Search Results (327)

Zinc oxide (ZnO) nanoparticles have attracted increasing attention in wood science due to their multifunctional properties, including antimicrobial activity, UV absorption, and photocatalytic behavior. Water-based deposition protocols offer clear advantages yet typically struggle with nanoparticle aggregation and limited adhesion to lignocellulosic substrates. This work introduces a rapid and scalable waterborne protocol combining catalyst-free aqueous seeding with microwave-assisted (MWA) growth under mild conditions. Pinus pinaster veneer samples were treated via dip-coating and spraying, with single and double seeding cycles, followed by MWA growth. Protocol efficiency was assessed through ZnO retention, SEM, and EDS analysis, while the impact of the substrate was assessed via mechanical testing, ATR-FTIR spectroscopy, and colorimetry. Dip-coating achieves significantly higher precursor uptake than spraying, while repeated seeding cycles further increase ZnO loading. Results suggest that incorporation may proceed through zinc–carboxylate bonds within the wood matrix, followed by localized ZnO nanostructures development. The effective integration did not weaken the mechanical properties, while color changes were significant for dip-coated samples and noticeable for sprayed ones. Overall, this methodology provides a fast, water-based, and minimally invasive route for ZnO incorporation into wood and a scalable pathway with retained mechanical and chemical properties and limited visual impact. Full article

Analytical studies of archeological materials often face challenges, such as the merging of heterogeneous, multidimensional datasets from complementary analytical techniques, and incorporating site- and user-defined parameters. In this study, a data fusion methodology is applied that combines micro-X-ray fluorescence (micro-XRF) spectrometry and handheld Raman spectroscopy to investigate degradation layers and identify pollution sources on two monuments in an urban background: the Temple of Hephaestus and the Byzantine Church of Ag. Theodoroi, in Athens, Greece. A total of 12 samples were collected for laboratory measurements and 32 in situ measurements were conducted. Statistical and unsupervised machine learning tools, namely correlation analysis, Principal Component Analysis and k-means clustering, were applied to the merged datasets. Additionally, selected elements’ ratios were calculated to infer their sources. The black crusts were identified as heterogeneous mixtures of calcium sulfate dihydrate, calcite, and particulate pollutants, with their composition reflecting their preservation state. Vehicular emission indicators were dominant in both sites, while secondary domestic heating pollutant indicators were more prevalent at Ag. Theodoroi. Orientation had a minor role compared to pollutant sources in differentiating degradation patterns. The integrated comparison of the different outputs highlighted the interpretive potential of the approach, particularly in improving the readability of the multivariate structure and supporting the development of targeted conservation strategies for monuments in polluted urban contexts. Full article

X-ray crystallography is still the most widely used and versatile method for structural studies of biological macromolecules. This study concerns the application of nanogels to facilitate protein crystallization, a prerequisite for X-ray crystallography. Nanogels (NGs) are nano-sized, highly crosslinked polymeric particles that have been extensively studied for chemical catalysis and drug delivery but not for protein crystal nucleation. The efficacy of six types of nanogels (three N-isopropylacrylamide-based and three acrylamide-based) was tested, with promising results. They were subsequently functionalised with active hydroxyl groups for further testing. Both functionalised and non-functionalised nanogels were tested on model (trypsin, thaumatin, proteinase K, ferritin and catalase) and target proteins (glulisine, α-crustacyanin and acriflavine resistance protein subunit AcrB) using both manual and automated techniques. All nanogels were found to be effective in promoting protein crystallization in both screening and optimization trials, giving crystal ‘hits’ that would have otherwise been missed. Overall, the functionalised nanogels were more effective. Nanogel effects are proposed to be due to a combination of surface porosity and surface chemistry. Full article

Plutella xylostella (L.) (Lepidoptera: Plutellidae), the diamondback moth (DBM), is a cosmopolitan pest of brassicas. To validate and compare the performance of yeast-derived sex pheromone components with chemically synthesized ones, we studied the behavioral and electrophysiological responses (EAGs) of male DBM adults. In addition, using gas chromatography coupled with electroantennographic detection (GC-EAD), we examined whether any residual impurities present in yeast-derived pheromone components can be perceived by the insects’ antennae and are thus capable of interfering with normal behavior. Furthermore, we assessed the performance of the yeast-derived pheromones under field conditions through monitoring trials conducted in cabbage crops in Greece. Electrophysiological and behavioral assays revealed equivalent responses from the insects to both the yeast-derived (BIO) and chemically synthesized (CHEM) pheromone blends. Consistent with this, GC-EAD results showed no significant differences in antennal response to minor impurities present in the BIO blend compared to the CHEM blend. Finally, it was demonstrated that the binary pheromone blend—comprising (Z)-11-hexadecenal and (Z)-11-hexadecenyl acetate derived from (Z)-11-hexadecen-1-ol produced by yeast cell-factories—was as efficient and specific for trapping male moths in cabbage fields as the conventional ternary synthetic blend [(Z)-11-hexadecenal and (Z)-11-hexadecenyl acetate and (Z)-11-hexadecen-1-ol]. The yeast-derived mixture contained small amounts of unoxidized (Z)-11-hexadecen-1-ol due to incomplete oxidation. Full article

Proton therapy offers superior dose localization, yet the biological effects of low-energy protons relevant to superficial tissues remain underexplored. We report the design and validation of a proton irradiation setup developed at the Tandem Accelerator of NCSR “Demokritos” for controlled radiobiological experiments. Monte Carlo simulations using Geant4 and Monte Carlo Damage Simulation (MCDS—Monte Carlo Damage Simulation) were used to determine proton energy spectra, linear energy transfer (LET), and predicted DNA damage yields. A single layer (15–20 μm in thickness) of human keratinocytes (HaCaT) was irradiated at doses from 0.65 to 3.65 Gy, and γ-H2AX foci were quantified as markers of tracks including one or more DNA double-strand breaks. The system achieved a uniform dose rate of 0.37 Gy/min, as calculated with Geant4, with a mean proton energy of 4.1 MeV (LET ≈ 8 keV/μm). A strong correlation (R² = 0.93) was observed between proton dose and γH2AX foci per nucleus (~10 foci/Gy), reflecting damage-inducing proton tracks rather than individual DNA double-strand breaks. At higher doses, an increased fraction of cells exhibited pan-nuclear γH2AX staining, characterized by a diffuse γH2AX signal throughout the nucleus and commonly associated with extensive or clustered DNA damage and global chromatin phosphorylation. These responses are consistent with the well-established dense ionization patterns produced by low-energy protons, as indicated by the LET spectrum and supported by MCDS-predicted clustered damage yields. While the γH2AX assay does not directly resolve simple versus complex DNA lesions, the agreement between Monte Carlo modeling and the observed cellular stress responses indicates that the irradiation platform reliably reproduces the expected biological signatures of low-energy proton exposure. Consequently, the developed system provides a robust experimental tool for systematic investigations of cellular radiosensitivity and radiotoxicity, with potential applications in skin dosimetry and radioprotection. Full article

Alzheimer’s disease (AD) is the most common neurodegenerative disorder worldwide, for which aging represents the main risk factor. As the global elderly population expands, the prevalence of Alzheimer’s disease escalates rapidly. Notably, as AD brain lesions may start 15–20 years before the appearance of the first symptoms, early diagnosis or prognosis of AD is of paramount importance for better patient treatment. Based on the absence of effective cure or early diagnosis of AD, this meta-analysis investigates the differentially expressed genes between Alzheimer’s and a healthy brain and identifies genes that can serve as risk factors for the disease or biomarkers of diagnostic, prognostic, or pharmacological value. Microarray datasets were collected from public repositories, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Quality control and data normalization were performed. Differentially expressed gene (DEG) lists were created for each study and combined through a Mosteller–Bush meta-analysis, resulting in a final list of DEGs. This list was filtered using an adjusted p-value cut-off of 0.001, and the included statistically significant DEGs were subjected to enrichment analyses. A total of eight microarray studies were identified, producing a combined list of 4218 DEGs, of which 1944 were up-regulated and enriched for immune response processes, and 2274 were down-regulated and enriched for synapse-related pathways. This meta-analysis reveals a distinct transcriptomic profile in Alzheimer’s disease characterized by the prevalence of immune response and inflammation alongside the collapse of essential synaptic and neuronal signaling. Full article

With climate change expected to intensify hazards across Europe, empowering communities and strengthening local adaptation is urgent. The challenge is bolstering the resilience of critical infrastructure (CI), which faces substantial risks. Transitioning from predominantly “grey” infrastructure to integrated “green-grey” solutions provides an effective way to safeguard societal and infrastructural assets against hazards and environmental degradation. Although several frameworks developed by international networks and regional authorities exist, they often fail to fully address the nuanced challenges of CI climate proofing, disaster risk reduction, and biodiversity protection. In response to these limitations and to address key societal challenges, the work here introduces an innovative, integrative blueprint framework. This framework synthesises existing approaches to CI climate adaptation, systematically strengthening resilience with nature-based solutions (NBS). The framework is partially applied and validated through the Public-Private-Civil Partnership (PPCP^®) approach, and operationalised in two climatically distinct but heatwave-prone regions: Egaleo (Greece) and Helsinki (Finland). These Labs have promoted more inclusive policymaking by supporting collaboration among key stakeholders, encouraging knowledge sharing and co-designing strategies to advance NBS implementation for heatwave mitigation. The approach facilitated the design of interconnected activities and simplified technical details. Adapting methods to local needs, such as site visits and participatory mapping, has led to concrete outcomes. The prefeasibility analysis outcomes and the targeted NBS-based strategies identified from these areas ensure that solutions are culturally relevant, technically feasible, and collectively owned, incorporating local knowledge and fostering long-term sustainability. Full article

This mini-review presents the major research trends in the synthesis, performance, and mechanisms of clay-supported magnetic iron oxide nanocomposites for the adsorption of heavy metals in water and wastewater treatment applications. The immobilization of iron oxide nanoparticles onto the hydrophilic natural or synthetic nanoclay matrices not only minimized the magnetic nanoparticles’ tendency to aggregate in aquatic solutions but also facilitated their recovery from the solutions via magnetic separation after adsorption. For these reasons, research on such materials emerged in the early 2010s, leading to the development of highly efficient nanocomposite adsorbents. At optimum conditions, including solution pH values between 5 and 7, rapid equilibrium times ranging from 30 to 180 min, and ambient or moderately elevated temperatures (up to 60 °C), maximum adsorption values of up to 225 mg/g were reported for certain heavy metals. Moreover, the nanocomposites demonstrated reusability, maintaining adsorption performance towards heavy metals for up to five adsorption–desorption cycles when common acids (such as HNO₃ and HCl) were used as regenerating agents. However, the current findings are all based on batch-scale laboratory experiments. To move toward industrial-scale applications, further research is necessary to address scale-up challenges and evaluate the performance of the clay-supported magnetic iron oxide nanocomposites under real-world conditions. All the critical limitations are highlighted in the context of this mini review to support future efforts toward achieving their economic and environmentally sustainable application for the adsorption of heavy metals from water/wastewater streams. Full article

This work presents the development of an automated and portable monitoring system for the point-of-need detection of tebuconazole and lambda-cyhalothrin. The system features nanoparticle/aptamer-modified electrochemical sensors that are integrated into a microfluidic chip based on polydimethylsiloxane (PDMS). More specifically, rapid and selective detection of both pesticides is achieved using target-specific aptamers immobilized on two-dimensional platinum nanoparticle films that serve as expanded nano-gapped electrodes to enhance sensor sensitivity. The effect of the device substrate (i.e., silicon versus flexible substrates) and measurement setup on biosensing performance has also been investigated. The final monitoring system is characterized by high sensitivity and selectivity in the cases of both target analytes and substrates. Τhe system features a limit of detection of 9.85 pM for tebuconazole, which is one of the lowest reported values in the literature; for lambda-cyhalothrin, it is worth noting that the results reported herein represent one of the few studies on an electrochemical aptamer-based sensor for this analyte, featuring a limit of detection of 48.5 pM. The system is also capable of selectively detecting both targets for complex cross-reactive sample matrices consisting of commercially available pesticides. Moreover, its use could be expanded to detect additional pollutants by functionalizing the biosensor surface with appropriate aptamers. Full article

Wildfires are increasingly recognized as a climatological hazard, able to threaten industrial and critical infrastructure safety and operations and lead to Natech disasters. Future projections of exacerbated fire regimes increase the likelihood of Natech disasters, therefore increasing expected direct damage costs, clean-up costs, and long-term economic losses due to business interruption and environmental remediation. While large industrial complexes, such as oil, gas, and chemical facilities have sufficient resources for the implementation of effective prevention and mitigation plans, small-to-medium-sized industrial hubs are particularly vulnerable due to their scattered distribution and limited resources for investing in comprehensive fire prevention systems. This study targets the vulnerability of these communities by proposing the deployment of Wireless Sensor Networks (WSNs) as cost-effective Early Wildfire Detection Systems (EWDSs) to safeguard wildland and industrial domains. The proposed approach leverages wildland–industrial interface (WII) geospatial data, simulated wildfire dynamics data, and mathematical optimization to maximize detection efficiency at minimal cost. The WII delimits the boundary where the presence of wildland fires impacts industrial activity, thus representing a proxy for potential Natech disasters. The methodology is tested in Cocentaina, Spain, a municipality characterized by a highly flammable Mediterranean landscape and medium-scale industrial parks. Results reveal the complex trade-offs between detection characteristics and the degree of protection in the combined wildland and WII areas, enabling stakeholders to make informed decisions. This methodology is easily replicable for any municipality and industrial installation, or for generic wildland–human interface (WHI) scenarios, provided there is access to wildfire dynamics data and geospatial boundaries delimiting the areas to protect. Full article

Excessive use of oxytetracycline (OTC) in veterinary medicine has increased the presence of antibiotics in food, which accelerates the development of antimicrobial resistance. We report the development of a highly sensitive electrochemical aptasensor for OTC detection, based on a glassy carbon electrode (GCE) modified with reduced graphene oxide (rGO) and multiwalled carbon nanotubes (MWCNTs) nanocomposite. DNA aptamers specific to OTC were covalently attached to the nanocomposite surface via carbodiimide chemistry. Differential pulse voltammetry (DPV) showed a decrease in peak current due to the binding of OTC to the aptamers. The sensor exhibited a limit of detection (LOD) of 1.72 ng/mL, which is below the maximum residue limit (MRL) for OTC (100 ng/mL) established by European Union. The sensor has been tested on a spiked milk sample. Full article

Identifying effective coalitions of agents for task execution within large multiagent settings is a challenging endeavor. The problem is exacerbated by the presence of coalitional value uncertainty, which is due to uncertainty regarding the values of synergies among the different collaborating agent types. Intuitively, in such environments, a hypergraph can be used to concisely represent coalition–task pairs in the form of hyperedges, along with their associated rewards. Therefore, this paper proposes harnessing the power of Hypergraph Neural Networks (HGNNs) that fit generic hypergraph-structured historical representations of coalitional task executions to learn the unknown values of coalitional configurations undertaking the tasks. However, the fitted model by itself cannot be used to provide suggestions on which coalitions to form; it can only be queried for the values of given coalition–task configurations. To actually provide coalitional suggestions, this work relies on informed search approaches that incorporate the output of the HGNN as an indicator of the quality of the proposed coalition configurations. The resulting approach is illustrated, via simulation results, to be able to effectively capture the uncertain values of multiagent synergies and thus suggest highly rewarding coalitional configurations. Specifically, the proposed novel hybrid approach can outperform competing baseline approaches and achieve close to 80% performance of the theoretical maximum in this setting. Full article

The reactions of UO₂(NO₃)₂·6H₂O or UO₂(O₂CMe)₂·2H₂O and 2,2′-{(1,2-ethanediyl)bis[nitrilo(phenyl)methylidene]}bisphenol (H₂L) in MeOH and DMF have provided access to complexes [UO₂(L)(MeOH)] (1) and [UO₂(L)(DMF)]·DMF (2·DMF), respectively. The molecular structures of the complexes are similar. The central U^VI atom is surrounded by five oxygen and two nitrogen atoms in a distorted pentagonal bipyramidal geometry; the two uranyl oxygen atoms are at the axial positions. Two phenolato oxygen and two imino nitrogen atoms from the tetradentate chelating (1.1111 using Harris notation) L²⁻ ligand are located at the equatorial plane, which is completed by the oxygen atom of a terminally ligated solvent (MeOH, DMF) molecule. Interestingly, the L²⁻ ligand adopts a chair (or stepped) conformation in 1 and a boat conformation in 2·DMF. The supramolecular features of 1 and 2·DMF are distinctly different due to the different H-bonding abilities of coordinated MeOH and DMF, and the presence of an extra-lattice solvent molecule in the latter. The solid complexes were studied by IR, Raman, electronic (UV/Vis), and emission spectroscopic techniques. Complex 1 decomposes in CHCl₃ and DMSO, whereas the molecular structure of 2 is retained in these solvents. A new polymorph of the free ligand, H₂L(B), has also been discovered and its crystal structure is described. Full article

This paper presents a hybrid fabrication method for producing anti-counterfeit optical elements on plastic products and surfaces targeting multidiscipline applications such as food, pharmaceuticals, luxury goods, and electronics industry. Our proposition combines the design flexibility and rapid prototyping capabilities of stereolithography three-dimensional (SLA 3D) printing with nanoimprint lithography (NIL) to create unique optical security tags onto plastic surfaces. The proposed approach is cost-effective, scalable, and tailored for mass production, addressing the increasing demand for secure and reliable authentication solutions. NIL is substrate agnostic, offering material selection versatility and realization of security tags onto polymer surfaces, which are widely used across various sectors such as packaging industry, medical devices, and flexible electronics. This enables integration into a wide range of materials, further enhancing applicability on flat and 3D shape surfaces. An evaluation method based on digital reconstruction has been used to ensure robust performance and verification of the produced optical security features. The results demonstrate that this hybrid approach provides a reproducible and technically feasible path for the development of optical anti-counterfeiting tags suitable for large-scale implementation, particularly within fast-moving consumer goods (FMCG). Full article