Individual Particles Research Articles

Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning

With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.

Direct Observation of Particle-To-Particle Variability in Ambient Aerosol pH Using a Novel Analytical Approach Based on Surface-Enhanced Raman Spectroscopy.

The pH of atmospheric aerosols is a key characteristic that profoundly influences their impacts on climate change, human health, and ecosystems. Despite widely performed aerosol pH research, determining the pH levels of individual atmospheric aerosol particles has been a challenge. This study presents a novel analytical technique that utilizes surface-enhanced Raman spectroscopy to assess the pH of individual ambient PM2.5-10 aerosol particles in conjunction with examining their hygroscopic behavior, morphology, and elemental compositions. The results revealed a substantial pH variation among simultaneously collected aerosol particles, ranging from 3.3 to 5.7. This variability is likely related to each particle's unique reaction and aging states. The extensive particle-to-particle pH variability suggests that atmospheric aerosols present at the same time and location can exhibit diverse reactivities, reaction pathways, phase equilibria, and phase separation properties. This pioneering study paves the way for in-depth investigations into particle-to-particle variability, size dependency, and detailed spatial and temporal variations of aerosol pH, thus deepening our understanding of atmospheric chemistry and its environmental implications.

Preserving Tracer Correlations in Moment‐Based Atmospheric Transport Models

AbstractA linear non‐diffusive algorithm for advective transport is developed that greatly improves the detail at which aerosols and clouds can be represented in atmospheric models. Linear advection schemes preserve tracer correlations but the most basic linear scheme is rarely used by atmospheric modelers on account of its excessive numerical diffusion. Higher‐order schemes are in widespread use, but these present new problems as nonlinear adjustments are required to avoid occurrences of negative concentrations, spurious oscillations, and other non‐physical effects. Generally successful at reducing numerical diffusion during the advection of individual tracers, for example, particle number or mass, the higher‐order schemes fail to preserve even the simplest of correlations between interrelated tracers. As a result, important attributes of aerosol and cloud populations including radial moments of particle size distributions, molecular precursors related through chemical equilibria, aerosol mixing state, and distribution of cloud phase are poorly represented. We introduce a new transport scheme, minVAR, that is both non‐diffusive and preservative of tracer correlations, thereby combining the best features of the basic and higher‐order schemes while enabling new features such as the tracking of sub‐grid information at arbitrarily fine scales with high computational efficiency.

Investigation of aluminum particle ignition dynamics in various propellant environments

The ignition delay of aluminum particles can be profoundly influenced by the agglomeration and combustion characteristics of solid propellants. In this experimental study, the ignition of individual aluminum particles in nitrate ester plasticized polyether (NEPE) and hydroxyl‑terminated polybutadiene (HTPB) propellant atmospheres was examined using laser ignition tests and high-speed photography. Focus was directed at the effects of the atmospheric composition, pressure, and aluminum particle size (500, 800 and 1000 µm). It was observed that the non-homogeneous reaction heat generation and convective heat exchange rates during particle ignition could be increased by increasing the CO2 content or ambient temperature, resulting in a shorter ignition delay time. This explains why aluminum particles tended to burn more efficiently in nitrate ester plasticized polyether than in hydroxyl‑terminated polybutadiene. In addition, it was found that the ignition delay time decreased with increasing pressure and was inversely proportional to the particle size squared. An ignition model for aluminum particle ignition in a multi-component atmosphere was developed and validated to describe the temperature change and energy transfer during ignition. The calculated ignition delay times were found to be within 5% of the experimental values. The model also showed that increasing the ambient temperature was more effective than increasing the CO2 content at enhancing ignition, and that CO2 was more effective than O2, which was itself more effective than H2O. Overall, this study provides useful insight into the ignition characteristics of aluminum particles in different propellant environments. The results could be used to develop more effective propellant formulations, optimize combustion processes, and improve safety in the handling and use of aluminum-based propellants.

Structural Evolution of GaOx-Shell and Intermetallic Phases in Ga-Pt Supported Catalytically Active Liquid Metal Solutions.

We present a comprehensive scale-bridging characterization approach for supported catalytically active liquid metal solutions (SCALMS) which combines lab-based X-ray microscopy, nano X-ray computed tomography (nano-CT), and correlative analytical transmission electron microscopy. SCALMS catalysts consist of low-melting alloy particles and have demonstrated high catalytic activity, selectivity, and long-term stability in propane dehydrogenation (PDH). We established an identical-location nano-CT workflow which allows us to reveal site-specific changes of Ga-Pt SCALMS before and after PDH. These observations are complemented by analytical transmission electron microscopy investigations providing information on the structure, chemical composition, and phase distribution of individual SCALMS particles. Key findings of this combined microscopic approach include (i) structural evolution of the SCALMS particles' GaOx shell, (ii) Pt segregation toward the oxide shell leading to the formation of Ga-Pt intermetallic phases, and (iii) cracking of the oxide shell accompanied by the release of liquid Ga-Pt toward the porous support.

Electrochemiluminescence-Based Single-Particle Tracking of the Biomolecules Moving along Intercellular Membrane Nanotubes between Live Cells.

Electrochemiluminescence (ECL) imaging, a rapidly evolving technology, has attracted significant attention in the field of cellular imaging. However, its primary limitation lies in its inability to analyze the motion behaviors of individual particles in live cellular environments. In this study, we leveraged the exceptional ECL properties of quantum dots (QDs) and the excellent electrochemical properties of carbon dots (CDs) to develop a high-brightness ECL nanoprobe (CDs-QDs) for real-time ECL imaging between living cells. This nanoprobe has excellent signal-to-noise ratio imaging capabilities for the single-particle tracking (SPT) of biomolecules. Our finding elucidated the enhanced ECL mechanism of CDs-QDs in the presence of reactive oxygen species through photoluminescence, electrochemistry, and ECL techniques. We further tracked the movement of single particles on membrane nanotubes between live cells and confirmed that the ECL-based SPT technique using CD-QD nanoparticles is an effective approach for monitoring the transport behaviors of biomolecules on membrane nanotubes between live cells. This opens a promising avenue for the advancement of ECL-based single-particle detection and the dynamic quantitative imaging of biomolecules.

Super Bound States in the Continuum on a Photonic Flatband: Concept, Experimental Realization, and Optical Trapping Demonstration.

In this Letter, we theoretically propose and experimentally demonstrate the formation of a super bound state in a continuum (BIC) on a photonic crystal flat band. This unique state simultaneously exhibits an enhanced quality factor and near-zero group velocity across an extended region of the Brillouin zone. It is achieved at the topological transition when a symmetry-protected BIC pinned at k=0 merges with two Friedrich-Wintgen quasi-BICs, which arise from the destructive interference between lossy photonic modes of opposite symmetries. As a proof of concept, we employ the ultraflat super BIC to demonstrate three-dimensional optical trapping of individual particles. Our findings present a novel approach to engineering both the real and imaginary components of photonic states on a subwavelength scale for innovative optoelectronic devices.

Understanding the Influence of the Buoyancy Sign on Buoyancy-Driven Particle Clouds

A numerical model was developed to investigate the behavior of round buoyancy-driven particle clouds in a quiescent ambient. The model was validated by comparing model simulations with prior experimental and numerical results and then applied the model to examine the difference between releases of positively and negatively buoyant particles. The particle cloud model used the entrainment assumption while approximating the flow field induced by the cloud as a Hill’s spherical vortex. The motion of individual particles was resolved using a particle tracking equation that considered the forces acting on them and the induced velocity field. The simulation results showed that clouds with the same initial buoyancy magnitude and particle Reynolds number behaved differently depending on whether the particles were more dense or less dense than the ambient fluid. This was found even for very low initial buoyancy releases, suggesting that the sign of the buoyancy is always important and that, therefore, the Boussinesq assumption is never fully appropriate for such flows.

Simulating Brownian motion in thermally fluctuating viscoelastic fluids by using the smoothed profile method

To investigate the Brownian motion of individual particles suspended in viscoelastic fluids, the stochastic smoothed profile method (SPm) for direct numerical simulation is developed by extending deterministic SPm for suspensions in viscoelastic media. To simulate viscoelastic flow driven by thermal fluctuations in the suspending medium, the random stress in the fluid momentum equation as well as the random driving force for the conformation tensor in the Oldroyd-B model are incorporated according to the fluctuating hydrodynamics and fluctuating viscoelasticity formalisms. The thermal equilibrium and dynamical properties calculated by using numerical simulations successfully reproduce the analytic predictions, validating the direct simulation for the coupled fluctuating Navier–Stokes and Oldroyd-B equations and for the coupling between the stochastic viscoelastic medium and individual particles. As an application of the stochastic SPm, we investigate finite system-size effects under periodic boundary conditions (PBCs) on the passive microrheological relationship between the mean-square displacement (MSD) of a Brownian particle and the medium's dynamic modulus. Comparing the modulus that was microrheologically calculated from the MSD with the input modulus reveals that the effect of periodic image cell interaction appears not only in the long-time diffusive regime but also in the short-time region. A frequency-dependent finite system-size correction is implemented by phenomenologically extending the long-time diffusive regime correction, allowing passive microrheology analysis under PBCs. This result can be directly applied to other mesoscale numerical simulations including coarse-grained molecular dynamics and dissipative particle dynamics simulations.

THE EFFECT OF SURFACE ROUGHNESS AND ELECTROSTATIC BONDS OF RESIDUAL GASES ON THE SETTLING OF THE GYROSCOPE ROTOR

Consider the rotor of an electrostatic gyroscope (ESG) for contactless operation. The rotors of the ESG are made in the shape of a ball and placed in an electric field. The gyroscope operates contactless between the rotor and the mutual oscillation force of the electrodes. Air is sucked out of the chamber in which the rotor is located. Due to the lack of air resistance and the lack of friction force, the rotor can rotate for a long time. But the rotor, located inside the vacuum, also slows down. The reason for this is the roughness and residual deposits of the rotor surface. This is the notorious effect of the rotor of these residual gases on braking. When calculating the effect of residual gases on the rotor, it is considered as separate gases, and not as a homogeneous medium. For this reason, the viscosity of the gas can be set without consideration. The effect of the action of individual particles on the rotor is determined by the roughness of its surface. That is, the momentum by which the particles somehow hit the "peaks" or "troughs" creates a torque that acts on the rotor.

Hydrodynamic fluctuations, diffusion and correlation functions in systems of particles adsorbed on solid surfaces

We present the detailed derivation of the equations describing the evolution of the hydrodynamic fluctuations of the coverage of particles adsorbed on homogeneous lattices. Using the method of the non-equilibrium statistical operator, we reduce the balance equation governing the behavior of the individual particles to the diffusion equation. On a macroscopic level, this equation describes the approach to equilibrium of the hydrodynamic coverage fluctuations. We obtain the analytical expressions for the Fickian diffusivity and Onsager phenomenological coefficient. These expressions are derived with account of the lateral interaction between the particles. They are simple functions of the thermodynamic quantities — derivatives of the thermodynamic potential over its arguments. The transport coefficients accurately describe the development of fluctuations in the entire coverage region and in the wide range of lateral interactions. We presented an elementary introduction to the theory of fluctuations in the lattice gas systems. For calculations of the correlation function and spectral density of fluctuations, we use the Langevin approach and the method of moments. The exact coincidence of the analytical expressions for the diffusion coefficients obtained by the two independent calculations is the direct proof of the accuracy of the approach developed in Chumak and Tarasenko (1980).

Design and optimisation of rotary vibrating screening process in silicon photovoltaic panel recycling: A particle-scale model study

The efficient separation of crushed solar panel particles is a critical step in photovoltaics (PV) recycling. In this paper, a DEM-based computer model is used to investigate the separation of crushed solar panel particles in a variety of shapes (including rod-like glass particles and chip-like solar cell particles and small broken residue) at the particle scale in a lab-scale rotary vibrating screen. The screening process of individual particles, including looseness, stratification, collision and penetration, can be well captured by the DEM simulations. Then the effects of several key vibration parameters on screening efficiency are studied, including circular displacements and oscillatory amplitude and frequency, and their underlying mechanism is explored at the particle scale. The simulation results show that with the increase of circular displacements from 1 to 3 mm, the screening efficiency increases from 89.5% to 91.8%. With the rise of oscillatory amplitude from 0.1° to 0.6°, the screening efficiency decreases significantly from 92.0% to 87.3%. With the increase of oscillatory frequency from 10 Hz to 25 Hz, the screening efficiency decreases slightly from 92.1% to 91.2%. The optimal screening condition is identified, i.e., >3 mm circular displacement, <0.1° oscillatory amplitude, and < 10 Hz oscillatory frequency, significantly enhancing screening efficiency. This study is helpful for rotary vibrating screen device scale-up and process optimisation in the PV recycling industry.

Accurate and High-Resolution Particle Mass Measurement Using a Peak Filtering Algorithm.

Charge detection quadrupole ion trap mass spectrometry (CD-QIT MS) is an effective way of achieving the mass analysis of microparticles with ultrahigh mass. However, its mass accuracy and resolution are still poor. To enhance the performance of CD-QIT MS, the resolution Rpeak of each peak in the mass spectra resulting from an individual particle was assessed, and a peak filtering algorithm that can filter out particle adducts and clusters with a lower Rpeak was proposed. By using this strategy, more accurate mass information about the analyzed particles could be obtained, and the mass resolution of CD-QIT MS was improved by nearly 2-fold, which was demonstrated by using the polystyrene (PS) particle size standards and red blood cells (RBCs). Benefiting from these advantages of the peak filtering algorithm, the baseline separation and relative quantification of 3 and 4 μm PS particles were achieved. To prove the application value of this algorithm in a biological system, the mass of yeast cells harvested at different times was measured, and it was found that the mixed unbudded and budded yeast cells, which otherwise would not be differentiable, were distinguished and quantified with the algorithm.

Effect of Particle Heterogeneity in Catalytic Copper-Containing Single-Chain Polymeric Nanoparticles Revealed by Single-Particle Kinetics.

Single-chain polymeric nanoparticles (SCPNs) have been extensively explored as a synthetic alternative to enzymes for catalytic applications. However, the inherent structural heterogeneity of SCPNs, arising from the dispersity of the polymer backbone and stochastic incorporation of different monomers as well as catalytic moieties, is expected to lead to variations in catalytic activity between individual particles. To understand the effect of structural heterogeneities on the catalytic performance of SCPNs, techniques are required that permit researchers to directly monitor SCPN activity at the single-polymer level. In this study, we introduce the use of single-molecule fluorescence microscopy to study the kinetics of Cu(I)-containing SCPNs towards depropargylation reactions. We developed Cu(I)-containing SCPNs that exhibit fast kinetics towards depropargylation and Cu-catalyzed azide-alkyne click reactions, making them suitable for single-particle kinetic studies. SCPNs were then immobilized on the surface of glass coverslips and the catalytic reactions were monitored at a single-particle level using total internal reflection fluorescence (TIRF) microscopy. Our studies revealed the interparticle turnover dispersity for Cu(I)-catalyzed depropargylations. In the future, our approach can be extended to different polymer designs which can give insights into the intrinsic heterogeneity of SCPN catalysis and can further aid in the rational development of SCPN-based catalysts.

Morphological and Elemental Classification of Freshly‐Emitted Individual Particles From Field and Laboratory Residential Biomass Burning

AbstractResidential biomass burning significantly contributes to air pollution in developing countries. However, the microscopic properties of individual particles in their emissions have not been well understood. In this study, individual primary particles from 14 kinds of biomass fuels (including firewood, crop residue, and animal dung) were collected in laboratory and field measurements, and their morphology, composition and mixing state were acquired using transmission electron microscope. These results constitute a source profile database of individual primary particles from residential biomass burning. The database reveals that different types of biomass fuels exhibit different emission characteristics, that is, residential firewood burning mainly emits pure carbonaceous particles (including organic matter (OM) and soot particles), crop residue burning mainly emits K‐containing particles (including OM‐K, soot(‐OM)‐K, and K‐rich particles), and animal dung burning mainly emits pure carbonaceous particles and K‐containing particles. Moreover, our results indicate that the emission characteristics obtained from laboratory and field measurements are different. Field measurements conducted in two selected villages in North China Plain exhibit a higher presence of soot particles compared to laboratory measurements, owing to their higher combustion temperatures. In contrast, field measurements conducted in one selected village in Qinghai‐Tibet Plateau contain less soot particles than those from laboratory measurements in plain areas, due to the deficient oxygen supply during combustion process in the high‐altitude regions. These results warn us that the emission estimation from residential biomass burning should notice the large emission differences among different biomass types and between field and laboratory measurements.

Cellular Distribution and Intracellular Localization of Different Sizes of Fluorescent Thiol-Organosilica Particles in Mouse Lungs.

We investigated the distribution of intratracheally administered thiol-organosilica (thiol-OS) particles in mouse lungs. Toward this end, single doses of thiol-OS particles containing fluorescein (140 nm in diameter) (F140) and rhodamine B (Rh) (Rh160, Rh280, Rh420, Rh640, and Rh1630 with diameters of 160, 280, 420, 640, and 1630 nm, respectively) were administered. After 24 h, fluorescence imaging revealed homogeneous fluorescence with a patchier pattern on the lung surface and no difference among the six particle sizes. Simultaneous dual administration of Rh and F140 particles did not reveal any size-dependent differences in the lung surface fluorescence. Fluorescence microscopy of the lung sections revealed a similar tissue distribution in the fluorescent areas of Rhs and F140. Some fluorescent areas showed one type of particle fluorescence or only one fluorescence. Cellular distribution of particles was observed in bronchoalveolar lavage cells and lung sections under a high magnification, and correlative light and electron microscopy revealed large cells with fluorescence corresponding to both particle types and small cells with fluorescence of individual particle types, indicating a cell-subset-dependent particle size effect. Rh280, Rh420, and Rh640 exhibited significant size effects and were taken up by alveolar macrophages. Extracellular particles were observed, indicating that saturation exceeded the particle dose threshold in the alveoli. F140 taken up by small and large macrophages colocalized with CD68, CD11c, and CD11b and correlated with CD11c. The size effect, intracellular localization, and extracellular distribution of particles provide insights into lung and systemic drug delivery.

From Streamline to Pathline: Visualizing Particle Trajectories Under Changing Velocity Fields

Summary Monitoring of subsurface fluid motion is critical for optimizing hydrocarbon production and CO2 sequestration. Streamlines are frequently used to visualize fluid flow; however, they provide only an instantaneous snapshot of the velocity field and do not offer an exact representation of fluid movement under varying field conditions. In contrast, pathlines are constructed by tracking individual particles within the fluid, enabling us to trace the movement of these particles as they traverse through changing velocity fields. Pathline is the trajectory that an individual fluid particle follows in the reservoir. It can be thought of as “recording” the path of a fluid element in the flow field for a given time interval. Pathlines are distinct from streamlines which represent a snapshot of the velocity field at a given instant. The direction the path takes is determined by the streamlines at a specific instant. To start with, streamlines are traced based on the grid face fluxes of finite-difference simulation. Streamline tracing continues till the time of flight equals the current time. The endpoints of the current streamlines become the starting points for the next tracing period. Thus, our formulation incorporates changing flow fields, and the process is repeated for each time interval until the end. The proposed injection monitoring method is tested using a 3D field-scale model with complex geologic features to demonstrate its power and utility. The pathlines were compared with streamlines, as well as the water saturation distribution. Three scenarios are tested: a constant well schedule, a changing well schedule with partial shut-in, and a changing well schedule with a whole field cessation. Results indicate that the pathline provides a more accurate swept volume, consistent with saturation distribution. The robustness of our algorithm and implementation is demonstrated with a complex embedded discrete fracture model (EDFM) to visualize flow patterns in discrete facture network. Pathlines display the fluid flow across fractures and are subsequently used to explore the sweep efficiency and the well connectivity.

Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy.

In light of the growing awareness regarding the ubiquitous presence of microplastics (MPs) in our environment, recent efforts have been made to integrate Artificial Intelligence (AI) technology into MP detection. Among spectroscopic techniques, Raman spectroscopy is preferred for the detection of MP particles measuring less than 10 μm, as it overcomes the diffraction limitations encountered in Fourier transform infrared (FTIR). However, Raman spectroscopy's inherent limitation is its low scattering cross section, which often results in prolonged data collection times during practical sample measurements. In this study, we implemented a convolutional neural network (CNN) model alongside a tailored data interpolation strategy to expedite data collection for MP particles within the 1-10 μm range. Remarkably, we achieved the classification of plastic types for individual particles with a mere 0.4 s of exposure time, reaching an approximate confidence level of 85.47(±5.00)%. We postulate that the result significantly accelerates the aggregation of microplastic distribution data in diverse scenarios, contributing to the development of a comprehensive global microplastic map.

Determination of non-combustible impurities in detonation nanodiamond powder

The study of the physicochemical (including adsorption) properties of nanodiamond requires the possibility of reproducible generation of the carbon surface of an individual particle without metal impurities of unknown composition. This kind of surface can be obtained through an additional deep cleaning of a commercially available sample. This article is dedicated to the study of the composition of non-combustible impurities of detonation nanodiamond powder. Using inductively coupled plasma mass spectrometry, iron and titanium were identified as the main metal components of the unburned residue, and the presence of Cr, Ni, Zr, As, and Sb was qualitatively established. The expected composition of the main molecular ions formed on the surface of the unburned residue in the course of laser desorption/ionisation mass spectrometry was presented. Based on the results of mass spectrometry analysis, a method of two-stage chemical treatment of detonation nanodiamond powder was proposed, which allowed reducing the mass fraction of non-combustible impurities during annealing in air from 2.0 to 0.1%.

Artificial Intelligence-Assisted Multiparameter Size Discrimination of Silver Nanoparticles through Electrochemical Collision.

Single particle collision is an important tool for size analysis at the individual particle level; however, due to complex dynamic behaviors of nanoparticles on the surface of an electrode, the accuracy of size discrimination is limited. A silver (Ag) nanoparticle (NP) was chosen as the research target, and the dynamic behavior of Ag NPs was simplified by enhancing adsorption between Ag NP and Au ultramicroelectrode (UME) in alkaline media. Immediately after, accurate dynamic and thermodynamic information on single Ag NP was accurately extracted from collision events, including current intensity, transferred charge, and duration time. On the basis that there were differences between parameters of different-sized Ag NPs, multiparameter size discrimination was proposed, which improved the accuracy compared to single-parameter discrimination. More intriguingly, multiparameter analysis was combined with artificial intelligence, a tool adept at processing multidimensional data, for the first time. Finally, artificial intelligence-assisted multiparameter size discrimination was successfully used to intelligently distinguish mixed Ag NPs, with an optimal accuracy of more than 95%. To sum up, the artificial intelligence-assisted multiparameter method showed an excellent ability to quickly achieve the most accurate size discrimination of nanoparticles at the level of individual particle and provide an effective guidance for the application of nanoparticles.