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26 pages, 6864 KB

Open AccessArticle

OCDBMamba: A Robust and Efficient Road Pothole Detection Framework with Omnidirectional Context and Consensus-Based Boundary Modeling

by Feng Ling, Yunfeng Lin, Weijie Mao and Lixing Tang

Sensors 2026, 26(2), 632; https://doi.org/10.3390/s26020632 (registering DOI) - 17 Jan 2026

Abstract

Reliable road pothole detection remains challenging in complex environments, where low contrast, shadows, water films, and strong background textures cause frequent false alarms, missed detections, and boundary instability. Thin rims and adjacent objects further complicate localization, and model robustness often deteriorates across regions [...] Read more.

Reliable road pothole detection remains challenging in complex environments, where low contrast, shadows, water films, and strong background textures cause frequent false alarms, missed detections, and boundary instability. Thin rims and adjacent objects further complicate localization, and model robustness often deteriorates across regions and sensor domains. To address these issues, we propose OCDBMamba, a unified and efficient framework that integrates omnidirectional context modeling with consensus-driven boundary selection. Specifically, we introduce the following: (1) an Omnidirectional Channel-Selective Scanning (OCS) mechanism that aggregates long-range structural cues by performing multidirectional scans and channel similarity fusion with cross-directional consistency, capturing comprehensive spatial dependencies at near-linear complexity and (2) a Dual-Branch Consensus Thresholding (DBCT) module that enforces branch-level agreement with sparsity-regulated adaptive thresholds and boundary consistency constraints, effectively preserving true rims while suppressing reflections and redundant responses. Extensive experiments on normal, shadowed, wet, low-contrast, and texture-rich subsets yield 90.7%

m A P_{50}

, 67.8%

m A P_{50 : 95}

, a precision of 0.905, and a recall of 0.812 with 13.1 GFLOPs, outperforming YOLOv11n by 5.4% and 5.6%, respectively. The results demonstrate more stable localization and enhanced robustness under diverse conditions, validating the synergy of OCS and DBCT for practical road inspection and on-vehicle perception scenarios. Full article

(This article belongs to the Section Intelligent Sensors)

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19 pages, 3156 KB

Open AccessArticle

Detecting Escherichia coli on Conventional Food Processing Surfaces Using UV-C Fluorescence Imaging and Deep Learning

by Zafar Iqbal, Thomas F. Burks, Snehit Vaddi, Pappu Kumar Yadav, Quentin Frederick, Satya Aakash Chowdary Obellaneni, Jianwei Qin, Moon Kim, Mark A. Ritenour, Jiuxu Zhang and Fartash Vasefi

Appl. Sci. 2026, 16(2), 968; https://doi.org/10.3390/app16020968 (registering DOI) - 17 Jan 2026

Abstract

Detecting Escherichia coli on food preparation and processing surfaces is critical for ensuring food safety and preventing foodborne illness. This study focuses on detecting E. coli contamination on common food processing surfaces using UV-C fluorescence imaging and deep learning. Four concentrations of E. [...] Read more.

Detecting Escherichia coli on food preparation and processing surfaces is critical for ensuring food safety and preventing foodborne illness. This study focuses on detecting E. coli contamination on common food processing surfaces using UV-C fluorescence imaging and deep learning. Four concentrations of E. coli (0, 10⁵, 10⁷, and 10⁸ colony forming units (CFU)/mL) and two egg solutions (white and yolk) were applied to stainless steel and white rubber to simulate realistic contamination with organic interference. For each concentration level, 256 droplets were inoculated in 16 groups, and fluorescence videos were captured. Droplet regions were extracted from the video frames, subdivided into quadrants, and augmented to generate a robust dataset, ensuring 3–4 droplets per sample. Wavelet-based denoising further improved image quality, with Haar wavelets producing the highest Peak Signal-to-Noise Ratio (PSNR) values, up to 51.0 dB on white rubber and 48.2 dB on stainless steel. Using this dataset, multiple deep learning (DL) models, including ConvNeXtBase, EfficientNetV2L, and five YOLO11-cls variants, were trained to classify E. coli concentration levels. Additionally, Eigen-CAM heatmaps were used to visualize model attention to bacterial fluorescence regions. Across four dataset groupings, YOLO11-cls models achieved consistently high performance, with peak test accuracies of 100% on white rubber and 99.60% on stainless steel, even in the presence of egg substances. YOLO11s-cls provided the best balance of accuracy (up to 98.88%) and inference speed (4–5 ms) whilst having a compact size (11 MB), outperforming larger models such as EfficientNetV2L. Classical machine learning models lagged significantly behind, with Random Forest reaching 89.65% accuracy and SVM only 67.62%. Overall, the results highlight the potential of combining UV-C fluorescence imaging with deep learning for rapid and reliable detection of E. coli on stainless steel and rubber conveyor belt surfaces. Additionally, this approach could support the design of effective interventions to remove E. coli from food processing environments. Full article

(This article belongs to the Special Issue Advanced Methods and Techniques: Ensuring and Improving Food Safety and Quality)

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46 pages, 5605 KB

Open AccessArticle

An Intelligent Predictive Maintenance Architecture for Substation Automation: Real-World Validation of a Digital Twin and AI Framework of the Badra Oil Field Project

by Sarmad Alabbad and Hüseyin Altınkaya

Electronics 2026, 15(2), 416; https://doi.org/10.3390/electronics15020416 (registering DOI) - 17 Jan 2026

Abstract

The increasing complexity of modern electrical substations—driven by renewable integration, advanced automation, and asset aging—necessitates a transition from reactive maintenance toward intelligent, data-driven strategies. Predictive maintenance (PdM), supported by artificial intelligence, enables early fault detection and remaining useful life (RUL) estimation, while Digital [...] Read more.

The increasing complexity of modern electrical substations—driven by renewable integration, advanced automation, and asset aging—necessitates a transition from reactive maintenance toward intelligent, data-driven strategies. Predictive maintenance (PdM), supported by artificial intelligence, enables early fault detection and remaining useful life (RUL) estimation, while Digital Twin (DT) technology provides synchronized cyber–physical representations for situational awareness and risk-free validation of maintenance decisions. This study proposes a five-layer DT-enabled PdM architecture integrating standards-based data acquisition, semantic interoperability (IEC 61850, CIM, and OPC UA Part 17), hybrid AI analytics, and cyber-secure decision support aligned with IEC 62443. The framework is validated using utility-grade operational data from the SS1 substation of the Badra Oil Field, comprising approximately one million multivariate time-stamped measurements and 139 confirmed fault events across transformer, feeder, and environmental monitoring systems. Fault detection is formulated as a binary classification task using event-window alignment to the 1 min SCADA timeline, preserving realistic operational class imbalance. Five supervised learning models—a Random Forest, Gradient Boosting, a Support Vector Machine, a Deep Neural Network, and a stacked ensemble—were benchmarked, with the ensemble embedded within the DT core representing the operational predictive model. Experimental results demonstrate strong performance, achieving an F1-score of 0.98 and an AUC of 0.995. The results confirm that the proposed DT–AI framework provides a scalable, interoperable, and cyber-resilient foundation for deployment-ready predictive maintenance in modern substation automation systems Full article

(This article belongs to the Section Artificial Intelligence)

20 pages, 741 KB

Open AccessArticle

Aging in Cross-Cultural Contexts: Transnational Healthcare Practices Among Older Syrian Refugees in the Greater Toronto Area

by Areej Al-Hamad, Yasin Mohammad Yasin, Sepali Guruge, Kateryna Metersky, Cristina Catallo, Hasina Amanzai, Zhixi Zhuang, Lu Wang, Lixia Yang, Lina Kanan and Yasmeen Chamas

J. Ageing Longev. 2026, 6(1), 13; https://doi.org/10.3390/jal6010013 (registering DOI) - 17 Jan 2026

Abstract

Despite the increasing number of older Syrian refugees in Canada, little is known about how they manage their health care needs while contending with language barriers, cultural dissonance, and systemic inequities. This qualitative study explored how older Syrian refugees in the Greater Toronto [...] Read more.

Despite the increasing number of older Syrian refugees in Canada, little is known about how they manage their health care needs while contending with language barriers, cultural dissonance, and systemic inequities. This qualitative study explored how older Syrian refugees in the Greater Toronto Area (GTA) navigate healthcare across Canadian and transnational contexts. The study was guided by the transnational circulation of care framework and used an interpretive descriptive design. Following research ethics approval, 20 older Syrian refugees were interviewed by bilingual research assistants. In-depth individual interviews were conducted in Arabic and analyzed using reflexive thematic analysis. Four interrelated themes emerged: (1) Navigating a New System; (2) Living in Two Worlds; (3) Medication Portability, Herbal Practices, and Supplement Culture; and (4) Digital Health Across Borders. Findings demonstrate that older Syrian refugees actively construct hybrid care pathways that integrate biomedical, cultural, and transnational practices. These strategies reflect resilience and adaptability but also expose gaps in the healthcare system. The study underscores the need for culturally responsive and age-friendly healthcare practices that acknowledge transnational realities. By illuminating how care circulates across borders, this study provides actionable guidance for designing responsive health systems. Full article

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17 pages, 2380 KB

Open AccessArticle

Photosynthetic Performance and Physiological Assessment of Young Citrus limon L. Trees Grown After Seed Priming

by Valentina Ancuța Stoian, Ștefania Gâdea, Florina Copaciu, Anamaria Vâtcă, Vlad Stoian, Melinda Horvat, Alina Toșa and Sorin Daniel Vâtcă

Horticulturae 2026, 12(1), 99; https://doi.org/10.3390/horticulturae12010099 (registering DOI) - 17 Jan 2026

Abstract

In the current context of climate change, special attention should be paid to assuring the security of food and fruits. Lemon trees struggle to keep their physiological traits stable in the context of all the cumulated challenges originating from climate stress. Therefore, our [...] Read more.

In the current context of climate change, special attention should be paid to assuring the security of food and fruits. Lemon trees struggle to keep their physiological traits stable in the context of all the cumulated challenges originating from climate stress. Therefore, our aim was to assess two seed priming methods’ long-term effects on some physiological parameters of young lemon trees. The relative chlorophyll content reveals that hydropriming shows 26% increases from E1 to E6, similar to the control, while osmopriming has a 31% higher value at the beginning and after three years. Leaf stomatal density has 80% lower values due to osmopriming compared to the control, while hydropriming show 15% lower values. Leaf area development was slightly similar between treatments, with more leaves being developed after hydropriming treatments. Guard cell width has similar values for priming, with both being with 40% higher than that of the control. Lemon trees grown after osmotic stress have the highest mass percentages of magnesium and potassium in the leaves. Hydropriming promotes calcium oxalate accumulation and a high mass percentage of phosphorus. The percentage allocation of carbon as dry matter is 32% for osmopriming, significantly higher than for the other treatments. The quantum yield of photosynthetic electron transport is the only significant photosynthetic parameter for osmoprimed lemon young trees. Physiological techniques successfully enhanced the overall growth of three-year-old lemon trees, especially osmopriming treatment. Full article

(This article belongs to the Special Issue Emerging Insights into Horticultural Crop Ecophysiology)

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16 pages, 2847 KB

Open AccessArticle

Monetary Policy and Fiscal Conditions: Interest Rates, Nominal Growth Rates, Tax Revenues, and Government Expenditures

by Yutaka Harada and Makoto Suzuki

J. Risk Financial Manag. 2026, 19(1), 75; https://doi.org/10.3390/jrfm19010075 (registering DOI) - 17 Jan 2026

Abstract

Two main perspectives exist regarding the interaction between fiscal deficits and expansionary monetary policy. The first perspective argues that fiscal deficits raise interest rates, thereby increasing interest payments and complicating monetary stabilization efforts. The second posits that expansionary monetary policy enhances nominal GDP [...] Read more.

Two main perspectives exist regarding the interaction between fiscal deficits and expansionary monetary policy. The first perspective argues that fiscal deficits raise interest rates, thereby increasing interest payments and complicating monetary stabilization efforts. The second posits that expansionary monetary policy enhances nominal GDP growth, which in turn reduces the government debt-to-GDP ratio and strengthens the fiscal position. Using panel data from the IMF World Economic Outlook covering advanced economies between 1980 and 2025, this study empirically evaluates which perspective is more consistent with observed data, while accounting for the dynamics of tax revenues, government expenditures, interest rates, and nominal GDP growth. Empirical evidence indicates that moderate monetary expansion—raising nominal GDP—tends to stabilize budget deficits, as government revenues generally outpace expenditures and interest rates do not increase proportionally with nominal growth. These results are further illustrated through case studies of Greece, Italy, Portugal, Spain, Japan, the United Kingdom, and the United States. Full article

(This article belongs to the Special Issue Monetary Policy and Debt)

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31 pages, 425 KB

Open AccessReview

Recent Research on the Role of Lactobacilli Probiotics in Cancer Management

by See-Hyoung Park

Nutrients 2026, 18(2), 297; https://doi.org/10.3390/nu18020297 (registering DOI) - 17 Jan 2026

Abstract

Lactobacilli strains are one of the major groups belonging to probiotics. Lactobacilli strains are known to be beneficial microbes widely studied and utilized for their health benefits and applications in various fields. Recently, Lactobacilli strains have emerged as promising agents in cancer management [...] Read more.

Lactobacilli strains are one of the major groups belonging to probiotics. Lactobacilli strains are known to be beneficial microbes widely studied and utilized for their health benefits and applications in various fields. Recently, Lactobacilli strains have emerged as promising agents in cancer management due to their ability to influence various physiological processes. Lactobacilli strains have shown potential in producing tumor-suppressive compounds, enhancing immune responses, and reshaping gut microbiota balance for the management of various cancer types. Lactobacilli strains demonstrated tumor-suppressive activity through mechanisms including induction of apoptosis, inhibition of migration, and regulation of key oncogenic signaling pathways. However, the effects of Lactobacilli strains appear to be strain- and cancer-type-dependent, necessitating further research to identify the most effective strains for the proper cancer type with the optimal treatment regimens. In this review article, we focus on Lactobacilli strains studied between 2021 and 2025 that have demonstrated tumor-suppressive properties in various experimental models. In addition, this article explores the current limitations in research methodologies and proposes potential avenues for future investigations in this area of study. Full article

(This article belongs to the Special Issue Prebiotics, Probiotics, and Gut Microbiota with Chronic Disease (2nd Edition))

22 pages, 1584 KB

Open AccessArticle

Highly Efficient Adsorption of Pb(II) by Magnesium-Modified Zeolite: Performance and Mechanisms

by Yuting Yang, Xiong Wang, Sumra Siddique Abbasi, Bin Zhou, Qing Huang, Shujuan Zhang, Xinsheng Xiao, Hao Li, Huayi Chen and Yueming Hu

Toxics 2026, 14(1), 85; https://doi.org/10.3390/toxics14010085 (registering DOI) - 17 Jan 2026

Abstract

In this study, magnesium-modified clinoptilolite (MZ) was successfully synthesized via precipitation and calcination to efficiently remove Pb(II) from aqueous solutions. The material was systematically characterized using BET, XRD, SEM-EDX, FT-IR, and XPS. Adsorption kinetics followed a pseudo-second-order model (R² = 0.9956), with [...] Read more.

In this study, magnesium-modified clinoptilolite (MZ) was successfully synthesized via precipitation and calcination to efficiently remove Pb(II) from aqueous solutions. The material was systematically characterized using BET, XRD, SEM-EDX, FT-IR, and XPS. Adsorption kinetics followed a pseudo-second-order model (R² = 0.9956), with MZ removing over 70% of Pb(II) within the first 3 h. Isotherm data were best described by the Langmuir model (R² = 0.9686), confirming monolayer chemical adsorption, with a maximum adsorption capacity (qₘ) of 1656 mg/g. Notably, MZ maintained high adsorption capacity across a pH range of 3.0~5.5, and its performance was largely unaffected by the presence of high concentrations of competing ions (0.1~1.0 M NaNO₃). Mechanistic analysis revealed that the loaded MgO facilitates the chemical conversion of Pb(II) to hydroxycarbonate (Pb₃(CO₃)₂(OH)₂) via surface complexation, which constitutes the primary removal mechanism. These findings demonstrate that magnesium modification can transform natural zeolites into high-capacity, stable adsorbents, offering promising potential for the treatment of Pb(II)-contaminated water. Full article

(This article belongs to the Special Issue Green and Advanced Technologies for the Removal of Emerging Contaminants from Aquatic Environments)

21 pages, 647 KB

Open AccessReview

A Critical Analysis of Agricultural Greenhouse Gas Emission Drivers and Mitigation Approaches

by Yezheng Zhu, Yixuan Zhang, Jiangbo Li, Yiting Liu, Chenghao Li, Dandong Cheng and Caiqing Qin

Atmosphere 2026, 17(1), 97; https://doi.org/10.3390/atmos17010097 (registering DOI) - 17 Jan 2026

Abstract

Agricultural activities are major contributors to global greenhouse gas (GHG) emissions, with methane (CH₄) and nitrous oxide (N₂O) emissions accounting for 40% and 60% of total agricultural emissions, respectively. Therefore, developing effective emission reduction pathways in agriculture is crucial [...] Read more.

Agricultural activities are major contributors to global greenhouse gas (GHG) emissions, with methane (CH₄) and nitrous oxide (N₂O) emissions accounting for 40% and 60% of total agricultural emissions, respectively. Therefore, developing effective emission reduction pathways in agriculture is crucial for achieving carbon budget balance. This article synthesizes the impact of farmland management practices on GHG emissions, evaluates prevalent accounting methods and their applicable scenarios, and proposes mitigation strategies based on systematic analysis. The present review (2000-2025) indicates that fertilizer management dominates research focus (accounting for over 50%), followed by water management (approximately 18%) and tillage practices (approximately 14%). Critically, the effects of these practices extend beyond GHG emissions, necessitating concurrent consideration of crop yields, soil health, and ecosystem resilience. Therefore, it is necessary to conduct joint research by integrating multiple approaches such as water-saving irrigation, conservation tillage and intercropping of leguminous crops, so as to enhance productivity and soil quality while reducing emissions. The GHG accounting framework and three primary accounting methods (In situ measurement, Satellite remote sensing, and Model simulation) each exhibit distinct advantages and limitations, requiring scenario-specific selection. Further refinement of these methodologies is imperative to optimize agricultural practices and achieve meaningful GHG reductions. Full article

(This article belongs to the Special Issue Gas Emissions from Soil)

24 pages, 3070 KB

Open AccessArticle

Early Vegetation Responses to Alien Plant Clearing in Communal Rangelands: A Case from Manzini, Eswatini

by Sihle Edmund Mthethwa and Sellina Ennie Nkosi

Ecologies 2026, 7(1), 10; https://doi.org/10.3390/ecologies7010010 (registering DOI) - 17 Jan 2026

Abstract

Invasive alien plant species pose significant threats to biodiversity and the ecological functioning of ecosystems, necessitating targeted clearing strategies. This study investigated the short-term recovery of native vegetation following the control of Lantana camara and Chromolaena odorata in communal lands of Manzini, Eswatini. [...] Read more.

Invasive alien plant species pose significant threats to biodiversity and the ecological functioning of ecosystems, necessitating targeted clearing strategies. This study investigated the short-term recovery of native vegetation following the control of Lantana camara and Chromolaena odorata in communal lands of Manzini, Eswatini. Nineteen sites were sampled across cleared and uncleared areas to assess changes in species diversity and veld condition. Cleared sites showed slightly reduced heterogeneity (D′ = 0.722) and higher diversity (H′ = 2.081) compared to uncleared sites (D′ = 0.732) and diversity (H′ = 2.032). Sites free from invasive alien plants had higher species richness (EXP (H′) = 35.693) than invaded sites (EXP (H′) = 28.237). Although statistical analyses showed no significant differences in stem counts between cleared and uncleared sites, effect sizes indicated potential practical significance for C. odorata. The Veld Condition Index (VCI) revealed high spatial variability with no consistent trend associated with clearing. Findings emphasise the complexity of early post-clearing dynamics and the importance of site-specific follow-up and monitoring. Full article

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18 pages, 1938 KB

Open AccessArticle

Reproductive Dynamics of the Blonde Ray (Raja brachyura) in Portuguese Waters: Timing, Maturity and Fecundity

by Catarina Maia, Ivone Figueiredo, Bárbara Serra-Pereira, Neide Lagarto, Inês Farias and Teresa Moura

Fishes 2026, 11(1), 61; https://doi.org/10.3390/fishes11010061 (registering DOI) - 17 Jan 2026

Abstract

Within the Rajidae family, the blonde ray (Raja brachyura) is considered one of the less resilient species to fishing pressure and other anthropogenic pressures, primarily due to its late maturity and large maximum size, which can exceed 120 cm total length. [...] Read more.

Within the Rajidae family, the blonde ray (Raja brachyura) is considered one of the less resilient species to fishing pressure and other anthropogenic pressures, primarily due to its late maturity and large maximum size, which can exceed 120 cm total length. This is the first study to provide comprehensive insights into the reproductive biology of Raja brachyura in the continental waters of Portugal, with insights into its timing, maturity, and fecundity. It was determined that egg-laying occurs from February to November, with a peak observed between April and September. Males were reproductively active throughout the year, with highest proportions of active males observed between January and May. The length at first maturity was estimated at 95.2 cm for females and 90.0 cm for males, corresponding to 85% of the maximum observed length in each sex. The potential fecundity was estimated at 115 follicles per female per year, and evidence suggests that the species has a determinate fecundity. The findings reinforce the appropriateness of current management measures in Portuguese continental waters, namely seasonal closure when overlapping with the peak of the reproductive season (May and June), and provide valuable scientific support for future conservation and management measures. Full article

(This article belongs to the Special Issue Ecology of Fish: Age, Growth, Reproduction and Feeding Habits)

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18 pages, 3693 KB

Open AccessArticle

Modeling and Performance Assessment of a NeWater System Based on Direct Evaporation and Refrigeration Cycle

by Yilin Huo, Eric Hu and Jay Wang

Energies 2026, 19(2), 468; https://doi.org/10.3390/en19020468 (registering DOI) - 17 Jan 2026

Abstract

At present, the global shortage of water resources has led to serious challenges, and traditional water production technologies such as seawater desalination and atmospheric water harvesting have certain limitations due to inflexible operation and environmental conditions. This study proposes a novel water production [...] Read more.

At present, the global shortage of water resources has led to serious challenges, and traditional water production technologies such as seawater desalination and atmospheric water harvesting have certain limitations due to inflexible operation and environmental conditions. This study proposes a novel water production system (called “NeWater” system in this paper), which combines saline water desalination with atmospheric water-harvesting technologies to simultaneously produce freshwater from brackish water or seawater and ambient air. To evaluate its performance, an integrated thermodynamic and mathematical model of the system was developed and validated. The NeWater system consists of a vapor compression refrigeration unit (VRU), a direct evaporation unit (DEU), up to four heat exchangers, some valves, and auxiliary components. The system can be applied to areas and scenarios where traditional desalination technologies, like reverse osmosis and thermal-based desalination, are not feasible. By switching between different operating modes, the system can adapt to varying environmental humidity and temperature conditions to maximize its freshwater productivity. Based on the principles of mass and energy conservation, a performance simulation model of the NeWater system was developed, with which the impacts of some key design and operation parameters on system performance were studied in this paper. The results show that the performances of the VRU and DEU had a significant influence on system performance in terms of freshwater production and specific energy consumption. Under optimal conditions, the total freshwater yield could be increased by up to 1.9 times, while the specific energy consumption was reduced by up to 48%. The proposed system provides a sustainable and scalable water production solution for water-scarce regions. Optimization of the NeWater system and the selection of VRUs are beyond the scope of this paper and will be the focus of future research. Full article

(This article belongs to the Special Issue Optimization and Innovations in Thermal Cycles and Heat Management Systems)

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23 pages, 2620 KB

Open AccessArticle

Secretome Profiling of Lactiplantibacillus plantarum CRL681 Predicts Potential Molecular Mechanisms Involved in the Antimicrobial Activity Against Escherichia coli O157:H7

by Ayelen Antonella Baillo, Leonardo Albarracín, Eliana Heredia Ojeda, Mariano Elean, Weichen Gong, Haruki Kitazawa, Julio Villena and Silvina Fadda

Antibiotics 2026, 15(1), 96; https://doi.org/10.3390/antibiotics15010096 (registering DOI) - 17 Jan 2026

Abstract

Background/Objectives. Lactiplantibacillus plantarum CRL681 has previously demonstrated a strong antagonistic effect against Escherichia coli O157:H7 in food matrices; however, the molecular mechanisms underlying this activity remain poorly understood. Since initial interactions between beneficial bacteria and pathogens occur mainly at the cell surface [...] Read more.

Background/Objectives. Lactiplantibacillus plantarum CRL681 has previously demonstrated a strong antagonistic effect against Escherichia coli O157:H7 in food matrices; however, the molecular mechanisms underlying this activity remain poorly understood. Since initial interactions between beneficial bacteria and pathogens occur mainly at the cell surface and in the extracellular environment, the characterization of the bacterial secretome is essential for elucidating these mechanisms. In this study, the secretome of L. plantarum CRL681 was comprehensively characterized using an integrated in silico and in vitro approach. Methods. The exoproteome and surfaceome were analyzed by LC-MS/MS under pure culture conditions and during co-culture with E. coli O157:H7. Identified proteins were functionally annotated, classified according to subcellular localization and secretion pathways, and evaluated through protein–protein interaction network analysis. Results. A total of 275 proteins were proposed as components of the CRL681 secretome, including proteins involved in cell surface remodeling, metabolism and nutrient transport, stress response, adhesion, and genetic information processing. Co-culture with EHEC induced significant changes in the expression of proteins associated with energy metabolism, transport systems, and redox homeostasis, indicating a metabolic and physiological adaptation of L. plantarum CRL681 under competitive conditions. Notably, several peptidoglycan hydrolases, ribosomal proteins with reported antimicrobial activity, and moonlighting proteins related to adhesion were identified. Conclusions. Overall, these findings suggest that the antagonistic activity of L. plantarum CRL681 against E. coli O157:H7 would be mediated by synergistic mechanisms involving metabolic adaptation, stress resistance, surface adhesion, and the production of non-bacteriocin antimicrobial proteins, supporting its potential application as a bioprotective and functional probiotic strain. Full article

(This article belongs to the Special Issue Foodborne Pathogens: Detection, Resistance Mechanisms, and Control Strategies)

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26 pages, 1052 KB

Open AccessArticle

Fast Computation for Square Matrix Factorization

by Artyom M. Grigoryan

Computers 2026, 15(1), 67; https://doi.org/10.3390/computers15010067 (registering DOI) - 17 Jan 2026

Abstract

In this work, we discuss a method for the QR-factorization of N × N matrices where N ≥ 3 which is based on transformations which are called discrete signal-induced heap transformations (DsiHTs). These transformations are generated by given signals and can be composed [...] Read more.

In this work, we discuss a method for the QR-factorization of N × N matrices where N ≥ 3 which is based on transformations which are called discrete signal-induced heap transformations (DsiHTs). These transformations are generated by given signals and can be composed by elementary rotations. The data processing order, or the path of the transformations, is an important characteristic of it, and the correct choice of such paths can lead to a significant reduction in the operation when calculating the factorization for large matrices. Such paths are called fast paths of the N-point DsiHTs, and they define sparse matrices with more zero coefficients than when calculating QR-factorization in the traditional path, that is, when processing data in the natural order x₀, x₁, x₂, …. For example, in the first stage of the factorization of a 512 × 512 matrix, a matrix is used with 257,024 zero coefficients out of a total of 262,144 coefficients when using the fast paths. For comparison, the calculations in the natural order require a 512 × 512 matrix with only 130,305 zero coefficients at this stage. The Householder reflection matrix has no zero coefficients. The number of multiplication operations for the QR-factorization by the fast DsiHTs is more than 40 times smaller than when using the Householder reflections and 20 times smaller when using DsiHTs with the natural paths. Examples with the 4 × 4, 5 × 5, and 8 × 8 matrices are described in detail. The concept of complex DsiHT with fast paths is also described and applied in the QR-factorization of complex square matrices. An example of the QR-factorization of a 256 × 256 complex matrix is also described and compared with the method of Householder reflections which is used in programming language MATLAB R2024b. Full article

22 pages, 6931 KB

Open AccessArticle

Biopolymer Casein–Pullulan Coating of Fe₃O₄ Nanocomposites for Xanthohumol Encapsulation and Delivery

by Nikolay Zahariev, Dimitar Penkov, Radka Boyuklieva, Plamen Simeonov, Paolina Lukova, Raina Ardasheva and Plamen Katsarov

Polymers 2026, 18(2), 256; https://doi.org/10.3390/polym18020256 (registering DOI) - 17 Jan 2026

Abstract

Introduction: Magnetic nanoparticles are widely investigated as multifunctional platforms for drug delivery and theranostic applications, yet their biomedical implementation is hindered by aggregation, limited colloidal stability, and insufficient biocompatibility. Hybrid biopolymer coatings can mitigate these issues while supporting drug incorporation. Aim: This study [...] Read more.

Introduction: Magnetic nanoparticles are widely investigated as multifunctional platforms for drug delivery and theranostic applications, yet their biomedical implementation is hindered by aggregation, limited colloidal stability, and insufficient biocompatibility. Hybrid biopolymer coatings can mitigate these issues while supporting drug incorporation. Aim: This study aimed to develop casein–pullulan-coated Fe₃O₄ nanocomposites loaded with xanthohumol, enhancing stability and enabling controlled release for potential theranostic use. Methods: Fe₃O₄ nanoparticles were synthesized through co-precipitation and incorporated into a casein–pullulan matrix formed via polymer complexation and glutaraldehyde crosslinking. A 3² full factorial design evaluated the influence of casein:pullulan ratio and crosslinker concentration on physicochemical performance. Nanocomposites were characterized for size, zeta potential, morphology, composition, and stability, while drug loading, encapsulation efficiency, and release profiles were determined spectrophotometrically. Molecular docking was performed to examine casein–pullulan interactions. Results: Uncoated Fe₃O₄ nanoparticles aggregated extensively, displaying mean sizes of ~292 nm, zeta potential of +80.95 mV and high polydispersity (PDI above 0.2). Incorporation into the biopolymer matrix improved colloidal stability, yielding particles of ~185 nm with zeta potentials near –35 mV. TEM and SEM confirmed spherical morphology and uniform magnetic core incorporation. The optimal formulation, consisting of a 1:1 casein:pullulan ratio with 1% glutaraldehyde, achieved 5.7% drug loading, 68% encapsulation efficiency, and sustained release of xanthohumol up to 84% over 120 h, fitting Fickian diffusion (Korsmeyer–Peppas R² = 0.9877, n = 0.43). Conclusions: Casein–pullulan hybrid coatings significantly enhance Fe₃O₄ nanoparticle stability and enable controlled release of xanthohumol, presenting a promising platform for future targeted drug delivery and theranostic applications. Full article

(This article belongs to the Special Issue Engineered Polymeric Particles for Next-Generation Nanomedicine)

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18 pages, 3926 KB

Open AccessArticle

Design and Simulation Study of an Intelligent Electric Drive Wheel with Integrated Transmission System and Load-Sensing Unit

by Xiaoyu Ding, Xinbo Chen and Yan Li

Energies 2026, 19(2), 461; https://doi.org/10.3390/en19020461 (registering DOI) - 17 Jan 2026

Abstract

Wheel load is a critical information source reflecting the status of vehicle load distribution and motion. Yet, existing in-wheel motor products are primarily designed as propulsion units and inherently lack the load-sensing capabilities required by intelligent vehicles. To address this research gap, this [...] Read more.

Wheel load is a critical information source reflecting the status of vehicle load distribution and motion. Yet, existing in-wheel motor products are primarily designed as propulsion units and inherently lack the load-sensing capabilities required by intelligent vehicles. To address this research gap, this paper presents a novel intelligent electric drive wheel (i-EDW) with an integrated transmission system and a load-sensing unit (LSU). The i-EDW adopts an Axial Flux Permanent Magnet Synchronous Motor (AFPMSM), while the integrated LSU ensures high-precision measurement of six-dimensional wheel forces and moments. According to this multi-axis force information, a real-time estimation and stability control method based on the tire–road friction circle concept is proposed. Instead of the complex decoupling and multi-objective optimization with the multi-actuator systems, this paper focuses on minimizing the tire load rate of i-EDWs, which significantly advances the state of the art in terms of calculation efficiency and respond speed. To validate this theoretical framework, a full-vehicle model equipped with four i-EDWs is developed. In the MATLAB R2022A/Simulink co-simulation environment, a virtual prototype is tested under typical driving scenarios, including the straight-line acceleration and double-moving-lane (DML) steering. The simulation results prove a reliable safety margin from the friction circle boundaries, laying a solid foundation for precise motion control and improved system robustness in future intelligent vehicles. Full article

(This article belongs to the Section E: Electric Vehicles)

16 pages, 3410 KB

Open AccessArticle

Systematic Evaluation of a Mouse Model of Aging-Associated Parkinson’s Disease Induced with MPTP and D-Galactose

by Tongzheng Liu, Xiaoyu Liu, Qiuyue Chen, Jinfeng Ren, Zifa Li, Xiao Qiu, Xinyu Wang, Lidan Wu, Minghui Hu, Dan Chen, Hao Zhang and Xiwen Geng

Biology 2026, 15(2), 169; https://doi.org/10.3390/biology15020169 (registering DOI) - 17 Jan 2026

Abstract

Parkinson’s disease (PD) is a common neurodegenerative disorder characterized by motor dysfunction and non-motor symptoms, including cognitive decline. Animal models that replicate PD’s clinical features are essential for therapeutic research. The widely used subacute 1-methyl-4-phenyl-1,2,3,6tetrahydropyridine (MPTP)-induced mouse model effectively mimics motor deficits but [...] Read more.

Parkinson’s disease (PD) is a common neurodegenerative disorder characterized by motor dysfunction and non-motor symptoms, including cognitive decline. Animal models that replicate PD’s clinical features are essential for therapeutic research. The widely used subacute 1-methyl-4-phenyl-1,2,3,6tetrahydropyridine (MPTP)-induced mouse model effectively mimics motor deficits but fails to fully represent aging-related non-motor symptoms. In this study, we established an aging-associated PD mouse model by combining MPTP with D-galactose treatment. Compared to mice treated with MPTP alone, MPTP + D-galactose-treated mice exhibited typical motor impairments alongside cognitive deficits in the Morris water maze and Y-maze tests. D-galactose alone induced cognitive impairment without motor dysfunction. Pathological analysis showed that the MPTP + D-galactose treatment caused tyrosine hydroxylase-positive neuron loss similar to MPTP, while D-galactose did not damage these neurons. Additionally, Micro-CT revealed bone loss in both the MPTP + D-galactose and D-galactose groups. This model recapitulates both the motor and aging-related non-motor symptoms of PD, including cognitive impairment and bone loss, providing a more comprehensive tool for studying PD pathogenesis and evaluating potential therapies. Full article

(This article belongs to the Special Issue Animal Models of Neurodegenerative Diseases)

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15 pages, 8399 KB

Open AccessArticle

Magnolol Ameliorates Cisplatin-Induced Acute Kidney Injury with Activation of Nrf2-Associated Antioxidant Responses

by Mi-Gyeong Gwon, Min Hui Park and Jaechan Leem

Curr. Issues Mol. Biol. 2026, 48(1), 96; https://doi.org/10.3390/cimb48010096 (registering DOI) - 17 Jan 2026

Abstract

Cisplatin (CDDP) is a cornerstone chemotherapeutic drug, yet its efficacy is frequently compromised by renal toxicity, primarily manifesting as acute kidney injury (AKI). Magnolol (MG) is a polyphenol from Magnolia officinalis and has been widely documented for its pronounced antioxidant and anti-inflammatory properties. [...] Read more.

Cisplatin (CDDP) is a cornerstone chemotherapeutic drug, yet its efficacy is frequently compromised by renal toxicity, primarily manifesting as acute kidney injury (AKI). Magnolol (MG) is a polyphenol from Magnolia officinalis and has been widely documented for its pronounced antioxidant and anti-inflammatory properties. This study evaluated the renoprotective effects of MG in a murine model of CDDP-induced AKI. Male C57BL/6 mice received MG (20 mg/kg) via daily intraperitoneal injection for four consecutive days, starting one day before a single CDDP injection. MG significantly reduced the serum concentrations of blood urea nitrogen and creatinine. Histopathological assessment revealed attenuated tubular damage and reduced expression of tubular injury markers. MG inhibited pro-inflammatory cytokines at both systemic and renal levels, alleviated endoplasmic reticulum stress, and suppressed activation of mitogen-activated protein kinase signaling pathways. Apoptotic damage was mitigated, as shown by the fewer TUNEL-positive cells and lowered expression of pro-apoptotic markers. In parallel, ferroptotic processes were alleviated through downregulation of pro-ferroptotic proteins and preservation of key antioxidant regulators. Importantly, MG restored nuclear factor erythroid 2-related factor 2 activity and upregulated downstream antioxidant effectors. These findings highlight the multi-targeted renoprotective actions of MG and support its possible utility as a therapeutic agent to prevent CDDP-induced renal injury. Full article

(This article belongs to the Special Issue Molecular Mechanisms and Treatment of Kidney Diseases)

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17 pages, 4414 KB

Open AccessArticle

Fast Helmet Detection in Low-Resolution Surveillance via Super-Resolution and ROI-Guided Inference

by Taiming He, Ziyue Wang and Lu Yang

Appl. Sci. 2026, 16(2), 967; https://doi.org/10.3390/app16020967 (registering DOI) - 17 Jan 2026

Abstract

Reliable detection of safety helmets is essential for ensuring personnel protection in large-scale outdoor operations. However, recognition becomes difficult when monitoring relies on low-resolution or compressed video streams captured by fixed or mobile platforms such as UAVs—conditions commonly encountered in intelligent transportation and [...] Read more.

Reliable detection of safety helmets is essential for ensuring personnel protection in large-scale outdoor operations. However, recognition becomes difficult when monitoring relies on low-resolution or compressed video streams captured by fixed or mobile platforms such as UAVs—conditions commonly encountered in intelligent transportation and urban surveillance. This study proposes a super-resolution-enhanced detection framework that integrates video super-resolution with ROI-guided inference to improve the visibility of small targets while reducing computational cost. Focusing on a single, carefully selected VSR module (BasicVSR++), the framework achieves an F1-score of 0.904 in helmet detection across multiple low-quality surveillance scenarios. This demonstrates the framework’s effectiveness for robust helmet monitoring in low-resolution and compressed surveillance scenarios. Full article

(This article belongs to the Section Transportation and Future Mobility)

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14 pages, 2202 KB

Open AccessArticle

Brushless Wound-Field Synchronous Machine Topology with Excellent Rotor Flux Regulation Freedom

by Muhammad Ayub, Arsalan Arif, Atiq Ur Rehman, Azka Nadeem, Ghulam Jawad Sirewal, Mohamed A. Abido and Mudassir Raza Siddiqi

Machines 2026, 14(1), 110; https://doi.org/10.3390/machines14010110 (registering DOI) - 17 Jan 2026

Abstract

This paper presents a nine-switch inverter for brushless operation of wound-field synchronous machines with excellent rotor flux regulation freedom. The manufacturing cost of permanent magnet machines is high due to the instability of rare-earth magnet prices in the global market. Moreover, conventional wound-field [...] Read more.

This paper presents a nine-switch inverter for brushless operation of wound-field synchronous machines with excellent rotor flux regulation freedom. The manufacturing cost of permanent magnet machines is high due to the instability of rare-earth magnet prices in the global market. Moreover, conventional wound-field synchronous machines (WFSMs) have problems with their rotor brushes and slip-ring assembly, wherein the assembly starts to malfunction in the long run. Furthermore, recently, some brushless WFSM topologies have been investigated to eliminate the problems associated with rotor brushes and slip rings, but they have either a high cost due to a double-inverter, or low flux regulation freedom due to a single inverter (−i_d). The proposed nine-switch topology achieves a low cost by using a single inverter with nine switches and excellent flux control through three variables (−i_d, i_q, and i_f), making it highly suitable for wide-speed applications. In the proposed topology, the machine’s armature winding is divided into two sets of coils: ABC and XYZ. A 12-slot and 8-pole machine stator is wound with armature winding coils ABC and XYZ, creating six terminals for injecting currents and two neutrals from each ABC and XYZ coil set. The current to the ABC and XYZ coils is supplied by a nine-switch inverter. The inverter is specially designed to supply rated currents to the ABC winding coils and half of the rated current to the XYZ winding coils. The number of turns of the ABC and XYZ winding coils are kept the same so they produce the same winding function. However, the current in the XYZ winding coils is half compared to that of the ABC winding coils, which creates an asymmetrical airgap magnetomotive force (MMF). The asymmetrical airgap MMF contains two working harmonics, i.e., fundamental MMF for torque production and an additional sub-harmonic MMF component for rotor field brushless excitation. The rotor field is controlled by the difference in current of the two armature winding coils: ABC and XYZ. The proposed topology is validated through theoretical analysis and finite element simulations of electromagnetic and flux regulation. A 2D finite-element analysis is performed to verify the idea. The proposed topology is capable of establishing a 9.15 A dc current in the rotor field winding coil, which consequently generates a torque of 7.8 N·m with a 20.30% torque ripple. Rotor field flux regulation was analyzed from the stator ABC and XYZ coils current ratio ζ. The ratio ζ is analyzed as 2 to 1.3; subsequently, the inducted field currents were 9.15 A dc to 4.8 A dc, respectively. Full article

(This article belongs to the Section Electrical Machines and Drives)

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24 pages, 542 KB

Open AccessSystematic Review

Dynamic Difficulty Adjustment in Serious Games: A Literature Review

by Lucia Víteková, Christian Eichhorn, Johanna Pirker and David A. Plecher

Information 2026, 17(1), 96; https://doi.org/10.3390/info17010096 (registering DOI) - 17 Jan 2026

Abstract

This systematic literature review analyzes the role of dynamic difficulty adaptation (DDA) in serious games (SGs) to provide an overview of current trends and identify research gaps. The purpose of the study is to contextualize how DDA is being employed in SGs to [...] Read more.

This systematic literature review analyzes the role of dynamic difficulty adaptation (DDA) in serious games (SGs) to provide an overview of current trends and identify research gaps. The purpose of the study is to contextualize how DDA is being employed in SGs to enhance their learning outcomes, effectiveness, and game enjoyment. The review included studies published over the past five years that implemented specific DDA methods within SGs. Publications were identified through Google Scholar (searched up to 10 November 2025) and screened for relevance, resulting in 75 relevant papers. No formal risk-of-bias assessment was conducted. These studies were analyzed by publication year, source, application domain, DDA type, and effectiveness. The results indicate a growing interest in adaptive SGs across domains, including rehabilitation and education, with DDA methods ranging from rule-based (e.g., fuzzy logic) and player modeling (using performance, physiological, or emotional metrics) to various machine learning techniques (reinforcement learning, genetic algorithms, neural networks). Newly emerging trends, such as the integration of generative artificial intelligence for DDA, were also identified. Evidence suggests that DDA can enhance learning outcomes and game experience, although study differences, limited evaluation metrics, and unexplored opportunities for adaptive SGs highlight the need for further research. Full article

(This article belongs to the Special Issue Serious Games, Games for Learning and Gamified Apps)

14 pages, 477 KB

Open AccessArticle

An SSI-Based Instructional Unit to Enhance Primary Students’ Risk-Related Decision-Making

by Miki Sakamoto, Etsuji Yamaguchi, Tomokazu Yamamoto, Motoaki Matano, Nobuko Ohmido and Rumiko Murayama

Educ. Sci. 2026, 16(1), 143; https://doi.org/10.3390/educsci16010143 (registering DOI) - 17 Jan 2026

Abstract

Socioscientific issues (SSIs) provide meaningful contexts for developing students’ competencies in scientific evaluation and decision-making. This study developed an SSI-based instructional unit to support primary school students in making decisions about genome-edited fish by considering risks and benefits and proposing risk mitigation. The [...] Read more.

Socioscientific issues (SSIs) provide meaningful contexts for developing students’ competencies in scientific evaluation and decision-making. This study developed an SSI-based instructional unit to support primary school students in making decisions about genome-edited fish by considering risks and benefits and proposing risk mitigation. The study aimed to examine the unit’s effectiveness in improving students’ risk-related decision-making and their attitudes toward critical thinking and risk. Sixty-three fifth-grade students participated in an 18-lesson unit comprising two phases: information gathering and risk management practice. Students completed three decision-making tasks and a post-unit questionnaire on related attitudes. Written arguments were analysed using a rubric based on claims, risk knowledge, benefit knowledge, and risk mitigation. The results indicated that the unit improved the quality of students’ socioscientific arguments. By the final task, about 60% of arguments reached the highest level, demonstrating integration of risk knowledge and corresponding mitigation. However, students’ risk–benefit emphasis ratings showed that their decisions remained predominantly risk-focused, and questionnaire data revealed a persistent zero-risk mindset. These findings provide empirical evidence that an SSI-based unit incorporating risk management practice can foster primary students’ risk-related socioscientific decision-making. Further refinement is needed to shift students’ risk attitudes and support more balanced risk–benefit reasoning. Full article

(This article belongs to the Special Issue Critical Thinking in Science Education: Nurturing Inquiry, Socio-Scientific Issues, and Innovation)

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13 pages, 1380 KB

Open AccessArticle

Correlation Between Meso-Defect and Fatigue Life Through Representing Feature Analysis for 6061-T6 Aluminum Alloys

by Liangxia Zhang, Yali Yang, Hao Chen and Shusheng Lv

Sensors 2026, 26(2), 631; https://doi.org/10.3390/s26020631 (registering DOI) - 17 Jan 2026

Abstract

Fatigue strength is vital for engineering applications of aluminum alloys. Accurate models incorporating mesoscopic defect-representing features are one of the issues for accurate fatigue strength prediction. A fatigue life prediction method based on meso-defect-representing features is proposed in this study. Based on staged [...] Read more.

Fatigue strength is vital for engineering applications of aluminum alloys. Accurate models incorporating mesoscopic defect-representing features are one of the issues for accurate fatigue strength prediction. A fatigue life prediction method based on meso-defect-representing features is proposed in this study. Based on staged fatigue damage, meso-defect data was obtained by X-ray CT. After 3D reconstruction and simplification, porosity, shape, and location were selected as the meso-defect-representing features using correlation coefficient analysis. Weights of meso-defect features were determined through FEM simulation. A mesoscopic damage variable incorporating the weights of porosity, shape, and location for meso-defect was defined. Correlation between fatigue life and meso-defect features was established through the mesoscopic damage variable. Experimental verification results showed that the prediction method is an effective method for fatigue life assessment. Full article

(This article belongs to the Special Issue Sensing Technologies in Industrial Defect Detection)

3 pages, 175 KB

Open AccessEditorial

Special Issue “Development, Characterization and Applications of Novel Polymeric Materials and Composites”

by Xiao Hu

Int. J. Mol. Sci. 2026, 27(2), 936; https://doi.org/10.3390/ijms27020936 (registering DOI) - 17 Jan 2026

Abstract

It is our pleasure to introduce this Special Issue (SI) on “Development, Characterization and Applications of Novel Polymeric Materials and Composites”, which brings together recent advances across polymer science, composite engineering, biotechnology, and emerging applications [...] Full article

(This article belongs to the Special Issue Development, Characterization and Applications of Novel Polymeric Materials and Composites)

33 pages, 1456 KB

Open AccessReview

Relevance and Safe Utilization of Amino Acids in Supplements for Human Nutrition: Lessons from Clinical and Preclinical Studies

by François Blachier

Nutrients 2026, 18(2), 296; https://doi.org/10.3390/nu18020296 (registering DOI) - 17 Jan 2026

Abstract

Amino acid availability is central for the synthesis of macromolecules and numerous bioactive compounds. Amino acids are also involved in ATP production, cell signaling, and the epigenetic regulation of gene expression in human cells. From clinical and experimental studies, it appears that supplementation [...] Read more.

Amino acid availability is central for the synthesis of macromolecules and numerous bioactive compounds. Amino acids are also involved in ATP production, cell signaling, and the epigenetic regulation of gene expression in human cells. From clinical and experimental studies, it appears that supplementation with specific amino acids may be relevant to correct for amino acid deficiency in the case of insufficient supply from dietary proteins with regards to the amounts needed for optimal metabolism and physiological functions. Clinical and experimental arguments suggest that amino acid supplementation may be indicated in specific situations under a specific nutritional context. However, it is essential not to overdose with excessive quantities of amino acids in supplements beyond the upper levels of safe intake (ULSI). In this narrative review, I recapitulate the protein and amino acid requirements for the general population and for subgroups of the population, and these requirements are compared to the usual consumption. Typical examples of clinical trials showing the benefits from amino acid supplementation in different physiological and pathophysiological contexts are presented together with results obtained from experimental studies. Parameters such as the no-observed-adverse-effect-level (NOAEL) values used to determine the ULSI for amino acid supplementation are defined, and values determined in clinical trials are given and discussed. Finally, prospects for future research in the field are proposed. Full article

(This article belongs to the Special Issue Relevance and Safe Utilization of Amino Acids in Dietary Supplements)

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21 pages, 5659 KB

Open AccessArticle

Development of High-Performance Catalytic Ceramic Membrane Microchannel Reactor for Carbon Dioxide Conversion to Methanol

by Aubaid Ullah, Nur Awanis Hashim, Mohamad Fairus Rabuni, Mohd Usman Mohd Junaidi, Ammar Ahmed, Mustapha Grema Mohammed and Muhammed Sahal Siddique

Membranes 2026, 16(1), 45; https://doi.org/10.3390/membranes16010045 (registering DOI) - 17 Jan 2026

Abstract

Conversion of carbon dioxide (CO₂) to methanol in a traditional reactor (TR) with catalytic packed bed faces the challenge of lower reactant conversion due to thermodynamic limitations. On the contrary, membrane reactors selectively remove reaction products, enhancing the conversion, but it [...] Read more.

Conversion of carbon dioxide (CO₂) to methanol in a traditional reactor (TR) with catalytic packed bed faces the challenge of lower reactant conversion due to thermodynamic limitations. On the contrary, membrane reactors selectively remove reaction products, enhancing the conversion, but it is still limited, and existing designs face challenges of structural integrity and scale-up complications. Therefore, for the first time, a ceramic membrane microchannel reactor (CMMR) system was developed with 500 µm deep microchannels, incorporated with catalytic membrane for CO₂ conversion to methanol. Computational fluid dynamic (CFD) simulations confirmed the uniform flow distribution among the microchannels. A catalytic LTA zeolite membrane was synthesized with thin layer (~45 µm) of Cu-ZnO-Al₂O₃ catalyst coating and tested at a temperature of 220 °C and 3.0 MPa pressure. The results showed a significantly higher CO₂ conversion of 82%, which is approximately 10 times higher than TR and 3 times higher than equilibrium conversion while 1.5 times higher than conventional tubular membrane reactor. Additionally, methanol selectivity and yield were achieved as 51.6% and 42.3%, respectively. The research outputs showed potential of replacing the current industrial process of methanol synthesis, addressing the Sustainable Development Goals of SDG-7, 9, and 13 for clean energy, industry innovation, and climate action, respectively. Full article

(This article belongs to the Special Issue Ceramic Membranes: Preparation, Modification and Multidisciplinary Applications)

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