Saved Queries

by Mislav Radić, Tina Bečić, Petra Šimac Prižmić, Josipa Radić, Hana Đogaš, Ivona Matulić, Ivana Jukić, Jonatan Vuković and Damir Fabijanić

Diagnostics 2026, 16(8), 1196; https://doi.org/10.3390/diagnostics16081196 (registering DOI) - 17 Apr 2026

Abstract

Systemic sclerosis (SSc) is a complex autoimmune connective tissue disease characterized by microvascular dysfunction, immune activation, and progressive fibrosis affecting multiple organs, including the heart. Myocardial involvement represents an important but frequently underrecognized manifestation of SSc and may develop even in the absence of overt clinical symptoms. Cardiac manifestations include ventricular dysfunction, arrhythmias, conduction abnormalities, and heart failure, contributing substantially to morbidity and mortality. The underlying pathophysiology involves coronary microvascular dysfunction, immune-mediated myocardial inflammation, and progressive myocardial fibrosis, which often precede clinically apparent cardiac disease. This review aims to summarize the current understanding of myocardial involvement in SSc and to provide a comprehensive overview of contemporary multimodality cardiovascular imaging techniques for its detection, characterization, and risk stratification. A comprehensive overview of the current literature was conducted focusing on established and emerging cardiovascular imaging modalities for the evaluation of myocardial involvement in SSc. Particular attention was given to echocardiography, cardiac magnetic resonance (CMR), nuclear imaging techniques including positron emission tomography (PET) and single-photon emission computed tomography (SPECT), and cardiac computed tomography (CT). Recent advances in imaging biomarkers, parametric mapping, myocardial strain analysis, and emerging technologies such as artificial intelligence (AI), radiomics, and molecular imaging were also considered. Multimodality cardiovascular imaging plays a central role in the early detection and comprehensive assessment of myocardial involvement in SSc. Advanced imaging techniques enable improved identification of subclinical myocardial dysfunction, microvascular impairment, inflammation, and fibrosis. An integrated imaging approach combining echocardiography, CMR, nuclear imaging, and CT may facilitate earlier diagnosis, enhance risk stratification, and ultimately improve cardiovascular outcomes in patients with SSc. Full article

(This article belongs to the Special Issue Novel Diagnostic Approaches in Cardiovascular and Metabolic Disorders: From Pathophysiology to Clinical Practice)

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20 pages, 1312 KB

Open AccessArticle

Maritime and Port Contributions to Coastal Nutrient Loading in the Baltic Sea: Apportionment and Regulatory Implications

by Suvi-Tuuli Lappalainen, Jonne Kotta, Deniece M. Aiken and Ulla Pirita Tapaninen

Sustainability 2026, 18(8), 3983; https://doi.org/10.3390/su18083983 (registering DOI) - 17 Apr 2026

Abstract

Eutrophication caused by excessive nitrogen and phosphorus input remains the most severe environmental threat to the Baltic Sea. While nutrient sources in general are widely studied and regulated, the relative importance of maritime nutrient inputs and their regulatory treatment remain insufficiently integrated into land-based nutrient assessments. This study applies a load-based source apportionment approach and quantifies the maritime- and port-related nutrient inputs to a Baltic Sea coastal system, in relation to other nutrient contributors (riverine, municipal, and industrial sources). Additionally, the stringency of the regulatory frameworks governing each source is assessed using a qualitative regulatory classification scale and compared to the proportion of each nutrient source. The results show that riverine inputs dominate total nutrient loading, accounting for over 90% of both nitrogen and phosphorus. Maritime sources contribute only a small share overall. However, fertilizer cargo handling constitutes the largest nitrogen point source, while ship wastewater inputs are negligible. In contrast, ship wastewater is subject to the strictest regulatory controls, whereas fertilizer handling operates under permits lacking explicit nutrient discharge limits. The findings reveal a governance mismatch between nutrient pressures and regulatory focus and highlight the need to better align nutrient management priorities with actual environmental pressures in semi-enclosed seas. Full article

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18 pages, 1496 KB

Open AccessReview

Cracking the Code: Computational Image Analysis Tools for Histopathological and Morphometric Insights

by Ana Luisa Teixeira de Almeida, Ana Beatriz Gram dos Santos and Debora Ferreira Barreto-Vieira

J. Imaging 2026, 12(4), 173; https://doi.org/10.3390/jimaging12040173 (registering DOI) - 17 Apr 2026

Abstract

The assessment of histopathological features has evolved considerably, transitioning from traditional manual measurements to more sophisticated, technology-assisted approaches. Classical histological evaluation, while foundational and highly reliable, is inherently labor-intensive and subject to inter-observer variability. With the advent of digital pathology, these practices have been progressively enhanced by image processing software, which offers capabilities such as segmentation, feature extraction, and data visualization. However, despite their promise, the integration of machine learning into this branch of pathology faces notable challenges, such as the need for large, high-quality annotated datasets and the integration into existing workflows, which remain significant hurdles. Looking forward, the role of specialists in histological evaluation remains crucial in this evolving landscape. While automation streamlines routine tasks, the expertise of pathologists is indispensable in validating results and interpreting findings in scientific contexts. This comprehensive review explores the trajectory of histological evaluation methods, from manual and classical strategies to cutting-edge digital tools, highlighting the benefits, limitations, and implications of each approach in contemporary practice. Full article

(This article belongs to the Special Issue AI-Driven Advances in Computational Pathology)

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22 pages, 329 KB

Open AccessArticle

Religious–Moral Values in Inclusive Education: A Mixed-Methods Study of Romanian Special Education Teachers

by Dorin Opriş and Alina-Mihaela Corici

Religions 2026, 17(4), 489; https://doi.org/10.3390/rel17040489 (registering DOI) - 17 Apr 2026

Abstract

This study examines the role of religious–moral values in supporting the inclusion of students with special educational needs (SEN) within the broader framework of inclusive education. Using a sequential explanatory mixed-methods design, the research combines a qualitative phase based on semi-structured interviews with special education teachers (N = 9 participants) and a quantitative phase involving a questionnaire administered to a larger sample (N = 324 respondents). The qualitative findings indicate that teachers associate religious–moral values with the development of socio-emotional competencies, such as empathy, respect, solidarity, and a sense of belonging, which are considered essential for inclusion. The quantitative results support these perspectives, showing high levels of agreement regarding the contribution of these values to fostering positive attitudes, social acceptance, and the classroom integration of students with SEN. The findings also suggest that teachers attribute greater importance to core values than to formal religious instruction and prefer adaptive, student-centered strategies, including narrative and experiential approaches. Overall, the study highlights the potential of religious–moral values as a resource for inclusive education when applied in a flexible, interdisciplinary, and context-sensitive manner. These findings contribute to ongoing discussions on the role of religion in education, particularly in relation to inclusion, equality, and respect for diversity. Full article

(This article belongs to the Section Religions and Humanities/Philosophies)

14 pages, 548 KB

Open AccessReview

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development

by Mia Yang Ang and Siew Woh Choo

Biology 2026, 15(8), 632; https://doi.org/10.3390/biology15080632 (registering DOI) - 17 Apr 2026

Abstract

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure–activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development. Full article

(This article belongs to the Section Medical Biology)

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20 pages, 1413 KB

Open AccessArticle

Risk Reduction Evaluation of Prescriptive Technical Codes for Hydrogen Refueling Stations Using LOPA

by Yonggyu Kim, Jongbeom Park, Shintak Han, Heewon Song, Heesoo Chung, Keunwon Lee, Gwyam Shin and Seungho Jung

Energies 2026, 19(8), 1933; https://doi.org/10.3390/en19081933 (registering DOI) - 17 Apr 2026

Abstract

This study evaluates the risk reduction performance of prescriptive technical codes applied to hydrogen refueling stations using a Layer of Protection Analysis (LOPA) approach. A representative accident scenario involving high-pressure hose rupture at the dispenser was selected as the initiating event, and the [...] Read more.

1.0 \times 10^{- 6}

/year, resulting in a required risk reduction factor (RRF) of

1.0 \times 10^{4}

. Safety devices specified in the Korean Gas Safety (KGS) Codes were identified as independent protection layers (IPLs), and their probability of failure on demand (PFD) values were assigned based on commonly accepted LOPA data. The combined PFD of the identified IPLs was estimated to be

1.0 \times 10^{- 5}

, leading to a mitigated event likelihood of

1.0 \times 10^{- 7}

/year, which satisfies the predefined TMEL. These results indicate that the prescriptive technical codes can provide a certain level of quantitative risk reduction when all required safeguards operate as assumed. However, the analysis also reveals structural limitations associated with independence assumptions, potential common cause failures, and maintenance conditions. The findings suggest that integrating functional safety concepts and systematic risk assessment with prescriptive codes could enhance the reliability of safety management for hydrogen refueling stations. Full article

(This article belongs to the Special Issue Advances in Hydrogen Storage: Strategies, Technologies, Safety and Future Directions)

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25 pages, 1338 KB

Open AccessArticle

Dimensional Synthesis and Optimization of Leading and Mixed-Leading Double Four-Bar Steering Mechanisms: A Comparative Metaheuristic Approach

by Yaw-Hong Kang and Da-Chen Pang

Machines 2026, 14(4), 445; https://doi.org/10.3390/machines14040445 (registering DOI) - 16 Apr 2026

Abstract

This study investigates the dimensional synthesis and optimization of multi-link steering mechanisms—namely, the leading and mixed-leading double four-bar configurations—for front-wheel-drive vehicles. To overcome the accuracy limitations of conventional steering at large angles (up to 70°), a comparative metaheuristic approach is employed, utilizing two popular metaheuristic optimizations, Improved Particle Swarm Optimization (IPSO) and Differential Evolution with golden ratio (DE-gr), to optimize the geometric parameters of these complex eight-bar steering systems. Using a track-to-wheelbase ratio of 0.5, the optimization minimizes a mean-squared structural-error objective function integrated with Grashof mobility constraints. The optimized mechanisms are validated via ADAMS kinematic simulations and further analyzed in MATLAB R2021 regarding steering accuracy, transmission angles, and mechanical advantage. The results reveal a distinct performance trade-off: mixed-leading configurations achieve superior geometric precision and mass reduction due to shorter link lengths, with IPSO yielding the highest accuracy. Conversely, leading-type mechanisms provide a more linear and stable mechanical advantage, ensuring predictable force transmission. While DE-gr exhibits faster convergence across both variants, both algorithms effectively exploit the complex parameter space of multi-link systems. Ultimately, this metaheuristic optimization-based approach offers a superior and robust framework for the dimensional synthesis of high-performance multi-link steering mechanisms, surpassing the constraints of traditional gradient-based methods. Our findings recommend the mixed-leading configuration for precision-focused applications and the leading configuration for scenarios requiring consistent mechanical performance. Full article

(This article belongs to the Special Issue Artificial Intelligence-Enabled Vehicle Systems: Modeling, Control Optimization and Fault Diagnosis)

21 pages, 293 KB

Open AccessArticle

Association Between Nutritional Risk and Mental Health in Older Adults: Focusing on Depression and Cognitive Function

by Seohyeon Cho, Keon Woo and Yoonsoo Choy

Healthcare 2026, 14(8), 1062; https://doi.org/10.3390/healthcare14081062 (registering DOI) - 16 Apr 2026

Abstract

Background: In the context of global population aging, nutritional risk has emerged as an important factor associated with both physical and mental health among older adults. This study aimed to examine the associations between nutritional risk, depression, and cognitive function in older adults and to explore potential variations across residential area, educational attainment, employment status, frailty status, and activities of daily living (ADL). Methods: Data were obtained from 9955 community-dwelling older adults aged 65 years and older who participated in the 2023 National Survey of Older Koreans. Nutritional risk was assessed using the DETERMINE checklist (21-point scale), a multidimensional screening tool reflecting dietary, functional, and social risk factors. Depression was measured using the Short-form Geriatric Depression Scale (15-point scale), and cognitive function was assessed using the Korean version of the Mini-Mental State Examination-2 (K-MMSE-2; 30-point scale). Hierarchical multiple linear regression, correlation, subgroup, and sensitivity analyses were conducted, adjusting for sociodemographic characteristics, health behaviors, and geriatric factors. Results: Correlation analyses showed significant associations between nutritional risk and cognitive function (r = −0.191, p < 0.05), nutritional risk and depression (r = 0.440, p < 0.05), and depression and cognitive function (r = −0.259, p < 0.05). Higher nutritional risk scores were significantly associated with greater depressive symptoms (B = 0.314, p < 0.001) and lower cognitive function (B = −0.051, p < 0.05). While some subgroup differences were observed, not all interaction effects reached statistical significance, and these findings should be interpreted with caution. Conclusions: These findings suggest that nutritional risk is associated with depressive symptoms and cognitive function in older adults. Given that the DETERMINE checklist reflects multidimensional vulnerability, the results should be interpreted as indicating broader risk contexts rather than direct nutritional status alone. These findings highlight the importance of integrated, multidimensional approaches to support older adults at nutritional risk in community settings. Full article

(This article belongs to the Section Mental Health and Psychosocial Well-being)

23 pages, 3446 KB

Open AccessArticle

Quality by Design-Based Scale-Up and Industrial Development of Turmeric Extract-Loaded Nanostructured Lipid Carriers

by Wipanan Jandang, Phennapha Saokham, Chidchanok Prathumwon, Siriporn Okonogi and Chadarat Ampasavate

Pharmaceutics 2026, 18(4), 492; https://doi.org/10.3390/pharmaceutics18040492 (registering DOI) - 16 Apr 2026

Abstract

Background/Objectives: A robust and scalable manufacturing framework for lipid-based nanocarriers remains a critical challenge, particularly for labile phytochemicals such as curcuminoids in turmeric. This study presents an integrated Quality by Design (QbD)-driven and Outcome-Based Design (ObD) strategy to establish a scalable, resource-efficient manufacturing process for curcuminoids-loaded nanostructured lipid carriers (NLCs). Methods: To overcome the limitations of conventional multivariate design of experiments (DOE), which require extensive experimental runs, a risk-based, knowledge-driven single-factor screening approach was employed. Guided by risk assessment tools, including Ishikawa diagrams and failure mode considerations, 12 representative processing conditions were selected to define the design space. Critical quality attributes (CQAs), namely, particle size, polydispersity index (PDI), and zeta potential, were predefined to establish a robust control strategy. A two-step homogenization process—high-shear homogenization (HSH) for pre-emulsification followed by high-pressure homogenization (HPH) for nanoscale refinement—was systematically optimized. Results: Multivariate data analysis using principal component analysis (PCA) and hierarchical cluster analysis (HCA) identified key critical process parameters (CPPs), particularly HSH speed, processing time, and HPH cycles, as dominant factors influencing nanoparticle characteristics. The optimized 1-h process enabled successful scale-up of NLCs from 100 g to 5000 g, demonstrating the capability to generate nanosized particles within 100–500 nm. The combined HSH–HPH approach produced smaller, more uniform nanoparticles with high encapsulation efficiency and physical stability, outperforming HSH alone. Conclusions: Overall, this study establishes a practical and industrially viable framework that integrates QbD principles with data-driven optimization tools, for enabling reliable translation from laboratories to semi-industrial production. Full article

(This article belongs to the Special Issue Feature Reviews in Pharmaceutical Technology, Manufacturing and Devices)

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23 pages, 516 KB

Open AccessArticle

Edge-Centric Federated Subgraph Isomorphism Counting via Residual Graph Neural Networks

by Jianjun Shi, Qinglong Wu and Xinming Zhang

Information 2026, 17(4), 375; https://doi.org/10.3390/info17040375 - 16 Apr 2026

Abstract

Subgraph isomorphism counting is a fundamental yet computationally challenging task in graph analysis, with broad applications in bioinformatics and social network mining. With the tightening of data privacy regulations and the emergence of data silos, traditional centralized Graph Neural Network (GNN) approaches face significant deployment hurdles. Existing federated subgraph counting methods are primarily designed for database federation scenarios, focusing on exact queries and the privacy and security concerns of databases. However, this rigid focus on exactness and heavy cryptographic security severely limits their scalability and generalizability to complex, arbitrary query patterns. To bridge this gap, we propose a general Federated Edge-Centric Framework for Subgraph Isomorphism Counting (FedCount), shifting the paradigm from exact querying on federated databases to neural approximate counting under federated architectures. Rather than relying on heavy cryptographic techniques, we exclusively leverage the inherent structural isolation of federated learning as a lightweight empirical privacy measure. While this framework does not theoretically defend against advanced gradient-based inference attacks, it successfully prevents the direct leakage of raw graph topology and node features, achieving high-precision approximate counting without the prohibitive cryptographic overheads. Specifically, we introduce two key technical innovations to enhance local counting capability: (1) we integrate a provable edge encoding scheme into the interaction-based GNN architecture, explicitly modeling edge-to-edge interactions to break the expressiveness bottleneck of standard message passing; (2) we design a Residual Edge-Centric Readout mechanism that mitigates the gradient vanishing problem, enabling the effective training of deeper networks to capture high-order topological dependencies. Extensive experiments on multiple benchmark datasets demonstrate that our framework significantly outperforms existing distributed enumeration baselines in terms of generalization and efficiency, approaching the performance of centralized state-of-the-art models. Full article

(This article belongs to the Special Issue Graph Learning and Graph Neural Networks: Techniques and Applications)

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14 pages, 573 KB

Open AccessArticle

Multifunctional Peptides from Equine Milk Lactoferrin: Evaluation of Antimicrobial Activity In Silico and In Vitro

by Meiramkul Narmuratova, Shara Atambayeva, Gulzhan Kaiyrmanova, Saltanat Orazova, Gulzhan Narmuratova and Bernard Faye

Animals 2026, 16(8), 1223; https://doi.org/10.3390/ani16081223 - 16 Apr 2026

Abstract

The rapid global spread of antimicrobial resistance among pathogenic microorganisms poses a serious challenge to both human and animal health, underscoring the urgent need for new strategies to combat resistance. Antimicrobial peptides (AMPs), key components of the innate immune system, are promising candidates because they disrupt the membranes of bacteria, fungi, and viruses, thereby reducing the risk of resistance development. Lactoferrin (LF), a multifunctional iron-binding glycoprotein abundant in mammalian milk, is a rich source of AMPs. Cationic peptide fragments such as lactoferricin and lactoferrampin exhibit more potent direct antimicrobial activity than the intact protein. Our previous studies have shown that peptides derived from Equine milk lactoferrin exhibit antihypertensive, anti-inflammatory, and anti-oncogenic activity in silico, highlighting their multifunctional bioactive potential. Building on these results, the present study aims to investigate the antimicrobial properties of these peptides. We used an integrated approach combining computer modeling and in vitro studies to identify and validate novel antimicrobial peptides from equine milk lactoferrin. Bioinformatics tools, including AMPScanner and CAMP, were used to predict antimicrobial domains, followed by experimental testing against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. The results showed that equine milk lactoferrin peptides possess potent and selective antimicrobial activity, with efficacy varying across bacterial species. These data expand the functional profile of lactoferrin-derived peptides, demonstrating their multifunctionality, and suggest that equine milk lactoferrin represents a promising natural source of antimicrobial agents, supporting alternative strategies to reduce antibiotic use in human and veterinary medicine. Full article

(This article belongs to the Section Equids)

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19 pages, 545 KB

Open AccessReview

Per- and Polyfluoroalkyl Substances in Fish: Global Occurrence, Bioaccumulation, Analytical Approaches, and Human Exposure Risks—A Review

by Ines Varga, Nina Bilandžić, Jelena Kaurinović, Andrea Gross Bošković and Tomislav Klapec

Toxics 2026, 14(4), 336; https://doi.org/10.3390/toxics14040336 - 16 Apr 2026

Abstract

Per- and polyfluoroalkyl substances (PFAS) are highly stable and persistent environmental contaminants. Their exceptional chemical stability prevents natural breakdown, leading to global distribution and bioaccumulation in aquatic organisms. Long-chain PFAS, such as perfluorooctane sulfonic acid (PFOS), tend to accumulate in the liver, kidneys, and muscle tissues, whereas short-chain PFAS remain largely dissolved in water and show lower accumulation. Freshwater fish generally contain higher PFAS levels than marine fish, with concentrations varying according to species, habitat, trophic level, contamination site, and other factors. Human exposure primarily occurs through the consumption of contaminated fish and seafood, as well as through drinking water, inhalation, and skin contact. Such exposure is associated with immunosuppression, high cholesterol, hormonal disruption, cancer, and other health risks. Regulatory limits exist for four PFAS compounds, while many others, including emerging compounds, remain unregulated. This review synthesizes the current knowledge on the global distribution of PFAS across various fish species, analytical approaches including sample preparation (e.g., SPE, QuEChERS) and instrumental techniques (e.g., LC-MS/MS, HRMS), human dietary exposure, and the related health risks. By integrating environmental distribution, bioaccumulation, analytical challenges, and health issues, this review provides an up-to-date perspective on PFAS in fish and emphasizes the need for ongoing monitoring and stricter regulatory frameworks to ensure food safety and protect both human health and ecosystems. Full article

(This article belongs to the Special Issue Persistent Pollutants and Biotoxins in Aquatic Environments: Ecotoxicological and Human Health Perspectives)

22 pages, 2845 KB

Open AccessArticle

Development and Comprehensive Evaluation of 3D-Printed Prosthetic Feet: Modeling, Testing and a Pilot Gait Study

by Anton Kurakin, Anton Sergeev, Darya Korostovskaya, Anna Kurenkova and Vladimir Serdyukov

Prosthesis 2026, 8(4), 40; https://doi.org/10.3390/prosthesis8040040 - 16 Apr 2026

Abstract

Background/Objectives: The modern prosthetic foot market is characterized by a pronounced polarization between affordable but low-function devices and high-performance yet costly composite prostheses. The aim of this study was to develop and comprehensively evaluate cost-effective, functional prosthetic feet manufactured by fused deposition modeling (FDM). Methods: An iterative design methodology was employed, combining finite element analysis to optimize the biomechanical response of the device, the incorporation of user-specific requirements and experimental validation. Two TPU 95A-based 3D-printed prosthetic foot designs were designed and developed, and their strength and functional characteristics were assessed numerically under the ISO 22675:2024 normative loading cycle. Bench-top mechanical tests were conducted on the fabricated prototypes. Functional performance was evaluated by a transtibial amputee using an inertial motion capture system to analyze gait kinematics. Results: The results demonstrated that both designs operate predominantly within the elastic range with an adequate safety margin. The pilot feasibility gait assessment indicated feasibility and plausibility within the tested protocol and participant for both prototypes. Conclusions: The developed TPU 95A-based FDM prosthetic feet demonstrated promising structural integrity and functional feasibility, supporting the potential of low-cost additive manufacturing as a viable approach for producing affordable prosthetic feet. Further studies with larger participant cohorts and extended testing are needed to confirm clinical applicability and long-term performance. Full article

(This article belongs to the Section Orthopedics and Rehabilitation)

25 pages, 3645 KB

Open AccessArticle

Pervaporation Mixed Matrix Membranes from Sodium Alginate/ZnO for Isopropanol Dehydration

by Roman Dubovenko, Mariia Dmitrenko, Anna Mikulan, Olga Mikhailovskaya, Anna Kuzminova, Aleksandra Koroleva, Anton Mazur, Rongxin Su and Anastasia Penkova

Molecules 2026, 31(8), 1300; https://doi.org/10.3390/molecules31081300 - 16 Apr 2026

Abstract

In this work, sodium alginate (NaAlg) membranes were enhanced with synthesized zinc oxide (ZnO) nanoplates to enable efficient pervaporation dehydration of isopropyl alcohol (IPA). A comprehensive suite of characterisation techniques—scanning electron (SEM) and atomic force (AFM) microscopy, Fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), contact angle and liquid uptake measurements—along with density functional theory (DFT) calculations, was employed to establish robust structure–property relationships and to elucidate filler–polymer interactions. Membranes with different ZnO contents were prepared, and membranes based on the optimal NaAlg-ZnO(5%) composite were cross-linked with CaCl₂ to improve stability in aqueous solutions, and supported membranes were developed for prospective applications by applying this composite onto the prepared porous cellulose acetate (CA) substrate. This developed cross-linked supported NaAlg-ZnO(5%)/CA membrane had a permeation flux increased by 2 times or more compared to a dense NaAlg membrane during dehydration of IPA (12–30 wt.% water) with a permeate water content above 99 wt.%. The integrated experimental–theoretical approach provides mechanistic insight into ZnO–NaAlg interactions and demonstrates the strong potential of these mixed matrix membranes for high-efficiency alcohol dehydration, offering a rational design paradigm for next-generation pervaporation membranes. Full article

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