Types Of Particles Research Articles

Total progeny in the near-critical multi-type Galton-Watson processes with immigration

In this paper, we study Galton-Watson branching processes with immigration. These processes are an extension of the classical Galton-Watson model, incorporating an additional mechanism where new individuals, called immigrants, enter the population independently of the reproduction dynamics of existing individuals. We focus on the multi-type case, where individuals are classified into several distinct types, and the reproduction law depends on the type.A crucial role in the study of multi-type Galton-Watson processes is played by the matrix $M$, which represents the expected number of descendants of different particle types, and its largest positive eigenvalue, $\rho$. Sequences of branching processes with primitive matrices $M$ and eigenvalues $\rho$ converging to $1$ are referred to as near-critical. Our focus is on the random vector $Y_n$, representing the total number of particles across all generations up to generation $n$, commonly called the total progeny, in near-critical multi-type Galton-Watson processes with immigration. Assuming the double limit $n(\rho - 1)$ exists as $n \to \infty$ and $\rho \to 1$, we establish the limiting distribution of the properly normalized vector~$Y_n$. This result is derived under standard conditions imposed on the probability generating functions of the offspring and immigration component.

Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics

Abstract. The continued increase in global plastic production and poor waste management ensures that plastic pollution will be a serious environmental concern for years to come. Because of their size, shape, and relatively low density, plastic particles between 1 and 1000 µm in size (known as microplastics or MPs) emitted directly into the environment (“primary”) or created due to degradation (“secondary”) may be transported through the atmosphere, similarly to other coarse-mode particles such as mineral dust. MPs can thus be advected over great distances, reaching even the most pristine and remote areas of Earth, and may have significant negative consequences for humans and the environment. The detection and analysis of MPs once airborne, however, remains a challenge because most observational methods are offline and resource-intensive and, therefore, not capable of providing continuous quantitative information. In this study, we present results using an online in situ airflow cytometer (SwisensPoleno Jupiter; Swisens AG; Emmen, Switzerland) – coupled with machine learning – to detect, analyze, and classify airborne single-particle MPs in near real time. The performance of the instrument in differentiating between single-particle MPs of five common polymer types (including polypropylene, polyethylene, polyamide, poly(methyl methacrylate), and polyethylene terephthalate) was investigated under laboratory conditions using combined information about their size and shape (determined using holographic imaging) and fluorescence measured using three excitation wavelengths and five emission detection windows. The classification capability using these methods was determined alongside other coarse-mode aerosol particles with similar morphology or fluorescence characteristics, such as a mineral dust and several pollen taxa. The tested MPs exhibit a measurable fluorescence signal that not only allows them to be distinguished from other fluorescent particles, such as pollen, but also differentiated from each other, with high (> 90 %) classification accuracy based on their multispectral fluorescence signatures. The classification accuracies of machine learning models using only holographic images of particles, only the fluorescence response, and combined information from holography and fluorescence to predict particle types are presented and compared. The last model, using both the holographic images and fluorescence information for each particle, was the most optimal model used, providing the highest classification accuracy compared to employing models using only the holography or fluorescence response separately. The results provide a foundation for significantly improving the understanding of the properties and types of MPs present in the atmosphere.

The study of hadronic rescattering on K∗0 resonance yield in baryon-rich QCD matter

Abstract The effect of hadronic rescattering on K ∗0 resonance yield can be studied by measuring K ∗0/K ratio as a function of centrality or multiplicity. This study investigates how the size of the system and the chemical composition (meson–meson versus meson–baryon interaction) of the matter formed in heavy-ion collisions impact the process of hadronic rescattering. It is shown that existing calculation of K ∗0/K ratio, which considers the interaction of K ∗0 and K mesons with only light mesons in the hadronic medium (neglecting interactions with baryons), fails to explain the measured K ∗0/K ratio at RHIC beam energy scan (BES) energies. To understand the multiplicity dependence of the K ∗0/K ratio at RHIC BES and SPS energies ( s NN < 20 GeV), the ultra relativistic quantum molecular dynamics (UrQMD) model is employed. UrQMD calculations suggest that for a given multiplicity, K ∗0/K is more suppressed in Au+Au collision at s NN = 7.7 GeV, compared to that in s NN = 200 GeV. This is possibly because of formation different type of quantum chromodynamics medium at 200 and 7.7 GeV, and change in interaction cross-section between hadrons with change in particle type and energy.

Amniotic fluid-derived stem cells: potential factories of natural and mimetic strategies for congenital malformations

Background:Mesenchymal stem cells (MSCs) derived from gestational tissues offer a promising avenue for prenatal intervention in congenital malformations although their application is hampered by concerns related to cellular plasticity and the need for invasive, high-risk surgical procedures. Here, we present naturally occurring exosomes (EXOs) isolated from amniotic fluid-derived MSCs (AF-MSCs) and their mimetic analogs (MIMs) as viable, reproducible, and stable alternatives. These nanovesicles present a minimally invasive therapeutic option, addressing the limitations of MSC-based treatments while retaining therapeutic efficacy.Methods:MIMs were generated from AF-MSCs by combining sequential filtration steps through filter membranes with different porosity and size exclusion chromatography columns. A physicochemical, structural, and molecular comparison was conducted with exosomes (EXOs) released from the same batch of cells. Additionally, their distribution patterns in female mice were evaluated following in vivo administration, along with an assessment of their safety profile throughout gestation in a mouse strain predisposed to neural tube defects (NTDs). The possibility to exploit both formulations as mRNA-therapeutics was explored by evaluating cell uptake in two different cell types(fibroblasts, and macrophages) and mRNA functionality overtime in an in vitro experimental setting as well as in an ex vivo, whole embryo culture using pregnant C57BL6 dams.Results:Molecular and physiochemical characterization showed no differences between EXOs and MIMs, with MIMs determining a threefold greater yield. Biodistribution patterns following intraperitoneal administration were comparable between the two particle types, with the uterus being among targeted organs. No toxic effects were observed in the dams during gestation, nor were there any malformations or significant differences in the number of viable versus dead fetuses detected. MIMs delivered a more intense and prolonged expression of mRNA encoding for green fluorescent protein in macrophages and fibroblasts. An ex-vivo whole embryo culture demonstrated that MIMs mainly accumulate at the level of the yolk sac, while EXOs reach the embryo.Conclusions:The present data confirms the potential application of EXOs and MIMs as suitable tools for prevention and treatment of NTDs and proposes MIMs as prospective vehicles to prevent congenital malformations caused by in utero exposure to drugs.

Prediction of the rheological properties of mortar compounded with desert sand and Pisha sandstone ceramic sand based on density-form coupling

Ordos, China has a large amount of environmentally hazardous Pisha sandstone and desert sand. Pisha sandstone ceramic sand and desert sand can be compounded to prepare light and fine aggregates, which are often used in construction mortar. However, it is unknown how the density and particle shapes of light and fine aggregates affect their rheological properties. In this study, we establish a predictive model for the rheological properties of light and fine aggregate mortar, using the Krieger-Dougherty and Chateau-Ovarlez-Trung models as a basis. First, density and particle morphology parameters are introduced and modified for light and fine aggregate mortars. Then, the morphological parameters of different types of fine aggregate particles were measured using image analysis techniques. This showed that the roundness of the particles decreases with decreasing density, and both the roughness and the aspect ratio increase. A regression model between the particle morphology parameters and characteristic viscosity was accordingly developed. The rheological properties of different types of mortars were examined. We find that as the particle density decreases, the yield stress and plastic viscosity decrease continuously, showing shear thickening behavior. Based on the coupled density-form effect, a modified Krieger-Dougherty model and Chateau-Ovarlez-Trung model are proposed. These proposed models result in more accurate predictions of plastic viscosity and yield stress for light and fine aggregate mortars.

Tumor Suppressor miR-34a: Potential Biomarker of TACE Response in HCC.

TACE induces variable systemic effects by producing factors that promote inflammation, oncogenesis, and angiogenesis. Here we compare concentrations of microRNAs (miR-21, miR-210 and miR-34a) and vascular endothelial growth factor (VEGF) in hepatocellular carcinoma (HCC) patients undergoing TACE with degradable (DSM) and nondegradable (DEB) particles and potential use of these biomarker changes for prediction of patient outcomes. Overall, 52 patients with HCC treated with DSM TACE (24 patients) and DEB TACE (28 patients) were included in this prospective study. Concentrations of studied biomarkers were measured from blood plasma preprocedurally, immediately (< 90min) postprocedurally, and 24-h after TACE. Levels were compared between DSM and DEB TACE and correlated with treatment response six and 12months after the first TACE. Both DSM and DEB TACE elevated plasma levels of miR-21, miR-34a, and miR-210 at 24h post-procedure compared to baseline levels (FC 1.25-4.0). MiR-34a elevation immediately after TACE was significantly associated with nonprogressive disease compared to those with progressive disease at both sixmonths (FCa: p = 0.014) and 12months (FCa: p = 0.029) post-TACE. No significant biomarker changes were found between the embolization particle groups. However, VEGF levels showed a decrease only in the DSM TACE group (FC24: p = < 0.001). Embolization particle type did not significantly impact miRNA or VEGF changes post-TACE. However, miR-34a elevation immediately after the procedure predicts better patient outcome and may prove useful as a biomarkers for the monitoring of clinical outcomes. Level 3 Prospective cohort study.

Effective pervaporation dehydration of acrylic acid using alginate/BaTiO3 and alginate/TiO2 mixed matrix membranes

To develop an efficient pervaporation technique for acrylic acid dehydration, four types of BaTiO3 and TiO2 particles with various shapes and structures (commercial solid TiO2-S, synthesized sea urchin-like TiO2-U, commercial solid BaTiO3-S, and synthesized porous BaTiO3-P) were incorporated into sodium alginate (NaAlg) to fabricate mixed matrix membranes (MMMs) for comparison. 18 h crosslinking with CaCl2 was conducted to improve the acid-resistant property of Alg-based membrane. The filler particles and MMMs were systematically characterized via SEM, FTIR, BET, XRD, AFM, EDX, and TGA. The hydrophilicities of MMMs were enhanced with increasing filler contents, evidenced by the decreased water contact angles and raised water swelling degrees. When applied to pervaporate 5 wt% water/acrylic acid solution at 25 ℃, the MMMs significantly improved total permeation fluxes and separation factors compared to the pristine Alg membrane. When TiO2 particles were incorporated, the Alg/TiO2-U MMMs obtained lower separation factors than the Alg/TiO2-S MMMs due to the increased acrylic acid transport through big nanorod gaps and interfacial voids formed by the sea urchin-like TiO2-U shapes. On the other hand, the Alg/BaTiO3-P MMMs were superior to the Alg/BaTiO3-S MMMs in the dehydration performance, which was attributed to their porous structures. The optimal separation efficiency occurred at Alg/2 % BaTiO3-P MMM (water purity of 99.8 wt% and separation factor of 8700) with a stable performance over 168 h. When the optimal filler content was exceeded, the separation factor would decrease owing to the extra voids resulted from particle agglomeration. Moreover, the pervaporation performance could be enhanced with increasing feed temperature. In contrast, the separation selectivity was reduced, but the total permeation flux was improved, with increasing feed water concentration. It was further confirmed that the Alg/2 % BaTiO3-P MMM could successfully pervaporate a three-component mixture (10.3 wt% water/acetic acid/acrylic acid) at 65 ℃ to produce high-purity water (98.3 wt%). In conclusion, the design of Alg/BaTiO3 and Alg/TiO2 MMMs provides a potential approach for effective acrylic acid dehydration.

Stimulated Pickering emulsion stabilized by binary particles with contrasting wettability and trace surfactant

The investigationof self-assembled particle structures at fluid interfaces has gained considerable interest across various fields. In particular, Pickering emulsions (PEs) stabilized by binary particles co-assembling at these interfaces have attracted even more attention. To explore the self-assembly process of superhydrophobic and superhydrophilic particles at oil–water (O/W) interfaces influenced by a trace amount of surfactant, a series of experiments were conducted. A small quantity (0.01 mmol·L−1) of dodecyltrimethylammonium bromide (DTAB) was introduced, establishing a variable stable emulsion system with high salt tolerance, capable of withstanding up to 6 mol·L−1 NaCl, in synergism with the particles. The DTAB concentration required to stabilize the O/W emulsion with particles was as low as 0.001 mmol·L−1. The stability and type of the resulting emulsion could be adjusted by varying the ratio of hydrophobic to hydrophilic particle (R) or by modifying acid/base conditions. The systems exhibited robust cyclic acid/base regulated demulsification and phase inversion behavior, maintaining stability over at least 10 cycles. Results indicated that the stability and nature of the emulsion were influenced by the curvature of the interface formed by the self-assembled interfacial particle structures, arising from the competitive adsorption of both types of particles and trace surfactant. Further analysis revealed a correlation between interfacial curvature and the surface charges of the particles, which could be modulated through DTAB adsorption. This study elucidated the behavior of hydrophilic and hydrophobic particle self-assembly at interfaces, provided a straightforward strategy for co-preparing switchable emulsions using superhydrophilic and superhydrophobic particles, and expanded the range of amphiphilic particles available for producing PEs. This approach presents significant potential for future research and applications in the field of PEs.

Feature Extraction and Attribute Recognition of Aerosol Particles from In Situ Light-Scattering Measurements Based on EMD-ICA Combined LSTM Model

Accurate identification and monitoring of aerosol properties is crucial for understanding their sources and impacts on human health and the environment. Therefore, we propose a feature extraction and attribute recognition method from in situ light-scattering measurements based on Bayesian Optimization, wavelet scattering transform, and long short-term memory neural network (BO-WST-LSTM), with empirical mode decomposition (EMD) and independent component analysis (ICA) algorithm for signal preprocessing. In this study, an experimental platform was utilized to gather light-scattering signals from particles with varying characteristics. The signals are then processed using the EMD-ICA noise reduction technique. Then, the wavelet scattering network is used to realize the adaptive extraction of the characteristics of the particle light-scattering signal, and the Bayesian Optimization model is used to optimize the hyperparameters of the LSTM neural network. The extracted scattering coefficient matrix is input into the LSTM for iterative training. Finally, the SoftMax layer’s probability classification method is applied to the identification of particle attributes. The results show that the multi-angle particle light-scattering signal detection system designed and built in this study performs well and is capable of effectively collecting particle light-scattering signals. At the same time, the proposed new method for particle property recognition demonstrates good classification performance for six different types of particles with a particle size of 2 μm, achieving a classification accuracy of 98.83%. This proves its effectiveness in recognizing particle properties and provides a solid foundation for particle identification.

Effects of carbon black particles on human monocyte-derived macrophages: type-dependent pro-inflammatory activation in vitro.

Carbon black is a key component of air-borne particulate matter, linked to adverse health outcomes, such as increased susceptibility to respiratory infections and chronic pulmonary disease exacerbations. Fine and ultrafine particles can penetrate the lungs, enter the bloodstream, and induce pathogenetic events. Macrophages play a crucial role in responding to inhaled particles, including carbon black, by initiating an innate immune response and upregulating pro-inflammatory cytokines and anti-oxidative enzymes. This study investigates the effects of carbon black particles on human monocyte-derived macrophages in vitro at a concentration of 10µg/ml, offering insights into their potential role in disease pathogenesis. We have compared two commercially available carbon black particle types using various physicochemical techniques and assessed their biological effects on monocyte-derived macrophages. We have evaluated changes in cell viability, morphology, and particle uptake/phagocytosis. Western blot, ELISA, and RT-qPCR measured inflammatory and oxidative stress biomarkers. Both types of carbon black particles induced similar responses in macrophages, including particle uptake, cytokine production, and oxidative stress-related protein expression. The observed changes suggest activation of the Nrf2-mediated antioxidant response, impaired autophagy, and decreased cellular defense against oxidative stress, indicating potential pathways for chronic inflammatory lung disease development.

Golf Ball-like Polymer Particles by Colloidal Etching.

The unique core-corona structure of raspberry-like particles makes it an ideal template for the fabrication of golf ball-like particles by selective removal of the corona particles. However, few types of raspberry-like polymer-polymer core-corona composite particles have been used as the templates, probably because it is relatively difficult to synthesize well-defined raspberry-like polymer-polymer composite particles. We describe, for the first time, the fabrication of golf ball-like polymer particles by colloidal etching of raspberry-like poly(glycidyl methacrylate)-polystyrene (PGMA-PS) composite particles in a tetrahydrofuran (THF)/water medium with a higher THF concentration. Well-defined golf ball-like polymer particles can be obtained within 20 s. The influence of various synthesis parameters including the THF concentration, etching time, and composite particle size on the morphology of the resulting particles has been investigated, which suggests that a sufficient THF concentration is a prerequisite for the rapid formation of the golf ball-like morphology.

Enhancing Skin Regeneration Efficacy of Human Dermal Fibroblasts Using Carboxymethyl Cellulose-Coated Biodegradable Polymer.

Polylactic acid (PLA) is extensively used in the medical and cosmetic industries for skin regeneration and as a dermal filler due to its biocompatibility and biodegradability. However, the effectiveness of PLA as a cosmetic filler is limited by its slow degradation rate and poor cell attachment properties. Recent studies have focused on enhancing the performance of PLA by combining it with other materials. This study aimed to evaluate the performance of carboxymethyl cellulose (CMC), known for its high biocompatibility, in comparison with the widely used hyaluronic acid (HA). Two types of PLA-based particles, HA-PLA and CMC-PLA were synthesized by combining PLA with HA and CMC, respectively. After characterizing the particles, we evaluated cell adhesion and viability using human dermal fibroblasts and analyzed gene and protein expression related to cell attachment and angiogenic paracrine factors. The CMC-PLA particles maintained a more uniform size distribution than the HA-PLA particles and exhibited superior cell adhesion properties. Cells attached on the CMC-PLA particles showed enhanced secretion of angiogenic paracrine factors, suggesting a potential improvement in therapeutic efficacy. CMC-PLA particles demonstrated superior cell adhesion and secretion capabilities compared with HA-PLA particles, indicating their potential for application in skin regeneration and tissue recovery. Further research, including in vivo studies, is required to fully explore and validate the therapeutic potential of CMC-PLA particles.

An Axial-Vector Photon in a Mirror World

We discuss a theory in which the left- and right-handed axial-vector photons refer to long- and short-lived bosons of true neutrality, respectively. Such a difference in lifetimes expresses the unidenticality of masses, energies, and momenta of axial-vector photons of the different components, generalizing the classical Klein-Gordon equation to the case of C-odd types of particles with a nonzero spin. Together with a new Dirac equation for truly neutral particles with the half-integral spin, the latter admits the existence of the second type of the local axial-vector gauge transformation responsible for origination in a Lagrangian of an interaction Newton component, which gives an axial-vector mass to all the interacting particles and fields. The quantum axial-vector mass, energy, and momentum operators constitute herewith a new Schrödinger equation, confirming that each of them can individually influence on the matter field. They define at the new level, namely, at the level of the mass-charge structure of gauge invariance another Euler-Lagrange equation such that it has an axial-vector nature.

Monitoring partially observed average kiloelectron-volt emissions using exponentially weighted moving average chart

The average lifespan of particles is a key parameter in nuclear physics, critical for identifying different particle types. Modern particle detectors are highly effective at detecting the arrival of individual radioactive nuclei and observing their decay events. However, challenges arise when matching arrivals with departures, especially when departures are only partially observed. One inefficient approach involves conducting experiments with very low arrival rates to facilitate matching. The kiloelectron-volt E(keV) emission is obtained during this radioactive process. We propose a Modified Distance Weighted Exponentially Weighted Moving Average (MDE) control chart for monitoring kiloelectron-volt E(keV) data. Control charts are vital in quality control, offering real-time insights into process stability by distinguishing between normal (common-cause) and unusual (special-cause) variations. This capability enables precise decision-making, showing when a process is within control or requires intervention, thus preventing unnecessary adjustments and costly errors. By highlighting trends and shifts, control charts foster continuous improvement, identifying areas where processes can be optimized. The proposed control chart is developed for Weibull lifetimes under type-I censoring. For constructing an efficient control charting structure, we employed the conditional median (CM) methods. The goal is to detect changes in the mean of Weibull lifetimes with censored data under both known and estimated parameter conditions. The performance of the proposed MDE chart is evaluated by the average run length (ARL). Besides a simulation study, a real-life data set on kiloelectron-volt E(keV) related to the alpha decays of the 177 Lutetium isotope is also discussed. It is observed that the proposed chart is more efficient than existing control charts for monitoring kiloelectron-volt E(keV) censored data.

Electrospray-Scanning Mobility Particle Sizer (ES-SMPS) Technique: Superior Sizing and Multimodal Characterization of Colloidal Nanoparticles Compared to NTA and DLS.

This study primarily employed three techniques─electrospray-scanning mobility particle sizer (ES-SMPS), nanoparticle tracking analysis (NTA), and dynamic light scattering (DLS)─to assess multimodal samples. For monodisperse particles, both ES-SMPS (all sizes) and NTA (for particles larger than 40 nm) accurately determined the mean size, while DLS overestimated it. The ES-SMPS technique demonstrated precision in particle counting for multimodal samples, with a standard deviation of around 2.5-4%. Conversely, NTA's ability to count particles potentially leads to misinterpretation. The ES-SMPS approach could identify particle peaks in multimodal (bimodal, trimodal, and tetramodal) samples and show the relatively accurate position of the mode diameter. In contrast to ES-SMPS, DLS and NTA have weaknesses in characterizing multimodal samples. While NTA's performance depends on the optical properties of particles and cannot measure silica particles smaller than 30-40 nm, ES-SMPS is independent of light scattering and can handle particles as small as ∼13 nm. The ES-SMPS also excelled in separating particle peaks of the bimodal sample with a size interval gap of 10 nm, whereas NTA needs at least 20-50 nm depending on the particle type. To sum up, the ES-SMPS method performs better and provides more accurate measurements for characterizing multimodal samples compared to NTA and DLS.

Dosimetric and biological impact of activity extravasation of radiopharmaceuticals in PET imaging.

The increasing use of nuclear medicine and PET imaging has intensified scrutiny of radiotracer extravasation. To our knowledge, this topic is understudied but holds great potential for enhancing our understanding of extravasation in clinical PET imaging. This work aims to (1) quantify the absorbed doses from radiotracer extravasation in PET imaging, both locally at the site of extravasation and with the extravasation location as a source of exposure to bodily organs and (2) assess the biological ramifications within the injection site at the cellular level. A radiation dosimetry simulation was performed using a whole-body 4D Extended Cardiac-Torso (XCAT) phantom embedded in the GATE Monte Carlo platform. A 10-mCi dose of 18F-FDG was chosen to simulate a typical clinical PET scan scenario, with 10% of the activity extravasated in the antecubital fossa of the right arm of the phantom. The extravasation volume was modeled as a 5.5mL rectangle in the hypodermal layer of skin. Absorbed dose contributions were calculated for the first two half-lives, assuming biological clearance thereafter. Dose calculations were performed as absorbed doses at the organ and skin levels. Energy deposition was simulated both at the local extravasation site and in multiple organs of interest and converted to absorbed doses based on their respective masses. Each simulation was repeated ten times to estimate Monte Carlo uncertainties. Biological impacts on cells within the extravasated volume were evaluated by randomizing cells and exposing them to a uniform radiation source of 18F and 68Ga. Particle types, their energies, and direction cosines were recorded in phase space files using a separate Geant4 simulation to characterize their entry into the nucleus of the cellular volume. Subsequently, the phase space files were imported into the TOPAS-nBio simulation to assess the extent of DNA damage, including double-strand breaks (DSBs) and single-strand breaks (SSBs). Organ-level dosimetric estimations are presented for 18F and 68Ga radionuclides in various organs of interest. With 10% extravasation, the hypodermal layer of the skin received the highest absorbed dose of 1.32±0.01Gy for 18F and 0.99±0.01Gy for 68Ga. The epidermal and dermal layers received absorbed doses of 0.07±0.01Gy and 0.13±0.01Gy for 18F, and 0.14±0.01Gy and 0.29±0.01Gy for 68Ga, respectively. In the extravasated volume, 18F caused an average absorbed dose per nucleus of 0.17±0.01Gy, estimated to result in 10.58±0.50 DSBs and 268.11±12.43 SSBs per nucleus. For 68Ga, the absorbed dose per nucleus was 0.11±0.01Gy, leading to an estimated 6.49±0.34 DSBs and 161.24±8.12 SSBs per nucleus. Absorbed doses in other organs were on the order of micro-gray (µGy). The likelihood of epidermal erythema resulting from extravasation during PET imaging is low, as the simulated absorbed doses to the epidermis remain below the thresholds that trigger such effects. Moreover, the organ-level absorbed doses were found to be clinically insignificant across various simulated organs. The minimal DNA damage at the extravasation site suggests that long-term harm, such as radiation-induced carcinogenesis, is highly unlikely.

Microwave absorption properties and mechanism of novel apatite-type materials Mn₂Gd₇.₅Ce₀.₅(SiO₄)₆O₂

Manganese minerals possess a high intrinsic magnetic moment, making them excellent materials for microwave absorption. Rare earth elements, with their unique electronic structures and interactions between spin electrons and orbitals, can further enhance the performance of absorbing materials. In this study, we designed a novel microwave absorbing material by incorporating manganese into an apatite structure with adjustable chemical composition. The material Mn₂Gd₇.₅Ce₀.₅(SiO₄)₆O₂, exhibiting specific microwave absorption properties, was synthesized using a high-temperature solid-phase method. The results indicate that at a sample thickness of 5 mm, the absorption frequency bandwidth below −10 dB within the 2–12 GHz range reaches 1.2 GHz, with a peak absorption of −21.78 dB. Additionally, smaller particles were prepared using the sol-gel method, achieving a peak absorption of −39.75 dB. The primary absorption mechanism for both particle types is attributed to magnetic loss. This work presents a new approach to designing microwave absorbing materials and significantly contributes to expanding the range of apatite-type materials.

Application of a modified set of GoogLeNet and ResNet-18 convolutional neural networks towards the identification of environmentally derived-MPLs in the Yadkin-pee dee river basin

Microplastic (MPL) abundance is a well-elucidated problem in the marine environment but not so much in the terrestrial environment. In order to contribute to this research gap, a field study was performed in the Yadkin-Pee Dee River Basin. Due to their heterogenous nature and difficulty in characterization, a diverse set of pictorial training data from µ-Raman was used to perform transfer learning on 2 CNNs of interest: GoogLeNet (GN) and ResNet-18 (RN). In the first trial, using 10% of the initial training dataset, the CNNs exhibited high levels of accuracy rates, generally above 90%. Irrespective of spectroscopic mode, marginal improvements in accuracy rates were seen, with the best improvements occurring in the Raman-based models (U[GN(FTIR), RN(Raman), GN(FTIR), and RN(Raman)]: 39, 42, 38.5, and 34.5; p-value: 1, .6753, .9719, and .4978). However, for the external trial, pictorial data from Primpke (FTIR) and DongMiller (Raman) was predicted less accurately, with the largest loss occurring across the following sets: U[GN(Raman) and RN(FTIR)], 45.5 and 35; p-value:, .3268 and .5476. However, set RN fared marginally better, and due to the usage of µ-Raman, and its performance in the 10% trial, RN18_ADAM_.0011 was selected as the champion model for the field study data. In the unknown microparticle (MP) trial, generally, the most ID’d polymer type was CA, PET, and PE representing a relative concentration range for a given water source and area (MPL/MP) of 4.17–37.5%, 4.17–8.33%, and 4.17–8.33% for CNN and OpenSpecy (OS). A FEDS algorithm, equipped with natural and synthetic polymer standards and biological material, used to compare the strength of each model determined similar frequency in ascertaining positive MPL results across both models with corroboration between the CNN and OS around 1/3 of the time. Results indicate the models detect MPLs with similar frequency elucidating comparable strength of the CNN as well as a focus on particle type distribution rather than individual identification. Moreover, the largest influential factor in this study appears to be either laundry wastewater effluent or atmospheric deposition, which is stressed as a primary focus of remediating MPLs in similar freshwater environments. Lastly, it appears that these MPL are of primary origin as opposed to secondary in the oceanic and coastal environments.

Experimental study on compaction characteristics of coarse-grained soils with typical particle shapes affected by vibration frequencies

Coarse-grained soils, common fillers in subgrades, bear vibration loads in the construction stage, the compaction effects of which are affected by internal and external factors such as particle shapes and vibration frequencies. To enhance the understanding of compaction performance, this study investigated the vibration-induced deformation and energy dissipation of coarse-grained soils with two typical-shaped particles (pebbles and rubbles) through vibration compaction tests with different frequencies. Results show that both resilience modulus and damping ratio increase with increasing frequency, and the resilience modulus of rubbles is more stable compared with pebbles. Energy dissipation decreases with increasing vibration cycles at low frequencies, yet it increases with increasing vibration cycles at high frequencies. The dry density increment of rubbles is greater than that of pebbles, which is closely related to energy dissipation. Furthermore, the relationships between energy dissipation and dry density increment affected by particle shapes were discussed by discrete element simulations.

Objective classification for solid hydrometeor particles using deep learning

Various small particles are present in the clouds. Hydrometeor videosonde (HYVIS) is an in situ instrument to observe such particles. The movies were used to capture hydrometeors with approximately 110,000 frames per sounding. When the particles in such movies were manually classified and measured sizes of particles for every several frames, it took an unrealistically long time to perform statistical analysis because of the large number of observed frames. Particle classification is subjective for the observers. This study developed a technique to classify cloud particles objectively using deep learning to overcome these problems and investigated the statistical microphysical characteristics of clouds. This study used the deep learning method You Only Looking Once for detection and classification. The training data were obtained using HYVIS in the Republic of Palau in 2013. The results trained using only HYVIS images showed low validity because some types of particles in the training data were insufficient. The data for typical particle shapes were augmented to improve the classification. Thus, the validity of classification using augmented data was improved. We used the Yonaguni Island HYVIS observation results from manual and artificial intelligence (AI) classification. The AI tended to classify the less irregular type than the manual, but no other significant differences were found. We believe this AI classification system will be for cloud microphysical studies on solid-phase particles.