Below are recent articles (co-)authored by Brunel academic staff. Please click the title of the article to access the full-text.
Phase and microstructure evolution of the hydration products of magnesium phosphate cements under sulfuric acid environments
Zhao, J.G. et al
Construction and Building Materials, Vol 418, Art No. 135465 (Mar 2024)
Magnesium phosphate cement is a good canditate of repairing material served under harsh environments and its acid resistivity has drawn a lot of interest, but rare detailed research on the evolution of its physiochemical features has been reported. In this work, the impacts of sulfuric acid solution (pH = 2 - 7) on the hydration products of magnesium potassium phosphate cement and magnesium ammonium phosphate cement. Results showed that the main hydration products, struvite/struvite-K, experienced partial corrosion in the absence of the sulfuric acid solution replacement. The phase transition of struvite-K is clearly visible, leading to the formation of the new crystalline hydration product Mg3(PO4)2 center dot 22 H2O. The initial blossoms cluster microstructure transformed into a diffused dispersion that resembled a plate. When the solution was replaced, struvite/struvite-K underwent significant corrosion and even complete decomposition in more acidic (pH = 2, 3) solutions.
Study on time-varying coefficient of thermal expansion of Carbon nanotubes-modified face slab concrete at early age
Zhao, Z. F. et al
Materials Letters, Vol 362, Art No. 136198 (May 2024)
Temperature stresses are a major cause of thermal cracking in face slab concrete (FSC) at an early age. Timevarying coefficient of thermal expansion (CTE) can reflect the real thermal cracking of FSC. Carbon nanotubes (CNTs) can improve the mechanical properties of concrete, but its thermal cracking performance is rarely reported. In this study, CTE of FSC at an early age was investigated based on the maturity theory. The mechanism of CNTs on the CTE of FSC at early age was revealed, and a time -varying model for CTE at early age was developed. The results showed that CNTs can effectively reduce the CTE of FSC at an early age and can significantly improve the cracking resistance of FSC at early age.
Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan
Ejaz, N. et al
Water, Vol 16, No 4, Art No. 597 (Feb 2024)
Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the "dynamic clustered Bayesian averaging (DCBA)" approach with those utilizing the "regional principal component analysis (RPCA)" under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan's diverse climatic zones-glacial, humid, arid, and hyper-arid-employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil's U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil's U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil's U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs.
Enhancing supercapacitor electrochemical performance through acetate-ion intercalation in layered nickel-cobalt double hydroxides
Zhang, Q. Q. et al
Journal of Colloid and Interface Science, Vol 660, p. 597-607 (Apr 2024)
Enhancing the performance of layered nickel-cobalt double hydroxides (NiCo-LDH) as electrode materials for supercapacitors represents a promising strategy for optimizing energy storage systems. However, the complexity of the preparation method for electrode materials with enhanced electrochemical performance and the inherent defects of nickel-cobalt LDH remain formidable challenges. In this study, we synthesized acetate-ion-intercalated NiCo-LDH (NCLA) through a simple one-step hydrothermal method. The physical and chemical structural properties and supercapacitor characteristics of the as-prepared NCLA were systematically characterized. The results indicated that the introduction of Ac- engendered a distinctive tetragonal crystal structure in NiCo-LDH, concomitant with a reduced interlayer spacing, thus enhancing structural stability. Electrochemical measurements revealed that NCLA-8 exhibited a specific capacitance of 1032.2 F g-1 at a current density of 1 A g-1 and a high specific capacitance of 922 F g-1 at 10 A g-1, demonstrating a rate performance of 89.3%. Furthermore, NCLA-8 was used to construct the positive electrode of an asymmetric supercapacitor, while the negative electrode was composed of activated carbon. This configuration resulted in an energy density of 67.7 Wh kg-1 at a power density of 800 W kg-1. Remarkably, the asymmetric supercapacitor retained 82.8% of its initial capacitance following 3000 charge-discharge cycles at a current density of 10 A g-1. Thus, this study demonstrates the efficacy of acetate-ion intercalation in enhancing the electrochemical performance of NiCo-LDH, establishing it as a viable electrode material for supercapacitors.
Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions
Abarkan, I. et al
Engineering Applications of Artificial Intelligence, Vol 132, Art No. 107952 (Jun 2024)
Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the crosssection resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes.