In this study, the electronic and optical properties of poly(p-phenylene vinylene) (PPV) were analyzed using density functional theory (DFT) with the ab initio simulation package VASP. PPV, a conjugated polymer, has garnered significant attention due to its promising applications in optoelectronic devices. The study focuses on the calculation of key electronic properties such as the density of states (DOSs) and the electronic band gap, which was found to be approximately 0.84 eV. This band gap is crucial for determining the material's suitability for devices that require efficient charge transport. The electronic structure reveals the characteristic behavior of delocalized π-electrons, which contribute to PPV's conductive properties, making it ideal for electronic and optoelectronic applications.

In addition to the electronic properties, the optical characteristics of PPV were also studied. The calculations show important parameters, including the absorption spectrum and dielectric constant, with significant absorption observed in both the visible and ultraviolet ranges. These features make PPV particularly attractive for applications requiring efficient light absorption and emission. Furthermore, the material's stability and performance under varying conditions were also evaluated, offering valuable insights into its practical applications. The combination of favorable electronic and optical properties suggests that PPV has strong potential for optoelectronic applications, particularly in organic photovoltaic cells, OLED lighting devices, and optical sensors, where light absorption, charge transport, and stability are essential for optimal performance.

According to the Food and Agriculture Organization of the United Nations (FAO), global rice production is projected to exceed 550 million tons by 2025. As one of the world's primary food crops, rice generates a remarkable amount of agro-industrial waste, particularly rice husks, which comprise about 20% of the total grain mass. This waste is often used for energy generation but releases polluting gases into the atmosphere, highlighting the need for more sustainable recovery alternatives. In this context, this study aimed to characterize the physicochemical properties of rice husks and evaluate their potential as a bioadsorbent. Ground rice husks were treated with NaOH solutions (less than 5% w/v) for delignification, followed by acidification with acetic acid under mild conditions. The samples underwent additional steam explosion pretreatment, followed by agitation in the Turrax. The physicochemical characterization of the samples was conducted using TG/DTG (model Q600 from TA Instruments, N₂ atmosphere, 25 to 600°C), SEM (model Quanta 400 FEG from FEI Company), and UV-Vis (model UV-2600i from Shimadzu). The TG curves revealed two significant decomposition stages between 25-100°C and 225-600°C. DTG curves indicated peaks at 40 and 325°C, linked to water loss and cellulose degradation, respectively. The micrographs indicated significant changes in fiber morphology after treatment, with exposure of cellulose nanofibers. Also, all samples were evaluated for color removal in beverage additives. Remarkably, as a bioadsorbent, the rice husks achieved an approximate 60% reduction in color intensity, especially in samples that underwent more extensive depressurization during steam explosion. These results reveal the potential of rice husks as a sustainable and effective material for color adsorption in aqueous solutions.

The development of advanced and sustainable materials for green technologies aims to lessen the environmental impact of current systems. In this context, polymer-based nanocomposites have garnered increasing attention due to their lightweight nature, adjustable properties, and environmental friendliness. This study examines the mechanical properties of polyvinylidene fluoride (PVDF), known for its piezoelectricity and semi-crystalline structure, both in its pure form and reinforced with barium titanate (BTO), a non-toxic, lead-free ceramic known for its mechanical and dielectric performance.

Using molecular dynamics (MD) simulations via Materials Studio, we analyzed four distinct systems: pure PVDF (PVDF-0), as well as three composites containing one (PVDF-1), two (PVDF-2), and three (PVDF-3) BTO inclusions, corre- sponding to weight fractions of 7.57 wt%, 14.07 wt%, and 19.72 wt%, respectively.

After energy minimization and equilibration, stiffness matrices were calculated to derive key mechanical parameters, Young’s modulus, shear, and bulk moduli, to evaluate the impact of nanoparticle reinforcement.

The results show a substantial improvement in the stiffness and mechanical performance of PVDF with BTO addition, especially at low concentrations. These findings confirm that small amounts of BTO act as an effective reinforcement strategy for polymer matrices.

Molecular dynamics simulations are a crucial predictive tool for understanding mechanical properties at the nanoscale. They provide fundamental insights, essential for designing and optimizing high-performance composite materials.

By combining this approach with multi-scale modeling, we are paving the way for the development of eco-friendly materials, energy-harvesting devices, and smart systems, representing a key step in driving sustainable technological innovation.

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.