Cloud Chamber Research Articles

Cloud droplet size distributions governed by condensation, collision–coalescence, and sedimentation. Part 1: Monte Carlo simulations and scaling relations for growth in a convection–cloud chamber

Abstract Atmospheric clouds are crucial to weather and climate, and the rate at which droplets collide and coalesce to form precipitation is one of the fundamental controlling processes. A convection-cloud chamber allows the interactions between aerosols and cloud droplets produced by condensation to be investigated within a turbulent environment. Studying the full range of microphysical conditions in atmospheric clouds is not possible, however, unless conditions for droplet growth by collision and coalescence are also achieved. In this study, we explore the conditions favorable to collision-coalescence growth in convection-cloud chambers, extending previous work on steady-state droplet size distributions due to condensation only to obtain analytic expressions for number concentration and supersaturation as a function of small-droplet injection rate, and for collision-coalescence rates. We derive several scaling laws and demonstrate consistency between these theoretical results and Monte-Carlo simulations of growth and precipitation within a convection-cloud chamber. Specifically, the coalescence rate is shown to scale as the square of the liquid water content, which in turn can be modulated in a convection-cloud chamber by varying the small-droplet injection rate as well as the applied temperature difference.

A CH4-Driven Ion Cloud-Stretched Approach Enables ICP-qMS for Multiplex Single-Cell Analysis.

In the last 40 years, inductively coupled plasma quadrupole (q) mass spectrometry (ICP-qMS) has been recognized as one of the best tools for the quantification of multiple elements/isotopes and even the biomolecules they labeled in a homogeneous solution sample. However, it meets a tough challenge when acquiring multi-m/z signals from an intact single-cell dispersed in a cell suspension, since the single-cell ion cloud generated in ICP presents an intermittently transient event with a duration time of hundreds of microseconds while the dwell time plus settling time of the q is at the similar time scale when peak-hopping between different m/z. Herein, we report CH4 is able to stretch the single-cell ion cloud duration time to more than 7,000 μs in collision-reaction-cell (CRC), allowing multi-m/z signals acquisition by ICP-qMS. Quantification of single-cell's multiple phenotype protein markers can thus be achieved on ICP-(CH4-CRC)-qMS, not only revealing the heterogeneity between the single cells but also enabling an unambiguous cell-classification of their subtypes. CH4-driven ion cloud-stretched approach breaks through the long-standing bottleneck limited single-cell multiplex analysis on ICP-qMS, paving a pathfor more important applications of ICP-qMS in the fields related to single-cell analysis.

How Porosity Influences the Heterogeneous Ice Nucleation Ability of Secondary Organic Aerosol Particles

AbstractDuring processing in deep convective cloud systems, highly viscous or glassy secondary organic aerosol (SOA) particles can develop a porous structure through a process known as atmospheric freeze‐drying. This structural modification may enhance their heterogeneous ice nucleation ability under cirrus conditions through the pore condensation and freezing mechanism. Pristine, compact SOA particles, on the other hand, are recommended to be treated as ice‐inactive in models. This recommendation also applies to internally mixed particles, where a coating layer of secondary organic matter (SOM) deactivates the intrinsic ice nucleation ability of the core, which may be a mineral dust grain. Ice cloud‐processing may also improve the ice nucleation ability of such a composite particle by inducing structural changes in the coating layer, which can release active sites on the mineral surface. In this work, we investigated the change in the ice nucleation ability of pure SOA particles from the ozonolysis of α‐pinene and two types of internally mixed particles (zeolite and coal fly ash particles coated with SOM) after being subjected to the atmospheric freeze‐drying process simulated in an expansion cloud chamber. For pure α‐pinene SOA, we found only a slight improvement in the ice nucleation ability of the ice cloud‐processed, porous particles compared to their pristine, compact counterparts at 221 and 217 K. In contrast, the zeolite and coal fly ash particles, which were initially deactivated by the organic coating, became significantly more ice‐active after atmospheric freeze‐drying, emphasizing that such composite particles cannot be excluded from model simulations of heterogeneous ice formation.

A Novel Model for Protecting the Privacy of Digital Images in Cloud Using Permutated Fragmentation and Encryption Algorithms

Maintaining privacy is becoming increasingly challenging due to growing reliance on cloud services and software, respectively. Our data is stored in a virtual environment on unreliable cloud machines, making it susceptible to privacy breaches if not handled properly. Encrypting data before uploading it can be a solution to this problem, but it can be time-consuming. However, all of the encryption methods used to safeguard digital data so far did not fulfillment privacy and integration requirements. This is because encryption cannot function independently. Data that is encrypted and stored on a single cloud server can still be accessed by attackers, compromising the privacy of the data. In this paper, we propose a new model based on the user's classification of privacy level. The proposed model divides the digital file into multiple fragments and separately encrypts each fragment; each fragment is encrypted as separated blocks. Additionally, permutation is implemented on encrypted fragments because they are stored in the cloud with replication fragments on another cloud service. This approach ensures that even if the attacker’s gains access to one fragment, they would not be able to access the entire file, thereby safeguarding the privacy of the data.

Real-time particle visualization in an electric-cooled cloud chamber using machine learning

This paper presents the development and implementation of an electric-cooled cloud chamber designed to visualize alpha and beta particle tracks. Traditional cloud chambers rely on dry ice for cooling, which can be cumbersome and impractical for extended use. Our approach employs a Peltier device coupled with a commercial CPU cooler, providing a stable, large field of view, and long-lasting cooling environment. Additionally, we have integrated a real-time visualization system utilizing OpenCV and machine learning with TensorFlow. This system accurately identifies and labels particle tracks, enhancing the educational experience. The cloud chamber operates seamlessly with consumer laptops, such as the MacBook Air with an M1 chip, making it accessible and convenient for educational purposes. Initial qualitative results demonstrate the system’s effectiveness. Future work will focus on refining detection accuracy and exploring additional applications. This advancement not only improves the usability of cloud chambers but also offers new possibilities for interactive scientific education.

Retraction notice: Efficient task scheduling on virtual machine in cloud computing environment

Retraction notice: Efficient task scheduling on virtual machine in cloud computing environment

Selective Gas-Phase Depletion of Chemical Contaminants in Dissolved Organic Matter Increases Compositional Coverage by FT-ICR Mass Spectrometry.

Fourier transform ion cyclotron resonance mass spectrometry of dissolved organic matter (DOM) extracted from environmental samples provides molecular speciation that enables visualization of compositional trends in the fate and cycling of biogenic and anthropogenic organics. Often, chemical contamination is introduced during field sampling (i.e., remote locations, cannot use glass). Further, preconcentration of DOM by solid-phase extraction often results in chemical contamination. When chemical noise is a dominant fraction of the ion signal, mass spectral performance is degraded by reduction of the ion trap analyte accumulation capacity and enhanced ion cloud dephasing during ICR detection. We have developed gas-phase ion depletion of unwanted chemical contaminants during ion injection into the linear RF ion trap of the hybrid linear ion trap 21 T FT-ICR mass spectrometer that improves detection of analytes by removing unwanted chemical noise. We demonstrate improvements in signal-to-noise ratio, dynamic range, and the number of observed analytes in dissolved organic matter samples that results in a 40-100% increase in the number of identified analytes. In many cases, the number of peaks observed per nominal mass more than doubles over select m/z regions. This gas-phase "clean-up" can salvage precious samples challenged by sampling location, sample volume, or collection protocols that cannot be avoided and maximizes the compositional information obtained. Further, this approach is generalizable and extendable to any hybrid linear ion trap instrument platform (e.g., LTQ-Orbitrap or linear ion trap-TOF). We highlight the power of gas-phase depletion with electrospray ionization, but this method is also applicable to other ionization modes.

Glaciation of mixed-phase clouds: insights from bulk model and bin-microphysics large-eddy simulation informed by laboratory experiment

Abstract. Mixed-phase clouds affect precipitation and radiation differently from liquid and ice clouds, posing greater challenges to their representation in numerical simulations. Recent laboratory experiments using the Pi Cloud Chamber explored cloud glaciation conditions based on increased injection of ice-nucleating particles. In this study, we use two approaches to reproduce the results of the laboratory experiments: a bulk scalar mixing model and large-eddy simulation (LES) with bin microphysics. The first approach assumes a well-mixed domain to provide an efficient assessment of the mean cloud properties for a wide range of conditions. The second approach resolves the energy-carrying turbulence, the particle size distribution, and their spatial distribution to provide more details. These modeling approaches enable a separate and detailed examination of liquid and ice properties, which is challenging in the laboratory. Both approaches demonstrate that, with an increased ice number concentration, the flow and microphysical properties exhibit the same changes in trends. Additionally, both approaches show that the ice integral radius reaches the theoretical glaciation threshold when the cloud is subsaturated with respect to liquid water. The main difference between the results of the two approaches is that the bulk model allows for the complete glaciation of the cloud. However, LES reveals that, in a dynamic system, the cloud is not completely glaciated as liquid water droplets are continuously produced near the warm lower boundary and subsequently mixed into the chamber interior. These results highlight the importance of the ice mass fraction in distinguishing the mixed-phase clouds and ice clouds.

The impact of ammonia on particle formation in the Asian Tropopause Aerosol Layer

During summer, ammonia emissions in Southeast Asia influence air pollution and cloud formation. Convective transport by the South Asian monsoon carries these pollutant air masses into the upper troposphere and lower stratosphere (UTLS), where they accumulate under anticyclonic flow conditions. This air mass accumulation is thought to contribute to particle formation and the development of the Asian Tropopause Aerosol Layer (ATAL). Despite the known influence of ammonia and particulate ammonium on air pollution, a comprehensive understanding of the ATAL is lacking. In this modelling study, the influence of ammonia on particle formation is assessed with emphasis on the ATAL. We use the EMAC chemistry-climate model, incorporating new particle formation parameterisations derived from experiments at the CERN CLOUD chamber. Our diurnal cycle analysis confirms that new particle formation mainly occurs during daylight, with a 10-fold enhancement in rate. This increase is prominent in the South Asian monsoon UTLS, where deep convection introduces high ammonia levels from the boundary layer, compared to a baseline scenario without ammonia. Our model simulations reveal that this ammonia-driven particle formation and growth contributes to an increase of up to 80% in cloud condensation nuclei (CCN) concentrations at cloud-forming heights in the South Asian monsoon region. We find that ammonia profoundly influences the aerosol mass and composition in the ATAL through particle growth, as indicated by an order of magnitude increase in nitrate levels linked to ammonia emissions. However, the effect of ammonia-driven new particle formation on aerosol mass in the ATAL is relatively small. Ammonia emissions enhance the regional aerosol optical depth (AOD) for shortwave solar radiation by up to 70%. We conclude that ammonia has a pronounced effect on the ATAL development, composition, the regional AOD, and CCN concentrations.

Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model

Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model

Characteristic parameters and radon exhalation rates of aerated concrete blocks

ObjectiveTo understand the characteristic parameters and radon exhalation rates of aerated concrete blocks. MethodsA total of 39 nationally inspected samples were measured. Their dry density was determined based on their mass and volumes, their porosities were measured on the principle of volumetric expansion, and their radium content was determined using a high-purity germanium (HPGe) detector for gamma-ray spectroscopy. Furthermore, their diffusion lengths were quantified by establishing a combined cumulative and diffusion chamber, and their radon exhalation rates were measured through closed-box testing using a continuous radon monitor. ResultsThe aerated concrete blocks exhibited dry densities ranging from 464 to 840 ​kg/m3 [average: (654.0 ​± ​82.5) kg/m3], open porosities from 67.1 ​% to 81.1 % [average: (74.3 ​± ​3.3) %, radium (226Ra) content from 12.3 to 136 Bq/kg [average: (63.0 ​± ​30.4) Bq/kg], diffusion lengths from 0.49 to 1.01 ​m [average: (0.70 ​± ​0.15) m], and radon exhalation rates from 0.6 to 22.8 Bq·m−2·h−1 [average: (7.3 ​± ​5.3) Bq·m−2·h−1]. ConclusionAerated concrete blocks exhibit significantly higher porosities, diffusion lengths, and radon exhalation rates than traditional concrete and clay bricks. These blocks might contribute to the high indoor radon concentration observed in modern buildings in China.

Effect of gas injection pattern on magnetically expanding rf plasma source

Abstract Argon gas is injected from a back plate having either a radial center hole or shower-patterned eight holes into a 13.3-cm-diameter and 25-cm-long radio frequency (rf) plasma source attached to a 43.7-cm-diameter and 65cm-long diffusion chamber under an expanding magnetic field, which resembles the magnetic nozzle rf plasma thruster. The source has a double-turn loop antenna powered by a 13.56 MHz rf generator at a maximum power level of ~2.8 kW in low-pressure argon, providing a plasma density of about 1018 m−3 in the source. A high plasma density and a slightly low electron temperature are obtained for the shower-pattered case in both the source tube and the diffusion chamber, compared with the center hole case, suggesting that the neutral density profile significantly affects the plasma density profile. This result will provide an improvement in the thruster performance by the gas injection pattern.

A root mucilage analogue from chia seeds reduces soil gas diffusivity

AbstractGas exchange in the soil is determined by the size and connectivity of air‐filled pores. Root mucilage reduces air‐filled pore connectivity and thus gas diffusivity. It is unclear to what extent mucilage affects soil pore connectivity and tortuosity. The aim of this study was to gain a better understanding of gas diffusion processes in the rhizosphere by explaining the geometric alterations of the soil pore space induced by mucilage. We quantified the effect of a root mucilage analogue collected from chia seeds without intrinsic respiratory activity on oxygen diffusion at different water contents during drying–rewetting cycles in a diffusion chamber experiment. Quantification of oxygen diffusion showed that mucilage decreased the gas diffusion coefficient in dry soil without affecting air‐filled porosity. Without mucilage, a hysteresis in gas diffusion coefficient during a drying–rewetting cycle was observed. The effect depended on particle size and diminished with increasing mucilage content. X‐ray computed tomography imaging indicated a hysteresis in the connectivity of the gas phase during a drying–rewetting cycle for samples without mucilage. This effect was attenuated with increasing mucilage content. Furthermore, electron microscopy showed that mucilage structures formed in drying soil increase with mucilage content, thereby progressively reducing the connectivity of the gas phase. In conclusion, the effect of mucilage on soil gas diffusion highly depends on soil texture and mucilage content. The diminishing hysteresis with the addition of mucilage suggests that plant roots secrete mucilage to balance oxygen availability and water content, even under fluctuating moisture conditions.

A trapped ion mobility enabled Fourier transform ion cyclotron resonance mass spectrometer for infrared ion spectroscopy at FELIX

We report the installation of a new infrared ion spectroscopy platform at the free-electron laser facility FELIX, based on a Bruker SolariX Fourier Transform ion cyclotron resonance (FT-ICR) mass spectrometer equipped with a trapped ion mobility spectrometry (TIMS) stage. The instrument allows one to record infrared multiple-photon dissociation (IRMPD) spectra for mass and mobility selected ions, produced by a range of ion sources. We describe two strategies to achieve consistent overlap between the laser beam and the ion packet. Removing the original ECD cathode significantly enhanced the IR transmission and the overlap with the ion cloud, resulting in improved IRMPD yield per pulse. Enhancement of the photodissociation yield is observed when multiple pulses are used, as there is negligible collisional deactivation in the ultra-high vacuum trapping region of the ICR cell. We compare IRMPD spectra recorded on the new platform with IRMPD spectra of the same species recorded on one of our 3D-quadrupole ion trap platforms. We demonstrate the instrument’s performance using a sample containing a mixture of two trisaccharides. Mobility selection allows us to record individual IR spectra for the two isomeric species. This multi-modal platform, encompassing liquid chromatography, ion mobility spectrometry, ultra-high resolution (tandem) mass spectrometry, infrared ion spectroscopy (and soon also mass spectrometry imaging), is available to users at the FELIX facility.

Methodology of integrated measurements with radon diffusion chamber

Calibration of Solid-State Nuclear Track Detectors - SSNTD in the diffusion chamber in most cases assumes a constant radon concentration outside the diffusion chamber and a steady state. Although this has been proven to be a valid approximation in most cases, the fact is that the radon concentration outside the chamber is not constant. In this way, the detectors are calibrated to measure the mean radon concentration. For this reason, there is doubt if the time-dependent concentration in dwellings results in the same number of tracks on the detector as the time-averaged radon concentration for the same exposure period. To obtain an answer, time-dependent and steady-state diffusion equations for radon and its progeny were solved. Then the number of tracks on the detector was calculated for these two cases and compared. For that goal, software for solving the time-dependent diffusion equation for time-varying external radon concentration was developed. Results show that concentrations measured using a diffusion chamber are the real concentrations at the measuring site. However, it was also found that the validity of results is not a universal property, as shown in the current paper. In some cases, results can be wrong. For some extreme conditions when diffusion coefficients are disrupted calibration using stationary conditions model will lead to incorrect results of real measurements in experiments where ambiental concentration is time-varying.

Seasonal Variation Influence to Water Image Properties to Retrieve Nearshore Bathymetry Based on Cloud Machine Learning Approach

Seasonal Variation Influence to Water Image Properties to Retrieve Nearshore Bathymetry Based on Cloud Machine Learning Approach

Exploring particle physics through diffusion chambers: detecting, visualizing, and analyzing subatomic phenomena

This study examines the development and utilization of diffusion chambers in particle physics research. Through meticulous experimentation, optimization of chamber parameters has been achieved to enhance particle detection while concurrently assessing background radiation levels, vital for minimizing interference. Furthermore, recent advancements have enabled the visualization of α – particles and mesons within these chambers, offering invaluable insights into their behaviors and interactions. These achievements highlight the pivotal role of diffusion chambers as indispensable tools in advancing our understanding of fundamental particles and their properties. As a result, diffusion chambers continue to serve as critical instruments in unraveling the mysteries of the subatomic world, promising continued contributions to particle physics research.

The state and vascularization of the bone marrow transplanted in the diffusion chamber to the rat neurovascular bundle

The state and vascularization of the bone marrow transplanted in the diffusion chamber to the rat neurovascular bundle

CCN Activation and Droplet Growth in Pi Chamber Simulations with Lagrangian Particle–Based Microphysics

Abstract Numerical simulations of turbulent moist Rayleigh–Bénard convection driving CCN activation and droplet growth in the laboratory Pi chamber are discussed. Supersaturation fluctuations come from isobaric mixing of warm and humid air rising from the lower boundary with colder air featuring lower water vapor concentrations descending from the upper boundary. Lagrangian particle–based microphysics is used to represent the growth of haze CCN and cloud droplets with kinetic, solute, and surface tension effects included. Dry CCN spectra in the range between 2- and 200-nm radii from field observations are considered. Increasing the total CCN concentration from pristine to polluted conditions results in an increase in the droplet concentration and reduction in the mean droplet radius and spectral width. These are in agreement with Pi chamber observations and numerical simulations, as well as with numerous past studies of CCN cloud-base activation in natural clouds. The key result is that a relatively small fraction of the available CCN is activated in the Pi chamber fluctuating supersaturations, from about a half in the pristine case to only a 10th in the polluted case. The activation fraction as a function of the dry CCN radius is similar in all simulations, close to zero at the CCN small end, increasing to a maximum at CCN radius around 50 nm, and decreasing to close to zero at the large CCN end. This is explained as too small supersaturations to activate small CCN as in natural clouds and insufficient time to allow large CCN reaching the critical radius. Significance Statement Impact of turbulence on the formation and growth of cloud droplets is an important cloud physics problem. Laboratory experiments in the Michigan Technological University cloud chamber provide key insights into this problem. Numerical simulations of cloud chamber processes discussed in this paper complement laboratory experiments by providing insights difficult or impossible to obtain in the laboratory. The study contrasts the formation and growth of cloud droplets in the laboratory cloud chamber with processes taking place in natural clouds. The differences documented in this paper pose questions concerning the impact of turbulence on the formation and growth of cloud droplets as a result of interactions of clouds with their environment.

Interacting dust grains in complex plasmas: Ion wake formation and the electric potential

Dust grains have been used as minimally invasive probes to determine plasma parameters including the plasma density, temperature, and electric field in a plasma discharge. However, the dust grains in a plasma generate local potential disturbances due to the collection of charge and the subsequent electrostatic interactions between the dust and charged plasma particles. Dust grains in close proximity to one another exhibit interesting non-reciprocal interactions and self-organize into structures such as one-dimensional filamentary chains, two-dimensional “zigzags,” and three-dimensional helices, among others. The formation of these structures suggests that although the dust grains may be less invasive than traditional plasma probes, the disturbance to the local plasma environment introduced by dust grains is non-trivial. Commonly used analytic forms of the electric potential describing complex plasmas have failed to resolve the near-dust region, and as a result are insufficient to provide insight about the formation of complex dust structures. Here, we use an N-body simulation to compute the electric potential from ion densities near various dust grain configurations. We provide an alternative description to the standard analytic model for the electric potential of dust and ion wakes based on a Gaussian shaped cloud of ions. The electric potential obtained from simulations is used to identify minimum energy configurations for two and three dust grains. It is further demonstrated that the minimum potential region identified for N dust grains and their associated ion wakes does not predict the minimum-energy configuration of N + 1 dust grains.