Pavement construction relies heavily on natural aggregates and conventional stabilizers such as Portland cement (PC) and lime, contributing to resource depletion, high energy demand, and significant greenhouse gas emissions. Geopolymerization, the alkaline activation of aluminosilicate materials, offers a more sustainable alternative. Mine tailings (MT), abundant mining by-products rich in silica and alumina, are attractive geopolymer precursors, although their reuse is constrained by variable composition and potential toxicity. In mining-intensive countries like South Africa, copper, cassiterite, and gold MT represent promising resources. This review critically synthesizes current knowledge on the physicochemical properties, mechanical performance, and environmental implications of geopolymerized MT in pavement applications. Unlike previous reviews that consider MT or geopolymers in isolation, it establishes a pavement-engineering–driven framework linking MT characteristics, geopolymer chemistry, and pavement performance requirements across laboratory, environmental, and field scales. The reviewed evidence indicates that MT-based geopolymers can achieve higher strength and superior durability than PC or lime-stabilized bases while reducing CO2 emissions by up to 80
Excessive consumption of synthetic food colourants such as tartrazine (TZ) poses significant health risks, highlighting the need for sensitive detection methods for food safety applications. Here, we report a binder-free electrochemical sensor based on synergistic integration of iron-based metal–organic framework MIL-100(Fe) with multiwalled carbon nanotubes (MWCNTs) on glassy carbon electrode (GCE). The nanocomposite leverages MIL-100's high porosity and accessible Fe3⁺ sites combined with MWCNT's superior conductivity, achieving an 11-fold enlarged electroactive surface area (0.786 cm2) and 19-fold enhanced exchange current density. Under optimized conditions using differential pulse voltammetry (DPV), the sensor exhibited a low detection limit (LOD) of 0.11 μM, a wide linear range (0.9 to 7.5 μM, R2 = 0.9891), and excellent selectivity over amaranth (AM) and common interferents. Mechanistic studies revealed adsorption-controlled, one-electron/one-proton irreversible oxidation with ultralow charge transfer resistance (0.073 kΩ). The sensor demonstrated robust performance with excellent repeatability (RSD = 2.11
We investigate a class of radiating star models with nonzero shear in general relativity by specifying a particular form of the gravitational potential associated with the areal 2-sphere. This choice enables the boundary condition equation to be transformed into a Riccati equation, which we integrate to obtain three distinct classes of exact solutions. These solutions represent a category of shearing metrics not previously studied. A comprehensive physical analysis confirms the physical plausibility of the solutions, highlighting their potential applicability to realistic radiating stellar structures.
This study investigated the acid rock drainage (ARD) potential of waste rocks (WR) in two abandoned mines in the Ekurhuleni region of the Gauteng Province, South Africa. ARD prediction characterization and techniques such as mineralogy, maximum potential acidity (MPA), net acid production potential (NAPP), acid neutralizing capacity (ANC), net acid generation (NAG) and biokinetic tests were performed to assess the ARD formation potential of the WR samples. WR-1 and 2 had sulphur contents of 0.34 and 2.3
There is mounting evidence from multiple cosmological probes that dark energy may be dynamical, with an equation of state that evolves over cosmic time. While this evidence is typically quantified using the Chevallier-Polarski-Linder (CPL) parametrization, based on a linear expansion of w(a) in the scale factor, nonparametric reconstructions frequently suggest nonlinear features, particularly at late times. In this work, we investigate four minimal one-parameter models of dark energy with nonlinear dependence on the scale factor. These models are constrained using cosmic microwave background data from Planck, lensing reconstruction from Planck and ACT-DR6, baryon acoustic oscillation measurements from DESI-DR2, and three type Ia supernovae (SNe) samples (PantheonPlus, DESY5, and Union3), considered independently. Although our conclusions depend on the choice of SNe sample, we consistently find a preference, as measured by the chi-squared statistic and the Bayesian evidence, for these dynamical dark energy models over the standard ACDM (cold dark matter) model. Notably, with the PantheonPlus dataset, one model shows strong Bayesian evidence (D ln B <^> 4.5) over CPL, favoring an equation of state that peaks near a <^> 0.7 and oscillates near the present day. These results highlight the impact of SNe selection and contribute to the growing collection of evidence for late-time deviations from ACDM.
