Search Results (725)

Understanding the ecological impacts of nanoplastics requires evaluation metrics beyond conventional mass-based concentrations. In this study, we investigated the generation, characterization, and phytotoxic effects of environmentally relevant plastic particles in hydroponically grown lettuce (Lactuca sativa), focusing on particle size, shape, and number concentration. Low-density polyethylene (LDPE) was degraded using an advanced oxidation process, demonstrating that substantial oxidative degradation is required for the formation of nanoplastics; the resulting LDPE particles exhibited a broad size distribution ranging from the nanoscale to the micrometer scale, containing nanoscale domains (peak size ~20 nm, average size ~30 nm), and showed predominantly ellipsoidal morphologies derived from cross-linked polymer regions. In contrast, polystyrene (PS) particles of defined sizes (~600 nm and ~2000 nm) were prepared via mechanical fragmentation, exhibiting sharp-edged, flake-like morphologies. Laser scanning microscopy revealed uptake and translocation of LDPE particles from roots to aerial tissues, whereas larger PS particles showed limited transport. Growth inhibition analysis based on particle number concentration (10¹⁰–10¹⁷ particles/mL) showed a stepwise concentration–response relationship for LDPE particles, with inhibition levels increasing from approximately ~30% at low concentrations to high levels of inhibition at the highest concentrations In contrast, PS particles caused significant root damage (e.g., clear surface disruption observed in microscopy) and growth inhibition (~30–40%) even at relatively low number concentrations (~10¹⁰–10¹² particles/mL), likely due to their sharp-edged morphology. Overall, plant responses to plastic particles were governed by an interplay of size, shape, and number concentration, highlighting the importance of particle morphology and concentration metrics in agroecosystem risk assessment. Full article

We present a novel algorithm for the retrieval of non-spherical particle microphysical parameters (PMP) from 3β + 2α + 3δ optical data taken with multiwavelength lidar. The 3β + 2α + 3δ optical datasets describe particle backscatter coefficients (β) at three wavelengths, λ = 355, 532, and 1064 nm, particle extinction coefficients (α) at two wavelengths, λ = 355 and 532 nm, and particle linear depolarization ratios (PLDR, δ) at three wavelengths, λ = 355, 532, and 1064 nm. The algorithm can be used for retrieving bimodal particle size distributions (PSDs). The PSDs can comprise mixtures of spheres and spheroids (SS). One or both modes can comprise spheroid-shaped particles or spherically shaped particles. The spheroids are used for approximating an arbitrary ensemble of non-spherical particles. The algorithm works on the basis of a combination of direct and analytical inversion methods. The algorithm uses the spheroid reference look-up table (RLUT) we developed and presented in part 1 of our research work. The algorithm uses constraints regarding the particle complex refractive index (CRI) and information on relative humidity (RH) in the atmosphere (in the case of aerosol lidar observation) for suppressing retrieval uncertainties. We carried out a numerical simulation study to evaluate the algorithm’s performance. In these numerical simulations, we considered perturbed synthetic 3β + 2α + 3δ optical data that mimic different organic carbon (OC)–dust (D) mixtures. Such mixtures are suitable examples for describing bimodal PSDs that consist of a fine mode of spherical particles and a coarse mode of non-spherical particles. The results of the numerical simulation show that (1) the PMPs of each mode of these particle mixtures can be found separately, (2) the mean retrieval errors of the effective radius, number, surface-area, and volume concentrations of these mixtures are 25%, 52%, 9%, and 28%, respectively, and (3) the mean retrieval error of single-scattering albedo (SSA) at 355 nm of these mixtures is as low as ±0.02. SSA retrieval accuracies at 532 and 1064 nm degrade because the complex refractive index (CRI) of OC and D particles depends on the measurement wavelength. In future studies, we will upgrade the algorithm such that it takes into account a spectrally dependent CRI. We also compare the results of our novel algorithm with our TiARA2.1 algorithm. The errors obtained from the TiARA2.1 algorithm are approximately three times larger compared to the errors we obtain with our novel ATLAS algorithm for the case of the OC-D mixtures considered in the present study. We explain the higher accuracy of the PMP retrievals by the use of three PLDRs and the extra constraints placed on CRI and RH. Full article

In this work, the development and validation of an AI- and sensor-based inline quality monitoring system for the analysis of particle size distributions (PSDs) of comminuted construction and demolition waste (CDW) material flows are described. In this, a custom-developed multitask CNN (CDW-MT-CNN) was developed using manually sieve analyzed particles. This model is able to rapidly and simultaneously predict the particle class and weight, essential for the determination of the PSD. The single particle data are then aggregated per raw image, usually consisting of around 1000 particles for full-scale experiments, to acquire a per-image PSD. The inline mounted RGB line scan sensor records high-resolution images in subsecond frequencies. With an inference time of around 54 ms for a single image, this model would be able to provide a PSD every minute in a full-scale plant. For the purpose of inline monitoring of CDW material flows in a comminution process, such intervals are sufficient according to experts and solve existing gaps regarding the upscaling of laboratory-developed systems. Together with the high predictive performance of the model, especially in terms of classification (82% accuracy), it is shown that this technology has potential for monitoring in full-scale plants, for instance by offering operators new insights to improve operation efficiency. Further research should focus on increasing the precision for weight prediction, for instance by increasing the labeled data set with a larger number of unique particles and on methods to verify the performance of the model on pilot or full-scale plants during live operation. Full article

This study investigates the morphological, compositional, and electrochemical properties of carbon materials derived from Pepsi (P) and Coca-Cola (CC) precursors, before and after chemical activation with ZnCl₂. Scanning electron microscopy revealed a lower density of surface cracks in non-activated hydrothermal carbon (NAHC) samples compared to activated carbons (ACs), indicating structural changes induced by the corrosive activation process. Particle size analysis showed an increase in average diameter after activation, particularly pronounced in CC-derived samples, which also exhibited a broader particle size distribution. Elemental mapping confirmed carbon as the dominant and homogeneously distributed element, while oxygen-containing functional groups decreased significantly after activation. Oxygen reduction reaction investigation demonstrated that all synthesized non-activated and activated samples are electrocatalytically active in alkaline solution. CC-NAHC demonstrated the lowest Tafel slope (99 mV dec⁻¹), while activated samples showed higher values, indicating slower kinetics and increased reaction limitations. Despite this, activated carbons—particularly CC-AC—displayed significantly higher diffusion-limited current densities (~−4.8 mA cm⁻² at 1600 rpm), suggesting improved mass transport and conductivity. Furthermore, electron transfer number (n) analysis indicated that P-NAHC and CC-AC follow a near four-electron ORR pathway (n ≈ 3.6–3.9). Full article

Italy is one of Europe’s largest consumers of wood pellets, while domestic production remains comparatively limited. In parallel, wood chips (WC) represent a strategic biofuel for power generation, where particle size distribution (PSD) affects handling and storage. Conventional PSD assessment relies on time-consuming methodology. This study proposes a patent-pending image-processing approach (Shadow Size Distribution—SSD analysis) for PSD classification of WC under bulk conditions. One hundred samples were characterized via both standard analysis and SSD. PSD data were aggregated into fine and coarse macro-fractions and used to define binary class labels. Multivariate analyses (PERMANOVA, PCA) and Support Vector Classifier (SVC) models were employed to evaluate the discriminative capability of SSD features. PCA revealed coherent relationships between PSD macro-variables and key shadow descriptors, particularly shadow number and area. The best SVC configuration achieved 0.77 test accuracy, with strong recall for coarse samples. Although overall performance was constrained by dataset size and imbalance, the results demonstrate that SSD features retain meaningful granulometric information, supporting further development toward automated, in-line PSD monitoring systems. From a sustainability perspective, the proposed SSD-based approach enables faster and potentially in-line monitoring of biomass quality, supporting more efficient combustion processes, reduced emissions, and improved resource management in bioenergy systems. Full article

The pantograph–catenary system is a crucial component of rail transit vehicles, performing the vital function of electric energy transmission. During train operation, the current-carrying components continuously emit particulate matter into the surrounding environment due to friction, and these particulate emissions have a significant impact on human health. However, research on the correlation between the current-carrying friction of carbon contact strips and particulate matter emission characteristics is rarely reported. Based on a semi-enclosed pin-on-disc current-carrying friction and wear test rig, this paper investigates the effects of varying current intensity under different contact load conditions on the friction and wear performance of carbon/copper pairs, as well as the associated particulate matter emission behavior. It reveals the damage characteristics of carbon contact strips, the particulate matter emission characteristics, and the relationship between them under different service conditions. The results indicate that the wear mechanism and particulate matter emission behavior of carbon contact strips are jointly influenced by current magnitude and contact load. In the absence of current, increasing the load exacerbates the mechanical wear on the carbon friction pair surface, while elevating the emission concentration of particles of various sizes and stabilizing the particle size distribution. Under current-carrying conditions, a higher contact load effectively reduces the frequency of arc discharges between the friction pair. Meanwhile, the degree of arc erosion on the contact surface worsens with increasing current intensity. Arc discharges instantaneously lead to a sharp increase in particulate emissions, and the higher the discharge intensity or the greater the number of discharges, the higher the particulate concentration around the contact pair. Full article

Precise in situ characterization of the mechanical properties of lunar regolith is critical for future lunar base construction and resource exploitation. However, existing detection methods predominantly rely on indirect inversion from rover wheel-soil interactions, which exhibit limitations in accuracy, real-time capability, and detection depth. Furthermore, specialized automated equipment capable of adapting to the complex lunar surface environment remains lacking. To address these challenges, this study presents the design and development of a novel autonomous in situ penetration-shear apparatus. The device automatically executes penetration and shear operations while recording real-time data, with a maximum penetration force of 25 N, shear torque of 2.5 N·m, penetration depth of 300 mm, and rotation angle of 360°. Given the maximum normal load constraint of 16 N imposed by the lunar rover platform, 24 probe configurations—varying in conicity, projected area, and vane number—were systematically evaluated using lunar soil simulants with three particle size distributions and two density levels. Multi-objective optimization was conducted to maximize detection efficiency, specifically penetration depth and shear torque, subject to a lightweight payload constraint (16 N). The multi-objective optimization reveals a fundamental trade-off: smaller conicity angles and projected areas favor deeper penetration, while larger projected areas enhance shear torque response. Under the 16 N constraint, the Pareto analysis identifies that a combination of moderate projected area, small conicity, and fewer vanes achieves the most balanced performance across all soil conditions. Results further demonstrate that increasing particle size and density substantially suppress both penetration capability and shear torque response, with compaction being the dominant factor limiting probe advancement under constrained normal loading. Results indicate that the optimal probe configuration comprises a 15° conicity, 324 mm² projected area, and two vanes, achieving an average penetration depth of 51.61 mm and average shear torque of 0.06 N·m across all test conditions. This study validates a complete automated system for characterizing lunar soil mechanical properties and provides an efficient, reliable hardware solution for future unmanned lunar exploration missions through optimized probe design. These findings establish a solid technical foundation for deep, high-precision in situ investigation of lunar soil structure and mechanical parameters, with significant implications for lunar base site selection and In Situ Resource Utilization (ISRU). Full article

Determining the compatible prestress and geometry under self-weight constitutes a key challenge in the form-finding of cable–truss structures. To overcome the limitations of experience-dependent trial methods and enhance computational efficiency, this paper proposes an automated and integrated methodology by synergistically combining a simplified mechanical model with an improved Particle Swarm Optimization (PSO) algorithm. The core of the method lies in formulating the form-finding process as an optimization problem, where the horizontal inclination angles of the lower-chord cables serve as the design variables for all radial cable–truss frames. To efficiently solve this high-dimensional optimization problem, an improved PSO algorithm is proposed, which introduces logistic chaotic mapping for particle initialization and a mutation operator within the iterative loop. Ablation studies confirm the individual contribution of each algorithmic enhancement. The algorithm intelligently searches for the optimal angle set, thereby simultaneously resolving the prestress and geometry. The proposed approach is rigorously validated through two representative numerical examples: a circular Type I and an elliptical Type II cable–truss, considering both cases with and without self-weight. The results demonstrate that the improved PSO-based solution achieves prestress distributions and nodal coordinates in excellent agreement with established benchmark data. More importantly, it attains this high precision with significantly reduced computational cost in terms of particle swarm size and iteration number. In conclusion, this improved PSO-based approach provides an efficient, accurate, and automated tool for the integrated prestress-geometry design of cable–truss structures, demonstrating strong potential for practical engineering application. Full article

Microplastics (MPs) in water treatment plants (WTPs) represent a critical environmental concern, particularly when treated effluent is reused for agricultural irrigation. This study investigates the occurrence, removal efficiency, and characterization of MPs in tertiary-treated wastewater destined for agricultural reuse in water-scarce regions. Additionally, the study examines the influence of sample volume on extrapolated MP concentrations. Despite advanced treatment processes including ultrafiltration achieving removal efficiencies of 89%, substantial quantities of MPs remain in final effluents at concentrations ranging from 89 to 399 MPs/m³ (equivalent to 0.1–0.4 MPs/L) with a mass load of 2 µg/L at the outlet. Morphological analysis revealed a shift from fragment-dominated influent (~50%) to film-dominated effluent (~51%), with blue particles being most prevalent. Size distribution analysis showed distinct peaks: 50–100 µm for fragments, 100–250 µm for films, and 250–500 µm for fibres. Polytetrafluoroethylene (PTFE) emerged as the dominant polymer across all morphotypes. Finally, converting particle counts to mass loads indicated an average decrease from ~11 µg/L at the inlet to ~2 µg/L at the outlet, underscoring that number- and mass-based metrics provide complementary information for risk assessment. Full article

Heritage rammed earth is a special soil material formed by manually selecting and ramming locally available Quaternary surface deposits layer by layer. However, the quantitative influence of material sourcing and rammed technology on the compressive properties of heritage rammed earth remains insufficiently understood, which limits the mechanical assessment and conservation planning of rammed earth sites. In this study, undisturbed rammed earth from 15 Ming Great Wall sites in Northwest China was investigated. Field 3D scanning, particle-size analysis, uniaxial compression testing, mesoscopic structural observation, and DEM analysis were combined to evaluate the effects of material characteristics and rammed technology on the compressive properties of heritage rammed earth. The results show clear regional differences in material characteristics and rammed technology parameters across the 15 sites. Across the five occurrence regions from the Extremely Arid Area to the Semi-Humid Area, dry density, silt fraction, curvature coefficient, and ramming pit distribution area ratio generally decreased, whereas clay and colloidal particle fraction, $d_{60}$, $C_u$, and rammed modulus generally increased. These variations were accompanied by changes in internal fabric, including aggregate proportion, coordination-number difference, high-stress particle proportion, and force-chain particle proportion. The peak stress and failure strain ranged from 0.48 to 1.01 MPa and from 0.03 to 0.07, respectively. Both parameters showed a decreasing regional trend from the extremely arid area to the semi-humid area, following the sequence: extremely arid area, arid area, semi-arid area, cold and humid area, and semi-humid area. From the Extremely Arid Area to the Semi-Humid Area, the shear failure mode changed from single-fork to mixed double-fork and then to intersecting double-fork. Regression analysis further showed that material and rammed technology parameters were closely related to mesoscopic structural parameters, with R² values generally greater than 0.75. These findings suggest that the regional differences in compressive behavior were closely associated with variations in material sourcing, rammed technology, internal fabric, and the load-bearing structure of rammed earth. Full article

In deep geothermal engineering, concrete slabs are prone to thermal cracking. The aggregates and pores are the core influencing factors for this failure behavior. However, existing research methods are unable to accurately capture the microscopic evolution process of thermal cracking and cannot clarify the intrinsic mechanism of how the characteristics of aggregates and pores affect the initiation and propagation of cracks. This limitation restricts the in-depth understanding of the laws of concrete thermal cracking. To address this deficiency, this study employs the discrete element method (DEM) and combines the particle flow program PFC2D to construct a microscopic model of concrete disks. By setting reasonable temperature parameters and thermal load boundaries, a numerical simulation system matching the actual deep geothermal high-temperature environment is established. Three sets of quantitative variables were designed: aggregate particle size (0.003, 0.004, 0.005, 0.006), aggregate volume fraction (0.35, 0.40, 0.45, 0.50), and porosity (0.11, 0.12, 0.13, 0.14). Through controlled variable simulations, the influence laws of each variable on the formation, propagation path, and time evolution of concrete thermal cracks were explored. The quantitative research results show that an increase in aggregate particle size significantly accelerates the generation and propagation of cracks. When the particle size is 0.006, the number of cracks is the highest and the propagation rate is the fastest. The aggregate volume fraction is negatively correlated with the final number of cracks, and 0.50 is the optimal fraction, at which the number of cracks is the smallest. A decrease in the fraction will lead to intensified stress concentration in the cement paste and a sudden increase in the number of cracks. An increase in porosity significantly disrupts the material continuity. When the porosity is 0.14, the bifurcation and connection of cracks are the most significant, while a low porosity of 0.11 can effectively inhibit the overall development process of thermal cracks. In addition, compared with traditional experimental methods and continuous medium numerical simulation techniques, the discrete element method has unique advantages in revealing the internal mechanism of concrete thermal cracking at the microscopic level. It can achieve real-time tracking of the evolution of discrete micro-cracks and the internal stress distribution characteristics. This study enriches the microscopic theoretical system of concrete thermal cracking and provides reliable quantitative references and technical support for the design of thermal crack resistance of concrete in deep geothermal engineering and the optimization of material composition. Full article

To solve the problems of poor fuel atomization effect, low combustion efficiency, and uneven temperature distribution of the evaporator tube of a certain micro turbojet engine, a structural optimization design method based on a genetic algorithm is proposed. Taking the inner diameter of the evaporator tube, the diameter of the nozzle hole, the number of nozzle holes as design variables, the fuel atomization particle size (d₅₀), combustion efficiency (η), and maximum wall temperature (T_max) as optimization objectives, a multi-objective optimization mathematical model is established. The iterative optimization is carried out through the selection, crossover, and mutation operations of the genetic algorithm, and the optimization effect is verified by combining CFD (Computational Fluid Dynamics) numerical simulation. The results show that when the inner diameter of the evaporator tube is 2.6 mm, the diameter of the nozzle hole is 0.8 mm and the number of nozzle holes is eight, the fuel atomization particle size of the evaporator tube is reduced by 18.3%, the combustion efficiency is increased by 7.6%, and the maximum wall temperature is decreased by 12.4%, which significantly improves the working performance of the evaporator tube and provides an effective reference for the optimization design of key components of micro turbojet engines. Full article

Marine-derived bioactive peptides have attracted increasing attention as value-added functional ingredients. In this study, peptides (<3 kDa) were prepared from yellowfin tuna processing by-products and further fractionated by Sephadex G-25 gel filtration. The major fraction (TBP-MF) exhibited markedly improved compositional homogeneity compared with the unfractionated hydrolysate (TBP), providing a well-defined peptide system for subsequent characterization and biological evaluation. Physicochemical analyses demonstrated that TBP-MF possessed enhanced thermal stability and a more ordered secondary structure, characterized by pronounced β-sheet enrichment, as revealed by TGA/DSC, FTIR, and circular dichroism analyses. Morphological and colloidal characterization further showed that TBP-MF formed relatively uniform lamellar and fibrous assemblies with a narrower particle size distribution and reduced electrostatic stabilization, indicating a higher tendency toward ordered self-association. Peptidomic profiling combined with in silico analysis revealed that TBP-MF was enriched in short peptides with relatively higher PeptideRanker scores and a functional motif distribution containing relatively more neuro-related annotations, although angiotensin-converting enzyme (ACE)- and dipeptidyl peptidase IV (DPP-IV)-related motifs remained predominant in both groups. In differentiated PC12 cells, TBP-MF exhibited excellent cytocompatibility and induced a stable, concentration-dependent increase in the Cell Counting Kit-8 (CCK-8) readout (OD₄₅₀), indicating enhanced cellular metabolic activity and/or increased cell number. In addition, TBP-MF significantly increased intracellular levels of key neurochemical factors associated with sleep-related regulation, including tetrahydrobiopterin (BH4), serotonin (5-HT), and γ-aminobutyric acid (GABA). Overall, this study highlights yellowfin tuna by-products as a promising marine resource for bioactive peptides and suggests that fractionation-driven structural refinement is associated with neuro-related biological activity in differentiated PC12 cells. These findings support the potential application of marine by-product-derived peptides as functional ingredients in health-related fields. Full article

As an efficient solid–liquid separation device, the hydrocyclone is widely applied in various industrial fields such as coal preparation and oil impurity removal, and its classification performance directly determines the efficiency of industrial separation operations., As the core separation zone of the hydrocyclone, the cone segment, its structure and the number of cone angles directly affect the flow field distribution characteristics and particle classification performance of the hydrocyclone. To reveal the regulation mechanism of the combined cone angles on the classification performance of hydrocyclones, numerical analysis and experimental verification methods were adopted to investigate the internal flow field and classification performance of hydrocyclones under different cone angle combinations. The evolution laws of velocity field, pressure field, turbulence characteristics, and particle classification effect under different configurations were systematically explored. The results show that the basic characteristics of the core flow field of the hydrocyclone do not change essentially with the increase in the number of cone segments, but the amplitude, distribution, and stability of flow field parameters are significantly regulated. The three-cone configuration achieves the optimal flow field synergy effect: the amplitude of the high turbulence intensity zone is lower and concentrated near the central axis; the zero-velocity envelope surface is stably maintained at approximately 8 mm in the core separation zone; and the full axial fluctuation of the air core is gentle, which effectively inhibits random particle diffusion and flow pattern mixing. In terms of separation performance, the three-cone configuration exhibits the highest classification efficiency in the core range of sub-coarse particles (10~30 μm), with the cut size (approximately 17.5 μm) in a reasonable range, the steepness index reaching a peak value (approximately 0.55), and the pressure drop (approximately 1.8 × 10⁵ Pa) and split ratio (2.8%) achieving synergistic optimization, balancing separation accuracy and energy consumption control. The single-cone configuration causes flow field disturbance due to the one-time contraction of the flow channel, while the four-cone configuration falls into the dilemma of “high pressure drop–marginal performance gain”, and neither achieves optimal performance. The regulation law of the number of cone segments revealed in this study provides a scientific basis for the structural optimization and engineering application of multi-cone hydrocyclones, and is of great significance for improving the particle classification efficiency in fields such as wastewater treatment and mineral processing. Full article

Human milk fat globules (MFGs)’ size characterization is key for evaluating milk quality and processing effects. Laser diffraction (LD) is widely used for particle size analysis but provides limited morphological information. This study applied image-based morphometric analysis (IBMA) to characterize MFGs’ size and shape distributions in human milk and compared the results with LD measurements. Milk samples from 12 women delivering term and preterm infants were analyzed. LD was performed using a Mastersizer 3000 (Malvern Panalytical, Malvern, UK) and IBMA using a Morphologi 4 (Malvern Panalytical, Malvern, UK), acquiring 2D images at 20× magnification covering particle sizes from ~1.5 to 130 µm. IBMA classified MFGs as individual particles (IP) (HS circularity ≥ 0.920; circle equivalent diameter < 25 µm) or agglomerates (HS circularity < 0.920; solidity < 0.970), extracting descriptors including circularity, elongation, and solidity. IP predominated, while agglomerates represented ~15% of particles. Number-mean diameters (D[1,0]) were 4.91 µm (total), 4.36 µm (IP), and 8.00 µm (agglomerates). Volume-weighted particle diameters (D[4,3]) were 7.21 µm for IP and 14.02 µm for agglomerates. The highest level of agreement between methods was observed for IP D[4,3], although minor differences may be clinically relevant. IBMA and LD provide complementary information; however, IBMA uniquely enables the characterization of MFG structural organization, including the identification of agglomerates, which cannot be resolved by LD. This added level of structural detail may have important implications for understanding the digestibility of human milk, particularly in preterm populations. Full article