The circular valorization of industrial residues as functional materials offers a promising strategy to reduce the environmental footprint of water treatment technologies. In this study, raw coal fly ash (FA), applied without pretreatment or structural modification, was evaluated as a waste-derived photocatalyst for the degradation of metamizole, a widely detected contaminant of emerging concern. A systematic assessment under five irradiation sources (visible light, UV-A, UV-B, UV-C, and V-UV) demonstrated that genuine photocatalytic activation occurred exclusively under UV-B irradiation, achieving 96.2% removal after 150 min and outperforming direct UV-B photolysis by 14.9 percentage points. In contrast, UV-C and V-UV induced near-complete photolysis independently of catalyst presence, while visible and UV-A irradiation produced negligible degradation, consistent with the measured band gap of FA (3.1 eV). Kinetic and scavenger experiments indicated a mechanism mainly governed by valence-band holes (h+) and hydroxyl radicals (●OH), with minor contribution from superoxide species (O2●-). The photocatalytic performance remained stable in the presence of common inorganic salts, confirming robustness under realistic water matrix conditions. HPLC-MS/MS analysis enabled the detection of fourteen potential transformation products and the proposal of a degradation pathway distinguishing photocatalytic activity from direct photolysis. Under optimized UV-B/FA conditions, COD and TOC removals reached 67.11% and 56.73%, respectively, while metal leaching remained minimal. Overall, unmodified coal fly ash behaves as an intrinsic semiconductor photocatalyst under UV-B irradiation and represents a promising low-cost material for sustainable and circular water treatment applications.
Transmission expansion delays represents a critical barrier to the timely and cost-effective development of modern power systems, particularly for integrating renewable energy. Although network congestions arising from postponed major infrastructure projects (e.g., new transmission lines) can severely impact system efficiency, these effects can be mitigated through the rapid deployment of flexible technologies such as FACTS devices and Battery Energy Storage Systems (BESS). In this paper, we propose a two-stage stochastic optimization framework that complements first-stage, asset-intensive network expansion decisions with second-stage, scenario-dependent investments in flexible transmission assets, including FACTS and BESS. The formulation explicitly captures the probabilistic nature of project delays, accounting for zonal variations in the likelihood of such delays and their implications for system planning. Through a set of case studies, we demonstrate that the strategic use of FACTS and BESS can serve as an effective hedge against the adverse impacts of transmission expansion delays.
This contribution proposes a reliability analysis framework to assess the stability of lunar lava tubes with explicit quantification of geometric shape uncertainty. A two-dimensional setting is adopted, where the variability in the cross-section geometry is modeled using a one-dimensional stationary Gaussian random field that is simulated via a Fourier series-based spatial averaging technique. The so-called gravity multiplier, a capacity-to-demand ratio for gravitational effects, is adopted as stability metric. Then, any given configuration is classified as insufficiently stable (failed) if its associated gravity multiplier is below a specified threshold. The corresponding failure probability provides an overall stability measure that explicitly accounts for geometric shape uncertainty and structural capacity requirements. To solve the reliability problem, a finite element limit analysis (FELA) technique is integrated with a stochastic simulation method. This approach not only strikes an appealing balance between efficiency and robustness, but also furnishes nontrivial insight about lava tube stability as a byproduct of the solution process. The resulting framework is designed in a modular manner, allowing different implementation aspects to be modified as needed. Numerical results suggest that the herein proposed framework can be regarded as a theoretically sound and flexible alternative for the reliability assessment of irregularly shaped lava tubes.
Heap leaching is widely used for the recovery of copper from low-grade oxide ores. Continuous monitoring of copper concentration in the pregnant leach solution (PLS) is essential for tracking leaching kinetics, determining optimal irrigation termination, and balancing metallurgical inventories at the individual-heap level; however, measurement at heap drainage channels remains operationally challenging owing to limited accessibility and reliance on manual sampling with delayed laboratory analysis. This study characterizes the photographic and modeling parameters of a laboratory prototype that estimates copper concentration in copper sulfate solutions using empirical, image-based colorimetric regression with low-cost consumer imaging hardware rather than a laboratory spectrometer, as a basis for future in-line sensing. Synthetic PLS solutions (0 to 40 g Cu/L in sulfuric acid) were imaged under controlled illumination (fixed-color, fixed-intensity LED light, diffused inside a dark chamber and not collimated) using a Nikon D3100 digital single-lens reflex (DSLR) camera across four channels (red, green, blue, and a white composite) at three intensity levels. For each channel, an absorbance-like feature was derived from the channel intensity relative to a zero-copper blank. A dataset of 432 images was subdivided into 256 spatial sub-samples per image across four channels, generating 442,368 channel-level observations, which were reduced to 15,120 physically coherent observations by sequential filtering (Kendall rank correlation, then linear and quadratic absorbance-concentration bounds). A feedforward artificial neural network (ANN) predicted copper concentration from the four color features and was benchmarked against a multivariate linear model used as a Lambert-Beer baseline. On a separate simulated, controlled dataset, the ANN achieved an RMSE of approximately 0.55 g/L (residual standard deviation of 0.40–0.41 g/L), a reduction of approximately 37 % in residual standard deviation relative to the best linear baseline (from 0.64 to 0.40 g/L) on simulated data. Experimental validation yielded a prediction error of approximately 25 %. This error is structured rather than random, arising from intensity-level displacement caused by specular reflections on the cylindrical sample cell, and is comparable to the ∼ 20 % uncertainty of current manual sampling; it is therefore a hardware and data-acquisition limitation rather than a limitation of the sensing concept. Under real PLS conditions, the error would be expected to increase, so the laboratory value should be read as a best-case bound. A flat-window flow cell, collimated illumination, and inclusion of the recorded illumination intensity as a model input are identified as the priority improvements.
Carbonic anhydrase isoforms I and II (hCA-I and hCA-II) are metalloenzymes involved in essential physiological processes and represent relevant therapeutic targets for disorders such as glaucoma and osteoporosis. Chalcones have emerged as promising scaffolds for carbonic anhydrase inhibition; however, their structure-activity relationships, particularly for non-sulfonamide derivatives, remain insufficiently explored from a computational point of view. In this study, a dataset of 118 chalcone derivatives has been analyzed by using a three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, which comprises Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA). The developed models exhibited strong internal consistency and predictive capability for both isoforms. For hCA-I, steric, electrostatic, hydrophobic, and hydrogen bond acceptor fields has been identified as key contributors to inhibitory activity, whereas for hCA-II, hydrogen bond donor features played a more prominent role. Molecular docking and molecular dynamics simulations have been employed as complementary approaches to analyze ligand-protein interactions and binding stability. In addition, quantum chemical descriptors, derived from density functional theory, that have been integrated with the 3D-QSAR analysis, reveal a consistent correspondence between contour map features and the distribution of frontier molecular orbitals and molecular electrostatic potential. Furthermore, ADME-based pharmacokinetic properties of the proposed compounds have been evaluated to assess their potential drug-likeness. Based on the integrated computational analysis, six new chalcone derivatives, with predicted inhibitory activity in the nanomolar range, are proposed. Overall, this study provides a consistent physicochemical framework for understanding the inhibitory activity of chalcone derivatives and highlights key molecular features that may guide the modulation of activity across hCA-I and hCA-II isoforms.
