Hydraulic stimulation is widely viewed as a key enabling technology for superhot enhanced geothermal systems (EGS), yet its effectiveness in rocks approaching the brittle-to-ductile transition (BDT) remains uncertain. In this regime, stimulation is not simply the creation of fractures in a brittle medium; permeability development is governed by the coupled effects of high temperature, evolving deformation mechanisms, fluid state, silica chemistry, and cooling-induced stress redistribution. This review synthesizes laboratory, numerical, theoretical, and field evidence to evaluate what hydraulic stimulation can create in superhot BDT rocks, why stimulation-induced permeability may degrade during operation, and what long-term feedbacks may develop over reservoir lifetimes. Existing evidence indicates that permeability may form through cloud-fracture networks, hydroshearing, distributed semi-ductile damage, and thermal microfracturing, but these processes do not necessarily produce durable transmissivity. Hydrothermal alteration, pressure solution, silica precipitation, nanoparticle clogging, aperture closure, and cooling-front migration can modify or reduce permeability over timescales from hours to decades. Cooling plays a complex role: it may improve near-wellbore injectivity through thermoelastic fracture opening, while promoting delayed fault reactivation at greater distances and longer times. Field evidence remains limited because drilling, completion, and wellbore-integrity challenges have often prevented full reservoir-scale validation. Overall, hydraulic stimulation should not be regarded as a standalone solution to the BDT challenge. Rather, BDT-controlled rock behavior determines whether stimulation can generate permeability that is useful, sustainable, and manageable. Future research should prioritize field-scale validation of permeability-stabilization criteria, scale-up of laboratory-observed fracture architectures, and long-term forecasting of cooling-driven mechanical feedbacks.
Adsorbent-modified membranes represent an evolving approach to improved water purification that has addressed the persistent trade-off between membrane selectivity and permeability, as well as the fouling associated with conventional membrane technology. This study will provide a critical evaluation of recent advancements in adsorptive membrane design, fabrication, and functional mechanisms for removing various contaminants, such as heavy metals, persistent organic pollutants, pharmaceuticals, per- and polyfluoroalkyl substances (PFAS), and microplastics. The emphasis of this study will be on combining adsorption chemistry with membrane separation via surface functionalization, nanomaterial incorporation, ion and molecular imprinting, and bio-inspired engineering strategies. Discussion will be provided on the mechanistic aspects of chelation, hydrophobic interactions, π–π stacking, electrostatic attraction, and catalytic coupling, in addition to performance metrics including selectivity, adsorption capacities, regeneration efficiency, and anti-fouling properties. Emerging concepts such as stimuli-responsive and dual-functional catalytic membranes show great potential to surpass traditional separation limitations, while also reducing energy requirements and minimizing secondary pollution. Although significant advancements have been reported at the laboratory scale, many unresolved challenges remain, including scalable manufacture, long-term stability, and environmentally sustainable regeneration. This review aims to bridge the gap between fundamental principles of membrane science and practical applications, thereby supporting next-generation membrane development for sustainable wastewater treatment.
Vascular disease (VD) is a medical condition that adversely affects the blood vessels. Peripheral Artery Disease (PAD), a significant form of VD, is a major global health concern. In developing countries such as Bangladesh, VDs often remain undiagnosed and untreated until reaching critical stages. Artificial Intelligence (AI) based methods have potential for early detection and improved treatment guidelines. This paper narratively reviews both the clinical and the AI aspects of VD, emphasizing PAD, by examining recent studies on clinical diagnostic methods, treatment strategies and clinical outcomes, highlighting AI-driven research, machine learning (ML) algorithms contributing to disease management. Existing research on VDs, addresses their epidemiology, diagnosis, treatment, prevalence and morbidity, and mortality. It shows the need for more context-sensitive treatment data, age-specific studies, better access to technology, precise treatment goals, and rigorous diagnostic methodologies. Several works have explored AI algorithms to analyze diverse data sources - such as electronic health records (EHR), radiology images, genetic data and pulse wave signals for PAD detection, achieving accuracy exceeding 94
Biochars produced from sawdust (SD) and manure pellets (MP) at pyrolysis temperatures of 300, 500, and 700 °C, with and without steam activation, were evaluated for their adsorption characteristics for methylene blue (MB) and methyl orange (MO). The physicochemical characteristics and surface functionalities of the biochars, previously studied as part of a larger project, provided insights into their dye adsorption behavior in this work. Increasing the pyrolysis temperature significantly increased the surface area of SD biochars, primarily due to devolatilization and pore development, with a further increase observed under steam activation. In contrast, MP biochars exhibited no significant changes in surface area, likely due to pore blockage by ash formed from mineral components. Scanning electron microscopy (SEM) confirmed enhanced porosity at higher temperatures, and X-ray diffraction (XRD) revealed amorphous structures in SD biochars and crystalline inorganic phases in MP. Fourier-transform infrared spectroscopy (FTIR) indicated a reduction in surface functional groups with increasing pyrolysis temperature, while steam activation at 500 °C may retain sufficient surface functionalities that contribute to dye adsorption. Biochars produced at 500 °C demonstrated the highest adsorption performance, reflecting a favorable balance between surface area and functional groups. Steam activation further increased dye adsorption by improving pore accessibility and introducing more active sites. Despite having lower surface areas, MP biochars exhibited competitive adsorption, likely due to their mineral-rich surfaces contributing to dye interactions. Adsorption followed the Langmuir isotherm model and pseudo-2nd-order kinetics, indicating monolayer chemisorption on homogeneous surfaces. These findings highlight the interplay between thermal processing and feedstock composition in governing adsorption behavior, providing design strategies for an efficient biochar-based dye adsorption system for environmental remediation.
CsSnxGe1-xI3 as lead-free perovskites are promising for next generation NIR emitting perovskite light emitting diodes (PeLEDs) due to their tunable bandgaps and stability. However, they suffer from poor light extraction efficiency (LEE), and accurate composition-specific optical data for these materials remain scarce. This study presents a density functional theory (DFT) informed finite-difference time-domain (FDTD) framework to optimize light extraction via compositional tuning and plasmonic enhancement. First, DFT calculations were performed to obtain composition-specific complex refractive index and extinction coefficient values for x = 0, 0.25, 0.5, 0.75, and 1. Results showed that the bandgap increased from 1.331 eV for CsSnI3 to 1.927 eV for CsGeI3 with increasing Ge content, while the refractive index ranged from 2.2 to 2.6 across compositions. These optical constants were then used as inputs for FDTD simulations of a PeLED structure with optimized Au/SiO2 core-shell nanorods for plasmonic enhancement. A 12.1-fold Purcell enhancement was achieved for CsSn0.25Ge0.75I3, while LEE reached 25% for CsSn0.5Ge0.5I3. A LEE enhancement of 36% was obtained for CsSnI3, and spectral overlap between emitter and plasmon resonance reached 96% for Sn-rich compositions. Among the studied compositions, CsSn0.5Ge0.5I3 provides the best balance between emission enhancement, light extraction efficiency (25%), Purcell enhancement (5.3 & times;), spectral matching (93%), and oxidation stability, while Ge-rich alloys exhibit stronger spontaneous emission rate enhancement. These results establish composition-aware design guidelines for lead-free perovskite emitters targeting flexible and wearable optoelectronic applications.
