Composition Of Mixture Research Articles

Impact of Waste Marble Powder as A Partial Alternative Material for Cement

Waste marble powder generated from the sawing and the mining activity of marble stone caused environmental problems that harmed human health. This current research aimed to investigate the influence of waste marble powder to substitute cement partially in mortar production. The mortar preparation with the mixture compositions of 0-50% marble powder was investigated for their resistance of compression and their porosity. The characterization was assessed with X-ray Fluorescence (XRF), Scanning Electron Microscopy-Energy Dispersive X-ray (SEM-EDX), Fourier Transform Infra-Red (FTIR), and X-ray Diffraction (XRD). The finding of this research revealed that a replacement by up to 10% marble powder obtained an increase in compressive strength after 28 days. A low level of marble powder to replace cement led to a less porous microstructure of the mortars. These confirmed that waste marble powder could be applied to manufacture mortars. In addition, the utilization of waste marble powder as an alternative building material would reduce the damage to the environment.

Soft computing models for prediction of bentonite plastic concrete strength

Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls in dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite is added to concrete mixes for the adsorption of toxic metals. The modified design of BPC, as compared to normal concrete, requires a reliable tool to predict its strength. Thus, this study presents a novel attempt at the application of two innovative evolutionary techniques known as multi-expression programming (MEP) and gene expression programming (GEP) and a boosting-based algorithm known as AdaBoost to predict the 28-day compressive strength ( ) of BPC based on its mixture composition. The MEP and GEP algorithms expressed their outputs in the form of an empirical equation, while AdaBoost failed to do so. The algorithms were trained using a dataset of 246 points gathered from published literature having six important input factors for predicting. The developed models were subject to error evaluation, and the results revealed that all algorithms satisfied the suggested criteria and had a correlation coefficient (R) greater than 0.9 for both the training and testing phases. However, AdaBoost surpassed both MEP and GEP in terms of accuracy and demonstrated a lower testing RMSE of 1.66 compared to 2.02 for MEP and 2.38 for GEP. Similarly, the objective function value for AdaBoost was 0.10 compared to 0.176 for GEP and 0.16 for MEP, which indicated the overall good performance of AdaBoost compared to the two evolutionary techniques. Also, Shapley additive analysis was done on the AdaBoost model to gain further insights into the prediction process, which revealed that cement, coarse aggregate, and fine aggregate are the most important factors in predicting the strength of BPC. Moreover, an interactive graphical user interface (GUI) has been developed to be practically utilized in the civil engineering industry for prediction of BPC strength.

Using genetic algorithms to solve the problem of finding the optimal composition of the reaction mixture

Using genetic algorithms to solve the problem of finding the optimal composition of the reaction mixture

Flavouring Group Evaluation 80, Revision 2 (FGE.80Rev2): Consideration of alicyclic, alicyclic-fused and aromatic-fused ring lactones evaluated by the JECFA (61st and 82nd meetings) structurally related to an aromatic lactone evaluated in FGE.27.

The EFSA Panel on Food Additives and Flavourings was requested to evaluate 14 flavouring substances assigned to the Flavouring Group Evaluation 80 (FGE.80), using the Procedure as outlined in the Commission Regulation (EC) No 1565/2000. Thirteen substances have already been considered in FGE.80 and its revision and in FGE.96 [FL-no: 10.005, 10.024, 10.025, 10.050, 10.061, 10.069, 10.070, 10.072, 10.169, 13.009, 13.012, 13.161 and 16.055]. The remaining flavouring substance 3a,4,5,7a-tetrahydro-3,6-dimethylbenzofuran-2(3H)-one [FL-no: 10.057] has been cleared with respect to genotoxicity in FGE.217Rev3 and it is considered in this revision 2 of FGE.80. The substance [FL-no: 10.057] was evaluated through a stepwise approach that integrates information on the structure-activity relationships, intake from current uses, threshold of toxicological concern (TTC) and available data on metabolism and toxicity. The Panel concluded that [FL-no: 10.057] does not give rise to safety concerns at its levels of dietary intake, when estimated on the basis of the 'Maximised Survey-derived Daily Intake' (MSDI) approach. Besides the safety assessment of the flavouring substance, the specifications for the material of commerce have also been considered and the information provided was complete for [FL-no: 10.057]. However, for the flavouring substance [FL-no: 10.057] in the present revision and for eight substances evaluated in previous revisions, the 'modified Theoretical Added Maximum Daily Intakes' (mTAMDIs) values are above the TTC for their structural class (III). For four substances previously evaluated in FGE.80Rev1 and in FGE.96, use levels are still needed to calculate the mTAMDI estimates. Therefore, in total for 13 flavouring substances, data on uses and use levels should be provided to finalise their safety evaluations. For [FL-no: 10.050, 10.069 and 13.161], information on the composition of stereoisomeric mixtures is needed.

New Amphiphilic Terpolymers of N-Vinylpyrrolidone with Acrylic Acid and Triethylene Glycol Dimethacrylate as Promising Drug Delivery: Design, Synthesis and Biological Properties In Vitro.

The terpolymers of N-vinylpyrrolidone (VP) with acrylic acid and triethylene glycol methacrylate were synthesized with more than 90% yield by radical copolymerization in ethanol from monomeric mixtures of different molar composition (98:2:2, 95:5: 2 and 98:2:5) and their monomer composition, absolute molecular masses and hydrodynamic radii in aqueous media were determined. Using the MTT test, these terpolymers were established to be low toxic for non-tumor Vero cells and HeLa tumor cells. Polymer compositions of hydrophobic dye methyl pheophorbide a (MPP) based on studied terpolymers and linear polyvinylpyrrolidone (PVP) were obtained and characterized in water solution. Quantum-chemical modeling of the MPP-copolymer structures was conducted, and the possibility of hydrogen bond formation between terpolymer units and the MPP molecule was shown. Using fluorescence microscopy, the accumulation and distribution of polymer particles in non-tumor (FetMSC) and tumor (HeLa) cells was studied, and an increase in the accumulation of MPP with both types of particles was found.

Modeling of hydrogen separation through Pd membrane with vacuum pressure using Taguchi and machine learning methods

The performance of hydrogen purification using palladium (Pd) membrane is analyzed by Taguchi and machine learning (ML) methods. Three system factors are considered: the feed gas mixture composition (i.e., H2, CO2, and H2O), retentate-side total pressure, and vacuum pressure. The Taguchi method designs the experimental cases based on the constructed orthogonal array. In addition, the hydrogen flux is investigated by the analyses of variance (ANOVA) and artificial neural networks (ANN) methods. The results show that the effects of the considered factors on the hydrogen permeation performance can be ranked as feed gas mixture composition > retentate-side total pressure > vacuum pressure. Both the retentate-side pressure and vacuum pressure present a positive effect on hydrogen flux. The average relative error of hydrogen flux between the experimental results and predictions by ANN is 2.1%, which proves that the ANN method is an effective technique for predicting the hydrogen flux through the Pd membrane. The ML classification test is then performed for hydrogen purity by three machine learning methods: decision tree (DT), support vector machine (SVM), and ensemble method. Among the various classification models, the Quadratic SVM model exhibits a relatively high average training accuracy but shows overfitting. However, the bagged model of the ensemble method can achieve an impressive training accuracy of 91.4% and a prediction accuracy of 85.7%.

Integrating Experimental and Numerical Data for Improved Steam Reforming Simulation with Deep Learning

Abstract In this paper, a data-driven methane steam reforming simulation is developed and used to predict the post-reaction mixture composition. Until today, methane steam reforming remains a predominant hydrogen production method, yet modeling its complex reactions remains a significant challenge due to the intricate interplay of process variables. Here, we show an artificial neural network simulator that can effectively model these reactions, offering precise predictions based on parameters like temperature, inlet gas composition, methane flow, and nickel catalyst mass. Our approach to data curation integrates experimental, interpolated, and theoretically calculated values and refining the model by assessing the relative importance of each data category. Various neural network structures were tested before ultimately identifying an optimal architecture with a 5-6-8-6-4 network structure. The network underwent 6000 epochs of training, leading to a model that demonstrates excellent agreement with experimental observations, as evidenced by the mean squared error of 0.000217 and the Pearson correlation coefficient of 0.965. Moreover, all process trajectories predicted by the network are characterized by a smooth course and are within a physical range of values. Therefore, this work overcomes a common challenge in chemical process simulation using neural networks and also sets a possible direction for future research in this field.

Chemical Mobilization-Based Capillary Isoelectric Focusing-Mass Spectrometry Using the nanoCEasy Interface for Pharmaceutical Protein Analysis.

Capillary isoelectric focusing (CIEF) coupled with electrospray ionization mass spectrometry (ESI-MS) is regarded as an outstanding approach for protein and proteoform analysis, combining a high-resolution separation technique and an enhanced detection technique. The few so-far developed CIEF-ESI-MS approaches exhibit limitations regarding sensitivity and separation performance. Here, we report a new generic methodology for CIEF-ESI-MS based on chemical mobilization, leading to highly efficient separation. This new integrated methodology relies on exchanging catholyte, initially introduced in the nanoCEasy interface in the focusing step, with sheath liquid (SL) in order to chemically mobilize the analytes into the ESI-MS system. The CIEF-MS method is evaluated by separation of a peptide set, model proteins, and monoclonal antibody charge variants. The effect of various parameters including master mixture composition, field strength, catholyte, SL composition, focusing time, and capillary conditions is optimized and discussed. Excellent separation performance can be achieved with a pI resolution down to 0.1 pH unit. The mobilization reproducibility is demonstrated with "migration time" RSDs below 10%. Additionally, the chemical mobilization is compared with the pressure assistance-chemical mobilization method, demonstrating that even a small pressure causes a strong decrease in separation performance, which clearly indicates the benefit of the chemical mobilization-based method. The applicability and separation power of the developed method are further exhibited by separation of Fc-conjugated insulins (mass = 62 kDa) differing in only one amino acid.

Evaluation of the shadowgraph method for the determination of mutual and thermal diffusivities.

The present work provides a systematic study on the influence of sample properties and experimental conditions on the reliable accessibility of Fick or mutual diffusion coefficients D11 and thermal diffusivities a in binary liquid mixtures using the shadowgraph method. For this, mixtures with varying magnitudes of the Soret coefficient ST and their optical contrast factors were studied at a temperature of 298.15K and pressures between (0.1 and 0.65) MPa with varying magnitudes and orientations of the applied temperature and concentration gradients ∇T and ∇c. Experimental signals obtained in these investigations were analyzed with respect to the intensities of the signal contributions from non-equilibrium fluctuations (NEFs) in concentration and temperature, and the reliability of the determined D11 and a data was assessed by comparison to literature data. Larger signal intensities from NEFs and, therefore, a more reliable determination of diffusivities were given for sufficiently large magnitudes of ST, the optical contrast factors, and the applied ∇T and ∇c. At very small fluid layer thicknesses L ≤ 0.30mm, the associated reduction of signal statistics outweighing the expected increase of signal intensities at larger magnitudes of ∇T and ∇c as well as the influence of confinement imposed limitations for the determination of diffusivities in some cases. Furthermore, an influence of the mixture composition on signal intensities from concentration-NEFs was identified, where too small mole fractions of one component can hinder the determination of D11 in mixtures with small magnitudes of the optical contrast factor (∂n/∂c)T,p.

Multipressure Sampling for Improving the Performance of MOF-based Electronic Noses.

Metal-organic frameworks (MOFs) are a promising class of porous materials for the design of gas sensing arrays, which are often called electronic noses. Due to their chemical and structural tunability, MOFs are a highly diverse class of materials that align well with the similarly diverse class of volatile organic compounds (VOCs) of interest in many gas detection applications. In principle, by choosing the right combination of cross-sensitive MOFs, layered on appropriate signal transducers, one can design an array that yields detailed information about the composition of a complex gas mixture. However, despite the vast number of MOFs from which one can choose, gas sensing arrays that rely too heavily on distinct chemistries can be impractical from the cost and complexity perspective. On the other hand, it is difficult for small arrays to have the desired selectivity and sensitivity for challenging sensing applications, such as detecting weakly adsorbing gases with weak signals, or conversely, strongly adsorbing gases that readily saturate MOF pores. In this work, we employed gas adsorption simulations to explore the use of a variable pressure sensing array as a means of improving both sensitivity and selectivity as well as increasing the information content provided by each array. We studied nine different MOFs (HKUST-1, IRMOF-1, MgMOF-74, MOF-177, MOF-801, NU-100, NU-125, UiO-66, and ZIF-8) and four different gas mixtures, each containing nitrogen, oxygen, carbon dioxide, and exactly one of the hydrogen, methane, hydrogen sulfide, or benzene. We found that by lowering the pressure, we can limit the saturation of MOFs, and by raising the pressure, we can concentrate weakly adsorbing gases, in both cases, improving gas detection with the resulting arrays. In many cases, changing the system pressure yielded a better improvement in performance (as measured by the Kullback-Liebler divergence of gas composition probability distributions) than including additional MOFs. We thus demonstrated and quantified how sensing at multiple pressures can increase information content and cross-sensitivity in MOF-based arrays while limiting the number of unique materials needed in the device.

Compositional transferability of deep learning potentials: a case study for LiCl-KCl melt.

One of the crucial issues related to machine learning potentials is the formation of representative dataset. For multicomponent systems, it is a general methodology to scan the composition range with a certain step. However, there is a lack of information on the compositional transferability of machine learning potentials. In this paper, we extend the knowledge in this area by studying the transferability of deep learning potential over the range of compositions of LiCl-KCl molten mixtures. The training dataset was formed using only the near-eutectic composition of 60% LiCl-40% KCl. Then, we tested the ability of the model to predict physicochemical properties of the melts far from the reference composition. It was found that for the composition range from 0 to 100% of LiCl, the calculated properties concur closely with those of other studies and ab initio calculations. Therefore, the model shows prominent non-intuitive compositional transferability. Moreover, the solid states and solid-liquid coexistence were reproduced. The calculated melting temperatures of LiCl and KCl show the errors of 6.6% and 0.4%, respectively. We argue that such good transferability stems from the local structure configurations that are typical both for pure LiCl and for pure KCl which are implicitly presented in the training dataset because of local fluctuations in composition. To collect the data for the initial dataset, density functional theory was employed. Then, the DeePMD package was used to train a neural network potential. To calculate the properties of the melts, standard equilibrium and non-equilibrium molecular dynamic approaches were utilized.

A comparative analysis of boosting and genetic programming techniques for predicting mechanical properties of soilcrete materials

This study aims to predict compressive strength (CS) and modulus of elasticity (E) of soilcrete mixes to foster their widespread use in the industry. Soilcfigrete has the potential to promote sustainable construction practices by making use of locally available raw materials. However, the accurate determination of mechanical properties of soilcrete mixes is inevitable to foster their widespread use. Thus, this study employs different machine learning algorithms including Extreme Gradient Boosting (XGB), Gene Expression Programming (GEP), AdaBoost, and Multi Expression Programming (MEP) for this purpose. The XGB and AdaBoost algorithms were implemented using python programming language while MEP and GEP were implemented using specialized softwares. The data used for model development was obtained from previously published literature containing five input parameters and two output parameters. This data was split into two sets named training and testing sets for training and testing of the algorithms respectively. The developed models for CS and E prediction were validated using several error metrices and residual comparison. The objective function value which should be closer to zero for an accurate model is the least for XGB model for prediction of both variables (0.0036 for CS and 0.00315 for E). Moreover, shapley analysis was carried out using XGB model to get insights into the underlying model framework. The results highlighted that water-to-binder ratio (W/B), metakaolin (MK), and ultrasonic pulse velocity (UV) are the most significant variables for predicting E and CS of soilcrete materials. These insights can be used practically to optimize the mixture composition of soilcrete mixes according to different site requirements.

Potential synthesis of nitrated products from 2,6,8,12‐tetraacetyl‐2,4,6,8,10,12‐hexaazaisowurtzitane derivatives with linear substituents

AbstractThe compact, nitrogen‐rich structure of 2,4,6,8,10,12‐hexaazaisowurtzitanes calls attention among researchers worldwide. These compounds have found the greatest utility as substrates for nitration to obtain the caged polynitramine, 2,4,6,8,10,12‐hexanitro‐2,4,6,8,10,12‐hexaazaisowurtzitane, which possesses a high‐energy performance. All new derivatives of hexaazaisowurtzitane are nitratable. The present study examined the nitration process of 2,6,8,12‐tetraacetyl‐2,4,6,8,10,12‐hexaazaisowurtzitane derivatives obtained through the condensation reaction with aldehydes under various conditions to yield CL‐20. The yield of CL‐20, a well‐known nitration product of hexaazaisowurtzitane compounds, was found to depend on the substrate structure and the nitrating mixture composition. The revealed dependence of the synthesis of various nitrated compounds on the holding time enables the synthesis of products with different physicochemical properties in a single process.

The Influence of Pre-Chamber Parameters on the Performance of a Two-Stroke Marine Dual-Fuel Low-Speed Engine

With increasing environmental pollution from ship exhaust emissions and increasingly stringent International Maritime Organization carbon regulations, there is a growing demand for cleaner and lower-carbon fuels and near-zero-emission marine engines worldwide. Liquefied natural gas is a low-carbon fuel, and when liquefied natural gas (LNG) is used on ships, dual-fuel methods are often used. The pre-chamber plays a key role in the working process of dual-fuel engines. In this paper, an effective three-dimensional simulation model based on the actual operating conditions and structural characteristics of a marine low-pressure dual-fuel engine is established. In addition, the effects of changing the Precombustion chamber (PCC) volume ratio and the PCC orifice diameter ratio on the mixture composition, engine combustion performance, and pollutant generation were thoroughly investigated. It was found that a small PPC volume ratio resulted in a higher flame jet velocity, a shorter stagnation period, and an acceleration of the combustion process in the main combustion chamber. When the PCC volume was large, the Nitrogen oxygen (NOx) ratio emission was elevated. Moreover, the angle of the PCC orifice affected the flame propagation direction of the pilot fuel. Optimizing the angle of the PCC orifice can improve combustion efficiency and reduce the generation of NOx. Furthermore, reasonable arrangement of the PCC structure can improve the stability of ignition performance and accelerate the flame jet velocity.

Particle-assisted formation of oil-in-liquid metal emulsions.

Gallium-based liquid metals (LM) have surface tension an order of magnitude higher than water and break up into micro-droplets when mixed with other liquids. In contrast, silicone oil readily mixes into LM foams to create oil-in-LM emulsions with oil inclusions. Previously, the LM was foamed through rapid mixing in air for an extended duration (over 2 hours). This process first results in the internalization of oxide flakes that form at the air-liquid interface. Once a critical fraction of these randomly shaped solid flakes is reached, air bubbles internalize into the LM to create foams that can internalize secondary liquids. Here, we introduce an alternative oil-in-LM emulsion fabrication method that relies on the prior addition of SiO2 micro-particles into the LM before mixing it with the silicone oil. This particle-assisted emulsion formation process provides a higher control over the composition of the LM-particle mixture before oil addition, which we employ to systematically study the impact of particle characteristics and content on the emulsions' composition and properties. We demonstrate that the solid particle size (0.8 µm to 5 µm) and volume fraction (1% to 10%) have a negligible impact on the internalization of the oil inclusions. The inclusions are mostly spherical with diameters of 20 to 100 µm diameter and are internalized by forming new, rather than filling old, geometrical features. We also study the impact of the particle characteristics on the two key properties related to the functional application of the LM emulsions in the thermal management of microelectronics. In particular, we measure the impact of particles and silicone oil on the emulsion's thermal conductivity and its ability to prevent deleterious gallium-induced corrosion and embrittlement of contacting metal substrates.&#xD.

Chemisorption of hydrogen on metal oxides with palladium (II) oxide additives

Chemisorption can lead to a change in electrical conductivity, which allows using semiconductor materials to create gas sensors. On the other hand, semiconductor sensors can be used as devices for studying the chemisorption of gases. In this work, we set two tasks. The first of them was to create a sensor for the selective determination of hydrogen in air, including in a mixture with other gases. The second task was to determine the mechanism of hydrogen chemisorption on the surface of metal oxides with palladium (II) oxide additives. We obtained and characterised nanodispersed materials based on SnO2 and WO3 with additions of 3% PdO by weight. We compared the electrical characteristics of these materials in stationary temperature conditions as well as with temperature modulation in the presence of hydrogen, methane, and their mixtures. The specific features of hydrogen chemisorption were determined on the surface of metal oxide semiconductors based on SnO2 and WO3 with PdO additives in the temperature modulation mode of the sensor. It was shown that the extrema in the dependence of the sensor’s electrical conductivity on temperature can be explained by the contribution of proton transfer to the total electrical conductivity. The presence of extrema in the electrical conductivity of sensors, which were observed in thermal modulation mode in the presence of hydrogen, allows conducting qualitative and quantitative analysis of not only single-component gas systems, but also of mixtures of hydrogen with other analyte gases. In particular, this work demonstrated that it was possible to determine the composition of a hydrogen-methane mixture in air using a single metal oxide sensor. The advantage of this approach is that multidimensional data arrays can be easily processed.

Exploring flexural performance and abrasion resistance in recycled brick powder-based engineered geopolymer composites

BackgroundDue to growing global concerns regarding the management of construction waste, this study investigates the feasibility of creating engineered geopolymer composites by replacing traditional industrial by-products (slag) with construction waste, specifically recycled brick waste powder.ResultsPolyvinyl alcohol fibers were incorporated into the engineered geopolymer composite mixtures. The substitution of slag with recycled brick waste powder was carried out at varying percentages: 0, 20, 40, 60, 80, and 100%, resulting in six different engineered geopolymer composite mixtures. The study evaluated the flexural strength, sorptivity, water absorption, and abrasion resistance of the engineered geopolymer composites, and also, microstructural characterization was conducted using scanning electron microscopy. The findings demonstrated that incorporating recycled brick waste powder into the engineered geopolymer composite mixes resulted in a decrease in flexural strength by 35.59% and a notable increase in midspan deflection by 339% when slag was replaced. Concurrently, there was a significant rise in water absorption and sorptivity by approximately 304 and 214%, respectively, when slag was entirely substituted with recycled brick waste powder. Conversely, abrasion resistance decreased, with the inclusion of recycled brick waste powder resulting in an 84% increase in volume change. The scanning electron microscopy (SEM) analysis showed active geopolymerization of recycled brick waste powder within the engineered geopolymer composite mixtures.ConclusionsThe results of this investigation demonstrate that it is feasible to produce engineered geopolymer composites using recycled brick waste powder instead of slag. The greater ductility and increased midspan deflection point to areas that require further optimization, even in spite of the observed decreases in flexural strength and abrasion resistance. The SEM examination reveals an active geopolymerization, highlighting the potential of recycled brick waste powder to produce environmentally friendly and sustainable construction materials. These results offer a good starting point for further studies that try to maximize the durability and performance of these composites.

(E)-2-Benzylidenecyclanones: Part XIX. Reaction of (E)-2-(4'-X-Benzylidene)-1-tetralones with Cellular Thiols: Comparison of Thiol Reactivities of Open-Chain Chalcones and Their Six- and Seven-Membered Cyclic Analogs.

Non-enzyme-catalyzed thiol addition onto the α,β-unsaturated carbonyl system is associated with several biological effects. Kinetics and diastereoselectivity of non-enzyme catalyzed nucleophilic addition of reduced glutathione (GSH) and N-acetylcysteine (NAC) to the six-membered cyclic chalcone analogs 2a and 2b were investigated at different pH values (pH 3.2, 7.4 and 8.0). The selected compounds displayed in vitro cancer cell cytotoxicity (IC50) of different orders of magnitude. The chalcones intrinsically reacted with both thiols under all incubation conditions. The initial rates and compositions of the final mixtures depended both on the substitution and the pH. The stereochemical outcome of the reactions was evaluated using high-pressure liquid chromatography with UV detection (HPLC-UV). The structures of the formed thiol-conjugates and the retro-Michael products (Z)-2a and (Z)-2b were confirmed by high-pressure liquid chromatography-mass spectrometry (HPLC-MS). Frontier molecular orbitals and the Fukui function calculations were carried out to investigate their effects on the six-membered cyclic analogs. Data were compared with those obtained with the open-chain (1) and the seven-membered (3) analogs. The observed reactivities do not directly relate to the difference in in vitro cancer cell cytotoxicity of the compounds.

Utilization of marble waste as fine aggregate in the composition of high performance concrete containing mineral additions

This study investigates the effect of adding marble waste as fine aggregate on the properties of high-performance concrete. To achieve this objective, 10%, 20%, 30%, and 40% of marble waste fine aggregate were substituted for ordinary fine aggregate in the composition of high-performance concrete mixtures containing silica fume and ground blast furnace slag. The fresh properties were studied using slump, fresh density and air content tests, while the hardened properties were assessed using compressive and flexural strengths, ultrasonic pulse velocity, water absorption, and chemical acid attack tests. The results obtained revealed that the addition of marble waste fine aggregate significantly improved the properties of high-performance concrete and increased its resistance to chemical acid attacks. The composition-based ground blast furnace slag containing 40% marble waste fine aggregate outperformed all other compositions, increasing compressive strength by 30.98%.

Retrieval and analysis of the composition of an aerosol mixture through Mie–Raman–fluorescence lidar observations

Abstract. In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie–Raman–fluorescence lidar, with the primary input information being the aerosol backscattering coefficient (β), particle depolarization ratio (δ), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (β, δ and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (δ and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and δ are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL (ATmospheric Observation at liLLe) observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France.