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This study investigates the performance of an electrooxidation (EO) process employing Sb₂O₅-doped RuO₂–ZrO₂|Ti anodes integrated into a concave-cover solar still for the degradation of Allura Red dye in aqueous solution and real river water. The anode was synthesized and characterized via scanning electron microscopy (SEM) and X-ray diffraction (XRD) to confirm its porous morphology and crystalline structure. Operational parameters—including supporting electrolyte concentration, initial solution pH, and current density—were systematically optimized. Under optimal conditions (pH 2–3 and 5 mA cm⁻²), the EO process was evaluated under natural solar irradiation. Sunlight exposure increased the solution temperature from approximately 20 °C to 50 °C, enhancing molecular diffusion and mass transport, thereby accelerating decolorization kinetics. Compared to EO performed under laboratory conditions, the solar-assisted system achieved an additional 20% increase in chemical oxygen demand (COD) removal and a fast reduction in color. When applied to real Lerma River water samples under these optimal conditions, the treatment achieved approximately 50% reduction in both COD and true color, demonstrating its applicability to complex environmental matrices. These results confirm that coupling electrooxidation with solar thermal input significantly improves pollutant degradation efficiency and energy performance, establishing this integrated approach as a promising and sustainable technology for advanced wastewater treatment. Full article

(This article belongs to the Special Issue Next-Generation Catalytic Solutions for Water Purification and Wastewater Remediation)

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20 pages, 2326 KB

Open AccessArticle

Metabolic Astrocytic Support with Decanoic Acid Enhances Energy Metabolism in Alzheimer’s Disease Models

by Aishat O. Ameen, Maja B. Rindshøj, Katarina Stoklund Dittlau, Karin Borges, Kristine K. Freude and Blanca I. Aldana

Cells 2025, 14(24), 2007; https://doi.org/10.3390/cells14242007 - 16 Dec 2025

Abstract

Alzheimer’s disease (AD) is increasingly recognized as a disorder of cerebral energy metabolism, where impaired glucose utilization contributes to disease pathology. Medium-chain fatty acids (MCFAs), such as decanoic acid (C10), have emerged as promising metabolic substrates due to their ability to bypass glycolytic deficits and support mitochondrial function. In this study, we investigated the metabolic impact of C10 in the 5xFAD mouse model of AD and in human induced pluripotent stem cell (hiPSC)-derived astrocytes carrying familial AD mutations. Utilizing stable ¹³C-labeled metabolic tracers, we demonstrated that while [U-¹³C]glucose metabolism was largely preserved in cortical slices of 6-month-old 5xFAD female mice, [1,2-¹³C]acetate uptake was significantly reduced, suggesting impaired astrocytic metabolism. [U-¹³C]C10 was efficiently metabolized in both WT and 5xFAD brain slices, particularly in astrocytes, as indicated by high labeling of glutamine and citrate. Furthermore, C10 competitively inhibited glucose and acetate metabolism, suggesting its potential as an auxiliary energy substrate. In hiPSC-derived astrocytes, AD-specific metabolic responses to C10 varied by mutation, with only partial alterations in oxidative glucose metabolism observed in APP and PSEN1 variants, highlighting genotype-dependent metabolic alterations. While AD-related mutations in the hiPSC models did not lead to robust deficits, the in vivo environment in the 5xFAD model is associated with measurable metabolic changes in astrocytes. These findings underscore astrocytic metabolic dysfunction in AD and suggest that C10 supplementation may restore brain energy by supporting astrocytic oxidative metabolism. Full article

(This article belongs to the Section Cellular Metabolism)

36 pages, 3748 KB

Open AccessArticle

Automated Image-to-BIM Using Neural Radiance Fields and Vision-Language Semantic Modeling

by Mohammad H. Mehraban, Shayan Mirzabeigi, Mudan Wang, Rui Liu and Samad M. E. Sepasgozar

Buildings 2025, 15(24), 4549; https://doi.org/10.3390/buildings15244549 - 16 Dec 2025

Abstract

This study introduces a novel, automated image-to-BIM (Building Information Modeling) workflow designed to generate semantically rich and geometrically useful BIM models directly from RGB images. Conventional scan-to-BIM often relies on specialized, costly, and time-intensive equipment, specifically if LiDAR is used to generate point clouds (PCs). Typical workflows are followed by a separate post-processing step for semantic segmentation recently performed by deep learning models on the generated PCs. Instead, the proposed method integrates vision language object detection (YOLOv8x-World v2) and vision based segmentation (SAM 2.1) with Neural Radiance Fields (NeRF) 3D reconstruction to generate segmented, color-labeled PCs directly from images. The key novelty lies in bypassing post-processing on PCs by embedding semantic information at the pixel level in images, preserving it through reconstruction, and encoding it into the resulting color labeled PC, which allows building elements to be directly identified and geometrically extracted based on color labels. Extracted geometry is serialized into a JSON format and imported into Revit to automate BIM creation for walls, windows, and doors. Experimental validation on BIM models generated from Unmanned Aerial Vehicle (UAV)-based exterior datasets and standard camera-based interior datasets demonstrated high accuracy in detecting windows and doors. Spatial evaluations yielded up to 0.994 precision and 0.992 Intersection over Union (IoU). NeRF and Gaussian Splatting models, Nerfacto, Instant-NGP, and Splatfacto, were assessed. Nerfacto produced the most structured PCs suitable for geometry extraction and Splatfacto achieved the highest image reconstruction quality. The proposed method removes dependency on terrestrial surveying tools and separate segmentation processes on PCs. It provides a low-cost and scalable solution for generating BIM models in aging or undocumented buildings and supports practical applications such as renovation, digital twin, and facility management. Full article

(This article belongs to the Special Issue Artificial Intelligence in Architecture and Interior Design)

25 pages, 1983 KB

Open AccessArticle

The Influence of Hydrazo and Azo Bonds on the Conformation of New 4-Methyl-3,5-dinitro-2-(2-phenylhydrazinyl)pyridine and Its Azo Derivative—Structural Properties, Vibrational Spectra and Quantum Chemical DFT Calculations

by Jacek Michalski, Edyta Kucharska, Iwona Bryndal, Lucyna Dymińska, Wojciech Sąsiadek, Anna Pyra, Radosław Lisiecki, Maciej Ptak and Jerzy Hanuza

Int. J. Mol. Sci. 2025, 26(24), 12106; https://doi.org/10.3390/ijms262412106 - 16 Dec 2025

Abstract

A review of studies has shown that aromatic azo and hydrazo derivatives are used in a wide spectrum of fields, including food, pharmaceutical, and cosmetic products, as well as in technical and electronic technologies, which has contributed to the development of new such compounds. In this work, the structures of newly obtained 4-methyl-3,5-dinitro-2-(2-phenylhydrazinyl)pyridine (4MDNPHP) and its azo derivative, 4-methyl-3,5-dinitro-2-[(E)-phenyldiazenyl]pyridine (4MDNPAP), were established by spectroscopic (NMR, IR, Raman, and UV-Vis) and emission studies. Single-crystal X-ray diffraction analysis was used to determine the molecular structure of the studied compounds, and the results were compared with DFT calculations (B3LYP/6-311G(2d,2p). The collected X-ray data revealed that the crystal of the hydrazo compound (4MDNPHP) belongs to the triclinic space group P

\bar{1}

(Z = 2), whereas the crystal of the azo compound (4MDNPAP) follows the symmetry of the monoclinic space group P2₁/n (Z = 4). Both presented derivatives crystallized with one molecule in the asymmetric unit. Specific properties of the hydrazo bridge C_ϕ-NH-NH-C_θ moiety and its azo counterpart C_ϕ-N=N-C_θ were considered in detail. Full article

(This article belongs to the Section Materials Science)

16 pages, 2138 KB

Open AccessFeature PaperArticle

γ-Valerolactone Pulping as a Sustainable Route to Micro- and Nanofibrillated Cellulose from Sugarcane Bagasse

by Roxana Giselle González, Nanci Ehman, Fernando Esteban Felissia, María Evangelina Vallejos and María Cristina Area

Processes 2025, 13(12), 4065; https://doi.org/10.3390/pr13124065 - 16 Dec 2025

Abstract

The study explores γ-valerolactone (GVL) pulps as a sustainable approach to producing microfibrillated (MFC) and nanofibrillated (NFC) cellulose from sugarcane bagasse, a widely available agro-industrial by-product. Pulp was obtained by acid-catalyzed organosolv delignification with a GVL–water system. MFC was generated through a simple disc refiner, while NFC was produced by TEMPO-mediated oxidation followed by mechanical treatment in a colloidal mill. NFC and MFC produced using the same methodology from a commercial sugarcane totally chlorine-free (TCF) soda–anthraquinone (soda–AQ) pulp served as a reference. Structural and physicochemical characterization involved optical transmittance, turbidity, conductimetry, X-ray diffraction, viscosity, FTIR, carboxyl content, cationic demand, degree of polymerization, and morphology by scanning electron microscopy (SEM). Results demonstrated that xylan and residual lignin contents influenced MFC formation, and the NFC showed properties comparable to those of the commercial pulp with fewer fibrillation passes. The study highlights GVL pulping as a greener, efficient alternative to conventional processes, opening new pathways for producing viscosity-controlled nanocellulose suspensions suitable for advanced applications. Full article

(This article belongs to the Special Issue Sustainable Nanocellulose Processes Toward New Products and Markets)

24 pages, 3087 KB

Open AccessArticle

Influence of Spark Plasma Sintering Parameters on the Microstructure, Mechanical and Tribological Characteristics of Air-Milled Aluminum

by Hanen Ammari, Sophie Le Gallet, Pierre-Henri Cornuault, Frédéric Herbst, Nicolas Geoffroy, Mahmoud Chemingui and Virgil Optasanu

Materials 2025, 18(24), 5652; https://doi.org/10.3390/ma18245652 - 16 Dec 2025

Abstract

This work investigates the influence of spark plasma sintering (SPS) parameters on the microstructure and mechanical properties of consolidated aluminum powders processed by high-energy ball milling under an air atmosphere. Sintering was performed under vacuum at various temperatures ranging from 550 °C to 625 °C and under pressures between 50 and 100 MPa. The particle size, crystallite size, and microstructure of the powders and the consolidated pellets were analyzed using laser granulometry, X-ray diffraction (XRD), scanning electron microscopy (SEM), and Archimedes’ density measurements. Mechanical properties were evaluated via Vickers microhardness, nanoindentation, and tribological testing. For comparison, unmilled aluminum powders were also consolidated and characterized. After 46 h of milling, the aluminum crystallite size was reduced from 74 nm to 68 nm. The sample’s density increased with higher sintering temperature and pressure. The aluminum sintered at 600 °C and 100 MPa after 46 h of milling exhibited the highest microhardness (187.5 HV). Nanoindentation tests were conducted to characterize different microstructural regions formed after SPS, revealing two distinct zones: one hard and one soft. The tribology results revealed that the SPS-consolidated samples of milled powders exhibited a reduction of 50% in specific wear rate and a reduction of 20% in the coefficient of friction compared to the SPS-sintered samples of unmilled powders. Full article

(This article belongs to the Section Advanced Nanomaterials and Nanotechnology)

32 pages, 4909 KB

Open AccessArticle

A Lightweight Hybrid Deep Learning Model for Tuberculosis Detection from Chest X-Rays

by Majdi Owda, Ahmad Abumihsan, Amani Yousef Owda and Mobarak Abumohsen

Diagnostics 2025, 15(24), 3216; https://doi.org/10.3390/diagnostics15243216 - 16 Dec 2025

Abstract

Background/Objectives: Tuberculosis remains a significant global health problem, particularly in resource-limited environments. Its mortality and spread can be considerably decreased by early and precise detection via chest X-ray imaging. This study introduces a novel approach based on hybrid deep learning for Tuberculosis detection from chest X-ray images. Methods: The introduced approach combines GhostNet, a lightweight convolutional neural network tuned for computational efficiency, and MobileViT, a transformer-based model that can capture both local spatial patterns and global contextual dependencies. Through such integration, the model attains a balanced trade-off between classification accuracy and computational efficiency. The architecture employs feature fusion, where spatial features from GhostNet and contextual representations from MobileViT are globally pooled and concatenated, which allows the model to learn discriminative and robust feature representations. Results: The suggested model was assessed on two publicly available chest X-ray datasets and contrasted against several cutting-edge convolutional neural network architectures. Findings showed that the introduced hybrid model surpasses individual baselines, attaining 99.52% accuracy on dataset 1 and 99.17% on dataset 2, while keeping low computational cost (7.73M parameters, 282.11M Floating Point Operations). Conclusions: These outcomes verify the efficacy of feature-level fusion between a convolutional neural network and transformer branches, allowing robust tuberculosis detection with low inference overhead. The model is ideal for clinical deployment and resource-constrained contexts due to its high accuracy and lightweight design. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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26 pages, 7801 KB

Open AccessArticle

Enhancing Sustainable Intelligent Transportation Systems Through Lightweight Monocular Depth Estimation Based on Volume Density

by Xianfeng Tan, Chengcheng Wang, Ziyu Zhang, Zhendong Ping, Jieying Pan, Hao Shan, Ruikai Li, Meng Chi and Zhiyong Cui

Sustainability 2025, 17(24), 11271; https://doi.org/10.3390/su172411271 - 16 Dec 2025

Abstract

Depth estimation is a critical enabling technology for sustainable intelligent transportation systems (ITSs), as it supports essential functions such as obstacle detection, navigation, and traffic management. However, existing Neural Radiance Field (NeRF)-based monocular depth estimation methods often suffer from high computational costs and poor performance in occluded regions, limiting their applicability in real-world, resource-constrained environments. To address these challenges, this paper proposes a lightweight monocular depth estimation framework that integrates a novel capacity redistribution strategy and an adaptive occlusion-aware training mechanism. By shifting computational load from resource-intensive multi-layer perceptrons (MLPs) to efficient separable convolutional encoder–decoder networks, our method significantly reduces memory usage to 234 MB while maintaining competitive accuracy. Furthermore, a divide-and-conquer training strategy explicitly handles occluded regions, improving reconstruction quality in complex urban scenarios. Experimental evaluations on the KITTI and V2X-Sim datasets demonstrate that our approach not only achieves superior depth estimation performance but also supports real-time operation on edge devices. This work contributes to the sustainable development of ITS by offering a practical, efficient, and scalable solution for environmental perception, with potential benefits for energy efficiency, system affordability, and large-scale deployment. Full article

(This article belongs to the Special Issue Sustainable Intelligent Transport Systems: AI-Driven Multi-Modal Fusion for Green Development)

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22 pages, 5738 KB

Open AccessReview

Probing Membrane Structure of Lipid Nanomedicines Using Solution Small-Angle X-Ray Scattering: Applications and Prospects

by Ke-Meng Li, Panqi Song, Xiao-Peng He and Na Li

Membranes 2025, 15(12), 382; https://doi.org/10.3390/membranes15120382 - 16 Dec 2025

Abstract

Lipid-based nanomedicines are already widely used in antitumor therapy and gene delivery. However, their complex structural features demand advanced mesoscopic structural characterization tools for effective research and development (R&D) and quality control. Synchrotron small-angle X-ray scattering (SAXS) is a powerful, non-invasive technique for probing nanoscale membrane organizations, monitoring in situ dynamic membrane assembly, and exploring the interactions of components in lipid-based drug delivery systems, including liposomes, lipoplexes, lipid nanoparticles (LNPs), and lyotropic liquid crystals (LLCs). Recent advances in high-flux synchrotron facilities, high-frequency detectors, and automated SAXS data processing pipelines permit a detailed structural characterization of lamellarity, bilayer spacing, internal phases, core–shell morphology, as well as “pump-probe” dynamic process studies for lipid nanomedicines. Though major challenges remain in sample polydispersity and model fitting, the advances in time-resolved synchrotron SAXS, high-throughput automation, and artificial intelligence (AI)-assisted modeling are rapidly reducing this barrier. This review summarizes SAXS methodology and introduces representative case studies in the field of lipid nanomedicines. The performance of BioSAXS beamline BL19U2 in the Shanghai synchrotron radiation facility (SSRF) and prospects of AI-guided drug screening at BL19U2 are highlighted to advance intelligent R&D and quality control for lipid nanomedicines. Full article

(This article belongs to the Special Issue Interfacial Interactions of Nanoparticles and Molecular Nanostructures with Model Membrane Systems)

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Graphical abstract

15 pages, 2043 KB

Open AccessArticle

Application of Vision-Language Models in the Automatic Recognition of Bone Tumors on Radiographs: A Retrospective Study

by Robert Kaczmarczyk, Philipp Pieroh, Sebastian Koob, Frank Sebastian Fröschen, Sebastian Scheidt, Kristian Welle, Ron Martin and Jonas Roos

AI 2025, 6(12), 327; https://doi.org/10.3390/ai6120327 - 16 Dec 2025

Abstract

Background: Vision-language models show promise in medical image interpretation, but their performance in musculoskeletal tumor diagnostics remains underexplored. Objective: To evaluate the diagnostic accuracy of six large language models on orthopedic radiographs for tumor detection, classification, anatomical localization, and X-ray view interpretation, and to assess the impact of demographic context and self-reported certainty. Methods: We retrospectively evaluated six VLMs on 3746 expert-annotated orthopedic radiographs from the Bone Tumor X-ray Radiograph dataset. Each image was analyzed by all models with and without patient age and sex using a standardized prompting scheme across four predefined tasks. Results: Over 48,000 predictions were analyzed. Tumor detection accuracy ranged from 59.9–73.5%, with the Gemini Ensemble achieving the highest F1 score (0.723) and recall (0.822). Benign/malignant classification reached up to 85.2% accuracy; tumor type identification 24.6–55.7%; body region identification 97.4%; and view classification 82.8%. Demographic data improved tumor detection accuracy (+1.8%, p < 0.001) but had no significant effect on other tasks. Certainty scores were weakly correlated with correctness, with Gemini Pro highest (r = 0.089). Conclusion: VLMs show strong potential for basic musculoskeletal radiograph interpretation without task-specific training but remain less accurate than specialized deep learning models for complex classification. Limited calibration, interpretability, and contextual reasoning must be addressed before clinical use. This is the first systematic assessment of image-based diagnosis and self-assessment in LLMs using a real-world radiology dataset. Full article

(This article belongs to the Section Medical & Healthcare AI)

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Graphical abstract

7 pages, 1130 KB

Open AccessShort Note

Benzyl 2,4-dichlorophenyl sulfoxide

by Maria Annunziata M. Capozzi, Joan F. Piniella Febrer and Cosimo Cardellicchio

Molbank 2025, 2025(4), M2113; https://doi.org/10.3390/M2113 - 16 Dec 2025

Abstract

Benzyl 2,4-dichlorophenyl sulfoxide was synthesized both in racemic and in an enantiopure form. This enantiopure sulfoxide is a further successful confirmation of our straightforward protocol to yield easily chiral aryl benzyl sulfoxides. We solved also the crystal structure of racemic benzyl 2,4-dichlorophenyl sulfoxide with a single crystal X-ray diffraction experiment. The main interactions building up the crystal structure were recognized and compared with other similar sulfoxides. Full article

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14 pages, 11633 KB

Open AccessArticle

Molybdenum Nitride and Oxide Layers Grown on Mo Foil for Supercapacitors

by Dong Hyun Lim and Young-Il Kim

Materials 2025, 18(24), 5649; https://doi.org/10.3390/ma18245649 - 16 Dec 2025

Abstract

In this study, thin molybdenum nitride (MoN_x) layers were directly synthesized on molybdenum foil via thermal treatment under an NH₃ atmosphere, and their phase evolution, structural characteristics, and electrochemical performance were investigated. The thickness and morphology of the MoN_x layers were controlled by varying ammonolysis time and temperature, while subsequent annealing in N₂ converted the nitride layer into MoO₂. Meanwhile, oxidation in air yielded crystalline MoO₃ layers. X-ray diffraction and X-ray photoelectron spectroscopy confirmed progressive oxidation of molybdenum, with Mo 3d binding energies increasing in the sequence of Mo < MoN_x < MoO₂ < MoO₃, consistent with their nominal oxidation states. Electrochemical characterization revealed that both MoN_x/Mo and MoO₂/Mo electrodes exhibit notable pseudocapacitive behavior in 0.5 M H₂SO₄ electrolyte, with areal specific capacitances reaching up to 520 mF cm⁻² at 10 mV s⁻¹. Increasing layer thickness led to enhanced capacitance, likely due to an increase in the electrochemically accessible surface area and the extension of ion diffusion pathways. MoO₂-coated samples showed stronger faradaic contribution and superior rate capability compared to MoN_x counterparts, along with a gradual shift from predominantly electric double-layer capacitance toward hybrid pseudocapacitive charge storage mechanisms. Full article

(This article belongs to the Special Issue Sustainable Functional Materials for Energy and Environmental Applications)

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17 pages, 4795 KB

Open AccessArticle

Structural Diversity, Thermal, and Semiconducting Characteristics of Two N,N′-bis(phosphonomethyl)-1,4,5,8-Naphthalenediimide-Based Compounds

by Kenya V. Medina, Juan L. Pinedo, Kimberly P. Hernandez, Julian I. Ramirez, Callah Preti, Dimitrios Bourmas, Kenya Rosas, Ryan A. Flores, Josemaria S. Soriano, Hadi D. Arman and Pius O. Adelani

Crystals 2025, 15(12), 1061; https://doi.org/10.3390/cryst15121061 - 16 Dec 2025

Abstract

Two crystals of N,N′-bis(phosphonomethyl)-1,4,5,8-naphthalenediimide were grown in the presence of neutral (water) and charged (imidazolium cation) species, yielding [(H₂O₃P)CH₂-(C₁₄H₄N₂O₄)-CH₂(PO₃H₂)]∙H₂O (1) and [C₃H₅N₂][(H_1.5O₃P)CH₂-(C₁₄H₄N₂O₄)-CH₂(PO₃H_1.5)] (2), respectively. The ligand N,N′-bis(phosphonomethyl)-1,4,5,8-naphthalenediimide was synthesized via the condensation of naphthalene-1,4,5,8-tetracarboxylic dianhydride with (aminomethyl)phosphonic acid in N,N′-dimethylformamide or imidazole. The flexible N-methyl phosphonic acid groups adopt a cis configuration in compound 1 and a trans configuration in compound 2. In compound 1, the phosphonate groups engage in extensive hydrogen bonding, as well as with water molecules and π–π stacking, resulting in a three-dimensional closely packed structure. Compound 2 forms a densely packed three-dimensional network stabilized by charge-assisted hydrogen bonding (anion-cation), anion–π interactions, and π–π stacking interactions. Hirshfeld surface analysis was conducted and the associated two-dimensional fingerprint plots were generated to further elucidate the nature and contributions of these noncovalent interactions. Direct bandgap measurements estimated from Tauc plots yielded values of 2.92 eV and 2.85 eV for compounds 1 and 2, respectively, highlighting their potential as promising n-type organic semiconductors. Thermal analysis reveals that compound 2 exhibits greater thermal stability than compound 1. Full article

(This article belongs to the Section Crystal Engineering)

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11 pages, 2469 KB

Open AccessArticle

Sonochemical Modification of ZrO₂ Nanoparticles with Thiamine Hydrochloride for the Development of Films with PLA for the Adsorption of Hexavalent Chromium

by Carlos Rodrigo Muñiz-Valdez, Nelly Abigaíl Rodríguez-Rosales, Juan Carlos Ortiz-Cuellar, Jesús Fernando Martínez-Villafañe, Josué Gómez-Casas, Gregorio Cadenas-Pliego, Christian Javier Cabello-Alvarado, Marlene Andrade-Guel and Jesús Salvador Galindo-Valdés

Coatings 2025, 15(12), 1484; https://doi.org/10.3390/coatings15121484 - 16 Dec 2025

Abstract

Industrial wastewater can be reused in other everyday processes to help combat water scarcity worldwide. One contaminant in industrial wastewater is hexavalent chromium, which is highly toxic and can cause kidney, liver, and respiratory problems, making its removal vital. In this study, PLA-based films containing modified zirconia nanoparticles were developed via a solution-mixing process for hexavalent chromium adsorption. Obtaining the films involved two stages: the first was the chemical modification of ZrO₂ nanoparticles with thiamine hydrochloride (vitamin B1) using fixed-frequency ultrasound at an output of 750 W and 50% amplitude for 60 min. The second stage involved preparing the films by mixing them in the solution using an ultrasonic bath. The nanoparticle concentrations were 0.25, 0.5, and 1 wt%. The results obtained from characterization using Fourier-transform infrared spectroscopy (FT-IR) revealed the characteristic bands of PLA and the characteristic peak of the Zr-O bond corresponding to the ZrO₂ nanoparticles. Thermogravimetric analysis (TGA) showed that the ZrO₂ nanoparticles provided thermal stability to the PLA polymer. X-ray diffraction (XRD) showed a broad peak of amorphous PLA at 16.8° and signals corresponding to the crystalline phase of ZrO₂. The morphology of a cross-section of the films was observed using scanning electron microscopy (SEM), revealing a rough surface with pores. Finally, hexavalent chromium adsorption tests were carried out, measuring the adsorption efficiency under the parameters of pH, concentration, and contact time. The PLAZrO₂ sample achieved an adsorption efficiency of 83% at pH 2. Full article

(This article belongs to the Special Issue New Trends in Films and Surfaces for Wastewater Treatment)

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13 pages, 1412 KB

Open AccessArticle

clpC-Mediated Translational Control Orchestrates Stress Tolerance and Biofilm Formation in Milk-Originated Staphylococcus aureus RMSA24

by Maofeng Zhang, Jie Hu and Ting Xue

Foods 2025, 14(24), 4333; https://doi.org/10.3390/foods14244333 - 16 Dec 2025

Abstract

Staphylococcus aureus is an important pathogen that can cause widespread infections as well as severe outbreaks of food poisoning. Recent studies have drawn attention to foodborne pathogens such as S. aureus endowed with the ability to form biofilms and increase resistance to antimicrobial agents as well as environmental stress, posing challenges to food safety. The Clp (caseinolytic protease) protein complex plays a crucial role in energy-dependent protein hydrolysis processes. This mechanism is a common way to maintain intracellular homeostasis and regulation in both prokaryotic and eukaryotic cells, especially under stress conditions. In S. aureus, multiple genes encoding Clp ATPase homologues have been identified: clpC, clpB, clpY, clpX, and clpL. This study investigated the roles of clpC in stress tolerance and biofilm formation of foodborne S. aureus RMSA24 isolated from raw milk. Our results showed that the deletion of the clpC gene significantly reduced the bacterium’s tolerance to heat, desiccation, hydrogen peroxide, and high osmotic pressure compared to wild type (WT). Furthermore, the clpC knockout mutant also exhibited a marked decrease in biofilm formation using Crystal Violet Staining (CVS) and Scanning Electron Microscopy (SEM). Finally, compared to WT, there was a total of 102 DEGs (differentially expressed genes), with a significant downregulation of genes related to biofilm formation (isaA and spa) and heat-shock response (clpP and danJ). These findings suggest that clpC regulates environmental tolerance in S. aureus by modulating the expression of stress- and biofilm-related genes, positioning it as a potential biomarker and a novel target for controlling contamination in the food industry. Full article

(This article belongs to the Section Food Microbiology)

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