3 were conducted on a Rich-Burn/Quick-Quench/Lean-Burn (RQL) aero-engine combustor. The soot emissions from the RQL combustor were analyzed via a high resolution transmission electron microscope (HRTEM), and the size distributions of primary particles at different power levels were determined based on the HRTEM images. Since the primary particle size is strongly related to the power level and follows an S-shaped curve, a phenomenological model with low-computational complexity and general applicability was established. Furthermore, the model was validated by using previously published experimental data of a CFM56–2C1 aero-engine burning four different fuels, including sustainable aviation fuels (SAF), demonstrating the influence of fuel properties on primary particle sizes at different power levels.Novelty and Significance Statement: In this paper, combustion experiments were conducted on an RQL combustor burning RP-3 fuel. Based on the HRTEM observation, the log-normal size distributions of primary particles at different power levels were determined. Furthermore, based on the fact that the relationship between primary particle size and power level followed an S-shaped curve, a phenomenological model linking primary particle size and combustor power was proposed and validated.

Analysis method for aerosol deposition behavior under sodium spray fire

Sodium spray fire is a significant containment-related safety concern in sodium-cooled fast reactors (SFR). In a highly-energetic accident such as a core disruptive accident (CDA), the sodium spray fire not only poses a threat to containment integrity but also causes the generation and release of sodium aerosol source terms with strong radioactivity and chemical hazards. In our method, the sodium aerosol generation model, log-normal size distribution model, agglomeration models, and deposition models were applied. In particular, different turbulent energy dissipation rates were considered in the agglomeration models according to the different thermal-hydraulic states of containment and confinement, and the simplified Crump model and Beresnev models have been selected to describe the gravity-diffusion and the thermophoresis deposition behavior respectively, due to the specific conditions of sodium spray fires. Simulation studies have been divided into two categories based on a potential design concept: HTCA, AB5, and AB6 experiments were introduced to describe the drastic thermodynamic state variations caused by spray fires in the confinement, while ATF and AB7 experiments characterized a relatively stable environment similar to the containment. From the simulation results, the analysis method can be considered reasonable with respect to experimental values. Moreover, calculations have been performed for a hypothetical sodium spray fire caused by a rotator plug failure case in a paper-based European SFR.

Statistical Characteristics of Droplets Formed due to the “Bag Breakup” Fragmentation Event at the Interface between Water and High-Speed Air Flow

Abstract Recent studies indicate that the dominant mechanism for generating sprays in hurricane winds is a “bag breakup” fragmentation. This fragmentation process is typically characterized by inflation and consequent bursting of short-lived objects, referred to as “bags” (sail-like pieces of water film surrounded by a rim). Both the number of spray droplets and their size distribution substantially affect the air–sea heat and momentum exchange. Due to a lack of experimental data, the early spray generation function (SGF) for the bag breakup mechanism was based on the assumed similarity with resembling processes. Here we present experimental results for the case with a single isolated bag breakup fragmentation event. These experiments revealed several differences from similar fragmentation events that control the droplet sizes, such as secondary disintegration of droplets in gaseous flows and bursting of bubbles. In contrast to the bubble bursting, the film thickness of the bag canopy is not constant but is random with lognormal distribution. Additionally, its average value does not depend on the canopy radius but is determined by the wind speed. The lognormal size distribution of the canopy droplets is observed in conjunction with the established mechanism of liquid film fragmentation. The rim fragmentation results in two types of droplets, and their size distribution has been found to be lognormal distribution. The constructed SGF is verified by comparing it with experimental data from the literature. The perspectives of transferring the results from laboratory to field environment have also been discussed. Significance Statement The “bag breakup” fragmentation is the dominant mechanism for generating spray in hurricane winds. The number and the sizes of the spray droplets substantially affect the heat transport from the ocean to the atmosphere and, thereby, the development of hurricanes. This paper presents experimental data and analysis that demonstrate how droplet formation occurs during bag breakup fragmentation. It also shows analysis of the quantity and size of droplets formed during a single fragmentation event. This work demonstrates how obtained experimental results can be applied to real field conditions in the context of hurricane prediction models.

Cascaded Compression of the Size Distribution of Nanopores in Monolayer Graphene

Nanoporous graphene has shown great promise for membrane separations. Its atomic thickness, remarkable mechanical, chemical, and thermal robustness could enable ultrahigh-flux membrane processes addressing persistent challenges in a wide range of separation needs. On the other hand, molecular separations across nanoporous graphene rely dominantly on size-based mechanisms (e.g, size exclusion) that is highly sensitive to the nanopore size and size distribution of nanopore ensemble created on the graphene. However, among existing nanopore creation methods, the process of nanopore nucleation is often coupled with nanopore expansion, which results in a lognormal nanopore size distribution with a long tail. It remains a challenge to obtain both high density and narrow size distribution of nanopores in graphene. Here, we report a cascaded compression approach to engineering nanopores in monolayer graphene for molecular separations with the assistance of electrical control of chemical vapor deposition of graphene[1]. The formation of nanopores is split into many small steps, in each of which the size distribution of all the existing nanopores is compressed by a combination of shrinkage and expansion, and at the same time of expansion, a new batch of nanopores is created, leading to increased nanopore density by each cycle. As a result, high-density nanopores with a short-tail size distribution are obtained by the cascaded compression that show high rejection and ultrafast organic solvent nanofiltration exceeding the state-of-the-art.Reference[1] Wang J, Park J H, Lu A Y, et al. Electrical Control of Chemical Vapor Deposition of Graphene. Journal of the American Chemical Society, 20

In situ characterization of particle formation in spray flame synthesis using wide-angle light scattering

Wide-angle light scattering (WALS) was used for in situ measurements of droplet and nanoparticle size distributions during the synthesis of titania and iron oxide particles from liquid precursor solutions in the standardized SpraySyn burner for spray flame synthesis. Titania was synthesized from titanium tetraisopropoxide (TTIP) and iron oxide from iron(III) nitrate nonahydrate (INN) using ethanol (EtOH) as solvent. Scattering images were taken at heights up to 120 mm above the burner surface and classified into droplet and particle scattering. Droplet size distributions were derived from a sequential analysis of scattering data containing the oscillating Mie pattern, the lognormal size distribution parameters for spherical and fractal particle fractions from a multivariate approach on averaged particle scattering data. The results show that the precursor addition leads to altered evaporation behavior and even droplet disruption probably induced by puffing or micro-explosions compared to pure EtOH. In the case of TTIP (a hygroscopic alkoxide), the synthesis of a large fraction of spheres was observed, while the nitrate INN leads to the formation of mostly fractal aggregates.

Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations

Abstract. In this study, we developed a novel algorithm based on the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations and dust vertical profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosol optical depth at 10 µm (DAOD10 µm) and the coarse-mode dust effective diameter (Deff) over global oceans. The accuracy of the Deff retrieval is assessed by comparing the dust lognormal volume particle size distribution (PSD) corresponding to retrieved Deff with the in situ-measured dust PSDs from the AERosol Properties – Dust (AER-D), Saharan Mineral Dust Experiment (SAMUM-2), and Saharan Aerosol Long-Range Transport and Aerosol–Cloud-Interaction Experiment (SALTRACE) field campaigns through case studies. The new DAOD10 µm retrievals were evaluated first through comparisons with the collocated DAOD10.6 µm retrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOP observations from our previous study (Zheng et al., 2022). The pixel-to-pixel comparison of the two DAOD retrievals indicates a good agreement (R∼0.7) and a significant reduction in (∼50 %) retrieval uncertainties largely thanks to the better constraint on dust size. In a climatological comparison, the seasonal and regional (2∘×5∘) mean DAOD10 µm retrievals based on our combined MODIS and CALIOP method are in good agreement with the two independent Infrared Atmospheric Sounding Interferometer (IASI) products over three dust transport regions (i.e., North Atlantic (NA; R=0.9), Indian Ocean (IO; R=0.8) and North Pacific (NP; R=0.7)). Using the new retrievals from 2013 to 2017, we performed a climatological analysis of coarse-mode dust Deff over global oceans. We found that dust Deff over IO and NP is up to 20 % smaller than that over NA. Over NA in summer, we found a ∼50 % reduction in the number of retrievals with Deff&gt;5 µm from 15 to 35∘ W and a stable trend of Deff average at 4.4 µm from 35∘ W throughout the Caribbean Sea (90∘ W). Over NP in spring, only ∼5 % of retrieved pixels with Deff&gt;5 µm are found from 150 to 180∘ E, while the mean Deff remains stable at 4.0 µm throughout eastern NP. To the best of our knowledge, this study is the first to retrieve both DAOD and coarse-mode dust particle size over global oceans for multiple years. This retrieval dataset provides insightful information for evaluating dust longwave radiative effects and coarse-mode dust particle size in models.

Parameterization of polydisperse aerosol optical properties during hygroscopic growth

Aerosol water uptake and the related influence on its optical properties owing to the hygroscopic growth of polydisperse aerosols have a significant effect on air quality and climate. This study develops an analytical parameterization for a single hygroscopic optical parameter and the scattering enhancement factor for polydisperse aerosol sizes. Polydisperse lognormal size distributions for geometric mean diameters of 0.05–1 μm and geometric standard deviations of 1.3–2.5 are considered with real refractive indices between 1.35-1.6. The analytical expression obtained for the optical parameter is compared to Mie theory and reasonable agreements within the size range investigated in this study are observed. Further, our results show that the analytical expression could express polydispersed aerosol optical properties as a function of geometric mean diameter and geometric standard deviation of a lognormal distribution and the refractive index. The obtained analytical expression can be efficiently used for 3D models with a reduced computational burden.

Studying the rate-dependent specific absorption rate in magnetic hyperthermia through multiscale simulations

In this work, the issue of whether the dynamic magnetic properties of monodispersed magnetic colloids, modeled using micromagnetic simulations, can be extrapolated to analyze magnetic particle hyperthermia data, i.e., specific absorption rate (SAR) values acquired at high frequencies of excitation fields, is addressed. Micromagnetic finite difference simulations were performed using the Object Oriented Micromagnetic Framework (OOMMF) software package in order to obtain the dynamic hysteresis loops under a 24 kA/m alternating magnetic field amplitude and for various frequencies (50–765 kHz). In OOMMF, the finite difference method was used to find the solution of the nonlinear Landau–Lifshitz Gilbert (LLG) equation, which describes the nanoparticles’ magnetization motion when applying an effective magnetic field. To create a system of randomly oriented magnetite nanoparticles having a certain volume fraction (0.02%) that coincides with the experimentally utilized concentration of 1 mg/ml, we start with a perfect simple cubic lattice with a large lattice spacing so that the particle–particle distance is large enough to neglect dipolar interactions (non-interacting nanoparticles). The system under study is a set of 40-nm magnetite nanoparticles with a lognormal size distribution. The simulations were performed assuming quasistatic conditions, an approach that is reasonable for ferromagnetic-like behavior. It is worth noting that the code considers not only the uniaxial anisotropy Ku but also the cubic magnetocrystalline one Kc as well. Kc is usually neglected in literature because the uniaxial contribution dominates, but this is not the case for magnetite since Ku = 9 kJ/m3 and Kc = −11 kJ/m3. Moreover, such an inclusion seems quite reasonable since the magnetocrystalline anisotropy is always present yet with a relative contribution. The SAR values at each frequency were determined after calculating hysteresis losses via the area of the simulated hysteresis loops. Interestingly, SAR values at low frequencies followed an exponential increase trend with a frequency indicating a deviation from the linear behavior usually reported in the literature. To validate our approach, we employed a coupled electromagnetic-thermal model based on COMSOL Multiphysics simulations that provides an accurate estimation of the magnetic field and temperature distribution within the ferrofluid. The time-dependent temperature curves are obtained after 30 min of magnetic particle hyperthermia treatment for the same alternating magnetic field amplitude used in OOMMF simulations (30 mT) and for two representative frequency values. One in the low (300 kHz) and one in the high (765 kHz) frequency regimes. The numerical curves were compared to the corresponding experimental ones and found to be in good agreement. Our findings provide new insight into the validity of dynamic micromagnetic simulation to analyze the frequency behavior of SAR within the framework of LLG and indicate that anisotropy selection plays a key role in the reliability of simulations.