Search

Low carbon and high performance have become key trends in the development of construction materials. Understanding the mechanism by which curing conditions affect the mechanical properties of high-ductility geopolymer concrete (HDGC) is of significant importance. This study investigated three sealing curing temperatures (room temperature, 45 °C, and 60 °C) and four curing durations (1 day, 3 days, 5 days, and 7 days), while considering two final curing ages (7 days and 28 days) to explore their effects on the axial tensile and compressive properties of HDGC. The results showed that both 45 °C and 60 °C could improve the brittle failure of HDGC under axial compressive loading. However, curing at 60 °C and for durations longer than 1 day in an oven would catalyze the formation of eight-faced zeolite crystals within the slag–fly ash geopolymer matrix, and it could weaken the matrix's pore structure and subsequently affect the material's later strength development. Nevertheless, oven heat curing enhanced the bridging effect between the fibers and the matrix, partially compensating for the reduction in the initial tensile strength of HDGC. This follows the pseudo-strain-hardening material's saturation cracking criterion to enhance the strain-hardening behavior of HDGC and improve its tensile energy absorption capacity. A curing condition of 45 °C for 5 days is recommended to maximize the ductility of HDGC. This study provides important theoretical support for the design and promotion of green, low-carbon, high-ductility composite materials. [ABSTRACT FROM AUTHOR]

Raman spectroscopy (RS) is a spectroscopic method which indirectly measures the vibrational states within samples. This information on vibrational states can be utilized as spectroscopic fingerprints of the sample, which, subsequently, can be used in a wide range of application scenarios to determine the chemical composition of the sample without altering it, or to predict a sample property, such as the disease state of patients. These two examples are only a small portion of the application scenarios, which range from biomedical diagnostics to material science questions. However, the Raman signal is weak and due to the label-free character of RS, the Raman data is untargeted. Therefore, the analysis of Raman spectra is challenging and machine learning based chemometric models are needed. As a subset of representation learning algorithms, deep learning (DL) has had great success in data science for the analysis of Raman spectra and photonic data in general. In this review, recent developments of DL algorithms for Raman spectroscopy and the current challenges in the application of these algorithms will be discussed. [ABSTRACT FROM AUTHOR]