To align with global climate targets, the construction industry, responsible for approximately 37% of emissions, must transition from a carbon source to a net carbon sink. This study pioneers a strategic blueprint for this transformation by identifying the most viable pathways for engineering genuinely carbon-negative concrete. We address the urgent need for a comprehensive framework that evaluates and compares emerging Carbon Capture, Utilization, and Storage (CCUS) technologies beyond isolated studies.

Our methodology involved a systematic analysis of nine distinct technologies sourced from scientific literature, patents, and industry reports. These approaches were organized into three foundational categories for comparison: direct mineral sequestration, bio-based additives, and engineered systems. Each technology was assessed against critical metrics for industrial adoption, including its CO₂ sequestration efficiency, resulting mechanical strength, and potential for scalable, energy-efficient implementation.

Our findings underscore an inherent tension between carbon uptake potential and material performance. Among the evaluated options, washout-pretreated biochar stands out as a promising solution, achieving a significant carbon sequestration of 150-200 kg of CO₂/m³ while preserving a structural-grade compressive strength of 27.6 MPa. This balanced performance contrasts with other methods, like enzymatic biomineralization, which yield stronger concrete but with substantially lower carbon removal capacity.

The research concludes that the most effective strategy for sectoral decarbonization lies not in a single "silver bullet" but in a versatile portfolio of CCUS solutions. This approach would combine partial cement substitution with specific CCUS admixtures tailored to different performance requirements. Realizing this vision requires a coordinated effort that integrates advances in materials science with supportive policy frameworks to de-risk investment and scale up supply chains. This work provides a clear roadmap to guide industry and policymakers, demonstrating how the built environment can become a key asset in mitigating climate change.

This study investigates the high-frequency capacitance behavior of metal/insulator/polysilicon (MIS) structures used in polycrystalline silicon (poly-Si) thin-film transistors (TFTs) through two-dimensional numerical modeling. A custom simulation code based on Poisson’s equation was developed to model the electrostatic potential and capacitance characteristics of an Al/SiO₂/poly-Si structure, accounting for the granular nature of poly-Si.

The poly-Si active layer is represented as a series of columnar grains separated by narrow, highly defective grain boundaries (GBs). These GBs, oriented perpendicular to the oxide interface, act as energy barriers that trap free carriers and reduce mobility. Simulations highlight how the number of GBs, grain size, layer thickness, and oxide thickness impact high-frequency capacitance and threshold voltage.

Results show that increasing the number of GBs shifts the capacitance–voltage C (V) curve, raising the threshold voltage due to enhanced charge trapping. Similarly, larger grain sizes and thicker active layers also lead to increased threshold voltages, with a quasi-linear relationship between grain size and layer thickness amplifying this effect. Thicker oxide layers reduce gate control, further increasing the threshold voltage.

A detailed electrostatic potential distribution confirms that GBs trap carriers and form potential barriers, especially under depletion conditions. These findings demonstrate the strong dependence of poly-Si TFT capacitance behavior on structural properties.

To optimize TFT performance, the study suggests increasing grain size and reducing GB density, which can be achieved through techniques like laser crystallization or rapid thermal annealing. These modifications lower defect density, improve carrier mobility, and enhance device performance.

In conclusion, the paper provides a valuable numerical tool and physical insights into the capacitive behavior of poly-Si TFTs, with direct implications for designing efficient electronic and display components.

According to the Food and Agriculture Organization (FAO), global pulp and paper production reached approximately 700 million tons in 2020. However, this impressive output comes at a cost, generating substantial amounts of lignocellulosic waste, particularly eucalyptus leaves, which are frequently discarded. For every 100 tons of pulp produced, an estimated 48 tons of waste are generated. The high content of organic extractives and lignin in eucalyptus leaves forms a natural barrier that complicates the extraction of nanocellulose, making delignification a crucial step in the process. This study evaluated the efficiency of treatments for obtaining nanocellulose from Eucalyptus urograndis leaves and characterized the resulting material using TG/DTG. Previously ground eucalyptus leaves were treated with NaOH solution (5% m/v), followed by washing protocols to achieve a neutral pH. The three samples then underwent steam explosion through cycles of pressurization and depressurization, followed by Turrax and sonication treatments. The treated samples were characterized by TG/DTG thermal analysis techniques in a TA Instruments Q600 simultaneous analyzer (25 to 600°C, 10 °C/min under N₂). The thermogravimetric curves of all samples showed two main mass loss stages: the first (below 100°C) corresponds to moisture evaporation, and the second (200 to 400°C) corresponds to hemicellulose and cellulose decomposition. The DTG curves exhibited three decomposition peaks around 50°C (moisture loss), 245°C (hemicellulose decomposition), and 305°C (cellulose decomposition). Notably, no lignin decomposition peak was observed, confirming the effectiveness of lignin removal. The results suggest that non-aggressive green treatment with a low alkaline reagent content was efficient in lignin removal, facilitating access to micro- and nanocellulose. This approach promotes the potential of agroforestry residues and supports sustainable applications in areas such as biodegradable packaging, polymeric reinforcements, and advanced biomaterials.

Hydride materials are widely recognized for their significant potential in hydrogen storage, a crucial component of renewable energy systems. This study employs density functional theory (DFT) to investigate the structural, electronic, optical, and hydrogen storage properties of novel double hydride perovskites, such as Na₂LiXH₆ (X = Al, Ga). The materials crystallize in a cubic structure (Fm-3m), the optimized structural parameters are obtained through energy–volume (E-V) curve analysis, and the negative formation enthalpies confirm the thermodynamic stability of these compounds.

Electronic structure calculations reveal that Na₂LiAlH₆ and Na₂LiGaH₆ are semiconductors with indirect band gaps of approximately 2.60 eV and 0.66 eV, respectively. These values suggest potential applications in semiconductor-based devices. Optical analyses including the dielectric function, absorption coefficient, refractive index, extinction coefficient, and optical conductivity indicate strong absorption in the ultraviolet region, highlighting the materials’ potential for optoelectronic applications such as UV detectors and solar energy harvesting.

Moreover, the predicted gravimetric hydrogen storage capacities C_wt(%) are favorable, and the hydrogen desorption temperatures T_d are calculated to be 373.9 K for Na₂LiAlH₆ and 337.1 K for Na₂LiGaH₆. These properties indicate practical viability for energy storage applications. Together, these characteristics position Na₂LiXH₆ hydrides as promising multifunctional materials for next-generation clean energy technologies, combining efficient hydrogen storage with valuable electronic and optical features. This work contributes to the ongoing search for sustainable materials supporting the transition to renewable energy.

Driving-related stress contributes to approximately 1.35 million traffic fatalities annually worldwide, necessitating innovative approaches to enhance automotive safety through real-time stress monitoring and adaptive comfort systems. This research proposes the development of AI-driven smart materials for automotive applications based on a comprehensive analysis of existing physiological databases. The methodology integrates large-scale driving stress datasets, including the MIT PhysioNet DriveDB containing physiological recordings from 17 drivers across various stress conditions, and the SHRP2 Naturalistic Driving Study encompassing over 3,400 drivers and 5 million miles of real-world driving data. Machine learning algorithms analyze heart rate variability, electromyography, and behavioral patterns to establish quantitative relationships between physiological stress indicators and optimal material property requirements. The Materials Project database, containing over 140,000 computed material properties, serves as the foundation for AI-predicted smart material compositions. Target materials include thermochromic polymers for visual stress feedback, shape memory materials for adaptive comfort adjustment, and conductive textiles for continuous physiological monitoring. Preliminary analysis demonstrates stress classification accuracy exceeding 85% using physiological parameters, with material property predictions validated against existing automotive-grade smart materials. Expected outcomes include validated AI algorithms for stress-responsive material design, optimized formulations for thermochromic, shape memory, and conductive polymer systems, and a comprehensive feasibility assessment for automotive industry implementation. This interdisciplinary approach establishes new paradigms for human-centered materials design, potentially reducing stress-related driving incidents by 15-25% through proactive comfort intervention and real-time physiological feedback systems.

Nanotechnology has advanced rapidly in recent years, revolutionizing various scientific fields, industries, and research areas through the development and application of metal and metal oxide nanoparticles. Among these nanomaterials, copper oxide nanoparticles (CuO NPs) have gained significant attention due to their p-type semiconducting properties, narrow band gap, and large surface area [1]. These characteristics provide CuO NPs with excellent thermal stability, chemical resistance, and catalytic performance [2]. As a result, they are widely applied in photocatalysis, environmental remediation, sensing, and biomedical fields, due to their strong antimicrobial, antioxidant, and multifunctional activities [3,4]. The present study aimed to synthesize copper oxide nanoparticles (CuO NPs) using pure ascorbic acid as a potential reducing and stabilizing agent through an environmentally friendly green synthesis approach, and to evaluate their antioxidant and photocatalytic activities. The formation of CuO NPs has been confirmed by using powder X-Ray diffraction (XRD), UV-Vis spectroscopy and Fourier Transform Infrared (FTIR) spectroscopy. The antioxidant potential of the synthesized CuO NPs was evaluated by assessing their scavenging activity against the stable DPPH free radical. The obtained results show that the CuO NPs possessed significant antioxidant capacity, with (IC₅₀ = 0.21 mg/ml). In comparison, pure ascorbic acid, used as a positive control, exhibited an IC₅₀ of 0.014 mg/mL. The photocatalytic activity was evaluated through the degradation of methylene blue under solar irradiation. The obtained results revealed that the biosynthesized CuO NPs were able to degrade approximately 80% of the dye within 120 minutes.

This study investigates the effect of powder-pack boriding on the microstructure, surface roughness, and corrosion behaviour of AISI 316 (EN X5CrNiMo17-12-2) stainless steel, with the aim of developing a mathematical model based on the obtained experimental results.

Boriding was performed at 850, 900, and 950 °C for durations of 2–4 h using commercial Durborid G powder. Surface roughness was measured before and after treatment, corrosion performance was assessed in 3.5 wt% NaCl solution by potentiodynamic polarisation with focus on corrosion current density (icorr), and boride layer thickness was analysed metallographically. Mathematical models were developed to describe the dependence of surface and electrochemical properties on process parameters.

Boride layer thickness value ranged from ~10 µm at 850 °C/2 h to nearly 95 µm at 950 °C/2 h. Surface roughness generally increased compared to the untreated steel, except for the 850 °C/3 h condition, which exhibited a smoother surface. Corrosion currents revealed a strong influence of boriding conditions. The untreated specimen showed icorr = 4.36 µA. At 850 °C, icorr ranged between 8.86 µA and 20.5 µA, indicating deterioration of corrosion resistance. At 900 °C, the best results were obtained: icorr decreased to 1.13 µA at 2 h and 3.17 µA at 3 h, representing up to a fourfold improvement compared to untreated steel. Boriding at 950 °C gave mixed results, with icorr values between 5.37 µA and 8.67 µA.

These findings demonstrate that optimised boriding, particularly at 900 °C for short to moderate durations, can markedly reduce corrosion current and improve the electrochemical stability of austenitic stainless steel in chloride environments.

The long-term reliability of stainless steel-316 (SS-316) components in high-temperature environments is a critical consideration in sectors such as energy production and aerospace engineering. In particular, welded or bonded joints in SS-316 structures are often the most vulnerable to creep deformation due to localized stress concentration and thermal exposure. This study advances the predictive capability of a recently developed creep model for SS-316 joints by incorporating artificial intelligence (AI)–based parameter optimization and machine learning (ML)–driven residual correction. Experimental creep data obtained from SS-316 joint specimens under varied stress and temperature conditions formed the basis for model calibration. Parameter refinement was carried out using Particle Swarm Optimization and Genetic Algorithms, both of which effectively reduced systematic prediction errors. Complementary ML models, including Support Vector Regression and Gradient Boosted Trees, were trained to identify and model complex nonlinear patterns that the analytical approach alone could not capture. Model accuracy was quantified using metrics such as the mean absolute percentage error (MAPE) and the coefficient of determination (R²). The optimized model exhibited a reduction in MAPE exceeding 25% compared to its unoptimized counterpart, while the hybrid analytical–ML framework achieved an R² of 0.98 on the validation dataset. These results confirm that integrating AI-driven optimization with ML-based correction significantly improves predictive accuracy and generalization for SS-316 joint creep behavior. The proposed approach not only enhances the modeling of high-temperature joint performance but also offers a transferable methodology for other materials and joint configurations subjected to complex thermo-mechanical loads, thereby contributing to safer and more efficient engineering design.

The formulation of printable ceramic inks for additive manufacturing via direct ink writing remains a complex and time-consuming task, as it requires experimentally tuning the composition and rheological properties of the ink to ensure its printability . This process is typically based on trial and error, increasing costs and material waste.

In this work, we present the first stage of a data-driven formulation system built upon a hybrid information extraction pipeline that combines regular expressions with named entity recognition based on language models. The goal is to systematically retrieve key formulation parameters from full-text scientific articles. The pipeline identifies relevant entities such as powder composition, binder types and content, and water percentage, viscosity, yield stress, and viscoelastic moduli. A manually curated subset was used to validate the system, which achieves an 80% entity recognition rate. This strategy offers a promising tool to accelerate the design of new ceramic ink formulations for 3D printing, while significantly reducing manual effort, experimental costs, and material consumption. This work lays the foundation for a fully artificial intelligence AI-driven formulation assistant, where missing parameters can be inferred through predictive models and integrated into a structured database to support automated ink design.

This research work has been funded by the European Commission – NextGenerationEU, through the Momentum CSIC Programme: "Develop Your Digital Talent"

Fuel cells (FCs) offer high-efficiency and low-emission energy conversion, making them a top contender in clean-energy technology. This paper presents the design and partial fabrication of a proton-exchange-membrane fuel cell (PEMFC) specifically adapted to climatic and infrastructural context in the Gulf region. PEMFC was selected due to its compact form factor, rapid start-up capability, moderate temperature operation, and high Technology Readiness Level (TRL 8-9). One main target was the development of the membrane, the key electrochemical component facilitating selective proton transport from anode to cathode, while blocking electron and reactant gas crossover. Performance enhancement objectives include improved proton conductivity, chemical stability, and mechanical integrity matching or surpassing current commercial membranes. Local development of such membranes is strategically significant given the absence of domestic FC-manufacturing capabilities in the Gulf region.

Four design concepts were generated and evaluated. The optimal solution was integration of a solar-powered electrolysis system for on-site hydrogen and oxygen production. These gases are fed directly into the PEMFC, converting chemical energy into electrical output to power a small motor or fan in a proof-of-concept demonstration. The integration of renewable hydrogen generation with FC technology provides a closed-loop, emission-free energy system.

An artificial intelligence (AI) model was developed to predict membrane performance under varying operational conditions, enabling design optimization and efficiency improvements without extensive physical prototyping. The combined experimental–computational approach establishes a foundation for high-performance PEMFC membranes, scalable to larger systems in subsequent phases. This work is based on the Final Year Design project of a group of undergraduate mechanical engineering students. It makes a small but significant contribution to sustainable energy technology by advancing region-specific PEMFC design and fabrication capabilities, offering pathways toward local manufacturing, reduced environmental impact, and enhanced energy security in the Gulf region.