Polymers最新文献

{"title":"Polymer Flooding for EOR: Field Applications, Recovery Performance, Injectivity, and Scale-Up Lessons (2005-2025).","authors":"Rafael E Hincapie","doi":"10.3390/polym18192443","DOIUrl":"10.3390/polym18192443","url":null,"abstract":"<p><p>Polymer flooding is one of the most widely applied chemical enhanced oil recovery (EOR) technologies and has progressed from conventional onshore sandstone applications to increasingly challenging reservoir environments. This study reviews global polymer flooding experience from 2005 to 2025 using a curated database of 54 documented field applications, including injectivity tests, pilots, and commercial projects across sandstone, carbonate, unconsolidated, heavy oil, offshore, high-salinity, and high-temperature reservoirs. Field data, laboratory investigations, and reported operational experience were evaluated to assess injectivity behavior, polymer retention, viscosity stability, recovery performance, surveillance practices, and implementation outcomes. The reviewed projects span reservoir temperatures from 16 to 124 °C and salinities from approximately 500 to 270,000 ppm total dissolved solids. The analysis shows that successful polymer flood applications are consistently associated with effective mobility control, sustained injectivity, appropriate polymer quality, surveillance-supported reservoir management, and disciplined operational execution. Advances in ATBS-containing and thermally stable polymers have expanded applicability into harsher reservoir conditions, although many developments remain at the injectivity assessment or pilot stage. The resulting synthesis provides a field-based framework linking reservoir conditions, injectivity, polymer performance, surveillance, and operational execution to support screening, qualification, design, scale-up, and decision-making for future polymer flood developments.</p>","PeriodicalId":20416,"journal":{"name":"Polymers","volume":"18 19","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149195638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Applications for Composite Materials: A Review.","authors":"Nada S Alharthi, Laila M Alqahtani, Albatool A Abaalkhail, Ibtehal S Baazeem, Mustafa Y Haddad, Mohammed T Alamoudi, Basheer A Alshammari","doi":"10.3390/polym18192439","DOIUrl":"10.3390/polym18192439","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Composite materials are not easy to model using traditional experiments and physics-based approaches due to their diverse properties and multiscale nature. This review summarizes how artificial intelligence (AI) methodologies, including machine learning, deep learning, and generative AI, have been increasingly transforming and how they have been increasingly applied to composite materials research. It combines their possible use across the prediction of their properties, damage detection, structural health monitoring, and materials design.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Searches were performed in Scopus and Google Scholar for articles published using combined keywords such as \"(artificial intelligence OR machine learning OR deep learning OR Generative AI) AND (composite OR fiber reinforced) AND (prediction OR optimization OR characterization)\". The search strategy was designed to capture both fundamental developments in AI methodologies relevant to materials research and their specific applications to composite materials. The identified publications underwent a two-step selection process: (i) an initial review of titles and abstracts, and (ii) a detailed full-text evaluation. Studies were included if they provided explicit descriptions of AI models, clearly specified datasets, and applied quantitative performance assessments to composite materials-related applications such as material property prediction, damage detection, characterization, or manufacturing process optimization. For each selected article, relevant information was extracted in a structured and consistent manner, including the type of composite material, AI methodology, input parameters, dataset characteristics and size, validation methods, and application domain. These data were used to support both quantitative trend analysis and the qualitative identification of research gaps and future research opportunities in composite materials research.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Supervised learning methods, in particular artificial neural networks, support vector machines, random forests, and decision trees, were commonly used and exhibited durable potential accuracy in predicting properties of composite materials. Unsupervised methods such as principal component analysis (PCA) and clustering were comparatively underused. Generative AI, including generative adversarial networks (GANs) and variational autoencoders (VAEs), and physics-informed models emerged as growing approaches for synthetic data generation and improved interpretability, respectively.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Limitations: &lt;/strong&gt;The reviewed studies were frequently limited by small or heterogeneous datasets, weak generalizability testing beyond training conditions, high computational requirements and limited model interpretability (\"black box\" behavior); a risk-of-bias assessment across included studies was not formally conducted.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;AI is shifting composite mat","PeriodicalId":20416,"journal":{"name":"Polymers","volume":"18 19","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13666869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical Laminate Screening of Pin-Loaded CFRP Driveshaft Joints Using Progressive Damage Analysis and Multi-Criteria Evaluation.","authors":"Liming Zheng, Lingze Yuan, Nengwen Wang, Xintian Liu","doi":"10.3390/polym18192435","DOIUrl":"10.3390/polym18192435","url":null,"abstract":"<p><p>Carbon fiber-reinforced polymer (CFRP) driveshafts offer significant lightweight potential, but the performance of pin-loaded joints is strongly affected by laminate architecture. To support laminate selection beyond peak strength alone, a numerical screening framework combining finite-element analysis, Hashin-based progressive damage modeling, and multi-criteria evaluation was developed. Seven representative layups were compared under axial compression considering off-axis fiber orientation, stacking order, hoop restraint, and mid-plane symmetry. The results showed that Layup 3 achieved the highest peak load of 28.38 kN but exhibited abrupt brittle failure, whereas Layup 1 reached 22.02 kN with more gradual post-peak degradation and localized progressive damage. Removal of hoop-oriented plies promoted longitudinal splitting-type deformation, while asymmetric stacking reduced deformation stability. By integrating peak load, effective energy absorption, and failure safety, Layup 1 achieved the highest comprehensive evaluation index of 0.612 and remained preferred when the safety weight exceeded approximately 0.2. Within the present numerical framework, the proposed approach provides a practical basis for preliminary laminate screening of CFRP driveshaft joints.</p>","PeriodicalId":20416,"journal":{"name":"Polymers","volume":"18 19","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical Recovery of Damaged Glass Fiber Reinforced Polypropylene via Repetitive Sub-Melting Thermal Annealing: A Molecular and Structural Study.","authors":"Tetsuo Takayama, Jun Funabashi","doi":"10.3390/polym18192441","DOIUrl":"10.3390/polym18192441","url":null,"abstract":"<p><p>This study clarifies the mechanical recovery and structural retention of damaged glass fiber-reinforced polypropylene (PP/GF) composites subjected to repetitive sub-melting thermal annealing at 160 °C. Initial thermal annealing increased matrix crystallinity from 0.30 to 0.37 (+23.3%), thereby elevating flexural strength from 103 MPa to 122 MPa (+18.4%). Following one repair cycle, flexural strength was completely restored to 103-105 MPa (100% recovery rate). However, after three repair cycles, short-duration annealing (120 s) resulted in severe mechanical degradation down to 30 MPa (-70.9%), whereas long-duration annealing (1800 s) retained a strength of 59 MPa (-42.7%, preserving 57.3% of initial strength). Concurrently, the crystallinity index increased to 0.49 (+63.3%) for 1800 s. These results establish that extended dwell time at sub-melting temperatures is essential for promoting molecular chain re-entanglement while suppressing void growth. These findings present an engineering guideline for low-energy, structure-preserving repair of thermoplastic composites.</p>","PeriodicalId":20416,"journal":{"name":"Polymers","volume":"18 19","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Qualification of a Swellable Hydrogel-Silicone Bimorph for Implantable Actuation.","authors":"Esma Dosdoğru, Adrian Onken, Sabine Hild, Patricia Torgau, David Schäffl, Jan Sündermann, Marc Müller, Johannes Aucktor, Hasso V Zychlinski, Theodor Doll","doi":"10.3390/polym18192437","DOIUrl":"10.3390/polym18192437","url":null,"abstract":"<p><p>To improve the structural adaptability and biological suitability of cochlear implant electrode carriers, this study systematically develops and evaluates novel 3D-printed, swellable hydrogel-silicone composites. A vinylphosphonic acid-acrylamide hydrogel powder (VPA-AAm) was pre-washed, milled, and sieved, incorporated into a silicone matrix, and 3D-printed before rapid thermal curing, followed by microstructural, swelling, and in vitro WST-1 cytotoxicity analyses. Pre-washing minimized particle agglomeration and facilitated high-resolution printing. Incorporating the hydrogel induced complete macroscopic opacity at 25 wt.% and localized microporosity during rapid thermal curing. The non-percolating morphology influenced swelling and mass transport through the composite, providing a mechanistic link between water uptake and the observed cell-viability response. While a 35 wt.% loading was associated with matrix degradation and cell viability below the 70% threshold, the 30 wt.% composition achieved the highest water absorption of approximately 400-460%, consistent with a favorable balance between osmotic swelling and matrix elasticity. This swelling behavior was associated with controlled release characteristics and non-cytotoxic extract responses at intermediate hydrogel loading. Consequently, the 30 wt.% composite represents a promising material combination for implantable medical devices, balancing swelling performance, structural stability, and favorable in vitro cell viability under the investigated extraction conditions.</p>","PeriodicalId":20416,"journal":{"name":"Polymers","volume":"18 19","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale Molecular Orientation in Polyacrylonitrile Precursors for Carbon Fibers: Processing Regulation, Characterization Methodologies, and Structure-Property Correlations.","authors":"Hailong Zhang, Chengyu Zhan, Ling Quan, Aijun Gao, Lianghua Xu","doi":"10.3390/polym18192442","DOIUrl":"10.3390/polym18192442","url":null,"abstract":"<p><p>Polyacrylonitrile (PAN) precursor fibers represent the dominant feedstock for manufacturing high-performance carbon fibers, and the hierarchical molecular orientation established during precursor processing fundamentally determines the tensile strength and elastic modulus of the final products. However, the precise regulation of multiscale orientational structures across spinning, hot-drawing, and thermal treatment stages remains a long-standing bottleneck for breaking the performance ceiling of carbon fibers. This review systematically elucidates the formation and evolution mechanisms of oriented structures at four hierarchical levels: molecular chains, crystalline domains, microfibrils, and fiber assemblies. Flow-induced orientation behaviors in three mainstream spinning routes (conventional wet spinning, dry-jet wet spinning, and electrospinning) are comparatively analyzed, followed by a discussion on crystallization-driven orientation during hot stretching and orientation inheritance throughout pre-oxidation and carbonization processes. Quantitative evidence from reported PAN precursor datasets confirms that increasing the orientation factor from 0.7 to 0.9 is associated with a tensile-strength gain from approximately 2.4 to 4.7 GPa and a modulus gain from 230 to 550 GPa in the corresponding carbon fibers, illustrating the magnitude of the orientation-performance relationship within specific material systems. We critically appraise widely used characterization techniques including X-ray scattering, polarized spectroscopy, optical birefringence, and electron microscopy, with particular focus on their spatial resolution, applicable scenarios, and inherent limitations, and propose combined characterization strategies for multiscale orientation analysis. Critical bottlenecks, including cross-scale modeling gaps, dynamic orientation kinetics under multi-field coupling, and industrial scalability limitations, are identified, and future research directions such as in situ synchrotron characterization and machine learning-assisted process optimization are outlined. This review builds a systematic framework linking molecular-level orientation control to macroscopic carbon fiber properties, providing theoretical guidance for the rational design of high-performance PAN precursors.</p>","PeriodicalId":20416,"journal":{"name":"Polymers","volume":"18 19","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Composition-Structure-Property Relationships in PLA-nHA-Mg Hybrid Biocomposites: Effect of Ceramic-Metallic Reinforcement Ratio on Mechanical and Physicochemical Performance.","authors":"K Hariharan, B Veera Siva Reddy, Dola Sundeep, C Chandrasekhara Sastry, Robert Cep","doi":"10.3390/polym18192438","DOIUrl":"10.3390/polym18192438","url":null,"abstract":"<p><p>Poly(lactic acid) (PLA) hybridized with nano-hydroxyapatite (nHA) and elemental magnesium (Mg) offers a route to tune load-bearing behavior while retaining a biodegradable polymer matrix. This study compares neat PLA, binary PLA-10Mg and PLA-10nHA controls, and three ternary hybrids containing a constant 10 wt.% total reinforcement at Mg:nHA ratios of 1:1, 3:1, and 1:3. Tensile, compressive, and three-point flexural responses were interpreted together with differential scanning calorimetry (DSC), X-ray diffraction (XRD), and Fourier-transform infrared (FTIR) spectroscopy. Ten specimens were tested per formulation in each mechanical assay (five initial specimens and five confirmatory specimens), and the mechanical data were consolidated across all ten specimens and are reported as an arithmetic mean ± standard deviation (SD). The recorded response was loading-mode-dependent: PLA-5Mg-5nHA combined a tensile stress of 52.0 ± 1.56 MPa with the largest tensile peak displacement ratio of 23.42 ± 0.70%; PLA-7.5Mg-2.5nHA showed the largest flexural peak load of 490.4 ± 14.71 N; and PLA-2.5Mg-7.5nHA combined 52.8 ± 1.58 MPa tensile stress, 93.7 ± 2.81 MPa compressive stress, and 431.5 ± 12.95 N flexural peak load. DSC showed a high-temperature heat-flow event at 365.6-372.6 °C under the stated mixed-purge conditions; this is treated as an operational thermal marker and not a melting or decomposition temperature. XRD remained matrix-dominated with reinforcement-sensitive features, while FTIR showed composition-dependent shifts in the C-O/C-O-C region. The results indicate that Mg:nHA partitioning alters the recorded loading-mode response within the tested 10 wt.% reinforcement window, while particle-scale mechanisms and biological performance require further validation.</p>","PeriodicalId":20416,"journal":{"name":"Polymers","volume":"18 19","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-Dimensional Monte Carlo Simulation of PHBV/TPU Blend Biodisintegration Under Composting Conditions: Role of TPU Network Percolation.","authors":"Idoia San Sebastian, Gaizka Erkizia, Eva Rodriguez Hortelano, Maider Iturrondobeitia","doi":"10.3390/polym18192440","DOIUrl":"10.3390/polym18192440","url":null,"abstract":"<p><p>Blending poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) with thermoplastic polyurethane (TPU) improves toughness but compromises compostability above a critical TPU content. To our knowledge, this is the first three-dimensional, voxel-based Monte Carlo (MC) framework for simulating the disintegration of PHBV/TPU blends under ISO 20200 composting conditions. It couples Monod-type microbial growth, a cardinal-temperature correction, biomass and moisture transport, and a blend morphology generated by random sequential adsorption (RSA) and calibrated against scanning electron microscopy (SEM) data. The transition between complete and incomplete disintegration is described by partitioning the TPU phase into reachable and trapped fractions derived from the connectivity of the generated TPU network. Calibrated against eight compositions (100/0 to 0/100; 58 °C; 41 days) with 22 adjusted parameters, the model yields a root-mean-square error (RMSE) of 3.97% over all 40 fitted points and reproduces complete disintegration up to 40 wt% TPU, the incomplete disintegration of the 50/50 blend and the minimal degradation of neat TPU. In a hold-out test, the compositions withheld from calibration (90/10 and 70/30) were predicted with an RMSE of 3.23%. Sensitivity and identifiability analyses reveal a composition-dependent parameter hierarchy, and virtual temperature experiments illustrate the thermal behaviour implied by the model assumptions.</p>","PeriodicalId":20416,"journal":{"name":"Polymers","volume":"18 19","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What Random Splits Measure in Polymer Glass-Transition Prediction: Chemistry Holdouts, Applicability Domains, and Prior Integration.","authors":"Deao Han, Yangkun Shao, Xueru Zhong, Zhanzhao Song, Quanzhong Zhang, Chuansheng Wang, Deshang Han, Lin Wang","doi":"10.3390/polym18192445","DOIUrl":"10.3390/polym18192445","url":null,"abstract":"<p><p>Predicting the glass-transition temperature (Tg) helps select polymers for a service-temperature window, but accuracy on familiar structures may not transfer to a new family. We compare validation protocols on 7367 curated homopolymers. Histogram gradient boosting gives R² = 0.899 and mean absolute error (MAE) = 23.9 K under random cross-validation, 0.857 and 28.5 K under scaffold holdout, a class-macro mean of 0.355 and 43.3 K under curator-class holdout, and -3.79 and 79.5 K under high-Tg extrapolation. Similarity explains only 6-11% of within-protocol absolute-error variance in the first three settings. A separate density functional theory (DFT) band-gap dataset shows a moderate fingerprint-cluster holdout penalty and poor property-range extrapolation for the tested baselines. Random-forest split-conformal empirical coverage falls from 0.899 under random splitting to 0.683 and 0.214 under class and property holdout; the tested prospective similarity gate provides little protection. A fold-fitted group-contribution prior is more sensitive to uniform additive bias in a squared-loss penalty than as a neural-network input, but this comparison is architecture-dependent. Class-holdout intervals and tests are descriptive or nominal because training sets overlap. The 279 multiply reported polymers support only the between-source variability analysis. Validation should match the intended polymer family and property range.</p>","PeriodicalId":20416,"journal":{"name":"Polymers","volume":"18 19","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149195788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Characterization of Carrageenan-Based Hydrogels Enriched with <i>Calendula officinalis</i> Extract for Topical Skin Applications.","authors":"Justyna Kozlowska, Anna Bakula, Katarzyna Noryca","doi":"10.3390/polym18192436","DOIUrl":"10.3390/polym18192436","url":null,"abstract":"<p><p>Carrageenan-based hydrogels are promising matrices for topical cosmetic applications because their highly hydrated polymer networks can be modified to incorporate bioactive compounds and adjust functional properties. In this study, <i>Calendula officinalis</i> flower extracts were incorporated into κ-carrageenan-based hydrogels either directly or after loading into chemically crosslinked gelatin microspheres. The obtained formulations were characterized in terms of morphology, compressive modulus, swelling behaviour, thermal stability, and selected biophysical skin parameters. The hydrogels showed formulation-dependent differences in surface morphology, mechanical properties, water uptake, and thermal behaviour. The compressive modulus ranged from 14.10 ± 1.15 to 23.20 ± 1.46 kPa, with the highest value observed for the hydrogel containing directly incorporated hydroglycolic extract. This formulation also exhibited the lowest degree of swelling, whereas higher swelling was generally observed in microsphere-containing systems. All formulations showed three main stages of thermal mass loss. Preliminary skin measurements showed no pronounced persistent increase in transepidermal water loss or marked changes in the A* parameter following hydrogel application. The results show that direct and microsphere-mediated incorporation represent distinct formulation strategies resulting in different physicochemical properties of carrageenan-based hydrogels. Preliminary skin measurements additionally provided initial information on their short-term response following topical application.</p>","PeriodicalId":20416,"journal":{"name":"Polymers","volume":"18 19","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13666785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}