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Leading agribusiness firms are pivotal to modernizing agricultural supply chains, yet evidence on how digital transformation translates into high-quality development remains fragmented. Using a 2024 sample of 30 Chinese national agribusiness leaders and the technology–organization–environment (TOE) framework, we integrate grey relational analysis with DEMATEL to quantify interdependencies among conditions, and combine fuzzy-set QCA with necessary condition analysis to identify both configurational pathways and binding constraints. The results of the analysis indicate that high-quality development rarely stems from a single driver; it emerges from complementary bundles linking digital technologies and R&D investment with organizational readiness (e.g., talent and governance) under supportive external conditions (e.g., policy incentives and market pressure). The findings provide a configurational explanation of digital upgrading in agribusiness and inform differentiated digital strategies for managers and policymakers. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

19 pages, 1063 KB

Open AccessReview

Endocrine and Metabolic Modulation of Vascular Dysfunction in the Diabetic Foot: A Narrative Review

by Luca Galassi, Erica Altamura, Elena Goldoni, Gabriele Carioti, Beatrice Faitelli, Matteo Lino Ravini, Niccolò Le Donne and Kristi Nika

Endocrines 2026, 7(1), 4; https://doi.org/10.3390/endocrines7010004 (registering DOI) - 25 Jan 2026

Abstract

Diabetic foot complications represent a major global health burden and arise from a multifactorial interaction between neuropathy, ischemia, infection, and impaired wound repair. Increasing evidence suggests that, beyond traditional vascular and metabolic risk factors, endocrine dysregulation plays a central role in shaping vascular dysfunction and tissue vulnerability in patients with diabetes. This narrative review provides an updated overview of the endocrine–vascular axis in the development, progression, and healing of diabetic foot ulcers (DFUs), integrating evidence from experimental and clinical studies identified through targeted searches of PubMed, Embase, and Scopus. We examine how alterations in insulin signaling, relative glucagon excess, adipokine imbalance, dysregulation of stress hormones, and thyroid dysfunction interact with chronic hyperglycemia, dyslipidemia, mitochondrial dysfunction, and low-grade inflammation to impair endothelial homeostasis. These disturbances promote oxidative stress, reduce nitric oxide bioavailability, and compromise microvascular perfusion, thereby creating a pro-ischemic and pro-inflammatory tissue environment that limits angiogenesis, extracellular matrix (ECM) remodeling, immune coordination, and effective wound repair. By linking pathophysiological mechanisms to clinical relevance, this review highlights potential biomarkers of endocrine–vascular dysfunction, implications for risk stratification, and emerging therapeutic perspectives targeting metabolic optimization, endothelial protection, and hormonal modulation. Finally, key knowledge gaps and priority areas for future translational and clinical research are discussed, supporting the development of integrated endocrine-based strategies aimed at improving DFU prevention, healing outcomes, and long-term limb preservation in patients with diabetes. Full article

(This article belongs to the Section Obesity, Diabetes Mellitus and Metabolic Syndrome)

18 pages, 7389 KB

Open AccessArticle

Enhanced Deep Convolutional Neural Network-Based Multiscale Object Detection Framework for Efficient Water Resource Monitoring Using Remote Sensing Imagery

by Sultan Almutairi, Mashael Maashi, Hadeel Alsolai, Mohammed Burhanur Rehman, Hanadi Alkhudhayr and Asma A. Alhashmi

Remote Sens. 2026, 18(3), 404; https://doi.org/10.3390/rs18030404 (registering DOI) - 25 Jan 2026

Abstract

Water resource monitoring can provide beneficial information supporting water management; however, present operational systems are small and provide only a subset of the information needed. Primary advancements consist of the clear explanation of water redistribution and water use from groundwater and river schemes, achieving better spatial detail and increased precision as evaluated against hydrometric observation. In such cases, Earth Observation (EO) satellite systems are persistently creating extensive data, which is now essential for applications in different fields. With readily available open-source satellite imagery, aerial remote sensing is progressively becoming a quick and efficient tool for monitoring land and water resource development actions, demonstrating time and cost savings. At present, the deep learning (DL) model will be beneficial for monitoring water resources and EO utilizing remote sensing. In this paper, a Deep Neural Network-Based Object Detection for Water Resource Monitoring and Earth Observation (DNNOD-WRMEO) model is introduced. The main intention is to develop an effective monitoring and analysis framework for water resources and Earth surface observations using aerial remote sensing images. Initially, the Wiener filter (WF) model was used for image pre-processing. For object detection, the Yolov12 method was used for identifying, locating, and classifying objects within an image, followed by the DNNOD-WRMEO methodology, which implements the ResNet-CapsNet model for the backbone feature extraction method. Finally, the temporal convolutional network (TCN) model was implemented for the classification of water resources. The comparison analysis of the DNNOD-WRMEO methodology exhibited a superior accuracy value of 98.61% compared with existing models under the AIWR dataset. Full article

(This article belongs to the Special Issue Remote Sensing in Natural Resource and Water Environment II)

38 pages, 6300 KB

Open AccessArticle

Fused Unbalanced Gromov–Wasserstein-Based Network Distributional Resilience Analysis for Critical Infrastructure Assessment

by Iman Seyedi, Antonio Candelieri and Francesco Archetti

Mathematics 2026, 14(3), 417; https://doi.org/10.3390/math14030417 (registering DOI) - 25 Jan 2026

Abstract

Identifying critical infrastructure in transportation networks requires metrics that can capture both the topological structure and how demand is redistributed during disruptions. Conventional graph-theoretic approaches fail to jointly quantify these vulnerabilities. This study presents a computational framework for edge-criticality assessment based on the Fused Unbalanced Gromov–Wasserstein (FUGW) distance, incorporating both structural similarity and demand characteristics of network nodes in an optimal transport tool. The three hyperparameters that influence FUGW accuracy—fusion weight, entropic regularization, and marginal penalties—were tuned using Bayesian optimization. This ensures the rankings remain accurate, stable, and reproducible under temporal variability and demand shifts. We apply the framework to a benchmark transportation network evaluated across four diurnal periods, capturing dynamic congestion and shifting demand patterns. Systematic variation in the fusion parameter shows seven consistently critical edges whose rankings remain stable across analytical configurations. It can be concluded from the results that monotonic scaling with increasing feature emphasis, strong cross-hyperparameter correlation, and low temporal variability confirm the robustness of the inferred criticality hierarchy. These edges represent both structural bridges and demand concentration points, offering α indicators of network vulnerability. These findings demonstrate that FUGW provides a solid and scalable method of assessing transportation vulnerabilities. It helps support clear decisions on maintenance planning, redundancy, and resilience investments. Full article

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17 pages, 1912 KB

Open AccessArticle

Kaempferia parviflora Extract Stabilizes Cartilage Homeostasis via TIMP-1–Associated Matrix Modulation in Monosodium Iodoacetate–Induced Rat Osteoarthritis

by DongHoon Lee, Jong Seong Ha, Anna Jo, HyeMin Seol, JiSoo Han, Seong-Un Jeong, Seol-Ji Baek and Wan-Su Choi

Pharmaceuticals 2026, 19(2), 206; https://doi.org/10.3390/ph19020206 (registering DOI) - 25 Jan 2026

Abstract

Background: Osteoarthritis (OA) is a degenerative joint disease characterized by extracellular matrix (ECM) breakdown, inflammation, and pain-associated functional impairment. Current pharmacological treatments primarily provide symptomatic relief without preventing cartilage degeneration. Kaempferia parviflora extract (KPE), rich in polymethoxyflavonoids, has been reported to have anti-inflammatory properties; however, its in vivo effects on cartilage homeostasis in OA remain incompletely defined. Methods: A monosodium iodoacetate (MIA)–induced rat model of knee OA was used to evaluate the therapeutic effects of KPE. Following OA induction, rats received oral KPE at low, medium, or high doses for 19 days. Pain-associated functional impairment was assessed by static weight-bearing analysis. Cartilage integrity was evaluated histologically, serum inflammatory and cartilage degradation biomarkers were quantified, and expression of matrix-degrading enzymes and their endogenous inhibitor, tissue inhibitor of metalloproteinase-1 (TIMP-1), was analyzed in articular cartilage. Results: MIA injection induced marked joint dysfunction, including an approximately 50% reduction in weight bearing on the affected limb. While KPE did not significantly reduce acute knee swelling, all KPE doses significantly improved weight-bearing imbalance compared with MIA controls. Histological analysis demonstrated preservation of cartilage structure and proteoglycan content in KPE-treated groups. Serum CTX-II levels were significantly reduced across all KPE doses, indicating attenuation of collagen degradation. Systemic inflammatory markers showed differential modulation: significant reductions in serum CRP and COX-2 at medium and high doses, while PGE2 showed a consistent downward trend that did not reach statistical significance. In articular cartilage, KPE treatment restored TIMP-1 expression, whereas modulation of individual MMPs was modest and variable. Conclusions: KPE alleviates OA-associated functional impairment and cartilage degeneration in an experimental OA model. The therapeutic effects are associated with reinforcement of TIMP-1–mediated matrix homeostasis and modulation of inflammatory pathways, supporting the potential of KPE as a natural adjunct candidate for OA management. Full article

(This article belongs to the Special Issue Anti-Inflammatory and Antithrombotic Natural Bioactives for Drug Development: Current Status and Future Perspectives)

22 pages, 8921 KB

Open AccessArticle

Delineating Bird Ecological Networks in Coastal Areas Based on Seasonal Variations and Ecological Guilds Differences

by Songyao Huai and Qianshuo Zhao

Animals 2026, 16(3), 380; https://doi.org/10.3390/ani16030380 (registering DOI) - 25 Jan 2026

Abstract

Habitat fragmentation and human disturbance pose major challenges to bird movement and ecological connectivity, highlighting the need for effective ecological network construction in conservation planning. Although coastal ecological networks have received increasing attention, few studies have simultaneously examined seasonally explicit patterns, functional guild differences, and seasonally varying recreational disturbance. Using a coastal case study, we analyzed seasonal (spring, summer, autumn, winter) and guild-specific (wading birds, songbirds, raptors, and swimming birds) variations in bird ecological networks by integrating systematic field surveys (2023–2024) with citizen science records (2020–2025). Results indicated clear differences among guilds and seasons: swimming birds exhibited relatively complex and well-connected networks, whereas wading birds showed lower connectivity. Conservation priority areas varied markedly across seasons, being more extensive in spring (28.62%), autumn (23.69%), and winter (22.09%), but substantially reduced in summer (17.07%). Our findings provide a locally grounded reference for adaptive conservation planning in rapidly changing coastal landscapes, with particular attention to the protection and connectivity of coastal and estuarine wetlands for wading birds. Full article

(This article belongs to the Special Issue Multi-Sensor Remote Sensing and Ecological Models for Bird and Wildlife Conservation)

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16 pages, 303 KB

Open AccessArticle

Teachers’ Reporting Attitude Scale for Child Sexual Abuse: An Italian Adaptation

by Matteo Angelo Fabris, Claudio Longobardi, Sofia Mastrokoukou, Bruno Luiz Avelino Cardoso and Diego Costa Lima

Behav. Sci. 2026, 16(2), 168; https://doi.org/10.3390/bs16020168 (registering DOI) - 25 Jan 2026

Abstract

This study presents an Italian adaptation of the Teachers’ Reporting Attitude Scale for Child Sexual Abuse (TRAS-CSA), aiming to assess teachers’ attitudes towards reporting suspected cases of child sexual abuse (CSA) and to explore the scale’s psychometric properties in the Italian context. Given the critical role schools play in identifying and addressing CSA, understanding teachers’ attitudes is vital for improving reporting rates and protecting victims. A sample of 1318 Italian teachers (12.8% male; age: 25–65; M = 46.71; SD = 10.25) from various educational levels participated in the study. Exploratory factor analysis identified two primary factors: Awareness of Role and Commitment to Reporting (Factor 1) and Concern and Distrust Towards Reporting (Factor 2). Results indicated that male teachers demonstrated significantly higher scores on the concern factor, while teachers from secondary schools exhibited higher commitment to reporting compared to those from preschool and primary levels. The adapted TRAS-CSA demonstrates solid psychometric properties, providing a valuable tool for future research and intervention strategies in Italy to enhance awareness and action against child sexual abuse within educational settings. Implications for educational policies and teacher training frameworks are discussed to bolster the preventive efforts against CSA. Full article

15 pages, 237 KB

Open AccessArticle

Prenatal Microarray Analysis of Pregnancies Without Ultrasound Anomalies: Establishment of Copy Number and Homozygosity Frequencies in Low-Risk Population

by Stuart Schwartz and Robert G. Best

Genes 2026, 17(2), 127; https://doi.org/10.3390/genes17020127 (registering DOI) - 25 Jan 2026

Abstract

Objectives: The overall objective of this study is to examine prenatal patients ascertained without an abnormal ultrasound (US) or an abnormal cell-free DNA (cfDNA) finding to provide a unique understanding of pathogenic copy number variants, identity by descent (IBD) and variants of uncertain significance (VUSs) in a normal population. Methods: This study retrospectively provides an analysis of over 28,362 prenatal specimens ascertained without an abnormal US or abnormal cfDNA finding utilizing an SNP microarray. These specimens include at least 10 different ascertainment groups, including advanced maternal age (AMA), anxiety, abnormal maternal serum screen (MSS) with/without AMA, and a previous or familial child/pregnancy with a chromosome abnormality or a genetic disorder. Results: This study provides a basic understanding of pathogenic copy number variants (CNVs), homozygosity and VUSs in an essentially normal population. This low-risk population has a frequency of pathogenic CNVs of ~1.26%; however, ~52% were associated with neurodevelopmental microdeletions/microduplications and ~13% were associated with incidental findings. Overall, ~1.32% of these patients showed an increase in homozygosity, the majority due to consanguinity. Lastly, VUSs were seen in 1.41% of this group, of which ~90% were familial. Conclusions: Overall, these findings provide a better estimate of the baseline frequencies and types of pathogenic CNVs and homozygosity in a low-risk population. It provides insight into the distribution of stretches of homozygosity associated with identity by descent in this population and gives a better understanding of the extent of variants of uncertain significance in phenotypically unaffected individuals. Full article

(This article belongs to the Special Issue Cytogenetics and Cytogenomics in Clinical Diagnostics: Innovations and Applications)

31 pages, 4489 KB

Open AccessArticle

A Hybrid Intrusion Detection Framework Using Deep Autoencoder and Machine Learning Models

by Salam Allawi Hussein and Sándor R. Répás

AI 2026, 7(2), 39; https://doi.org/10.3390/ai7020039 (registering DOI) - 25 Jan 2026

Abstract

This study provides a detailed comparative analysis of a three-hybrid intrusion detection method aimed at strengthening network security through precise and adaptive threat identification. The proposed framework integrates an Autoencoder-Gaussian Mixture Model (AE-GMM) with two supervised learning techniques, XGBoost and Logistic Regression, combining deep feature extraction with interpretability and stable generalization. Although the downstream classifiers are trained in a supervised manner, the hybrid intrusion detection nature of the framework is preserved through unsupervised representation learning and probabilistic modeling in the AE-GMM stage. Two benchmark datasets were used for evaluation: NSL-KDD, representing traditional network behavior, and UNSW-NB15, reflecting modern and diverse traffic patterns. A consistent preprocessing pipeline was applied, including normalization, feature selection, and dimensionality reduction, to ensure fair comparison and efficient training. The experimental findings show that hybridizing deep learning with gradient-boosted and linear classifiers markedly enhances detection performance and resilience. The AE–GMM-XGBoost model achieved superior outcomes, reaching an F1-score above 0.94 ± 0.0021 and an AUC greater than 0.97 on both datasets, demonstrating high accuracy in distinguishing legitimate and malicious traffic. AE-GMM-Logistic Regression also achieved strong and balanced performance, recording an F1-score exceeding 0.91 ± 0.0020 with stable generalization across test conditions. Conversely, the standalone AE-GMM effectively captured deep latent patterns but exhibited lower recall, indicating limited sensitivity to subtle or emerging attacks. These results collectively confirm that integrating autoencoder-based representation learning with advanced supervised models significantly improves intrusion detection in complex network settings. The proposed framework therefore provides a solid and extensible basis for future research in explainable and federated intrusion detection, supporting the development of adaptive and proactive cybersecurity defenses. Full article

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30 pages, 3807 KB

Open AccessReview

Flapping Foil-Based Propulsion and Power Generation: A Comprehensive Review

by Prabal Kandel, Jiadong Wang and Jian Deng

Biomimetics 2026, 11(2), 86; https://doi.org/10.3390/biomimetics11020086 (registering DOI) - 25 Jan 2026

Abstract

This review synthesizes the state of the art in flapping foil technology and bridges the distinct engineering domains of bio-inspired propulsion and power generation via flow energy harvesting. This review is motivated by the observation that propulsion and power-generation studies are frequently presented separately, even though they share common unsteady vortex dynamics. Accordingly, we adopt a unified unsteady-aerodynamic perspective to relate propulsion and energy-extraction regimes within a common framework and to clarify their operational duality. Within this unified framework, the feathering parameter provides a theoretical delimiter between momentum transfer and kinetic energy extraction. A critical analysis of experimental foundations demonstrates that while passive structural flexibility enhances propulsive thrust via favorable wake interactions, synchronization mismatches between deformation and peak hydrodynamic loading constrain its benefits in power generation. This review extends the analysis to complex and non-homogeneous environments and identifies that density stratification fundamentally alters the hydrodynamic performance. Specifically, resonant interactions with the natural Brunt–Väisälä frequency of the fluid shift the optimal kinematic regimes. The present study also surveys computational methodologies and highlights a paradigm shift from traditional parametric sweeps to high-fidelity three-dimensional (3D) Large-Eddy Simulations (LESs) and Deep Reinforcement Learning (DRL) to resolve finite-span vortex interconnectivities. Finally, this review outlines the critical pathways for future research. To bridge the gap between computational idealization and physical reality, the findings suggest that future systems prioritize tunable stiffness mechanisms, multi-phase environmental modeling, and artificial intelligence (AI)-driven digital twin frameworks for real-time adaptation. Full article

(This article belongs to the Special Issue Bio-Inspired Flapping Wing Aerodynamics for Propulsion and Power Generation: 2nd Edition)

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14 pages, 21879 KB

Open AccessArticle

Comparison of Different Numbers of White Base Coat Layers on Metallized Cardboard for Obtaining High Print Quality After Rubbing

by Dino Priselac, Maja Rudolf, Ivana Plazonić and Irena Bates

Coatings 2026, 16(2), 158; https://doi.org/10.3390/coatings16020158 (registering DOI) - 25 Jan 2026

Abstract

Metallized papers or cardboards, used when high barrier properties are required in packaging, are usually coated with white ink prior to printing to ensure accurate colors and high print quality. The coating provides well-controlled sorption properties at a certain thickness, allowing for better printability and reduced penetration of ink components into the substrate. The white ink used for coating ensures the dimensional stability of the substrate after the drying process is complete. This research compares how different numbers of white base coat layers affect the print quality of multicolor offset prints onto metallized cardboard after rubbing. A high print quality assessment after rubbing was obtained based on spectrophotometric and gloss measurements. A comparison of the number of white base coat layers on metallized cardboard indicated that multicolor prints with two base coat layers have lower reflectance, better color stability, and high print quality after rubbing. Gloss measurements showed that prints with one layer of white base coat exhibited higher gloss values, while rubbing led to a moderate increase in gloss for all samples. Ultimately, a thicker layer of white base coat enhances mechanical resistance while maintaining acceptable optical properties in multicolor prints on metallized cardboards. Full article

(This article belongs to the Section Environmental Aspects in Colloid and Interface Science)

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26 pages, 2167 KB

Open AccessArticle

AI-Powered Service Robots for Smart Airport Operations: Real-World Implementation and Performance Analysis in Passenger Flow Management

by Eleni Giannopoulou, Panagiotis Demestichas, Panagiotis Katrakazas, Sophia Saliverou and Nikos Papagiannopoulos

Sensors 2026, 26(3), 806; https://doi.org/10.3390/s26030806 (registering DOI) - 25 Jan 2026

Abstract

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article

(This article belongs to the Section Sensors and Robotics)

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19 pages, 1494 KB

Open AccessReview

The Ketogenic Diet in Type 2 Diabetes and Obesity: A Narrative Review of Clinical Evidence

by Julia Kilian, Dominika Szlęzak, Malgorzata Tyszka-Czochara, Elżbieta Filipowicz-Popielarska and Patrycja Bronowicka-Adamska

Nutrients 2026, 18(3), 397; https://doi.org/10.3390/nu18030397 (registering DOI) - 25 Jan 2026

Abstract

Type 2 diabetes mellitus (T2DM) and obesity represent a growing global public health challenge, strongly associated with excess body weight, unhealthy dietary habits, and a sedentary lifestyle. The ketogenic diet (KD), characterized by very low carbohydrate intake, moderate protein intake, and high fat consumption, induces a metabolic state known as ketosis, in which the body switches from glucose to fat as its primary energy source. KD has gained increasing interest as a strategy to improve glycemic control, reduce body weight, and improve lipid profiles in individuals with obesity and T2DM. The purpose of this narrative review is to summarize the current scientific evidence on the effects of KD on key metabolic parameters, including blood glucose levels, glycated hemoglobin (HbA1c), body weight, and body composition. The analysis is based on peer-reviewed articles retrieved from PubMed, Embase, and Scopus with particular emphasis on clinical studies that provide robust evidence on the efficacy and safety of KD in the treatment of metabolic disorders. Full article

(This article belongs to the Special Issue Endocrine Disturbances and Nutritional Therapies)

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17 pages, 4618 KB

Open AccessArticle

A Method for Identification and Adjustment of Key Variables for Power Flow Convergence in Bulk Power Systems Based on Unbalanced Power Characteristics of Intermediate Power Flow

by Yuxi Fan and Yibo Zhou

Energies 2026, 19(3), 628; https://doi.org/10.3390/en19030628 (registering DOI) - 25 Jan 2026

Abstract

In the operation mode arrangement of bulk power systems, unreasonable reactive power injection data at nodes tend to result in power flow calculation non-convergence. Owing to the extremely high dimension of the variable space and the heterogeneous impacts of different variables on power flow convergence, it is imperative to accurately identify the key variables inducing non-convergence and provide physical justifications. For this purpose, this paper proposes a data-driven key variable identification and adjustment method: firstly, based on the blocking cut-set theory and the characteristic that the active unbalanced power ΔP of intermediate power flow exhibits opposite signs at the sending and receiving ends of the cut-set, a blocking cut-set identification method leveraging the characteristics of the active unbalanced power of intermediate power flow is developed; secondly, relying on the feature that the reactive unbalanced power ΔQ of intermediate power flow is less than zero, a key variable identification method based on the characteristics of the reactive unbalanced power of intermediate power flow is presented; finally, a key variable adjustment method grounded in the numerical value of ΔQ is proposed. The validity of the proposed approach was validated via simulated computations using both the IEEE 39 bus system and a practical bulk power system. Full article

(This article belongs to the Section F1: Electrical Power System)

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40 pages, 8707 KB

Open AccessReview

Interphase-Centric and Mechanism-Driven Advances in Polymer Composites Reinforced with Nano-, Synthetic, and Inorganic Fillers

by Sachin Kumar Sharma, Lokesh Kumar Sharma, Reshab Pradhan, Yogesh Sharma, Mohit Sharma, Sandra Gajević, Lozica Ivanović and Blaža Stojanović

Polymers 2026, 18(3), 323; https://doi.org/10.3390/polym18030323 (registering DOI) - 25 Jan 2026

Abstract

Polymer composites reinforced with nanofillers, synthetic fibers, and inorganic fillers have progressed rapidly, yet recent advances remain fragmented across filler-specific studies and often lack unified mechanistic interpretation. This review addresses this gap by presenting an interphase-centric, mechanism-driven framework linking processing routes, dispersion and functionalization requirements, interphase formation, and the resulting structure–property relationships. Representative quantitative datasets and mechanistic schematics are integrated to rationalize nonlinear mechanical reinforcement, percolation-controlled electrical/thermal transport, and thermal stabilization and barrier effects across major filler families. The review highlights how reinforcement efficiency is governed primarily by interfacial adhesion, filler connectivity, and processing-induced microstructural evolution rather than filler loading alone. Key challenges limiting scalability are critically discussed, including dispersion reproducibility, viscosity and processability constraints, interphase durability, and recycling compatibility. Finally, mechanism-based design rules and future outlook directions are provided to guide the development of high-performance, multifunctional, and sustainability-oriented polymer composite systems. Full article

(This article belongs to the Special Issue Sustainable and Functional Polymeric Nanocomposites)

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