Imam Hossein University

The Ti3AlC2 MAX phase exhibits excellent properties such as high electrical conductivity, toughness, and machinability. In this work, Ti3AlC2 MAX phase was synthesized via spark plasma sintering (SPS) with a high purity of 97.9

Directional simulation, among techniques for reliability analysis of engineering structures, is well known. In this context, directional simulation in load space, because of the advantages of lower dimension of working space, is of considerable interest. As expected due to structural resistance variability, the location of the relevant limit state(s) along any simulated direction, is no longer constant and must be taken as a random variable. The accuracy of the results obtained using this type of simulation therefore depends on how well this random variable probabilistically is modeled in analysis. In order to describe probabilistic properties of this random variable, one simplified solusion is to use only the first two moments which implies the use of normal distribution. This paper intends first to verify how taking this distribution may affect the accuracy of the results (i.e. estimation of the probability of failure) and second, to propose alternative solution(s) in order to enhance the quality of the method in the cases where the above simplified method may lead to poor results. In the examples given in this paper, the quality and workability of the proposed solution(s) are compared with the simplified solution and finally some practical recommendations are given.

Abstract This study presents the design of an artificial neural network (ANN) to evaluate and predict the viscosity behavior of Al2O3/10W40 nanofluid at different temperatures, shear rates, and volume fraction of nanoparticles. Nanofluid viscosity ( $${\mu }_{nf}$$ μ nf ) is evaluated at volume fractions ( $$\varphi$$ φ =0.25% to 2%) and temperature range of 5 to 55 °C. For modeling by ANN, a multilayer perceptron (MLP) network with the Levenberg–Marquardt algorithm (LMA) is used. The main purpose of this study is to model and predict the $${\mu }_{nf}$$ μ nf of Al2O3/10W40 nanofluid through ANN, select the best ANN structure from the set of predicted structures and manage time and cost by predicting the ANN with the least error. To model the ANN, $$\varphi$$ φ , temperature, and shear rate are considered as input variables, and $${\mu }_{nf}$$ μ nf is considered as output variable. From 400 different ANN structures for Al2O3/10W40 nanofluid, the optimal structure consisting of two hidden layers with the optimal structure of 6 neurons in the first layer and 4 neurons in the second layer is selected. Finally, the R regression coefficient and the MSE are 0.995838 and 4.14469E−08 for the optimal structure, respectively. According to all data, the margin of deviation (MOD) is in the range of less than 2% < MOD < + 2%. Comparison of the three data sets, namely laboratory data, correlation output, and ANN output, shows that the ANN estimates laboratory data more accurately.

Covalent post-synthetic modification (PSM) represents a robust and irreversible strategy for precisely engineering the chemical functionality of metal–organic frameworks (MOFs) without compromising their structural integrity or porosity. This review surveys recent developments in covalent PSM approaches that enhance catalytic performance by introducing diverse functional groups—such as amines, carboxylic acids, sulfonic acids, phosphonic acids, imines, and guanidines—onto MOF organic linkers. These modifications enable fine-tuning of active site distribution, electronic environments, and acid–base properties, thereby facilitating cooperative and multifunctional catalysis. Through detailed analysis of structure–activity relationships and representative catalytic systems, covalent PSM is established as a powerful strategy for designing efficient, selective, and recyclable MOF-based heterogeneous catalysts applicable to a broad range of organic transformations.

The growing emissions associated with cement production have necessitated the development and utilization of sustainable alternatives such as green concretes, notably geopolymers. Although numerous studies have investigated the effects of elevated temperatures on concrete, the compressive behavior of these materials under repeated heating-cooling cycles, particularly in high-temperature industrial and infrastructural applications, has rarely been addressed. In the present study, the influence of successive thermal cycling on the performance of geopolymer concrete incorporating ground granulated blast furnace slag (GGBFS) and fly ash (FA) was examined and compared with that of conventional concrete. The evaluated variables included the type of concrete (RC as the reference mix, G1 with 100% GGBFS, and G2 with a blend of 60% GGBFS and 40% FA, all designed with the same binder content), the number of thermal cycles (1, 3, and 6 cycles), exposure soaking time (2, 4, and 6 h), and target temperatures (20, 300, and 600 & ring;C). The performance indicators comprised compressive strength, electrical resistivity, water absorption, and porosity of the concrete specimens. The results revealed that the reduction in compressive strength due to thermal exposure was not significantly influenced by different soaking times. This trend was consistently observed in both geopolymer and conventional concretes across all thermal cycling regimes. At elevated temperatures, conventional concrete generally exhibited a lower loss in compressive strength compared to geopolymer concretes, and this trend remained consistent across different soaking times and thermal cycles. Furthermore, among the geopolymer mixes, the specimen containing only GGBFS demonstrated superior compressive strength at ambient conditions relative to the blend of GGBFS and FA; however, this behavior was completely reversed at higher temperatures. The influence of exposure soaking time and the number of thermal cycles on the reduction of electrical resistivity in both geopolymer and conventional concretes was found to be minor, with an average impact of less than 10%. By changing the concrete type from normal concrete to geopolymer concrete, the average electrical resistivity (considering all thermal cycles and soaking times) decreased by 78, 70, and 50% at ambient temperature, 300, and 600 & ring;C, respectively. Water absorption and porosity in ordinary concrete were lower than those of geopolymer concretes under ambient conditions, but became higher at elevated temperatures. In addition, the effects of soaking time and the number of thermal cycles on these two parameters were more pronounced in geopolymer concretes than in conventional concrete. Finally, by applying nonlinear regression to the experimental dataset, high-accuracy predictive equations were developed to estimate the compressive strength of both geopolymer and conventional concretes as a function of temperature, number of thermal cycles, and soaking time.