Search Results (19)

Silica fume (SF) has been widely used in engineering; however, its densification during transportation reduces its original pozzolanic activity. This paper investigates the effects of wet grinding and chemical activation on the mechanical properties and hydration products of silica fume in cement-based materials, revealing the mechanism by which wet grinding improves these properties. The results indicate that wet grinding effectively reduces the particle size of silica fume. Under optimal excitation conditions (250 r/min, 20 min), the median particle size is reduced to 12.1 μm, 2.27 times smaller than before excitation. The 28-day compressive strength of the silica fume cement paste reaches 60.8 MPa, 23.7% higher than that of the reference group. This approach effectively mitigates nano-agglomeration, enhances the pozzolanic activity of silica fume, and promotes AFt and C-S-H gel formation. The findings demonstrate that wet grinding activation can further enhance the utilization rate of silica fume. Full article

This research provides a comparative analysis of the optimization of ultra-high-performance concrete (UHPC) using artificial neural network (ANN) and response surface methodology (RSM). By using ANN and RSM, the yield of UHPC was modeled and optimized as a function of 22 independent variables, including cement content, cement compressive strength, cement type, cement strength class, fly-ash, slag, silica-fume, nano-silica, limestone powder, sand, coarse aggregates, maximum aggregate size, quartz powder, water, super-plasticizers, polystyrene fiber, polystyrene fiber diameter, polystyrene fiber length, steel fiber content, steel fiber diameter, steel fiber length, and curing time. Two statistical parameters were examined based on their modeling, i.e., determination coefficient (R²) and mean square error (MSE). ANN and RSM were evaluated for their predictive and generalization capabilities using a different dataset from previously published research. Results show that RSM is computationally efficient and easy to interpret, whereas ANN is more accurate at predicting UHPC characteristics due to its nonlinear interactions. Results show that the ANN model (R = 0.95 and R² = 0.91) and RSM model (R = 0.94, and R² = 0.90) can predict UHPC compressive strength. The prediction error for optimal yield using an ANN and RSM was 3.5% and 7%, respectively. According to the ANN model’s sensitivity analysis, cement and water have a significant impact on compressive strength. Full article

In today’s digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus of current research work is to improve knowledge regarding application of one of the new ML techniques, i.e., gene expression programming (GEP), to anticipate the ultra-high-performance concrete (UHPC) properties, such as flowability, flexural strength (FS), compressive strength (CS), and porosity. In addition, the process of training a model that predicts the intended outcome values when the associated inputs are provided generates the graphical user interface (GUI). Moreover, the reported ML models that have been created for the aforementioned UHPC characteristics are simple and have limited input parameters. Therefore, the purpose of this study is to predict the UHPC characteristics while taking into account a wide range of input factors (i.e., 21) and use a GUI to assess how these parameters affect the UHPC properties. This input parameters includes the diameter of steel and polystyrene fibers (µm and mm), the length of the fibers (mm), the maximum size of the aggregate particles (mm), the type of cement, its strength class, and its compressive strength (MPa) type, the contents of steel and polystyrene fibers (%), and the amount of water (kg/m³). In addition, it includes fly ash, silica fume, slag, nano-silica, quartz powder, limestone powder, sand, coarse aggregates, and super-plasticizers, with all measurements in kg/m³. The outcomes of the current research reveal that the GEP technique is successful in accurately predicting UHPC characteristics. The obtained R², i.e., determination coefficients, from the GEP model are 0.94, 0.95, 0.93, and 0.94 for UHPC flowability, CS, FS, and porosity, respectively. Thus, this research utilizes GEP and GUI to accurately forecast the characteristics of UHPC and to comprehend the influence of its input factors, simplifying the procedure and offering valuable instruments for the practical application of the model’s capabilities within the domain of civil engineering. Full article

Durable concrete significantly reduces the spalling caused by chemical damage. The objective of current research is to substitute cement with supplementary such as fly ash (FA), ground granulated blast furnace slag (GGBS), and alccofine (AF). Additionally, the impact of nano-silica (NS) and silica fumes (SF) on the GPC durability when cured at various temperatures has been attempted. In order to perform this, GPC samples were produced by combining NS and SF at proportions of 0.5% NS + 5% SF, 1% NS + 10% SF, and 1.5% NS + 15% SF, and then cured at temperatures of 27 °C, 60 °C, 90 °C, and 120 °C, respectively. In this research, all concrete specimens were continuously immersed for twelve weeks under four different chemicals, i.e., HCl (2%), H₂SO₄ (2%), NaCl (6%), and Na₂SO₄ (6%). The influence of chemical attack on the qualities of concrete was examined by evaluating the water absorption, sorptivity, loss of mass, and loss of GPC strength. The durability aspect is also studied by visual appearance and mass loss under harmful chemical attack. The combination of GPC with integrated NS and SF affords great resistance against chemical attacks. The percentages of these two components are 1.5% and 15%. For GPC specimens, when cured at 90 °C, the resultant strength is found at its maximum. Full article

Chemical mechanical planarization (CMP) is a wafer-surface-polishing planarization technique based on a wet procedure that combines chemical and mechanical forces to fully flatten materials for semiconductors to be mounted on the wafer surface. The achievement of devices of a small nano-size with few defects and good wafer yields is essential in enabling IC chip manufacturers to enhance their profits and become more competitive. The CMP process is applied to produce many IC generations of nanometer node, or those of even narrower line widths, for a better performance and manufacturing feasibility. Slurry is a necessary supply for CMP. The most critical component in slurry is an abrasive particle which affects the removal rates, uniformity, defects, and removal selectivity for the materials on the wafer surface. The polishing abrasive is the source of mechanical force. Conventional CMP abrasives consist of colloidal silica, fume silica or other inorganic polishing particles in the slurries. We were the first to systematically study nanoparticles of the polymer type applied in CMP, and to compare traditional inorganic and polymer nanoparticles in terms of polishing performance. In particular, the polymer nanoparticle size, shape, solid content dosing ratio, and molecular types were examined. The polishing performance was measured for the polishing removal rates, total defect counts, and uniformity. We found that the polymer nanoparticles significantly improved the total defect counts and uniformity, although the removal rates were lower than the rates obtained using inorganic nanoparticles. However, the lower removal rates of the polymer nanoparticles are acceptable due to the thinner film materials used for smaller IC device nodes, which may be below 10 nm. We also found that the physical properties of polymer nanoparticles, in terms of their size, shape, and different types of copolymer molecules, cause differences in the polishing performance. Meanwhile, we used statistical analysis software to analyze the data on the polishing removal rates and defect counts. This method helps to determine the most suitable polymer nanoparticle for use as a slurry abrasive, and improves the reliability trends for defect counts. Full article

Bio-oil emulsions were stabilized using coconut shell coke, modified amphiphilic graphene oxide, and hydrophobic nano-fumed silica as solid emulsifiers. The effects of different particles on the stability of bio-oil emulsions were discussed. Over 21 days, the average droplet size of raw bio-oil increased by 64.78%, while that of bio-oil Pickering emulsion stabilized by three particles only changed within 20%. The bio-oil Pickering emulsion stabilized by Ni/SiO₂ was then used for catalytic hydrodeoxygenation. It was found that the bio-oil undergoes polymerization during catalytic hydrogenation. For raw bio-oil hydrodeoxygenation, the polymerization reaction was little affected by the temperature below 200 °C, but when the temperature raised to 250 °C, it was greatly accelerated. However, the polymerization of monocyclic aromatic compounds in the reaction process was partially inhibited under the bio-oil Pickering emulsion system. Additionally, a GC-MS analysis was performed on raw bio-oil and hydrodeoxygenated bio-oil to compare the change in GC-MS-detectable components after hydrodeoxygenation at 200 °C. The results showed that the Pickering emulsion catalytic system greatly promoted the hydrodeoxygenation of phenolic compounds in bio-oil, with most monocyclic phenolic compounds detected by GC-MS converting to near 100%. Full article

The findings of an extensive experimental research study on the usage of nano-sized cement powder and other additives combined to form cement–fine-aggregate matrices are discussed in this work. In the laboratory, dry and wet methods were used to create nano-sized cements. The influence of these nano-sized cements, nano-silica fumes, and nano-fly ash in different proportions was studied to the evaluate the engineering properties of the cement–fine-aggregate matrices concerning normal-sized, commercially available cement. The composites produced with modified cement–fine-aggregate matrices were subjected to microscopic-scale analyses using a petrographic microscope, a Scanning Electron Microscope (SEM), and a Transmission Electron Microscope (TEM). These studies unravelled the placement and behaviour of additives in controlling the engineering properties of the mix. The test results indicated that nano-cement and nano-sized particles improved the engineering properties of the hardened cement matrix. The wet-ground nano-cement showed the best result, 40 MPa 28th-day compressive strength, without mixing any additive compared with ordinary and dry-ground cements. The mix containing 50:50 normal and wet-ground cement exhibited 37.20 MPa 28th-day compressive strength. All other mixes with nano-sized dry cement, silica fume, and fly ash with different permutations and combinations gave better results than the normal-cement–fine-aggregate mix. The petrographic studies and the Scanning Electron Microscope (SEM) and Transmission Electron Microscope (TEM) analyses further validated the above findings. Statistical analyses and techniques such as correlation and stepwise multiple regression analysis were conducted to compose a predictive equation to calculate the 28th-day compressive strength. In addition to these methods, a repeated measures Analysis of Variance (ANOVA) was also implemented to analyse the statistically significant differences among three differently timed strength readings. Full article

A new electrochemical sensor for the detection of levofloxacin (LV) was efficiently realized. The aim was to develop a new, cheap, and simple sensor for the detection of LV, which is used in various infections due to its pharmacological importance. It consists of carbon paste (CP) enhanced with nano-sized fumed silica (NFS). NFS has a very low bulk density and a large surface area. The carbon paste-enhanced NFS electrode (NFS/CPE) showed great electrocatalytic activity in the oxidation of 1.0 mM LV in Britton–Robinson buffer (BR) at pH values ranging from 3.0 to 8.0. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used; the peak current value (I_p) of the NFS/CPE sensor was 2.7 times that of the bare electrode, ensuring its high electrocatalytic activity. Electrochemical impedance spectroscopy (EIS) was performed at a peak potential (E_p) of +1066 mV, yielding a resistance of 10 kΩ for the designed NFS/CPE sensor compared to 2461 kΩ for the bare electrode, indicating the high conductivity of the modified sensor and verifying the data observed using the CV technique. Surface descriptions were determined by scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDX). The variation in the concentration of LV (2.0 to 1000 µM) was considered in BR buffer (pH = 5.0) at a scan rate (SR) of 10 mV/s by the NFS/CPE. The detection and quantification limits were 0.09 µM and 0.30 µM, respectively. To evaluate the application of LV in real samples, this procedure was established on Quinostarmax 500 mg tablets and human plasma samples. Reasonable results were obtained for the detection of LV. Full article

The main objective of this work is to develop a variety of hybrid high-density polyethylene (HDPE) micro- and nanocomposites and to investigate their thermal, mechanical, and morphological characteristics as a function of number of fillers and their contents percentage. In this study, 21 formulations of the composites were prepared using fillers with different sizes including micro fillers such as talc, calcium carbonate (CaCO₃), as well as nano-filler (fumed silica (FS)) though the melt blending technique. The morphological, mechanical, and thermal properties of the composite samples were evaluated. The morphological study revealed negligible filler agglomerates, good matrix–filler interfacial bonding in case of combined both CaCO₃ and FS into the composites. Sequentially, improvements in tensile, flexural and Izod impact strengths as a function of fillers loading in the HDPE matrix have been reported. The maximum enhancement (%) of tensile, flexural and impact strengths were 127%, 86% and 16.6%, respectively, for composites containing 25% CaCO₃ and 1% FS without any inclusion of talc filler; this indicates that the types/nature, size, quantity and dispersion status of fillers are playing a major role in the mechanical properties of the prepared composites more than the number of the used fillers. Full article

This paper proposes a new chemical combustion method for the synthesis of nano-low-carbon belite cement via a simple one-step process without using any oxidizers, and related mechanisms are briefly introduced. The starting materials used, including micro-silica (silica fume) as a byproduct of the metallurgic industry and CaCO₃ powders, are of great abundance, and the processing parameters involved were optimized using a series of systematic experiments based on X-ray diffraction (XRD) and the Rietveld fitting method. Besides, the properties of the synthesized belite cement were characterized by the Brunauer–Emmett–Teller (BET) technique and scanning electron microscopy (SEM). Experimental results revealed that the optimized fuel agent was urea with a dosage of 4.902 times that of the starting materials by mass, and the corresponding holding temperature and time were 1150 °C and 2 h, respectively. In addition, the CaO/(SiO₂ + CaO) for the starting materials should be set at 62.5% by mass ratio. BET and SEM results showed that the obtained belite cement had a specific surface area of 11.17 m²/g and a size of around 500 nm or even smaller in spherical shapes, suggesting that this method was successfully implemented. Thus, it can be a promising approach for the synthesis of nano-belite particles as a low-carbon construction material, which could be used more in the near future, such as for low-carbon concrete productions. Full article

The low tensile strain capacity and brittle nature of high-strength concrete (HSC) can be improved by incorporating steel fibers into it. Steel fibers’ addition in HSC results in bridging behavior which improves its post-cracking behavior, provides cracks arresting and stresses transfer in concrete. Using machine learning (ML) techniques, concrete properties prediction is an effective solution to conserve construction time and cost. Therefore, sophisticated ML approaches are applied in this study to predict the compressive strength of steel fiber reinforced HSC (SFRHSC). To fulfil this purpose, a standalone ML model called Multiple-Layer Perceptron Neural Network (MLPNN) and ensembled ML algorithms named Bagging and Adaptive Boosting (AdaBoost) were employed in this study. The considered parameters were cement content, fly ash content, slag content, silica fume content, nano-silica content, limestone powder content, sand content, coarse aggregate content, maximum aggregate size, water content, super-plasticizer content, steel fiber content, steel fiber diameter, steel fiber length, and curing time. The application of statistical checks, i.e., root mean square error (RMSE), determination coefficient (R²), and mean absolute error (MAE), was also performed for the assessment of algorithms’ performance. The study demonstrated the suitability of the Bagging technique in the prediction of SFRHSC compressive strength. Compared to other models, the Bagging approach was more accurate as it produced higher, i.e., 0.94, R², and lower error values. It was revealed from the SHAP analysis that curing time and super-plasticizer content have the most significant influence on the compressive strength of SFRHSC. The outcomes of this study will be beneficial for researchers in civil engineering for the timely and effective evaluation of SFRHSC compressive strength. Full article

(1) Background: Synthetic amorphous silica (SAS) is widely used as a food additive and contains nano-sized particles. SAS can be produced by fumed and precipitated methods, which may possess different physiochemical properties, toxicokinetics, and oral toxicity. (2) Methods: The toxicokinetics of fumed SAS and precipitated SAS were evaluated following a single-dose oral administration in rats. The tissue distribution and fate of both SAS particles were assessed after repeated oral administration in rats for 28 d, followed by recovery period for 90 d. Their 28-d repeated oral toxicity was also evaluated. (3) Results: Precipitated SAS showed higher oral absorption than fumed SAS, but the oral absorption of both SAS particles was low (<4%), even at 2000 mg/kg. Our tissue-distribution study revealed that both SAS particles, at a high dose (2000 mg/kg), were accumulated in the liver after repeated administration for 28 d, but the increased concentrations returned to normal levels at 29 d, the first day of the recovery period. A higher distribution level of precipitated SAS than fumed SAS and decomposed particle fates of both SAS particles were found in the liver at 28 d. No significant toxicological findings were observed after 28-d oral administration, suggesting their low oral toxicity. (4) Conclusions: Different manufacturing methods of SAS can, therefore, affect its oral toxicokinetics and tissue distribution, but not oral toxicity. Full article

Elastomers with supreme mechanical adaptability where the increasing stress under continuous deformation is significantly inhibited within a large deformation zone, are highly desired in many areas, such as artificial muscles, flexible and wearable electronics, and soft artificial-intelligence robots. Such system comprises the advantages of recoverable elasticity and internal compensation to external mechanical work. To obtain elastomer with supreme mechanical adaptability, a novel liquid crystal-based organosilicon elastomer (LCMQ) is developed in this work, which takes the advantages of reversible strain-induced phase transition of liquid crystal units in polymer matrix and the recoverable nano-sized fillers. The former is responsible for the inhibition of stress increasing during deformation, where the external work is mostly compensated by internal phase transition, and the latter provides tunable and sufficient high tensile strength. Such LCMQs were synthesized with 4-methoxyphenyl 4-(but-3-en-1-yloxy)benzoate (MBB) grafted thiol silicone oil (crosslinker-g-MBB) as crosslinking agent, vinyl terminated polydimethylsiloxane as base adhesive, and fumed silica as reinforcing filler by two-step thiol-ene “click” reaction. The obtained tensile strength and the elongation at break are better than previously reported values. Moreover, the resulting liquid crystal elastomers exhibit different mechanical behavior from conventional silicone rubbers. When the liquid crystal content increases from 1% (w/w) to 4% (w/w), the stress plateau for mechanical adaptability becomes clearer. Moreover, the liquid crystal elastomer has no obvious deformation from 25 °C to 120 °C and is expected to be used in industrial applications. It also provides a new template for the modification of organosilicon elastomers. Full article

In the present research work, the ternary effects of varying dosages of graphene oxide encapsulated silica fume (GOSF), nano-silica sol (SS), and fly ash (FA) on the rheological properties, compressive strength, the content and crystal size of calcium hydroxide (CH), porosity, water absorption, and microstructure of cement were studied. The results showed that the incorporation of SF improved the dispersion of GO in the cement matrix. GOSF was prepared through electrostatic attraction between the positively charged SF and negatively charged GO. The ternary mixture of GOSF, SS, and FA had a synergistic effect that improved the performance of cement. When the curing age of cement reached 28 days, the compressive strength reached a peak of 86.5 MPa, the minimum values of content and crystal size of CH were 11.55% and 45.69 nm, respectively, and the minimum values of water absorption and porosity were 0.0011 cm/s^0.5 and 13.56%, respectively. In terms of microstructure, the denser structure of C-S-H gel and the regular arrangement of CH were noticed. Full article

There have been numerous studies on shotcrete based on strength and durability. However, few studies have been conducted on rheological characteristics, which are very important parameters for evaluating the pumpability and shootability of shotcrete. In those studies, silica fume has been generally used as a mineral admixture to simultaneously enhance the strength, durability, pumpability, and shootability of shotcrete. Silica fume is well-known to significantly increase the viscosity of a mixture and to prevent material sliding at the receiving surface when used in shotcrete mixtures. However, the use of silica fume in shotcrete increases the possibility of plastic shrinkage cracking owing to its very high fineness, and further, silica fume increases the cost of manufacturing the shotcrete mixture because of its cost and handling. Colloidal silica is a new material in which nano-silica is dispersed in water, and it could solve the above-mentioned problems. The purpose of this research is to develop high-performance shotcrete with appropriate levels of strength and workability as well as use colloidal silica for normal structures without a tunnel structure. Thereafter, the workability of shotcrete with colloidal silica (2, 3, and 4%) was evaluated with a particle size of 10 nm and silica fume replacement (4 and 7%) of cement. In this study, an air-entraining agent for producing high-performance shotcrete was also used. The rheological properties of fresh shotcrete mixtures were estimated using an ICAR rheometer and the measured rheological parameters such as flow resistance and torque viscosity were correlated with the workability and shootability. More appropriate results will be focusing on the Bingham model properties such that the main focus here is to compare all data using the Bingham model and its performance. The pumpability, shootability, and build-up thickness characteristics were also evaluated for the performance of the shotcrete. This research mainly focuses on the Bingham model for absolute value because it creates an exact linear line in a graphical analysis, which provides more appropriate results for measuring the shotcrete performance rather than ICAR rheometer relative data. Full article