Particle Size Measurements Research Articles

Particle size characteristics of sliding-zone soil and its role in landslide occurrence: a case study of the Lanniqing landslide in Southwest China

In landslide studies, particle size is a key quantitative indicator, reflecting the formation and development of the sliding zone. It plays a crucial role in understanding the mechanisms and evolutionary processes that lead to landslide occurrences. Precise measurement of particle size is crucial. This study centered on soil samples from the Lanniqing landslide in Southwest China. To begin, seven distinct methods were used to preprocess the soil samples. Next, the particle size frequency distribution was measured using the Mastersizer 2000 laser particle size analyzer. Key parameters, including median particle size, mean particle size, sorting coefficient, skewness, and kurtosis, were then compared and analyzed to determine the most appropriate preprocessing method for evaluating the characteristics of the soil samples. The mechanism of landslide occurrence was subsequently analyzed by examining the particle size characteristics, mechanical properties, and mineral composition of the soil samples. The results suggested that method C provides the most reliable analysis of particle size characteristics in soil samples. The observed coarsening of coarse particles, along with a significant increase in clay content within the sliding zone, indicates that the sliding surface has undergone multiple shear and compression events. The interplay of the upper traffic load and slope cutting at the front edge set the stage for the Lanniqing landslide, prompting the initial development of potential sliding surfaces. Rainfall acts as a catalyst for slope instability. The high clay content, combined with the formation of a low-permeability layer rich in clay minerals on the sliding surface, leads to excessive pore water pressure and mineral lubrication. These factors inherently trigger and accelerate the occurrence of the landslide.

Real-time detection of coke particles in blast furnace operations using machine learning: Case of a steel plant in India

The efficiency of blast furnace operations in a steel plant relies on the quality of raw materials, including coke. The particle size distribution (PSD) of coke plays a vital role in ensuring the efficiency of the blast furnace. Conventional methods of measuring average particle size are time-consuming and labour-intensive leading to delayed operations. To address the issue, various studies have used different techniques for real-time measurement of coke particle size, including laser diffraction, acoustic sounding, X-ray computed tomography, and computer vision. While these methods have shown successful applications, there have been limitations in their lower prediction accuracy and detection speed. Therefore, our study proposes a practical approach that leverages the integration of computer vision algorithms to enhance accuracy and detection speed. The proposed methodology is compared with the conventional method of measuring particle size in the lab at a blast furnace. Our results reveal that the YOLOv8 algorithm outperforms the conventional method, providing efficient detection of 5.419 frames per second and with a mean absolute error of [Formula: see text]1.77 mm within the lab results. YOLOv8 can perform object segmentation providing much more precise polygon masks of the coke particles instead of simply providing rectangular dimensions using traditional object detection, as explored in previous studies. This approach enables real-time monitoring of PSD and timely alerts to the onsite team, significantly improving the efficiency of blast furnace operations.

Recovery of Selenium-Enriched Polysaccharides from Cardamine violifolia Residues: Comparison on Structure and Antioxidant Activity by Different Extraction Methods.

The residues from selenium-enriched Cardamine violifolia after the extraction of protein were still rich in polysaccharides. Thus, the recovery of selenium polysaccharides (SePSs) was compared using hot water extraction and ultrasonic-assisted extraction techniques. The yield, extraction rate, purity, specific energy consumption, and content of total and organic selenium from different SePS extracts were determined. The results indicated that at conditions of 250 W (ultrasonic power), 30 °C, and a liquid-to-material ratio of 30:1 extracted for 60 min, the yield of SePSs was 3.97 ± 0.07%, the extraction rate was 22.76 ± 0.40%, and the purity was 65.56 ± 0.35%, while the total and organic selenium content was 749.16 ± 6.91 mg/kg and 628.37 ± 5.93 mg/kg, respectively. Compared to traditional hot water extraction, ultrasonic-assisted extraction significantly improves efficiency, reduces energy use, and boosts both total and organic selenium content in the extract. Measurements of particle size, molecular weight, and monosaccharide composition, along with infrared and ultraviolet spectroscopy, revealed that ultrasonic-assisted extraction breaks down long-chain structures, decreases particle size, and changes monosaccharide composition in SePSs, leading to lower molecular weight and reduced dispersity. The unique structure of SePSs, which integrates selenium with polysaccharide groups, results in markedly improved antioxidant activity and reducing power, even at low concentrations, due to the synergistic effects of selenium and polysaccharides. This study establishes a basis for using SePSs in functional foods.

Development of N-alkylated benzimidazole based cubosome hydrogel for topical treatment of burns.

The current study focuses on assessing the activity of the N-alkylated benzimidazole based cubosomal hydrogel (cubogel) for the topical treatment of burn wounds. The study involves the synthesis of six benzimidazole derivatives (1-6) and their characterization by FT-IR and 1H and 13C NMR spectroscopy. The further study involves the design and formation of nanoparticles known as cubosomes loaded with selected 1-benzyl-1-benzimidazole (API 6) and the development of a cubogel for the topical treatment of burn wounds. Cubosomes were prepared by the homogenization method, using glyceryl monooleate (GMO) as a lipid polymer and poloxamer 407 (P407) as a surfactant. Cubosomes undergo in vitro characterizations (measurement of particle size, zeta potential, polydispersity index (PDI), % entrapment efficiency, drug release in phosphate buffer saline of pH 6.8, and surface morphology by utilizing TEM (transmission electron microscopy). Formulation D3 (2.5% of GMO, 1% of P407, and 2.5% of PVA) emerged as the optimized formulation, displaying a minimum particle size (PS) of 129.9 ± 1 nm, entrapment efficiency (%EE) of 96.67 ± 0.89%, and a drug release of 86 ± 2.7% at 24 h. Carbopol 940 hydrogel was prepared and incorporated with the optimized formulation to prepare cubogel. This optimized cubogel provided 92.56 ± 0.014% in vitro drug release within 24 h. An in vivo histopathological study was conducted on an animal model (rabbit) to assess the efficacy of cubogel in wound healing and wound contraction. Then cubogel was compared with the commercially available creams Clotrimazole® and Polyfax®. The wound treated with newly developed cubogel has maximum wound contraction (96.70%) as compared to the standard creams. The findings revealed that the newly formulated cubogel was highly effective in treating burns, showing superior performance to commercial products without inducing side effects. Additionally, benzimidazole derivative loaded cubogel caused a sustained release for treating burn wounds without any bacterial infections. The current results further suggested phase 0 clinical trials.

Extended Monte Carlo modeling for submicron particle size measurement under high light extinction conditions

The light extinction method (LEM) based on Lambert Beer's law (LB) is widely used to characterize particle size distribution (PSD) in multiphase flow. However, as the optical thickness increases with concentration, the phenomenon of multiple scattering is enhanced, potentially leading to reduced accuracy and a limited application range. In this study, an extended Monte Carlo-based light extinction model (EMC) was developed, which was initially employed to calculate and evaluate the impact of multiple scattering. Subsequently, it was used to predict the extinction spectrum that incorporates multiple scattering effects under varying concentrations. To validate the modeling, a compact setup was constructed to perform a series of experiments on polystyrene and silica dioxide (SiO2) suspensions. A differential evolution algorithm was also implemented for the inversion of PSD based on spectral content, with the incorporation of an improved coefficient matrix that considers multiple scattering effects. In comparison to the linear LB model, the EMC exhibits a markedly enhanced capacity in handling higher particle concentrations and improving measurement accuracy. For a 700 nm PS suspension, the particle size inversion error of the EMC can be kept within 4% (93.86% for the LB model) compared with the nominal value, even with an optical thickness of 5.

Establishment of a semi-continuous nano-production line using the Microfluidizer® technology for the fabrication of lipid-based nanoparticles part 1: Screening of critical parameters and Design of Experiment optimization studies

A variety of strategies for producing high-quality nanoparticles have been reported in recent years. Batch-based bottom-up and top-down technologies are generally the most efficient methods, but present a number of challenges, particularly in terms of variability, safety, sustainability and large-scale production. In this study, a scalable, semi-continuous production line was built by connecting individual processing units, including a high shear mixing device, the Microfluidizer® technology and a cooling system. Each unit was equipped with an adequate temperature control to allow solvent-free production of solid lipid nanoparticles (consisting of Precirol® ATO 5 or Gelucire® 43/01) and nanostructured lipid carriers (additionally comprising Labrafac™ lipophile WL 1349). Subsequently, critical formulation parameters and critical process parameters (CPPs) of the individual processing units and their effects on particle size (i.e., critical quality attribute (CQA)) were investigated to identify appropriate input parameters for the subsequent Design of Experiment (DoE) studies conducted after linking the process units to a semi-continuous production line. For particle size monitoring, spatially resolved dynamic light scattering (SR-DLS) measurements were conducted and compared to standard DLS measurements to evaluate the applicability of SR-DLS as an inline monitoring tool. It was found that matrix composition, emulsifier concentration, pressure and number of cycles when processing through Microfluidizer® processor were the most influencing parameters. By optimizing these parameters, five-times higher throughputs could be achieved by the semi-continuous manufacturing line. In addition, the particle size measurements with SR-DLS confirmed the feasibility of implementing this technology for real-time particle size monitoring as an important safety factor in quality control.

Evaluation of protein-polysaccharide interactions through ζ-potential and particle size measurements to assess their functionality in wine.

Protein-polysaccharide-tannin interactions are important in every aspect of red wine production from physical stability to color, astringency, and body. For this model study, bovine serum albumin (BSA) was selected as the protein, while carboxymethyl cellulose (CMC), mannoproteins, and pectin were the model polysaccharides. Each protein-polysaccharide combination was analyzed for zeta (ζ) potential and particle size at neutral pH and within the wine-like solution. Mixtures were assessed regarding their protective, affinitive, and aggregative behaviors. Based on their individual ζ-potentials, pectin and mannoprotein were most stable at lower concentrations. At higher concentrations, they reduced the suspension's stability and increased the aggregate sizes. CMC consistently increased the stability of any solution under neutral pH conditions. However, with increasing concentrations, these large aggregates are expected to precipitate. Fruit pectin (FP) and BSA interactions seemed to be the main factors in the formation of visible precipitates at neutral pH. FP and the mannoprotein decreased stability enough to cause precipitation without haze formation. The mannoprotein decreased particle sizes, in both the suspension and precipitation, which may indicate greater selectivity toward proteins. FP also decreased the suspended particle sizes under wine conditions. These findings demonstrate the use of ζ-potential and particle size values to characterize macromolecular interactions in model systems and can also be used to indicate effective fining agents. PRACTICAL APPLICATION: This work demonstrates the capabilities of ζ-potential analysis paired with size particle measurements to predict and characterize the interactions between macromolecules in complex systems. The interactions between model wine macromolecules can be evaluated with this technology at a level that cannot be reached with any other analytical technique.

Fabrication of ultrafine Himalayan walnut oil Pickering emulsions by ultrasonic emulsification: Techno-functional properties of emulsions and microcapsules

In present scenario, much of the attention has been put on the production and utilization of Pickering emulsions deciphering enhanced stability and applicability over wide environmental conditions. In this context the present study was carried out to elaborate effect of different wall materials and pH systems on the physicochemical, structural and morphological properties of Himalayan walnut oil Pickering emulsions by ultrasonic emulsification. In this study, concentrated Pickering emulsion of Himalayan walnut oil (HWO) was prepared utilizing soy protein isolate (SPI), maltodextrin (MD) stabilized by pectin at varying concentrations and pH systems (4.0, 7.0). With increase in pectin and SPI concentration and lowering MD, stable emulsions were obtained as deciphered by an Emulsion stability index (ESI) of 100 for 7 days at ambient storage. HWO Pickering emulsions were analysed for particle size measurements (2.13–13.64 µm) and depicted negative zeta potential values (−3.70 to −18.58). Lyophilized HWO microcapsules depicted moderate encapsulation efficiency (44.69–57.63 %) whereas the hygroscopicity values of the microcapsule ranged from (0.21–12.10 %). Thermogravimetric analysis (TGA) of the samples depicted the temperature of maximum degradation rate up to 550 °C whereas XRD spectra depicted amorphous nature of oil microcapsules. FTIR spectra revealed a close association between the SPI-MD-Pectin matrix. SEM analysis revealed stable oil globules entrapped in protein-polysaccharide matrix with no visible cracks and fissures.

Corrosion performance and mechanism of clay-lignin biocomposite: Effective bio based coating with strong adhesion character for extended carbon steel protection

The development of sustainable and eco-friendly anticorrosion coating for metal substrates is a pivotal advancement in various engineering fields. Hence, the novel sepiolite-lignin (SP-L) and bentonite-lignin (BN-L) biocomposites were successfully introduced in an alkyd resin 5.0 wt% and applied on carbon steel (CS), to prepare the alkyd SP-L (A/SP-L) and BN-L (A/BN-L) coatings. Particle size and zeta potential measurements were conducted to determine the average particle size and assess the stability of the reinforcing pigments. The corrosion performance of A/SP-L and A/BN-L coatings revealed higher protection with an impedance modulus of about 5.18 × 105 Ω.cm² and 7.86 × 105 Ω.cm², respectively, after 6weeks of immersion in 3 % NaCl solution, measured by electrochemical impedance spectroscopy (EIS), which were extremely higher than the reference alkyd (RA) 1.13 × 103 Ω.cm². The adhesion of the three coated samples before and after immersion was assessed using pull-off test, while their hydrophobicity and impermeability were evaluated using saline corrosion and water absorption tests. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were utilized to examine the corrosion mechanism. The results demonstrated an excellent coating quality and interfacial adhesion against corrosion.

Preparation and biological evaluation of a glucose-responsive block copolymer nanoparticle with the ability to ameliorate diabetic kidney damage

In this study, a p(AAPBA-b-Daidzein) block copolymer was designed and synthesised using glucose-responsive 3-acrylamidophenylboronic acid (AAPBA) and esterified daidzein as structural units; this copolymer possessed anti-diabetic pharmacological activity. After confirming the chemical structure of the copolymer, nanoparticles of the copolymer were prepared using the water-in-oil method. The results of the in vitro experiments showed that nanoparticles swelled when the pH was >7 or when the glucose concentration exceeded 1.0 mg/mL. Particle size measurements showed that the size of the nanoparticles was kept in the range of 100–300 nm before and after the glucose response, which helped avoid their clearance by the liver and kidneys. As the nanoparticles swelled, insulin and esterified daidzein were continuously released. The nanoparticles subcutaneously injected into mice with streptozotocin-induced diabetes were able to quickly and continuously reduce the blood sugar levels; significantly improve renal pathological damage and reduce renal function damage, inflammatory factors and oxidative stress indicators. In summary, the p(AAPBA-b-Daidzein) glucose-responsive nanoparticles prepared in the current study possess good pH value and glucose sensitivity, can achieve a precise and controlled release of insulin and exert the pharmacological activity of esterified daidzein. Therefore, it shows potential clinical application value in the treatment of diabetes and its complications.

Deep learning based automated quantification of powders used in additive manufacturing

This study proposes a novel deep learning technique for efficient powder morphology characterization, crucial for successful additive manufacturing. The method segments powder particles in microscopy images using Pix2Pix image translation model, enabling precise quantification of size distribution and extraction of critical morphology parameters like circularity and aspect ratio. The proposed approach achieves high accuracy (Structural Similarity Index of 0.8) and closely matches established methods like laser diffraction in measuring particle size distribution (within a deviation of ∼7 %) and allows determination of additional particle attributes of aspect ratio and circualarity in a reliable, repeated, and automated way. These findings highlight the potential of deep learning for automated powder characterization, offering significant benefits for optimizing additive manufacturing processes.

Enhanced properties of hard metal WC-Ni processed from nanostructured powders

In this study, we investigated the effects of nanostructured composite powders (WC-Ni), nickel binder content, and sintering techniques on the morphology and mechanical properties of tungsten carbide. An alternative low-temperature gas-solid reaction route with a short reaction time was employed to produce nanostructured composite powders with 5 and 15 wt% Ni. The powders were consolidated using spark plasma sintering (SPS) and conventional vacuum furnace sintering at temperatures of 1350 °C and 1450 °C. The composite powders were characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and particle size measurements. The expansion/contraction of the compacted nanocomposite powders was studied through dilatometric analysis. The composite powders exhibited a uniform distribution of Ni, in both compositions. For the WC-5 wt% Ni nanocomposite powders, the average crystallite sizes for WC and Ni were 41.6 nm and 46.2 nm, respectively. While, for the WC-15 wt% Ni nanocomposite, the crystallite sizes for WC and Ni were 45.6 and 38.4 nm. The sintered composites were evaluated through XRD, SEM, measurements of density and Vickers hardness. The enhanced sinterability of the nanostructured powders enabled their consolidation into (WC-Ni) hard metal with densities approaching the theoretical relative density of 96.07 %, even with low binder content (5 wt% Ni). This is attributed to the uniform distribution of Ni and an extensive Ni-WC interface present prior to sintering. The SPS process at 1350 °C produced sintered bodies (WC-5 wt% Ni and WC-15 wt% Ni) with higher Vickers hardness (2322 HV and 1512 HV, respectively) and superior microstructural quality compared to samples produced by conventional vacuum furnace techniques. These results demonstrated that the mechanical properties of the system WC-Ni processed by SPS are superior to those obtained by vacuum furnace. Therefore, WC-Ni hard metals processed by this approach is a promising alternative to conventional hard metals (WC-Co) for a wide range of applications.

Rheological, mechanical, and environmental performance of printable graphene-enhanced cementitious composites with limestone and calcined clay

As extrusion-based 3D concrete printing gains wider acceptance in construction, there is a growing imperative to incorporate supplementary cementitious materials (SCMs) into printable mixtures to address their high cement content and promote sustainability. Conventional SCMs like fly ash and slag are becoming increasingly scarce, underscoring the need for alternative solutions such as limestone and calcined clay. Additionally, the utilization of nanomaterials in printable mixtures holds potential for enhancing the mechanical properties of 3D printed structures. These properties are often compromised by interlayer interfaces and void formations during printing, resulting in lower mechanical performance compared to conventionally cast concrete. Graphene nanoplatelets (GNPs), with their high aspect ratios and exceptional mechanical characteristics, offer promising avenues for reinforcing printable cementitious composites. This study explores the rheological, mechanical, and environmental aspects of graphene-enhanced cementitious composites incorporating limestone and calcined clay. Graphene nanoplatelets (GNPs) were dispersed utilizing a surfactant-assisted sonication technique, and their dispersion characteristics were evaluated through absorbance, particle size, and zeta potential measurements. Then, the influence of varying GNP concentrations on the rheological properties of limestone-calcined clay (LC2) cementitious composites was assessed. Compressive and flexural strength tests were conducted on 3D-printed LC2 samples with different GNP ratios, alongside cast specimens for comparison. Microstructural examination of failed specimens was performed using scanning electron microscopy. Furthermore, a life cycle assessment was conducted to compare the environmental impacts of printable LC2 mixtures to conventional printable mixtures. Incorporating GNPs at a ratio of 0.05 % by weight of cement in printable LC2 mixtures enhances compressive strength by 23 %, leading to a reduction of approximately 31 % in environmental impacts compared to conventional printed mixtures.

Convolvulus pluricaulis - Lipid hybrid nanoparticles to enhance in vivo exposure and uptake of rivastigmine for alzheimer disease

This study investigates Rivastigmine Hydrogen Tartrate (RHT)-loaded polymeric lipid hybrid nanoparticles (PLHNs) incorporating Shankhpushpi (Convolvulus Pluricaulis; C. Pluricaulis) extract. C. Pluricaulis was extracted using methanol, and PLHNs were formulated using a modified film hydration method with RHT, oleic acid, soy lecithin, Tween 80, PLA polymer, and Pluronic F-68. Characterization involved particle size, PDI, and zeta potential measurements using Dynamic Light Scattering (DLS), and morphology assessments via TEM and SEM. Drug-excipient interactions were analyzed using DSC and FTIR. In vitro release was evaluated using dialysis bags, and in vivo efficacy was tested in scopolamine-induced amnesic mice using the Morris Water Maze (MWM) test. The optimized formulation (NF6) demonstrated suitable particle size, PDI, zeta potential, encapsulation efficiency, and drug loading. NF6 significantly improved memory and exhibited antioxidant activity, indicating its potential for neurodegenerative disorder therapy.

Mercury Ion Selective Adsorption from Aqueous Solution Using Amino-Functionalized Magnetic Fe2O3/SiO2 Nanocomposite.

This study focuses on the development of new amino-functionalized magnetic Fe2O3/SiO2 nanocomposites with varying silicate shell ratios (1:0.5, 1:1, and 1:2) for the efficient elimination of Hg2+ ions found in solutions. The Fe2O3/SiO2-NH2 adsorbents were characterized for their structural, surface, and magnetic properties using various techniques, including Fourier transform infrared spectrum (FT-IR), powder X-ray diffraction (XRD), scanning electron microscopy (SEM), Braunauer-Emmett-Teller (BET), thermogravimetric analysis (TGA), zeta-potential, and particle size measurement. We investigated the adsorption circumstances, such as pH, dosage of the adsorbent, and duration of adsorption. The pH value that yielded the best results was determined to be 5.0. The Fe2O3/SiO2-NH2 adsorbent with a silicate ratio of (1:2) exhibited the largest amount of adsorption capacity of 152.03 mg g-1. This can be attributed to its significantly large specific surface area of 100.1 m2 g-1, which surpasses that of other adsorbents. The adsorbent with amino functionalization demonstrated a strong affinity for Hg2+ ions due to the chemical interactions between the metal ions and the amino groups on the surface. The analysis of adsorption kinetics demonstrated that the adsorption outcomes adhere to the pseudo-second-order kinetic model. The study of adsorption isotherms revealed that the adsorption followed the Langmuir model, indicating that the adsorption of Hg2+ ions with the adsorbent occurred as a monomolecular layer adsorption process. Furthermore, the thermodynamic analyses revealed that the adsorption of Hg2+ ions using the adsorbent was characterized by a spontaneous and endothermic process. Additionally, the adsorbent has the ability to selectively extract mercury ions from a complex mixture of ions. The Fe2O3/SiO2-NH2 nanocomposite, which is loaded with metal, can be easily recovered from a water solution due to its magnetic properties. Moreover, it can be regenerated effortlessly through acid treatment. This study highlights the potential use of amino-functionalized Fe2O3/SiO2 magnetic nanoparticles as a highly efficient, reusable adsorbent for the removal of mercury ions from contaminated wastewater.

Correlation between beverage consumption and droplet production during respiratory activity using interferometric Mie imaging experiment

This study investigates the effects of beverage consumption on droplet production during coughing and speaking. Interferometric Mie imaging (IMI) measures particle size using the diffraction characteristics of light and was used to examine the particle size distribution and particle count concentration of exhaled droplets without water (WW), with still water (SW), and with carbonated water (CW). The parameters of the IMI technique were calibrated using glass beads and respiratory droplets were measured for 16 subjects, which showed that drinking beverages had a significant impact on the particle size distribution during coughing and speaking. Another important aspect of this study was the variability in particle emissions among individuals. The results showed that the consumption of SW and CW led to a significant increase in total particle count concentrations in the coughing condition when compared with WW, with no significant difference among beverage type. Individuals with relatively high particle emissions WW showed more particle generation when consuming SW and CW. When speaking, SW ingestion significantly increased the total particle count concentrations when compared with the WW condition, whereas CW consumption did not increase the total particle count concentrations to the same extent as that in the SW condition. These results emphasize that the consumption of beverages such as SW and CW have the potential to significantly increase particle production during respiratory activities, amplifying the potential risks associated with infection transmission.

Synthesis, Characterization, and Biological Evaluation of Chitosan Nanoparticles Cross-Linked with Phytic Acid and Loaded with Colistin against Extensively Drug-Resistant Bacteria.

The natural evolution of microorganisms, as well as the inappropriate use of medicines, have accelerated the problem of drug resistance to many of the antibiotics employed today. Colistin, a lipopeptide antibiotic used as a last resort against multi-resistant strains, has also begun to present these challenges. Therefore, this study was focused on establishing whether colistin associated with chitosan nanoparticles could improve its antibiotic activity on an extremely resistant clinical isolate of Pseudomonas aeruginosa, which is a clinically relevant Gram-negative bacterium. For this aim, nanoparticulate systems based on phytic acid cross-linked chitosan and loaded with colistin were prepared by the ionic gelation method. The characterization included particle size, polydispersity index-PDI, and zeta potential measurements, as well as thermal (DSC) and spectrophotometric (FTIR) analysis. Encapsulation efficiency was assessed by the bicinchoninic acid (BCA) method, while the antimicrobial evaluation was made following the CLSI guidelines. The results showed that colistin-loaded nanoparticles were monodispersed (PDI = 0.196) with a particle size of around 266 nm and a positive zeta potential (+33.5 mV), and were able to associate with around 65.8% of colistin and decrease the minimum inhibitory concentration from 16 μg/mL to 4 μg/mL. These results suggest that the association of antibiotics with nanostructured systems could be an interesting alternative to recover the antimicrobial activity on resistant strains.

Revolutionizing treatment for topical fungal infections: evaluating penetration-enhancer-containing vesicles as a fluconazole delivery system: Ex-vivo and in-vivo dermal testing

Fungal infections pose a significant challenge in numerous developing nations and worldwide, necessitating urgent solutions. Oral administration of antifungal medications often leads to severe adverse reactions. Hence, employing topical delivery systems is preferred to ensure efficient dermal delivery of antifungal agents while minimizing side effects. Furthermore, the incorporation of penetration enhancers into nanocarriers loaded with antifungal agents has demonstrated enhanced efficacy in combating mycotic infections. Consequently, ultra-deformable penetration enhancer-containing vesicles (PEVs) were developed to explore this promising approach. In this study, Labrasol® and Transcutol® were used as penetration enhancers in formulating ultra-deformable PEVs containing the antifungal agent Fluconazole (FCZ). The PEVs underwent comprehensive characterization, including measurements of particle size (PS), charge, and entrapment efficiency (EE%). The results revealed that the size of tested PEVs ranged from 100 to 762 nm. All particles exhibited a negative charge, with a minimum zeta potential (ZP) of −38.26 mV, and an intermediate entrapment efficiency (EE%) that reached approximately 40%w/w. Ex-vivo studies demonstrated the ability of PEVs to deliver FCZ to the dermis while minimizing transdermal delivery. The selected formula was tested in-vivo using candidiasis-induced rat model and showed a superiority in its antifungal effect against Candida Albicans compared to the drug control. Stability studies were executed for the selected formula, and revealed good stability shown by the insignificant change in the PS, ZP& EE% over a six-month period.

Measurement of particle size in suspension based on VIS-NIR-RGB sensors and gradient-boosted regression tree

To better utilize the spectral features of near-infrared and visible light and address issues in traditional suspension particle size detection, such as long measurement cycles, high costs, and complex operation, we propose a new method that combines multispectral sensor characterization technology with machine learning algorithms for rapid particle size measurement. By integrating a lab-designed light source driving circuit and spectral data acquisition device, we obtained 15 spectral data points for visible light, near-infrared, and red–blue–green (RGB) values to build a prediction model. The near-infrared-based model obtained a coefficient of determination (R2) of 0.9130, whereas models combining RGB-near-infrared and visible-near-infrared features performed best, with R2 values of 0.9670 and 0.9845. These models improved performance by 5.91% and 7.83%, effectively overcoming the limitations of single spectral features in different suspensions. This combination of visible and near-infrared light with machine learning proves effective for detecting average particle size in suspensions.

Revolutionizing Grain and Particle Size Measurement in Metals: The Role of Sensor-Assisted Metallographic Image Analysis.

Metallographic image analysis is vital in the field of metal science due to its potential to automate the sensing process for grain and particle size estimation. To ensure the good quality and reliability of metal products, analysis of the integrity of metallic components is required. In contemporary manufacturing processes, microscopic analysis is a crucial step, mainly when complex systems like gearboxes, turbines, or engines are assembled using various components from multiple suppliers. A final product's quality, durability, and lifespan are determined via the quality analysis of properties of a material with proper tolerances. A flaw in a single component can cause the breakdown of the entire finished product. To ensure the good quality of a material, micro-structural analysis is necessary, which includes the routine measurement of inclusions. The particle and grain sizes of particulate samples are the most crucial physical characteristics of metals. Their measurement is routinely conducted across various industries, and they are frequently considered essential parameters in the creation of many products. This paper discusses the role of sensors in enhancing the accuracy and efficiency of metallographic image analysis, as well as the challenges and limitations associated with this technology. The paper also highlights the potential applications of sensor-assisted metallographic image analysis in various industries, such as aerospace, automotive, and construction. The paper concludes by identifying future research directions for this emerging field, including the development of more sophisticated algorithms for grain and particle size estimation, the integration of multiple sensors for more accurate measurements, and the exploration of new sensing modalities for metallographic image analysis.