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Characterization of pitch carbon coating properties affecting the electrochemical behavior of silicon nanoparticle lithium-ion battery anodes

Silicon is an exciting material for next-generation lithium-ion battery anodes, due to its high theoretical capacity and availability, but its widespread implementation has been limited by extensive volume changes during cycling that causes mechanical damage and limits cycle life. One approach to mitigating these deleterious effects while still maintaining silicon's benefits is the addition of a pitch carbon coating to nanometer-sized silicon particles. Here, in this study, we present characterization of pitch-coated silicon electrodes without annealing and annealed at 700 °C and 1000 °C to determine the optimum temperature treatment, as well as pitch-only and silicon-only electrodes with and without annealing to elucidate the impacts of each component on early electrochemical behavior. Using Fourier transform infrared and Raman spectroscopies, atomic force and scanning spreading resistance microscopy, and electrochemical analysis, we find 700 °C to be the optimal annealing temperature, as amorphous carbon is created, which improves conductivity prior to cycling and facilitates ion storage during cycling that increases capacity. We also present in situ Raman spectroscopy data demonstrating the heterogeneous aging that occurs across the electrode surface during cycling.

25 ENERGY STORAGE↗

On the Interaction Between a Flow Instability and Inertial Particles in Supersonic Jet Plume-Surface Interaction

Extraterrestrial landings often require spacecraft to deploy supersonic jets directed at dusty planetary surfaces to ensure controlled descents. This interaction, termed plume-surface interaction (PSI), critically influences crater formation and the dynamics of ejected particles, which can obscure vision, damage onboard sensors, and impact nearby infrastructure with significant particle velocities. The physics of PSI remain poorly understood due to the complexities in simulating the involved multiphysics and experimentally replicating reduced atmospheric pressure conditions. This article discusses a collaborative effort to investigate PSI in reduced atmospheric pressure environments using a Mach 5.3 jet impinging on a granular bed. The results highlight observations of an azimuthal pattern in the ejecta distribution, characterized by alternating ejecta streaks of varying particle concentrations. This phenomenon suggests the presence of a low Reynolds number instability, a notable discovery given the supersonic nature of the jet.

PSI↗

A New Generation Fiber Optic Probe: Characterization of Biological Fluids, Protein Crystals and Ophthalmic Diseases

A new fiber optic probe developed for determining transport properties of sub-micron particles in fluids experiments in a microgravity environment has been applied to characterize particulate dispersions/suspensions in various challenging environments which have been hitherto impossible. The probe positioned in front of a sample delivers a low power light (few nW - 3mW) from a laser and guides the light which is back scattered by the suspended particles through a receiving optical fiber to a photo detector and to a digital correlator. The probe provides rapid determination of macromolecular diffusivities and their respective size distributions. It has been applied to characterize various biological fluids, protein crystals, and ophthalmic diseases.

Ansari, Rafat R.↗

Nearby Sites Show Similar Upwind Sources and Differing Semivolatile Concentrations in Coastal Aerosol Particles

The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) characterized aerosol composition using measurements at two sites within 3 km (Scripps Pier and Mt. Soledad) from 15 February 2023 to 14 February 2024. Comparing the two sites shows the strong influence of upwind sources that results in similar monthly compositions at both sites. The seasonal changes in chemical mass concentrations were largely driven by the upwind source regions, with coastal northwesterly back-trajectories occurring 63−65% of the year and bringing submicrometer mass concentrations that were lower than the EPCAPE average for all trajectories at each site. In contrast, refractory black carbon (rBC) and nonrefractory (NR)-organics and nitrate mass concentrations exceeded EPCAPE average concentrations for back-trajectories from urban areas such as Los Angeles-Long Beach. For hourly measurements, NR-organics and non-sea-salt (NSS)-sulfate mass concentrations at Mt. Soledad were correlated strongly (r = 0.73−0.82) to those measured at Scripps Pier, but NR-nitrate was correlated only moderately (r = 0.63). The explanation for the lower correlation of NR-nitrate is both emissions between the sites and semivolatility, with semivolatility accounting for site-to-site changes in daily averages of +0.01 μg m −3 per percentage site-to-site difference in relative humidity and −0.07 μg m −3 per degree Celsius site-to-site difference in temperature. On average, comparing Scripps Pier to Mt. Soledad, NR-nitrate was higher by 29% because of relative humidity and lower by −26% because of temperature. NR-nitrate and rBC mass concentrations at Scripps Pier for nighttime were 13−15% higher than those for daytime because land breezes brought higher inland concentrations. Concentrations of rBC were 52% higher at Mt. Soledad than those measured at Scripps Pier, accompanied by increases in tracers for brake wear because of traffic on the steep roads within 10 m of that site. The implications are that these nearby sites had comparable monthly concentrations of measured components due to their similar backtrajectories, but hourly and daily concentration differences supported quantification of the meteorological effects from relative humidity and temperature on semivolatile NR-nitrate as well as minor differences from land−sea breezes and local emissions.

Aerosols↗

RU Net for Automatic Characterization of TRISO Fuel Cross Sections

TRistructural ISOtropic (TRISO) particle fuel is a type of nuclear fuel known for its high-temperature and high-burnup performance. Each sub-millimeter diameter TRISO particle consists of uranium-oxycarbide (UCO) or UO2 fuel kernel, coated with buffer, inner pyrolytic carbon (IPyC), silicon carbide (SiC), and outer pyrolytic carbon (OPyC) layers. The SiC layer acts as the main containment barrier for the TRISO particle to retain the fission products, while the IPyC and OPyC layers provide additional barriers to the release of fission products, especially fission gases. During irradiation, phenomena like kernel swelling, buffer densification, and IPyC fracture may impact fuel performance. Post-irradiation microscopy on entire compact cross sections or samples of individual particles deconsolidated from compacts is often used to identify these irradiation-induced changes in morphology. However, each fuel compact generally contains thousands of TRISO particles. To get statistical information on these phenomena, it is cumbersome work if done manually. For example, to get information about swelling/densification behaviors of different layers or kernels after irradiation, researchers previously manually measured the perimeter of each TRISO layer in hundreds of particles after four rounds of iterative grinding and polishing encompassing more than 2000 cross-section images for a total of four fuel compacts. To attempt to reduce the subjectivity inherent in that process and accelerate data analysis, we conducted a study on the automatic TRISO layer segmentation on cross-sectional microscopic images using Convolutional Neural Networks (CNNs). CNNs are a class of machine learning algorithms specifically designed for processing structured grid data that have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we have generated the large irradiated TRISO layer dataset with more than 2000 cross-section TRISO microscopic images and the corresponding annotated images. Based on these annotated images, we have employed different CNNs for automatic segmentation of different TRISO layers. These include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net has the best performance in terms of intersection-over-union (IoU). Through the aid of these CNN models, we can expedite the analysis of TRISO particle cross-sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

Convolutional Neural Networks↗

Transport and losses of fusion-born alpha particles in the presence of tearing modes using the new Toroidal Accelerated Particle Simulator (TAPaS)

Here, the transport and losses of fusion-born alpha particles are studied in the presence of a single-helicity tearing mode, characterized by (m = 2, n = 1). The analysis is performed by means of the recently developed Toroidal Accelerated Particle Simulator (TAPaS). Although such modes have usually been believed to result only in a local flattening of the radial profiles, it is shown that the density profile can exhibit a global modification leading to significant losses of alpha particles. This is due to the fact that, although the magnetic field does not exhibit any chaotic behaviour, the trajectories of alpha particles do, as revealed by their Poincaré maps. Such results are in qualitative agreement with past observations and simulations of energetic particles generated by neutral beam injection in TFTR, DIII-D and AUG tokamaks. In-depth analysis is carried out to characterize the impact of the tearing mode on the transport and losses of fusion-born alpha-particles with a realistic density profile. The impact of the amplitude is evidenced. Moreover, the effect of the island rotation frequency is assessed based on a detailed analysis of the linear resonances in phase-space, in agreement with the simulation results. Finally, the probability density function of the exit time has been computed and the transport of alpha particles has been found to be anomalous.

anomalous transport↗

Theory for the anomalous phase behavior of inertial active Brownian particles

In contrast to equilibrium systems, inertia can profoundly impact the phase behavior of active systems. This has been made particularly evident in recent years, with motility-induced phase separation (MIPS) exhibiting several intriguing dependencies on translational inertia. Here, we report extensive simulations characterizing the phase behavior of inertial active Brownian particles and develop a mechanical theory for the complete phase diagram without appealing to equilibrium notions. Our theory qualitatively captures all aspects of liquid-gas coexistence, including the critical value of inertia above which MIPS ceases. Notably, our findings highlight that particle softness, and not inertia, is responsible for the MIPS reentrance effect at the center of a proposed active refrigeration cycle.

Feng, Jiechao↗

Alpha-Particle Gas-Pressure Sensor

An approximate model was developed to establish design curves for the saturation region and a more complete model developed to characterize the current-voltage curves for an alpha-particle pressure sensor. A simple two-parameter current-voltage expression was developed to describe the dependence of the ion current on pressure. The parameters are the saturation-current pressure coefficient and mu/D, the ion mobility/diffusion coefficient. The sensor is useful in the pressure range between 0.1 and 1000 mb using a 1 - mu Ci(241) Am source. Experimental results, taken between 1 and up to 200 mb, show the sensor operates with an anode voltage of 5 V and a sensitivity of 20 fA/mb in nitrogen.

Buehler, M. C.↗

Alpha-Particle Gas-Pressure Sensor

An approximate model was developed to establish design curves for the saturation region and a more complete model developed to characterize the current-voltage curves for an alpha-particle pressure sensor.

alpha-particle↗

Nanoelectric Materials Laboratory Development

The Ultracapacitor Research and Development project is a collaborative effort between the NASA Marshall Space Flight Center's (MSFC's) ES43 Parts, Packaging, and Fabrication Branch and the EM41 Nonmetallic Materials Branch. NASA's Ultracapacitor Research is an effort to develop solid-state energy storage devices through processing of ceramic materials into printable dielectric inks, which can be formed and treated to produce solid state ultracapacitor cells capable of exceeding lithium-ion battery energy density at a fraction of the weight. Research and development efforts into solid state ultracapacitors have highlighted a series of technical challenges such as understanding as-received nature of ceramic powders, treatment and optimization of ceramic powders, dielectric and conductor ink formulation, and firing of printed (green) ultracapacitor cells. Two facilities have been continually developed since project inception: the Additive Electronics Lab in Bldg. 4487 and the Nanoelectric Materials Lab in Bldg. 4602. The Nanoelectric Materials Lab has become a unique facility at MSFC, capable of custom processing a wide range of media for additive electronics. As research has progressed, it was discovered that additional in-house processing was necessary to achieve smaller, more uniform particle diameters. A vibratory mill was obtained that can agitate powder and media in three directions, which has shown to be much more effective than ball milling. However, in order to understand the effects of milling, a particle size analysis system has been installed to characterize as-received and milled materials Continued research into the ultracapacitor technology included advanced milling and optimization of ceramic nanoparticles, fluidized bed treatment of atomic-layer deposition- (ALD-) coated ceramic particles, custom development of dielectric and conductor inks, as well as custom ink precursors such as polyvinylidene diflouride- (PVDF-) loaded vehicles. Experiments with graphene-based inks were also conducted.

Allen, Lee↗

The Beam Dump eXperiment

Hadronic matter makes about 14% of the known universe. The remaining 86% is Dark Matter (DM). Since it does not interact with the ordinary matter via electromagnetic force, DM is not visible and, to date, it escaped detection. The search for Dark Matter (DM) is one of the hottest topic in modern physics. Despite the increasing number of astrophysical and cosmological observations proving the existence, so far no particle physics experiment has detected DM yet. Up to now, most of the experimental efforts have been focused on the so called WIMP (Weakly Interacting Massive Particle) paradigm, which predicts heavy DM (10 GeV-10 TeV mass range) interacting with Standard Model (SM) particles via the weak force mediators (W or Z bosons). More recently, due to the lack of clear evidence of WIMPs, other models of DM gained the interest of the physics community. These models consider Light DM particles (LDM), in MeV-GeV mass range. Among LDM theories, the Dark Photon theory predicts the existence of a Dark Sector interacting with SM particles via a new massive vector boson (Dark Photon, Heavy Photon or A0), mediator of a new force. This scenario, despite being theoretically well motivated, is remarkably experimentally unexplored. The Beam Dump eXperiment (BDX), is an approved experiment at Jefferson Lab (JLab), aiming to discover the DM predicted witihn the Dark Photon theory. The experiment uses the CEBAF (Continuous Electron Beam Accelerator Facility) 11 GeV electron beam, impinging on the JLab Hall-A beam-dump, to produce a beam of DM particles, detected by a ~ 1 m3 detector made of thallium doped cesium iodide (CsI(Tl)) crystals located ~ 20 m downstream. In order to achieve excellent background rejection, the experimental setup includes active vetos and passive shielding surrounding CsI(Tl) crystals. Given the weakness of the DM-SM interaction, the scattering of a DM particle in the BDX detector is a rare event. Therefore, the characterization of the expected background is a critical aspect of the experiment. Both cosmogenic and penetrating SM particles produced by the beam interaction in the dump contribute to the background of the experiment. While the cosmogenic contribution can be measured during the experiment when the beam is off, beam-related background can only be estimated via Monte Carlo (MC) simulations. Therefore, a careful assessment of possible systematics introduced by the MC is needed. The beam-correlated background characterization was made by measuring the muon flux produced by the interaction of the CEBAF beam with Hall-A dump in a dedicated experimental campaign in spring 2018 [1]. The comparison to the ?ux predicted by MC allowed us to validate the BDX simulation framework. The reach of BDX, i.e. the region that the experiment can probe in the LDM theory parameter space, depends critically on the background rejection capability and signal detection effciency. For this reason, the detector setup was fine-tuned through a dedicated study. The response to LDM and background events was evaluated for different setups and selection cuts. As a result of this procedure, the configuration resulting in the best sensitivity was selected [2]. The reach calculation was performed taking into account the different LDM production mechanisms, including the contribution of secondary particles produced in the beam-dump. In particular, the effect of the secondary positrons annihilation was found to be extremely significant. In the majority of the sensitivity studies, this contribution is neglected, but recent results [3] [4] demonstrated that this process significantly enhances the sensitivity of lepton beam-dump experiments. Currently, the BDX collaboration is focused on the deployment and operation of a small detector, called BDX-MINI, built to perform a preliminary physics measurement searching for LDM at JLab. This test will pave the way to the realization of the full BDX experiment. The measurement is currently ongoing but results are expected by the end of this year. During my PhD I was involved in all aspects of the BDX experiment: design, simulation, prototyping and data analysis. The main results of my work are reported in this thesis. This manuscript is organized as follows: the first Chapter provides an introduction to the theory of LDM, with particular attention to the Dark Photon paradigm; the second Chapter illustrates the BDX experimental setup, the LDM production and detection mechanisms and the expected backgrounds; Chapter 3 and 4 describe, respectively, the BDX-HODO measurement, with a detailed description of the simulations, and the BDX experimental setup and analysis cuts optimization. Chapter 5 reports about BDXMINI detector characterization, calibration and sensitivity estimate. Finally, Chapter 6 describes in detail the calculation of the secondary positron annihilation contribution to the sensitivity of BDX and other electron-beam thick-target experiment.

Marsicano, Luca↗

Explosive dispersal of particles in high speed environments

In this paper, we present the results of the explosive dispersal of particles in high-speed environments. We carry out Euler–Lagrange numerical simulations of a source at quiescent ambient conditions as well as moving at Mach numbers of 3 and 6. Particle volume fractions of 0%, 1%, and 4.5% are presented. The detonation profile is computed with the Jones–Wilkins–Lee equation of state using a reactive burn model. Non-static cases provide a framework to consider the effect of a bow shock and pre-existing high-speed flow conditions on the dispersal process. We also compute averages of both static and dynamic pressures, as well as impulse density histories on virtual probe planes to characterize the momentum of the flow and particles that would deposit on a target. Results suggest that the presence of the particles can have a substantial effect on the pressure average of the virtual target planes.

Durant, B. A.↗

Viewing internal bubbling and microexplosions in combusting metal particles via x-ray phase contrast imaging

The combustion performance of metal particles for explosives, propellants, pyrotechnics, and bio-agent defeat can be optimized by controlling the chemistry, size, and coatings of the particles. Many pure and composite metal fuels used in these applications have been observed to microexplode during combustion. This causes particles to fragment, revealing fresh surfaces, which may enhance burn rates and efficiencies. Despite these potential benefits, few have attempted to control the bubbling and the microexplosion of metal powders as they combust in molten states. Here, we choose Al:Zr composite powders as a representative system to study bubbling and microexplosions and to identify active mechanisms. Using synchrotron x-rays and phase contrast imaging, we observe molten metal particles of various sizes as they burn in air at temperatures ranging from 2700 to 3500 K. We characterize bubble nucleation and growth in the interior of the particles during the combustion process, and we identify heterogeneous nucleation, slow growth and coalescence of bubbles, as well as rapid bubble growth leading to microexplosions. Bubble growth rates and Laplace pressures are calculated, and we find that during slow expansion growth rates are similar to classical predictions of bubble growth in superheated liquids. For rapid expansions we find that a critical growth rate of ~0.5m s –1 , independent of the initial particle size, is necessary to enable microexplosions. We calculate the rate of gas generation that is needed to enable this growth and we conclude that nitrogen is the gas most likely driving rapid bubble growth and fragmentation. Furthermore these results provide the first in situ observations of the mechanisms that control bubbling and microexplosions during the combustion of metal particles.

36 MATERIALS SCIENCE↗

Application of an alkylammonium method for characterization of phyllosilicates in CI chondrites

Many meteorites and interplanetary dust particles (IDP's) with primitive compositions contain significant amounts of phyllosilicates, which are generally interpreted as evidence of protoplanetary aqueous alteration at an early period in the solar system. These meteorites are chondrites of the carbonaceous and ordinary varieties. Characterization of phyllosilicates in these materials is important because of the important physico-chemical information they hold, e.g., from well characterized phyllosilicates, thermodynamic stability relations and hence the conditions of formation of phyllosilicates in the parent body of the meteorite can be predicted. Although we are at a rudimentary level of understanding of the minerals resulting from the aqueous alteration in the early solar nebula, we know that the most common phyllosilicates present in chondritic extraterrestrial materials are serpentines, smectites, chlorites, and micas. The characterization of fine grained minerals in meteorites and IDP's rely heavily on electron beam instruments, especially transmission electron microscopy (TEM). Typically, phyllosilicates are identified by a combination of high resolution imaging of basal spacings, electron diffraction analysis, and chemical analysis. Smectites can be difficult to differentiate from micas because the smectites loose their interlayer water and the interlayers collapse to the same basal spacing as mica in the high vacuum of the TEM. In high-resolution TEM (HRTEM) images, smectite basal spacings vary from 1 nm up to 1.5 nm, while micas show 1 or 2 nm basal spacings. Not only is it difficult to differentiate smectites from micas, but there is no way of identifying different classes of smectites in meteorites and IDP's. To differentiate smectites from micas and also to recognize the charge differences among smectites, an alkylammonium method can be employed because the basal spacings of alkylammonium saturated smectites expand as a function of alkylamine chain length and the layer-charge density of the 2:1 expanding phyllosilicate, and the final product is significantly more stable under electron beam examination. Such a method was tested on standard clays and several meteorite samples using four alkylammonium salts.

Golden, D. C.↗

Filamentary surface plasma discharge flow length and time scales

Nanosecond surface dielectric barrier discharges (ns-SDBDs) are a class of plasma actuators that utilize a high-voltage pulse of nanosecond duration between two surface-mounted electrodes to create an electrical breakdown of air, along with rapid heating. These actuators usually produce multiple filaments when operated at high pulse frequencies, and the rapid heating leads to the formation of shock waves and complex flow fields. In this work we replicate a single filament of the ns-SDBDs and characterize the induced flow using velocity measurements from particle image velocimetry and density measurements from background-oriented schlieren. The discharge is produced by a high voltage electrical pulse between two copper electrodes on an acrylic base. A hot gas kernel characterizes the flow field formed close to the electrodes that expands and cools over time and a vortex ring that propagates away from the surface while entraining cold ambient fluid. The gas density deficit inside the kernel displays a power-law decay over time. Based on the observations, we develop a simplified theoretical model based on vortex-driven cooling and perform a scaling analysis to obtain the induced flow length and time scales. The results show that the cooling process's time scales correspond to a circulation-based time scale of the vortex ring, and the length scale of the kernel corresponds to the vortex ring radius. Furthermore, these findings can guide the choice of optimal filament spacing and pulse frequencies in the design, deployment, and operation of ns-SDBDs for flow control.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Rapid Measurements of Aerosol Ionic Composition and 3-10 nm Particle Size Distributions On The NASA P3 To Better Quantify Processes Affecting Aerosols Advected From East Asia

The Particle Into Liquid Sample (PILS) was deployed on the NASA P3 for airborne measurements of fine particle ionic chemical composition. The data have been quality assured and reside in the NASA data archive. We have analyzed our data to characterize the sources and atmospheric processing of fine aerosol particles advected from the region during the experiments. Fine particle water-soluble potassium was found to serve as a useful aerosol tracer for biomass smoke. Ratios of PILS potassium to sulfate are used as a means of estimating the percent contribution of biomass burning to fine particle mass in mixed plumes advecting from Asia. The high correlations between K+ and NO3(sup -) and NH4(sup +)' indicated that biomass burning was a significant source of these aerosol compounds in the region. It is noteworthy that the air mass containing the highest concentrations of fine particles recorded in all of ACE-Asia and TRACE-P appeared to be advecting from the Bejing/Tientsin urban region and also had the highest K(+), NO3(sup -) and NH4(sup +) concentrations of both studies. Based on K+/SO4(sup 2-) ratio's, we estimated that the plume was composed of approx. 60% biomass burning emissions, possibly from the use of bio-fuels in the urban regions.

Weber, Rodney J.↗

Small field campaign: aerosol – ice formation closure pilot study. Final Report

Prediction of atmospheric ice formation from aerosol particles by heterogeneous nucleation represents one of the grand challenges in atmospheric science. Our insufficient predictive understanding of primary ice formation is the reason that climate models typically do not include heterogeneous ice nucleation with subsequent effects on climate uncertainty. Mixed-phase clouds, where supercooled water droplets and ice crystals coexist play globally an important role regulating climate. This is especially the case for the Arctic region that experiences the greatest warming due to climate change compared to other regions in the world. Immersion freezing initiated by ice-nucleating particles (INPs) in supercooled water droplets is recognized as the dominant primary ice formation pathway in mixed-phase cloud regimes. For this reason, it is crucial to evaluate our capability to predict immersion freezing for a given ambient aerosol population. The goal of this project is conducting a field-based pilot study at the U.S. DOE Atmospheric Radiation Measurement (ARM) user facility at Southern Great Plains (SGP) to evaluate our capability to predict the number concentration of aerosol particles that serve as INPs in the immersion freezing mode. Successful prediction of INP number concentrations is also termed “closure”. This field-observational approach represents a first-of-its kind attempt of an aerosol–ice formation closure study (AEROICESTUDY). Very few closure studies related to INPs have been conducted, and to our knowledge, none using robust size-resolved ambient aerosol composition measurements as a starting point. Achievement of aerosol–ice formation closure relies on our ability to characterize the ambient aerosol population with respect to particles size and composition and to determine INP number concentrations for specified freezing temperatures. This requires numerous online and offline instrumentation resulting in this pilot field campaign being a multi-institutional and community-collaborative effort. We chose the ARM SGP megasite for this first aerosol-ice formation closure pilot study due to its significant measurement capabilities available to obtain detailed physical characterization of the local aerosol population including size distribution, mass loading, and chemical composition of non-refractory aerosol particles. The overall objective of this project is to identify ice nucleation parameterizations that produce the most robust predictions of INP numbers and thus are best suited to be included in cloud and climate models. This objective includes the following goals for this field and laboratory-based project: i) What are the crucial aerosol physicochemical property measurements needed to accurately guide ice nucleation representations in models and long-term INP measurements? ii) What level of parameter details needs to be known to achieve aerosol–ice formation closure? iii) What are the leading causes for climate model bias in INP predictions? We found that the advances in our understanding of immersion freezing garnered over the last 20 years allowed us to yield partial and full closures of atmospheric immersion freezing from ambient aerosol particles. When the aerosol population is physicochemically complex and parameterizations for representative INP types are not yet available, we still struggle to accurately predict INP number concentrations. This project clearly demonstrates that with more laboratory and field measurements that are accompanied by particle composition analysis, the necessary datasets to achieve aerosol–ice formation closure for various locations will emerge, thus providing a robust foundation for guiding the representation of INPs in cloud and climate models.

54 ENVIRONMENTAL SCIENCES↗