Semiconductors are fundamental components of modern industry, as they serve as the backbone of electronics and energy technologies. Among their many characteristics, thermal properties play a crucial role in ensuring both performance and long-term reliability. Doped semiconductors, in particular, display unique and useful electronic properties; however, the introduction of impurities generally alters their thermal behavior, often leading to a reduction in thermal conductivity. To study this effect, equilibrium molecular dynamics (EMD) combined with the Green–Kubo formalism can be employed to calculate the thermal conductivity of doped semiconductor systems. In addition, the use of interatomic potentials, such as the Tersoff model, provides a framework to capture the underlying atomic interactions. By integrating machine learning techniques with molecular dynamics, it becomes possible to predict thermal properties across different doping levels and defect concentrations. Machine learning models, trained on simulation data, can reduce the computational cost of traditional simulations, which are typically both time- and resource-intensive. The results highlight how doping and defects modify thermal conductivity and help establish practical limits for impurity levels that still allow semiconductors to remain attractive for technological applications. The results are also of interest to determine the figure of merit of doped semiconductors. Understanding this relationship is essential for designing advanced materials that balance performance, efficiency, and reliability.

The development of advanced energy harvesting technologies requires piezoelectric architectures that combine high efficiency with mechanical flexibility. In this study, we conduct a comparative investigation of zinc oxide (ZnO) nanowires (NW) arrays grown on rigid Au/Pt-coated substrates and on flexible titanium foils, with the aim of evaluating their potential for integration into next-generation piezoelectric devices.

Vertically aligned ZnO NWs were grown via a low-temperature hydrothermal process on a crystalline seed layer prepared by sol–gel spin deposition. To enhance structural integrity and device compatibility, the NW arrays were encapsulated within a polymethylmethacrylate polymer matrix. The nanostructures were characterized throughout fabrication using transmission electron microscopy, scanning electron microscopy, and spectroscopic ellipsometry, confirming uniform morphology, high crystallinity, and consistent alignment. Piezoelectric properties were directly evaluated by measurements of the effective longitudinal piezoelectric coefficient (d₃₃), enabling a quantitative comparison between rigid and flexible device platforms. The results revealed a significant enhancement in piezoelectric performance for ZnO NWs integrated on titanium foil compared to those grown on Pt/Au substrates. This improvement is attributed to superior vertical integration and enhanced mechanical adaptability of the NWs when supported by a flexible substrate. These findings highlight the critical role of substrate choice in optimizing nanoscale piezoelectric performance. Moreover, the demonstrated low-cost, solution-processed growth of ZnO NWs on flexible metal foils underscores their potential for scalable fabrication of energy harvesting systems.

These results indicate that solution-processed ZnO nanowires networks on metal foils provide a cost-effective, scalable pathway to developing efficient, environmentally sustainable energy harvesting devices.

The rising incidence of fungal infections and the limited effectiveness of current antifungal drugs emphasize the need for new active substances. Cryptococcus neoformans is a major opportunistic pathogen, responsible for cryptococcal meningitis in immunocompromised patients and associated with high mortality worldwide. Thiosemicarbazones and their metal complexes are reported to exhibit diverse biological activities, including antifungal effects.

For our study, we have synthesized 3-(morpholin-4-yl)propane-2,3-dione 4-phenylthiosemicarbazone (HL) and its mixed ligand–copper(II) complexes [CuL(Im)NO₃], [CuL(Py)NO₃], [CuL(β-Pic)NO₃], and [CuL(γ-Pic)NO₃]. The composition and structure of all compounds were confirmed using ¹H and ¹³C NMR, FTIR, elemental analysis, molar conductivity measurements, and X-ray crystallographic analysis.

The antifungal activity of all synthesized compounds was evaluated towards Cryptococcus neoformans (CECT 1043) using the broth microdilution method. The mixed ligand–copper(II) complexes exhibited higher activity compared to both the HL and the copper(II) nitrate complex from which they were obtained. This finding confirms that the introduction of an additional ligand into the inner coordination sphere can enhance biological activity. The most active compound was [CuL(γ-Pic)NO₃], which showed MIC and MBC values of 7.81 µg/mL and 15.63 µg/mL, respectively.

These results highlight the potential of mixed ligand–copper(II) complexes as promising leads for the development of new antifungal agents, particularly against pathogenic fungi such as Cryptococcus neoformans.

The work was performed with financial support from subprogram 010602 of the institutional project.

The large-scale production of various synthetic dyes has contributed significantly to the release of large volumes of wastewater into the environment. It is estimated that approximately 14,000 tons of dyes from the textile industry are released directly into the environment annually, significantly compromising water quality [1]. The objective of this work was to prepare activated carbons (ACs) from agricultural waste from Mozambique for use in removing methylene blue (MB) dye. These ACs were simultaneously modified by impregnation with nitrogen-containing chemicals, such as urea, to increase their porosity and surface area. Three natural precursors were used in the preparation of the urea-modified ACs, with KOH as the activating agent in a 1:2:1 ratio (precursor–activating agent–urea). Kinetic studies, concerning methylene blue (MB) removal, from the aqueous phase, were performed at 298 K and pH 6 for 168 hours. Adsorption isotherms were obtained using 10 mg of the modified AC, in 25 mL of MB solution, with concentrations ranging from 0 to 250 mg L ^-1. The mixtures were stirred at 20 rpm and 298 K, for 24 hours. In general, ACs are efficient in removing MB dye. Modified ACs with the precursor–KOH–urea ratio of 1:2:1 presents better performance, while the ratio 1:2:0.5 resulted in lower adsorption capacity.

[1] Rendón-Castrillón, L., Ramírez-Carmona, M., Ocampo-López, C., González-López, F., Cuartas-Uribe, B., & Mendoza-Roca, J. A. (2023). Treatment of water from the textile industry contaminated with indigo dye: A hybrid approach combining bioremediation and nanofiltration for sustainable reuse. Case Studies in Chemical and Environmental Engineering, 8, 100498. doi.org/10.1016/j.cscee.2023.100498

Introduction
This study presents a biomimetic approach to enhance the photoconversion of CO₂ into methane. A catalyst based on halloysite (HNT) and kojic acid (K) was developed and further improved with xanthopterin, a natural pigment found in oriental wasp wings. The integration of this component significantly increased both efficiency and selectivity, highlighting the potential of nature-inspired strategies for sustainable solar-driven CO₂ conversion.

Methods
The synthesis involved functionalizing HNTs with chlorokojic acid, then complexing with CuCl₂ and reducing to Cu(I) with ascorbic acid. The resulting HNT-K/Cu(I) was combined with xanthopterin or riboflavin in DMSO and purified. Antenna loading was quantified by UV analysis, functionalization confirmed with FTIR and TGA, and optical properties evaluated by UV-DRS.

Results
Catalysts lacking Cu(I), including HNT, K, HNT-K, and Xant:HNT-K, showed negligible activity, confirming the essential role of cuprous ions. Even with Cu(I), activity remained low unless kojic acid was present, demonstrating its importance in stabilizing the active sites. The best-performing system, Xant:HNT-K/Cu(I), achieved 40.9% CO₂ conversion with 85.1% methane selectivity, far surpassing HNT-K/Cu(I). This performance arises from the synergistic effect of Cu(I), kojic acid, and xanthopterin. Compared with other catalysts that yield higher conversion but more CO, this system favored the formation of methane. It also remained stable over four cycles.

Conclusions
The combination of HNTs, kojic acid, and xanthopterin yields an efficient, selective, and stable catalyst for CO₂ photoreduction to methane. These results highlight the promise of biomimetic strategies and natural materials for designing sustainable CO₂ conversion systems.

Green synthesis of nanoparticles provides an alternative to conventional methods, which often involve harsh conditions and hazardous chemicals. This study utilized Tithonia diversifolia, an invasive plant species in Sri Lanka. Rich in phytochemicals, it is a promising candidate for green synthesis while converting it into a useful application. CuSO₄·5H₂O (Copper (II) sulfate) was used as the metal precursor. This research compared the petals and leaves of T. diversifolia to determine the optimal source for green synthesis of copper oxide (CuO) nanoparticles. Plant extracts (dried, ground, and sieved petals and leaves) were prepared by dissolving the powders in distilled water [1:10 (w/v)], heating at 70 °C for 15 minutes, and then centrifuging at 10,000 rpm. The supernatant was mixed with CuSO₄·5H₂O (0.5 M) and distilled water in a 1:1:1 ratio, heated to 70 °C for 15 minutes, and stirred at room temperature. The colour change indicated nanoparticle production. The solution was centrifuged at 10,000 rpm, washed three times with distilled water, dried at 60 °C, and characterized. Results for petals and leaves were as follows: UV–Vis absorbances at 200–250 nm and 265–285 nm; SEM revealed spherical shapes (20-50 nm) and flake-like structures (~100 nm); FTIR confirmed Cu–O bonding at 500–650 cm⁻¹ with phytochemical residues; zeta potential values were -0.2 mV (petals) and -1.8 mV (leaves), with hydrodynamic diameters of 262 nm and 385 nm, respectively. The study confirms that both are effective, with petals showing improved characteristics. Further research on purification and applications is recommended.

This study investigates the synthesis and characterization of Ni-Zr melamine based coordination polymer (CP) modified using charcoal by mechanochemical synthesis. The materials were analyzed using XRD, SEM-EDS, BET, TGA, and FTIR techniques. The results show that Ni-Zr melamine CP exhibit significantly enhanced surface area at (1178.176 m²/g) and porosity (2.800 nm) compared to the pure melamine ligand (83.803 m²/g). The incorporation of charcoal further increased the surface area to 1873.196 m²/g and pore diameter to 3.120 nm. TGA analysis indicated improved thermal stability of the CP, with total weight loss of 7% compared to 17% of the melamine ligand. X-ray diffraction analysis showed distinct peaks of Ni-Zr melamine at 27.7^o and 29.58^o, contrasting with pure melamine’s broader peak range (13-55^o) , which suggests new crystalline phases due to metal ligand interaction. The charcoal-modified composite exhibited a lower angle XRD peak (3-36^o), indicating increase disorder and charcoal-induced structural changes. Fourier transform infrared spectroscopy identified characteristic N-H, C≡N, and C=O vibrations, with new peaks in the composite (2849 cm^-1) indicating the influence of charcoal. Scanning electron microscopy revealed a complex, agglomerated morphology for the composite compared to the uniform crystalline structure of pure melamine, highlighted enhanced porosity. Energy-dispersive X-ray spectroscopy revealed dominant nitrogen (77.91%) and carbon (17.64%) content, consistent with melamine chemical composition (C₃H₆N₆), with trace impurities (Al, Na, Mg, Ti, Si) suggesting residual contaminants. The study highlighted the synergistic effects of combining CP with charcoal, leading to enhanced material properties and performance. Ni-Zr melamine CPs, especially those modified with charcoal, demonstrated promising characteristics for various environmental applications.

Introduction and Aim

Steel-reinforced concrete is an integral part of construction, but its durability and reliability can be compromised by problems arising from the interaction between steel and concrete. Differences in the properties of these materials often lead to microcracking, reduced load-bearing capacity, increased corrosion, adhesion problems, and a shorter service life of structures.

The study's main aim is to investigate the effectiveness of multicomponent composite materials (MCM) that improve the interaction between steel and concrete.

Methods

The study was conducted in two stages. First, we selected materials, among which were the following: acrylic polymer compositions; epoxy resins with modified hardeners; liquid glass-based materials with mineral fillers; hybrid organo-mineral systems.

In the second stage, experimental tests were conducted, which included assessment of adhesive strength, corrosion resistance, crack resistance, and durability under cyclic freezing/thawing conditions.

RESULTS and DISCUSSION

The results demonstrate significant improvement in the compatibility of steel and concrete through the use of MCM. In particular, the adhesive strength increased by 20-40% compared to unmodified samples, indicating a denser and more homogeneous structure at the interface. MCM also provided effective protection of permanent formwork, reducing the corrosion rate in aggressive environments by 50% or more. The MCM matrix's polymer components partially compensated for stresses from thermal expansion differences, reducing the risk of microcracking.

CONCLUSION

The results of the study confirm that MCM are an effective solution for ensuring the compatibility of steel and concrete. The prospects of using such materials in construction will significantly increase the reliability of structures and extend their service life.

This study investigates the mechanochemical modification of graphite electrodes with zinc oxide (ZnO) to enhance their electrochemical performance, particularly in the anodic response to ferricyanide. The structural and morphological characteristics of the modified graphite were analyzed using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM). SEM images revealed the smoothened surface morphology of the graphite post-ZnO incorporation, indicating an increase in surface area critical for electrochemical reactions. X-ray Diffraction (XRD) analysis confirmed the formation of new compounds upon the mechanochemical synthesis of ZnO-modified graphite, suggesting the successful integration of ZnO into the graphite matrix. Cyclic voltammetry experiments demonstrated a significant enhancement in anodic response when ferricyanide was used as an electrolyte, with increased peak currents observed at elevated scan rates. Varying the scan rate allowed for the differentiation between diffusion-controlled and surface-controlled processes, providing insights into charge transfer mechanisms and the stability of the electrode material. Higher scan rates revealed surface-adsorbed species or fast electron transfer, while lower scan rates are more indicative of diffusion-limited processes. This helped in optimizing the electrode's performance for energy storage applications. These findings indicate that the mechanochemically modified ZnO-graphite electrode exhibits superior electrochemical properties compared to unmodified graphite, positioning it as a promising candidate for future electrical and battery applications. The results underscore the potential of mechanochemical methods in developing advanced materials for energy storage technologies.

Buildings consume approximately 40% of national energy in the U.S., significantly contributing to greenhouse gas emissions. Data-driven analysis and accurate prediction of energy consumption is vital for advancing sustainability and climate objectives. In this regard, Large Language Models (LLMs), advanced AI systems trained on vast datasets to process and generate human-like text, are pivotal in enhancing data-driven analysis and prediction of building energy use. With a variety of LLMs available, selecting the most effective model is critical for optimizing energy consumption forecasts. This study investigates, analyzes, and benchmarks multiple LLMs to identify the optimal model for data-driven analysis of energy consumption in U.S. residential buildings, leveraging data from the Residential Energy Consumption Survey (RECS). Through rigorous evaluation of model performance across different end-use energy levels—including space heating, air conditioning, water heating, lighting, and appliances—this research identifies the most accurate and efficient LLM. The identification of the best-performing LLM informs retrofit planning, energy policy development, and demand-side management, enabling more effective energy-saving strategies.