大模型分子结构设计用于POSS聚氨酯阻燃和阻尼研究

… are introduced into polyurethane to improve flame resistance. The prepared polyurethane elastomer exhibits excellent comprehensive performance, featuring an effective damping (tanδ…

… POSS compounds to polymers provides a significant improvement in the properties of hybrid materials, such as thermal and mechanical stability, flammability resistance… of damping time …

ABSTRACT Polyurethane (PU) composites have emerged as versatile materials with superior mechanical, thermal, electrical, and functional properties due to the incorporation of advanced reinforcements such as graphene, carbon nanotubes (CNTs), metal oxides, and bio-based fillers. This review provides a comparative analysis of these reinforcements, highlighting their effectiveness in enhancing PU properties for diverse industrial applications. Graphene-based additives exhibit the highest improvement in mechanical performance, with tensile strength increasing by up to 320% and modulus by 40%. Multi-walled carbon nanotubes (MWCNTs) enhance electrical conductivity by 70% and improve electromagnetic interference (EMI) shielding effectiveness by 50%, making them superior for electronic applications. In contrast, nano-clays and silica-based fillers significantly improve thermal insulation, reducing thermal conductivity by 40% while also increasing compressive strength and abrasion resistance. Metal oxide nanoparticles, such as boron nitride and molybdenum disulfide, contribute to 30% higher thermal stability, 50% improved flame retardancy, and enhanced corrosion resistance, making them ideal for protective coatings. Bio-based fillers, including cellulose nanofibrils, improve sustainability by increasing tensile strength by 15%, enhancing water resistance, and reducing environmental impact. Hybrid reinforcement strategies integrating multiple fillers demonstrate the highest versatility, achieving balanced mechanical, thermal, and electrical performance. Additionally, recent advancements in dispersion techniques have mitigated agglomeration challenges, ensuring uniform filler distribution and consistent property enhancement. Despite these advancements, scalability remains a key challenge, particularly in maintaining cost-effectiveness while integrating eco-friendly bio-based fillers. Future research should focus on optimizing recyclability and multifunctionality to develop next-generation high-performance PU composites tailored for aerospace, automotive, and biomedical applications. Graphical Abstract

… -functional polyurethane elastomer with high damping, water resistance and flame retardancy. … of epoxy/polyurethane grafted copolymer by adding glycidyl POSS. Chinese Journal of …

… the interaction between nanofillers and PU at the molecular levels, thereby enabling them to … various molecular modeling approaches along with the case studies on PU nanocomposites…

… of PU and POSS are elucidated from the molecular … of POSS on PU more intuitively, in the following, the structural characteristics of POSS and PU are introduced at the molecular level, …

The physical properties of silsesquioxane materials are commonly controlled by varying the proportion of silane coupling agents with different substituents. In contrast, with cage silsesquioxanes (polyhedral oligomeric silsesquioxane, POSS) that allow for precise molecular design, the material properties can be controlled by isomers, similar to organic materials. In this study, we synthesized polyurethanes with corner‐opened POSS (CO‐POSS) and completely condensed POSS (CC‐POSS) in the main and side chains, respectively. For the substituents on POSS, we used a combination of phenyl and isobutyl groups and investigated their effects on the physical properties by changing this combination. Notably, the thermal and mechanical properties changed in polymers where the substituents of CO‐POSS (main chain) and CC‐POSS (side chain) were interchanged. This demonstrates that the isomeric structure significantly affects the properties of silsesquioxane‐based materials and provides important design guidelines for these materials.

… of polyhedral oligomeric silsesquioxane (POSS) molecules with different functional groups (… Incorporating POSS into the PU matrix had multiple benefits for the coating material, such as …

Polyurethane elastomer (PUE), which is widely used in coatings for construction, transportation, electronics, aerospace, and other fields, has excellent physical properties. However, polyurethane elastomers are flammable, which limits their daily use, so the flame retardancy of polyurethane elastomers is very important. Reactive flame retardants have the advantages of little influence on the physical properties of polymers and low tendency to migrate out. Due to the remarkable needs of non-halogenated flame retardants, phosphorus flame retardant has gradually stood out as the main alternative. In this review, we focus on the fire safety of PUE and provide a detailed overview of the current molecular design and mechanisms of reactive phosphorus-containing, as well as P-N synergistic, flame retardants in PUE. From the structural characteristics, several basic aspects of PUE are overviewed, including thermal performance, combustion performance, and mechanical properties. In addition, the perspectives on the future advancement of phosphorus-containing flame-retarded polyurethane elastomers (PUE) are also discussed. Based on the past research, this study provides prospects for the application of flame-retarded PUE in the fields of self-healing materials, bio-based materials, wearable electronic devices, and solid-state electrolytes.

… structure–property relationships based on available polymer … the screening of promising polymers that satisfy the targeted … , the combinable polymer genes can be optimized through …

Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid‐state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer–biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine–human interface are shared.

The properties of polymer materials are closely related to their structures. A deep understanding of quantitative relationships between the structures and properties of polymers is crucial for the design and preparation of high-performance polymer materials. However, these relationships are inherently complex and difficult to model with limited trial and error experimental data. In recent years, machine learning (ML) has become an effective multidimensional relationship modeling method, playing an important role in the construction of quantitative relationships between the structures and properties of polymer materials. This review first provides an overview of the ML workflow, with a focus on the feature engineering of polymers and commonly used ML algorithms in the application of ML processes. Afterward, the progress of ML in the quantitative relationship between the structures and properties of polymer materials was summarized and evaluated from the aspects of mechanical properties, thermal conductivity, glass transition temperature (Tg), compatibility, dielectric properties, and refractive index of polymers. Finally, the application prospects of ML in polymer material research were proposed.

Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, and models. They can target various definitions, distributions, and correlations of concerned physical parameters in given polymer systems, and have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages in building quantitative multivariate correlations have largely enhanced the capability of scientific understanding and discoveries, thus facilitating mechanism exploration, target prediction, high‐throughput screening, optimization, and rational and inverse designs. This article summarizes representative progress in the recent two decades focusing on the design, preparation, application, and sustainable development of polymer materials based on the exploration of key physical parameters in the composition–process–structure–property–performance relationship. The integration of both data‐driven and physical insights through ML approaches to deepen fundamental understanding and discover novel polymer materials is categorically presented. Despite the construction and application of robust ML models, strategies and algorithms to deal with variant tasks in polymer science are still in rapid growth. The challenges and prospects are then presented. We believe that the innovation in polymer materials will thrive along the development of ML approaches, from efficient design to sustainable applications.

Artificial intelligence (AI) plays a significant role in advancing polymer science and engineering. Considering the critical role of the glass transition temperature (Tg) in determining the physical properties of polymers, this study systematically investigates the influence of their composition and sequence structure on Tg using machine learning (ML) models. To clarify the complex relationship between polymer composition and Tg, the k-nearest neighbor mega-trend diffusion (kNNMTD) method was employed for data augmentation, and various ML models were constructed for Tg prediction. Among them, the Random Forest model demonstrated the best performance for the generated data, achieving an R2 of 0.85 and an RMSE of 0.38. To explore the effect of polymer sequence structure on Tg, we further introduced natural language processing (NLP) techniques to represent polymer sequences. The data was augmented using the Wasserstein generative adversarial network (GAN) with gradient penalty (WGAN-GP) model, and Tg predictions were made using a convolutional neural network-long short-term memory (CNN-LSTM) model. This integrated framework achieved excellent predictive performance, with an R2 of 0.95 and an RMSE of 0.23, and demonstrated strong generalization across different data sets. In summary, this study introduces an innovative application of kNNMTD for augmenting polymer composition data combined with NLP techniques for representing polymer sequences. The proposed ML framework offers a valuable contribution to the advancement of polymer material design and optimization.

… extract structure–property relationships, predict polymer properties, and optimize molecular … This section focuses on property prediction and thus specifically refers to structure–property …

The traditional research paradigm for polymer materials relies heavily on time-consuming and inefficient trial-and-error methods, which are no longer sufficient to meet the demands of modern research and development. With the rapid advancement of big data and artificial intelligence technologies, machine learning has emerged as a powerful data analysis tool, revolutionizing polymer material research and development. This paper provides an overview of machine learning techniques, summarizes common machine learning algorithms, and reviews recent progress in machine learning-assisted polymer material design and development. Key areas include polymer sequence design, material property prediction, classification and identification, and applications leveraging computer vision technologies. Furthermore, this study discusses several critical challenges currently faced by the field and offers perspectives on future directions .

Molecular descriptors and machine learning are useful tools for extracting structure–property relationships from large, complex polymer data, and accelerating the design of novel polymers with tailored functionalities.

Polymers have a wide range of applications in materials science, chemistry, and biomedical domains. Conventional design methods for polymers are mostly event-oriented, directed by intuition, experience, and abstract insights. Nevertheless, they have been effectively utilized to determine several essential materials; these techniques are facing important challenges owing to the great requirement of original materials and the huge design area of organic polymers and molecules. Enhanced and inverse materials design is the best solution to these challenges. With developments in high-performing calculations, artificial intelligence (AI) (particularly Deep learning (DL) and Machine learning (ML))-aided materials design is developing as a promising tool to show development in various domains of materials science and engineering. Several ML and DL methods are established to perform well for polymer classification and detection presently. In this paper, we design and develop a Simplified Molecular Input Line Entry System Based Polymer Property Detection and Classification Using Pareto Optimization Algorithm (SMILES-PPDCPOA) model. This study presents a novel deep learning framework tailored for polymer property classification using SMILES input. By integrating a one-dimensional convolutional neural network (1DCNN) with a gated recurrent unit (GRU) and optimizing the model via Pareto Optimization, the SMILES-PPDCPOA model demonstrates superior classification accuracy and generalization. Unlike existing methods, our model is designed to capture both local substructures and long-range chemical dependencies, offering a scalable and domain-specific solution for polymer informatics. Furthermore, the proposed SMILES-PPDCPOA model executes a one-dimensional convolutional neural network and gated recurrent unit (1DCNN-GRU) technique for the classification process. Finally, the Pareto optimization algorithm (POA) adjusts the hyperparameter values of the 1DCNN-GRU algorithm optimally and results in greater classification performance. Results on a benchmark dataset show that SMILES-PPDCPOA achieves an average classification accuracy of 98.66% (70% Training, 30% Testing) across eight polymer property classes, with high precision and recall metrics. Additionally, it demonstrates superior computational efficiency, completing tasks in 4.97 s, outperforming other established methods such as GCN-LR and ECFP-NN. The experimental validation highlights the potential of SMILES-PPDCPOA in polymer property classification, making it a promising approach for materials science and engineering. The simulation result highlighted the improvement of the SMILES-PPDCPOA system when compared to other existing techniques.

Artificial intelligence (AI) technology has made remarkable progress in polymer materials, which has changed polymer science significantly. However, this community still relies heavily on the traditional research paradigm instead of the data-driven paradigm. This review advocates for a fundamental paradigm shift in polymer research from traditional experience-driven methods to data-driven approaches enabled by AI. While AI has made transformative advances in polymer design, property prediction, and process optimization, the field remains anchored in conventional methodologies. AI’s computational advantages against persistent barriers are also evaluated, such as data scarcity, inadequate material descriptors, and algorithmic complexity. Potential solutions, including collaborative data platforms, domain-adapted descriptor frameworks, and active learning strategies, are also discussed. Furthermore, we demonstrate how high-quality data and explainable AI methodologies overcome computational limitations while ensuring result credibility in other areas, which can benefit polymer research. Ultimately, this work provides a roadmap for accelerating the sustainable convergence of data-driven AI innovation with polymer science.

… predicted optimal interfacial properties of the composite with PDA and 3-glycidyloxypropyl-POSS (… efficient and straightforward approach to the interfacial modification of the composites. …

… polyurethanes (LCPU) modified by trisilanolphenyl-POSS … Using partially cross-linked POSS particles, an enhancement … In summary, it was found that the addition of POSS causes a …

Molecular generative models based on deep learning have increasingly gained attention for their ability in de novo polymer design. However, there remains a knowledge gap in the thorough evaluation of...

A grand challenge in polymer science lies in the predictive design of new polymeric materials with targeted functionality. However, de novo design of functional polymers is challenging due to the vast chemical space and an incomplete understanding of structure–property relations. Recent advances in deep generative modeling have facilitated the efficient exploration of molecular design space, but data sparsity in polymer science is a major obstacle hindering progress. In this work, we introduce a vast polymer database known as the Open Macromolecular Genome (OMG), which contains synthesizable polymer chemistries compatible with known polymerization reactions and commercially available reactants selected for synthetic feasibility. The OMG is used in concert with a synthetically aware generative model known as Molecule Chef to identify property-optimized constitutional repeating units, constituent reactants, and reaction pathways of polymers, thereby advancing polymer design into the realm of synthetic relevance. As a proof-of-principle demonstration, we show that polymers with targeted octanol–water solubilities are readily generated together with monomer reactant building blocks and associated polymerization reactions. Suggested reactants are further integrated with Reaxys polymerization data to provide hypothetical reaction conditions (e.g., temperature, catalysts, and solvents). Broadly, the OMG is a polymer design approach capable of enabling data-intensive generative models for synthetic polymer design. Overall, this work represents a significant advance, enabling the property targeted design of synthetic polymers subject to practical synthetic constraints.

… applied to generative molecular design, elucidating their … (‘Generative molecular design tasks’ section). We categorize models by design criteria and highlight trade-offs (‘Generative …

Generative artificial intelligence (GenAI) models have emerged as a transformative tool for addressing the complex challenges of drug discovery, enabling the design of structurally diverse, chemically valid, and functionally relevant molecules. Despite significant advancements, the rapid expansion of GenAI applications still faces challenges related to prediction accuracy, molecular validity, and optimization for drug-like properties. This review provides a comprehensive analysis of recent techniques and strategies aimed at enhancing the performance of GenAI models in molecular design. We explore key generative architectures, including variational autoencoders, generative adversarial networks, and transformer-based models, highlighting their unique contributions to drug discovery. Additionally, we discuss critical advancements such as reinforcement learning, multi-objective optimization, and the integration of domain-specific chemical knowledge, which collectively enhance molecular validity, novelty, and drug-likeness. Also, the review examines persistent challenges, including data quality limitations, model interpretability, and the need for improved objective functions, while offering insights into future research directions. By mapping the evolving landscape of GenAI-driven molecular design and providing strategic guidance for overcoming existing limitations, this review serves as an essential resource for researchers leveraging GenAI in drug discovery.

… in synthesis prediction and molecular optimization. For generative molecular design, the … While SMILES 24,25 has long been the de facto standard for encoding molecules/polymers, …

In this work, we introduce a polymer discovery platform to efficiently design polymers with tailored properties, exemplified by the discovery of high-performance polymer electrolytes. The platform integrates three core components: a conditioned generative model, a computational evaluation module, and a feedback mechanism, creating a self-improving system for material innovation. To demonstrate the efficacy of this platform, it is used to design polymer electrolyte materials with high ionic conductivity. A simple conditional generative model, based on the minGPT architecture, can effectively generate candidate polymers that exhibit a mean ionic conductivity that is significantly greater than those in the original training set. This approach, coupled with molecular dynamics simulations (MD) for testing and a specifically planned acquisition mechanism, allows the platform to refine its output iteratively. Notably, after the first iteration, we observed an increase in both the mean and the lower bound of the ionic conductivity of the new polymer candidates. The platform's effectiveness is underscored by the identification of 14 polymer repeating units, each displaying a computed ionic conductivity surpassing that of Polyethylene Oxide (PEO). The performance of these polymers in MD simulations verifies the platform's efficacy in generating potential polymer candidate materials. Acknowledging current limitations, future work will focus on enhancing modeling techniques, evaluation processes, and acquisition strategies, aiming for broader applicability in polymer science and machine learning.

Designing polymers for targeted applications and accurately predicting their properties is a key challenge in materials science owing to the vast and complex polymer chemical space. While molecular language models have proven effective in solving analogous problems for molecular discovery, similar advancements for polymers are limited. To address this gap, we propose polyBART, a language model-driven polymer discovery capability that enables rapid and accurate exploration of the polymer design space. Central to our approach is Pseudo-polymer SELFIES (PSELFIES), a novel representation that allows for the transfer of molecular language models to the polymer space. polyBART is, to the best of our knowledge, the first language model capable of bidirectional translation between polymer structures and properties, achieving state-of-the-art results in property prediction and design of novel polymers for electrostatic energy storage. Further, polyBART is validated through a combination of both computational and laboratory experiments. We report what we believe is the first successful synthesis and validation of a polymer designed by a language model, predicted to exhibit high thermal degradation temperature and confirmed by our laboratory measurements. Our work presents a generalizable strategy for adapting molecular language models to the polymer space and introduces a polymer foundation model, advancing generative polymer design that may be adapted for a variety of applications.

The BigSMILES notation, a concise tool for polymer ensemble representation, is augmented here by introducing an enhanced version called generative BigSMILES. G-BigSMILES is designed for generative workflows, and is complemented...

This work introduces, for the first time, an innovative bio-based flame retardant (FR) system for biocomposites, integrating experimental insights and machine learning (ML) to optimize both composition and performance. By...

… The design of flame-retardant polymeric composites (FRPC) … In this work, we performed data-driven multi-objective … of the approach in designing the FRPC for industrial applications. …

The growing demand for lightweight, high-performance, and fire-safe polymer materials has accelerated research into advanced flame-retardant composites. Traditional experimental approaches to designing sustainable flame-retardant biodegradable polymer composites still rely heavily on empirical formulation and iterative testing, which are time-consuming and costly, and they often struggle to capture the coupled effects of chemical composition, processing conditions, and material performance. Recent advances in artificial intelligence (AI) provide opportunities to address these challenges by learning formulation–structure–performance relationships from curated datasets and by translating materials chemistry and flame-retardant mechanisms into data-ready descriptors and targets. This review summarizes recent progress of AI-assisted approaches to design sustainable flame-retardant biodegradable polymer composites, emphasizing machine learning, deep learning, and active learning methods for predicting and optimizing key fire performance metrics, including limiting oxygen index and heat release-related parameters. Biodegradable-specific limitations, including narrow processing window, thermal degradation, and moisture sensitivity, are discussed in the content of descriptor selection and constraint-aware optimization, together with the role of interpretable/explainable models in supporting experimentally actionable guidance. Current challenges such as limited data availability, protocol variability, model transferability, and interpretability are highlighted, and emerging solutions, including data harmonization, standardized fire testing, and physics-informed models are outlined. AI-assisted strategies are expected to play a central role in accelerating efficient, sustainable, halogen-free, and performance-driven development of next-generation flame-retardant biodegradable polymer composites.

… into flame-retardant mechanisms … design of flame-retardant polypropylene materials. This work provides a transferable strategy for the efficient development of polymer flame-retardant …

It is of significant importance to design flame-retardant polymeric composites (FRPCs) with superior flame retardancy and appropriate mechanical properties. However, discovering such materials is often reliant on serendipity, as the conventional “trial-and-error” approach is inadequate for navigating the vast virtual space. To overcome this challenge, we propose an active generative design framework to accelerate the development of FRPCs within the expansive virtual space. This framework operates as a closed-loop system, integrating machine learning, knowledge-embedded generative model, and experimental exploration. Through this approach, we derived two interpretable linear expressions and identified a key composition threshold that when the mass fraction of zinc stannate is below 2.5% and that of piperazine pyrophosphate exceeds 12.5%, the flame retardancy of polypropylene (PP)-based FRPCs is significantly enhanced. By processing and characterizing 10 FRPCs, we successfully designed two composites with flame retardancy improved by 1% compared to the top-performing reference FRPC in the initial dataset - without compromising mechanical properties. This work effectively resolves the trade-off between flame retardancy and mechanical performance at a low cost, demonstrating a promising pathway for the accelerated discovery of PP-based FRPCs with balanced properties.

… mechanisms but also integrating emerging data-driven methodologies such as deep learning, … Traditional flame retardants and flame-retardant polymeric materials struggle to meet the …

… the future design direction of nano flame retardants, concentrating … flame-retardant polymer composites but also provided a strong foundation for further development of big-data-driven …

… The design of novel flame-retardant systems compatible with diverse … of data-driven approaches, machine learning has opened new avenues for the prediction of flame-retardant …

Machine learning algorithms have emerged as an effective and popular decision-making tool for solving complicated engineering problems and challenges. Although introducing these algorithms can accelerate the optimization of fire retardants for polymeric materials by replacing traditional tedious and time-consuming trial-and-error methods, this tool remains at the elementary stage of designing fire retardants for polymeric materials, and thus to date there is a lack of insightful yet review on this topic. Herein, we review the most practical and accurate algorithms used to predict flame retardancy features, such as limiting oxygen index (LOI) and cone calorimetry results, of their polymeric materials. We highlight the merits of some current algorithms, including artificial neural network (ANN), Lasso, Ridge, ANN (L-ANN), and extreme gradient boosting (XGB). Finally, key challenges with existing algorithms for predicting next-generation fire retardants, followed by some proposed solution and future directions. This review will help expedite the development of optimized fire retardants accelerated by machine learning.

Organic polymer materials, as the most abundantly produced materials, possess a flammable nature, making them potential hazards to human casualties and property losses. Target polymer design is still hindered due to the lack of a scientific foundation. Herein, we present a robust, generalizable, yet intelligent polymer discovery framework, which synergizes diverse capabilities, including the in situ burning analyzer, virtual reaction generator, and material genomic model, to achieve results that surpass the sum of individual parts. Notably, the high-throughput analyzer created for the first time, grounded in multiple spectroscopic principles, enables in situ capturing of massive combustion intermediates; then, the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information; further, the proposed feature engineering tool, which embedded both polymer hierarchical structures and massive intermediate data, develops the generalizable genomic model with excellent universality (adapting over 20 kinds of polymers) and high accuracy (88.8%), succeeding discovering series of novel polymers. This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.

Understanding how dangling fragments of polyurethanes (PU) affect its microphase separation structure and damping properties can provide insights for designing desired materials at the molecular scale. By varying the types of diol extenders (such as 1,2‐ethyleneglycol, 1,2‐propanediol, and 1,2‐butanediol), PU samples with different dangling residues were successfully synthesized. Using atomic force microscopy and small‐angle x‐ray scattering, we confirmed that the introduction of dangling chains disrupts microphase separation and demonstrated a correlation between the degree of suppressed microphase separation and the size of the dangling residues. Fourier transform infrared spectroscopy and molecular dynamics simulations confirmed that dangling chains reduce the hydrogen bonding index while increasing the phase compatibility between soft and hard segments. Differential scanning calorimetry (DSC) measurements revealed an increased glass transition temperature (Tg), indicating hindered movement of backbone segments due to dangling chains. Moreover, lengthening the dangling chains further decreased Tg. Dynamic mechanical analysis (DMA) demonstrated improved damping properties with the introduction of dangling chains, although increasing chain length led to deteriorating damping properties, consistent with observations from DSC. These findings suggest that the variations in macroscopic properties of PU induced by dangling chains are linked to hydrogen bonding interactions and micromorphology at the molecular level.

… traditional AA simulations are limited by computational efficiency and are unable to simulate … target RDF from the reference AA trajectory, α is a damping factor (set as 0.2 in this work (29)…

… computation times associated with the MD method complicate the attainment of complex combinatorial results for the various components of polyurethane … multicomponent polyurethane …

… This study provides a strategic approach for enhancing the flame-retardant efficacy of GO-based fillers, along with remarkable advancements in smoke suppression, sound absorption, …

… This work highlights MA-POSS as a versatile modifier for VER, enabling intrinsic flame retardancy, water resistance, and optical stability, with potential as a standalone thermoset in high-…

Inadequate fire resistance of polymers raises questions about their advanced applications. Flexible polyurethane (PU) foams have myriad applications but inherently suffer from very high flammability. Because of the dependency of the ultimate properties (mechanical and damping performance) of PU foams on their cellular structure, reinforcement of PU with additives brings about further concerns. Though they are highly flammable and known for their environmental consequences, rubber wastes are desired from a circularity standpoint, which can also improve the mechanical properties of PU foams. In this work, melamine cyanurate (MC), melamine polyphosphate (MPP), and ammonium polyphosphate (APP) are used as well-known flame retardants (FRs) to develop highly fire-retardant ground tire rubber (GTR) particles for flexible PU foams. Analysis of the burning behavior of the resulting PU/GTR composites revealed that the armed GTR particles endowed PU with reduced flammability expressed by over 30% increase in limiting oxygen index, 50% drop in peak heat release rate, as well as reduced smoke generation. The Flame Retardancy Index (FRI) was used to classify and label PU/GTR composites such that the amount of GTR was found to be more important than that of FR type. The wide range of FRI (0.94–7.56), taking Poor to Good performance labels, was indicative of the sensitivity of flame retardancy to the hybridization of FR with GTR components, a feature of practicality. The results are promising for fire protection requirements in buildings; however, the flammability reduction was achieved at the expense of mechanical and thermal insulation performance.

… polyurethane chains to form a network structure named CNTs-PDA-PU, which improved the ability of polyurethane … enhanced the mechanical and damping properties of nanocomposites…

… hygrothermal ageing resistance and flame retardancy are … polyurethane (FTPU) and synergistic flame retardant. The … ) can meet UL94 V-0 flame-retardant standard while the counterpart …

… silsesquioxane (T 7 -POSS). The interlayer adhesion and … materials obtained by pyrolyzing POSS and aligned CF. The … Furthermore, during the printing process, POSS decomposes …

… In a water environment at 25 C, the PU-0.5%SiO 2 obtained a certain degree of water swelling resistance compared to PU, and the water swelling resistance of the PUCW-SiO 2 series …

Biologically based polyamide 1012 (PA1012) challenges limitations in fire-sensitive applications due to its inherent flammability, while conventional flame retardants compromise mechanical/dielectric performance. Herein, based on an ingenious micromorphology design, a POSS derivative submicrometer sphere architecture (POSS(Ph-Li)-POM(Mo)@POSS(Ph-Li)) coated with a Janus supramolecular nanolayer was designed to address the critical dilemma of balancing flame retardancy, mechanical integrity, and dielectric performance in PA1012. The Janus architecture conferred dual functionality: lithium-phenyl-decorated POSS strengthened interfacial compatibility with the PA1012 matrix, while polyoxometalate (POM) clusters catalyzed char formation through radical scavenging and carbonization catalysis. In this article, H3PMo12O40 and Li-Ph-POSS were assembled into a composite flame retardant, POSS(Ph-Li)-POM(Mo)@POSS(Ph-Li), using an electrostatic self-assembly method, and the composite flame retardant was prepared by blending with PA1012. Crucially, incorporating 10 wt % of POSS(Ph-Li)-POM(Mo)@POSS(Ph-Li) into PA1012, the composite retained 91% of its tensile strength (45.15 MPa vs 49.69 MPa), while the dielectric constant decreased to 3.09-2.73 compared with 3.70-3.26 for PA1012. Additionally, thermal analysis revealed an 8.4 wt % increase in residual char yield at 800 °C. Combustion performance tests indicated that the addition of POSS(Ph-Li)-POM(Mo)@POSS(Ph-Li) significantly enhanced the flame retardancy of PA1012, elevating the limiting oxygen index (LOI) from 23.6% to 26.2%. This improvement was attributed to the rapid formation of compact char layers during combustion, which effectively suppressed fire propagation by reducing the peak heat release rate (p-HRR), peak smoke production rate (p-SPR), and peak CO production rate (p-COP) by 39.7%, 35.4%, and 53.3%, respectively. This work developed a paradigm for creatingmultifunctional flame retardants through Janus supramolecular engineering, simultaneously addressing the critical challenges of flammability mitigation and performance preservation in biologically based polyamide.

… capability to polyurethane elastomers. (21) These studies highlight the growing interest in developing intrinsic flame retardants that can endow PU with both good fire retardancy and …

… Polyhedral oligomeric silsesquioxane (POSS), as a nanofiller, offers excellent thermal stability and flame retardancy, yet studies on the electro-thermal aging resistance and chemical …

… is introduced to enhance the flame retardancy of thermoplastic polyurethane (TPU) using a … impact for CPP@ZrP nanohybrid in dampening the emission of combustible fragments, …

… , PU/M-GNP nanocomposites showed exceptional resistance to acidic and alkaline environments in comparison to neat PU. The … aerospace, vibration damping, and protective coatings. …

… to polyurethane foams, such as improvements in the thermal, mechanical, fire resistance and … Thus, this article presents a systematic review of graphene-modified polyurethane foam …

… This produced three different protective materials (POSS-PDMS, POSS-APTS, and POSS-AHAPTMS) that greatly improved the weather resistance of sandstone monuments, especially …

… improve the flame resistance of flexible polyurethane foam by incorporating or adding flame retardants into the polymer network of flexible polyurethane foams. It is in this way that liquid …

Polyhedral oligomeric silsesquioxanes (POSS) are a class of well‐defined organic–inorganic stereo molecules comprising inorganic SiOSi cores and peripheral organic moieties. Since they were first reported in 1946 by Scott et al., there have been a great number of investigations on the use of POSS macromers as the building blocks to access the organic–inorganic composites with polymers. In most of cases, monofunctional POSS macromers are employed and the linear hybrid polymers are obtained. Under this circumstance, POSS cages act as the side or end groups whereas the main chains of the polymers remain unchanged. Occasionally, octafunctional POSS macromers are involved, resulting in the generation of crosslinked (or network‐like) hybrids. Recently, well‐defined difunctional POSS macromers have increasingly provoked a considerable attention of investigators. From the synthetic methodology of POSS macromers to the approaches to introduce them into polymers, difunctional POSS macromers have the features quite different from mono‐ (or octa‐) functional POSS. More importantly, the related organic–inorganic hybrids possess the different morphologies and properties. In the past years, there has been a rapid increase in the number of literatures on the studies on well‐defined difunctional POSS and the related organic–inorganic hybrids. Nonetheless, the related review is lacking. In this contribution, we would summarize the recent progress in this regard, from the synthesis of POSS macromers, the approaches of introducing the POSS macromers into polymers to the correlation of morphologies with properties of the organic–inorganic hybrids. In addition, perspectives and challenges for the further advancement are envisaged and discussed.

Abstract POSS-DGEBA was synthesized by the ring opening reaction of the epoxy group of bisphenol A diglycidyl ether (DGEBA) with the amine group of aminopropyl isobutyl polyhedral oligomeric silsesquioxane (NH2-POSS). POSS-DGEBA was incorporated to PU as the chain extender for improving thermal stability and the shape memory behavior in hybrid polyurethane. PU prepolymer was prepared by the reaction of MDI with polyols having different chain length which are poly(ethylene glycol) or poly(tetramethylene ether) glycol. The hybrid polyurethanes (HPU-PEG and HPU-PTMG) with different contents of POSS-DGEBA were fabricated by using PU prepolymer, a chain extender, and different mole ratios of POSS-DGEBA.

ABSTRACT Polyhedral oligomeric silsesquioxanes (POSS) are promising nanostructured additives for enhancing the mechanical, thermal, and flame-retardant properties of epoxy resins, though their tendency to aggregate and induce phase separation often limits their performance. In this study, a novel POSS derivative functionalized with eight long, flexible polyether chains terminating in amine groups (OPEA-POSS) was synthesized and employed as a co-curing agent for epoxy resin. The structure of OPEA-POSS was confirmed via FT-IR, 1H-NMR, 13C-NMR, GPC and XRD. Differential Scanning Calorimetry (DSC) analysis identified 5 wt% OPEA-POSS (S2) as the optimal composition, with increased curing enthalpy and a higher glass transition temperature (Tg) compared to neat epoxy (S0), despite a lower crosslinking density. Kinetic studies revealed a reaction order of 1.66 and an activation energy (Ea) of 58.1 kJ/mol (KAS method). Thermogravimetric analysis (TGA) demonstrated enhanced thermal stability, with the hybrid system showing increased degradation temperatures and a char yield improvement from 3.5% (S0) to 11.55% (S2), attributed to the siloxane framework. EDX mapping confirmed the homogeneous distribution of OPEA-POSS in the epoxy matrix without phase separation. The incorporation of OPEA-POSS enhanced the thermal stability, flame retardancy, and processing behavior of the hybrid system, making it a potential co-curing agent for high-performance epoxy composites. Graphical Abstract

… design is presented. Polyhedral oligomeric silsesquioxane-based nanophotoinitiators (POSS-PIs) were synthesized by functionalizing POSS … Unlike conventional initiators, POSS-PIs …

Polyhedral oligomeric silsesquioxane (POSS), a cage silsesquioxane, is a versatile building block for the design of organic–inorganic hybrid materials. Numerous POSS‐tethered copolymers have been developed to enhance the properties of commercial polymers. In contrast, homopolymers that incorporate POSS units into their side chains are rare because of the intrinsically high crystallinity of POSS, which often results in brittle and turbid polymer films. In this study, we propose a novel molecular design strategy for obtaining homogeneous and optically transparent POSS‐based poly (methacrylate) films. The key structural feature is the introduction of hydrogen bonding (HB) sites such as urethane and urea groups. These intra‐ and/or inter‐molecular HB networks effectively suppress the crystallization of POSS moieties. The thermal and mechanical properties of the resulting materials are significantly influenced by the nature of the HB sites.

This study provides a comprehensive overview of the preparation methods for polyhedral oligomeric silsesquioxane (POSS) monomers and polymer/POSS nanocomposites. It focuses on the latest advancements in using POSS to design polymer nanocomposites with reduced dielectric constants. The study emphasizes exploring the potential of POSS, either alone or in combination with other materials, to decrease the dielectric constant and dielectric loss of various polymers, including polyimides, bismaleimide resins, poly(aryl ether)s, polybenzoxazines, benzocyclobutene resins, polyolefins, cyanate ester resins, epoxy resins, and more. Additionally, the research investigates the impact of incorporating POSS on improving the thermal properties, mechanical properties, surface properties, and other aspects of these polymers. The entire study is divided into two parts, discussing systematically the role of POSS in reducing dielectric constants during the preparation of POSS composites using both physical blending and chemical synthesis methods. The goal of this research is to provide valuable strategies for designing a new generation of low dielectric constant materials suitable for large-scale integrated circuits in the semiconductor materials domain. This article is protected by copyright. All rights reserved.

Recently, silsesquioxanes (SSQ) and polyhedral oligomeric silsesquioxanes (POSS) have gained much interest in the area of biomaterials, mainly due to their intrinsic properties such as biocompatibility, complete non-toxicity, the ability to self-assemble and to form a porous structure, facilitating cell proliferation, creating a superhydrophobic surface, osteoinductivity, and ability to bind hydroxyapatite. All the above has resulted in new developments in medicine. However, the application of POSS-containing materials in dentistry is still at initial stage and deserves a systematic description to ensure future development. Significant problems, such as reduction of polymerization shrinkage, water absorption, hydrolysis rate, poor adhesion and strength, unsatisfactory biocompatibility, and corrosion resistance of dental alloys, can be addressed by the design of multifunctional POSS-containing materials. Because of the presence of silsesquioxanes, it is possible to obtain smart materials that allow the stimulation of phosphates deposition and repairing of micro-cracks in dental fillings. Hybrid composites result in materials exhibiting shape memory, as well as antibacterial, self-cleaning, and self-healing properties. Moreover, introducing POSS into polymer matrix allows for materials for bone reconstruction, and wound healing. This review covers the recent developments in the field of POSS application in dental materials and gives the future perspectives within a promising field of biomedical material science and chemical engineering.

… multi-branched molecular structure design characteristics of … The unique molecular design of the multi-branched cage-like … arrays of supramolecular polyurethane, showcasing strong …

Self‐healing polymers are promising for diverse applications in wearable technology and electronic skin. Polyurethanes are versatile polymers that can incorporate various monomer structures. Waterborne polyurethanes (WPUs) emerge as an environmentally conscious choice due to water usage instead of organic solvents, thereby minimizing the generation of volatile organic compounds. This study introduces a novel approach to enhance the self‐healing properties of WPUs by integrating disulfide bonds. These dynamic disulfide bonds undergo exchange reactions upon heating, facilitating the renewal of cross‐links on damaged film surfaces. Self‐healing WPUs with a low glass transition temperature achieve excellent self‐healing efficiency under mild conditions. Self‐healing adhesives applied to various flexible substrates retain stable peel strength, which confirms their potential as self‐healing solutions. Furthermore, the WPU hydrogel electrolyte is prepared with dihydroxyhexyl viologen (DHHV), and the prepared electrochromic gel exhibits good ionic conductivity while maintaining high transparency. The flexible electrochromic device exhibited excellent performances, including low coloration voltage, high coloration efficiency, and long‐term stability. The transmittance difference is exceptional, with over 99%, and no decay after repeated bending cycles is observed. The current results demonstrate the feasibility of self‐healing WPUs in improving the operation and durability of high‐performance flexible electrochromic devices.

… POSS-based supramolecular gel with polyalkylcyclopentane (MAC) as the base oil through molecular design… or simulated space environments due to the POSS structure and high load-…

Liquid‐like surfaces (LLSs) with dynamic repellency toward various pollutants (e.g., bacteria, oil, and ice), have shown enormous potential in the fields of biology, environment, and energy. However, most of the reported LLSs cannot meet the demands for practical applications, particularly in terms of de‐wettability and durability. To solve these problems, considerable progress has been made in enhancing the de‐wettability and durability of LLSs in complex environments. Therefore, this review mainly focuses on the recent progress in LLSs, encompassing designed structures and repellent capabilities, as well as their diverse applications, offering greater insights for the targeted design of desired LLSs. First, a detailed overview of the development of LLSs from the perspective of their molecular structural evolution is provided. Then highlight recent approaches for enhancing the dynamic de‐wettability and durability of LLSs by optimizing their structural designs, including linear, looped, crosslinked, and hybrid structures. Later, the diverse applications and unique advantages of recently developed LLSs, including repellency (e.g., liquid anti‐adhesion/transportation/condensation, anti‐icing/scaling/waxing, and biofouling repellency) are summarized. Finally, Perspectives on potential innovative advancements and the promotion of technology selection to advance this exciting field are offered.

ABSTRACT Polyhedral oligomeric silsesquioxane (POSS) is an inorganic-organic nanoreinforcement for polymers. To enhance the physical/chemical interactions and compatibility of POSS molecules with organic matrices, POSS-nanocarbon nanostructures have been developed. POSS has been essentially modified to form POSS-fullerene, POSS-graphene, POSS-graphene oxide, POSS-carbon nanotube, and POSS-nanodiamond nanohybrids. The state-of-the-art review highpoints design, essential features, and developments of thermoplastic/thermosetting nanocomposite familiarized with POSS-nanocarbon nanofiller. Inclusion of POSS-nanocarbon has found to enhance structural, morphological, crystallinity, thermal, mechanical, wear, adhesion, anti-corrosion and other physical characteristics of nanocomposites. Recent progress in field of polymer/POSS-nanocarbon nanocomposite unveils potential toward solar cell, electromagnetic interference shielding devices, and membrane application. Graphical abstract

With the rapid development of technologies such as 5G/6G communication, artificial intelligence, and high-performance computing, electronic devices are continuously evolving toward higher frequencies, higher speeds, higher integration, and miniaturization. This has placed greater demands on the dielectric properties and thermal stability of the substrates and interconnection media. Traditional low dielectric materials such as polyimide, cyanoacrylate, and benzocyclobutene, due to their poor dielectric stability, easy bubble formation during curing, and high costs, have become unable to meet the future application requirements. Polybenzoxazine (PBZ), as a new type of thermosetting resin, has become a research hotspot for low dielectric and high-performance materials due to its flexible molecular design, near-zero volume shrinkage, and excellent heat resistance. This article reviews the latest research progress of low dielectric constant (Dk) PBZ-based materials. Starting from the fundamental principles of reducing Dk and the molecular characteristics of benzoxazine, this paper focuses on elaborating three major approaches to achieving ultralow Dk: molecular structure design, blending and composite strategies, and nanocomposite and hybrid materials. It systematically analyzes the influence mechanisms of various methods on dielectric, thermal stability, mechanical, and water absorption properties. Finally, we summarize the current challenges and look forward to the future development direction, providing theoretical and technical references for the development and application of the new generation of high-performance low dielectric materials.

The increasing availability of diverse experimental and computational data has accelerated the application of deep learning (DL) techniques for predicting polymer properties. A literature review was conducted to show recent advances in DL applied to this field. For example, Li et al. (2023) achieved an \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2>0.96$$\end{document} for predicting stiffness tensors of carbon fiber composites using a hybrid CNN–MLP model trained on microstructure images and two-point statistics. Aligning with this approach, Xue et al. (2023) compared DNN performance with genetic programming and minimax probability machine regression in predicting the lateral confinement coefficient for CFRP-wrapped RC columns, showing competitive predictive capability. These studies demonstrate that specialized architectures, including hybrid CNN–MLP models, feedforward ANNs, graph convolutional networks, and DNNs, provide high accuracy in predicting mechanical, thermal, and chemical properties of polymer composites and biodegradable plastics. Among these, DNNs have consistently shown superior performance in capturing complex nonlinear relationships within heterogeneous datasets, highlighting their suitability for materials characterization and optimization tasks. Building on these insights, this study investigates the effects of four natural fibers (flax, cotton, sisal, hemp) with densities around 1.48–1.54 g/cm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^3$$\end{document}, incorporated at 30 wt.% into three polymer matrices (PLA, PP, epoxy resin) with varying surface treatments (untreated, alkaline, silane). Samples were prepared via extrusion and injection molding (or casting for epoxy) under controlled processing conditions. Mechanical properties (tensile strength, modulus, elongation at break, impact toughness) were measured per ASTM standards, and density was determined by Archimedes’ method. Using 180 experimental samples, augmented up to 1500 using bootstrap technique, several regression models–linear, random forest, gradient boosting, DNNs–were developed to predict mechanical behavior. Best DNN model architecture (four hidden layers (128–64–32–16 neurons), ReLU activation, 20% dropout, a batch size of 64, and the AdamW optimizer with a learning rate of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^{-3}$$\end{document}) obtained through hyperparameter optimization using Optuna, delivered the best performance (R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document} up to 0.89) and MAE reductions of 9–12% compared to gradient boosting, driven by the DNN’s ability to capture nonlinear synergies between fiber-matrix interactions, surface treatments, and processing parameters while aligning architectural complexity with multiscale material behavior.

Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure-property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties.

Polyimide (PI) is widely used in modern industry due to its excellent properties. Its synthesis methods and property research have significantly progressed. However, the design and regulation of PI structures through traditional technologies are slow and expensive, which make it difficult to meet the practical demand of modern materials. With the rapid development of high-throughput computing and data-driven technology, machine learning (ML) has become an important method for exploring new materials. Data-driven ML is envisaged as a decisive enabler for new PIs discovery. This paper first introduces the basic workflow and common algorithms of ML. Secondly, applications of ML in material properties prediction, assisting computational simulation technologies and inverse design for desired structures are reviewed. Finally, we discuss the main challenges and possible solutions of ML in PI research.

… structure-property using machine learning (ML) is beneficial to shorten the development cycle of polymer … the dataset for the application of machine learning. An initial step toward …

The construction of material prediction models using machine learning algorithms can aid in the polyimide structural design and screening of materials as well as accelerate the development of new materials. There is a lack of research on predicting the optical properties of polyimide materials and the interpretation of the structural features. Here, we collected 652 polyimide molecular structures and used seven popular machine learning algorithms to predict the glass transition temperature and cut-off wavelength of polyimide materials and extract key feature information of repeating unit structures. The results showed that the root mean square error of the glass transition temperature prediction model was 33.92 °C, and the correlation coefficient was 0.861. The root mean square error of the cut-off wavelength prediction model was 17.18 nm, and the correlation coefficient was 0.837. The elasticity of the molecular structure was also found to be the key factor affecting glass transition temperature, and the presence and location of heterogeneous atoms had a significant effect on the cut-off wavelengths. Finally, eight polyimide materials were synthesized to test the accuracy of the prediction models, and the experimental characterization values agreed with the predicted values. The results would contribute to the development of polyimide structural design and materials preparation for flexible display.

… However, most of the past machine learning studies in … Collectively, 176 machine learning models were trained for … SHAP analysis was used to explain the optimal model for each …

Sustainable polymers from renewable resources have been gaining importance due to their recyclability and reduced environmental impact. However, their development through conventional trial-and-error methods remains inefficient and resource-intensive. Machine learning has emerged as a powerful tool in polymer science, enabling rapid prediction and discovery of new chemicals and materials. In this review, we examine emerging trends in machine learning applications for sustainable polymer development, focusing on catalyst discovery, property optimization, and new polymer design. We analyze unique challenges in applying machine learning to sustainable polymers and evaluate proposed solutions, providing insights for future development in this rapidly evolving field.

… of the interactions between the sequence polymer. Optimization of the hyperparameter space … In examining the results from such optimization, we did not see substantial variation in the …

This review analyzes the current practices in the data-driven characterization, design and optimization of disordered nanoporous materials with pore sizes ranging from angstroms (active carbon and polymer membranes for gas separation) to tens of nm (aerogels). While the machine learning (ML)-based prediction and screening of crystalline, ordered porous materials are conducted frequently, materials with disordered porosity receive much less attention, although ML is expected to excel in the field, which is rich with ill-posed problems, non-linear correlations and a large volume of experimental results. For micro- and mesoporous solids (active carbons, mesoporous silica, aerogels, etc.), the obstacles are mostly related to the navigation of the available data with transferrable and easily interpreted features. The majority of published efforts are based on the experimental data obtained in the same work, and the datasets are often very small. Even with limited data, machine learning helps discover non-evident correlations and serves in material design and production optimization. The development of comprehensive databases for micro- and mesoporous materials with low-level structural and sorption characteristics, as well as automated synthesis/characterization protocols, is seen as the direction of efforts for the immediate future. This paper is written in a language readable by a chemist unfamiliar with the data science specifics.

… This study highlights the enhanced properties of the PU/HCNT film resulting from the … in biosensing and provide support for disease prediction and diagnosis. [ 141-144 ] Compared to …

We revealed that bulky substituents on POSS and similar emission quantum yields between the POSS (ΦDonor,film) and the π-conjugated polymer (ΦAcceptor,film) are the key factors for creating hybrid films with thermally stable white-light emission.

We present the synthesis and comprehensive characterization of a new class of hybrid inorganic–organic materials: polyhedral oligomeric silsesquioxane (POSS) cages functionalized with 1-haloacetylene groups (Cl, Br, I). These building blocks...

… -PCUU nanofibers to selectively increase their superficial properties while maintaining the … with the predicted results by the model. Results showed that the modified PAAc-POSS-PCUU …

The crystallinity, solubility, and physical properties of polyhedral oligomeric silsesquioxane (POSS) compounds are highly dependent on their organic substituents. We previously synthesized a series of isobutyl-substituted star-shaped POSS derivatives with aliphatic chain linkers of different length. In this study, we prepared C3- and C6-linked phenyl-substituted star-shaped POSS derivatives (3Ph-C3 and 3Ph-C6) by the hydrosilylation of heptaphenylallyl- and hexenyl-POSS (1a and 1b) and octadimethylsiloxy-Q8-silsesquioxane (Q8M8H) (2), respectively, and their properties were compared with those of the corresponding isobutyl-substituted derivatives (5iBu-C3 and 5iBu-C6). Although 3Ph-C6 was only soluble in chloroform and insoluble in tetrahydrofuran (THF) and toluene, 3Ph-C3 was soluble even in THF and toluene, suggesting that the shorter linkers of the derivative afford a wider range of solvents for dissolution. Differential scanning calorimetry analysis showed that 3Ph-C3 exhibited a baseline shift at 190 °C and an endothermic peak at 316 °C. However, no clear baseline shift was observed for 3Ph-C6. Thermogravimetric analysis showed that the shorter linker in the phenyl-substituted star-shaped POSS derivative significantly increased the decomposition temperature compared with the longer linker. The annealed cast film of 3Ph-C3 at 340 °C above its melting temperature formed a transparent film even after cooling to room temperature. However, an opaque whitish film was formed in the case of 3Ph-C6. Poly(methyl methacrylate) (PMMA) films containing 2 wt % 3Ph-C3 and 3Ph-C6 were prepared by casting their chloroform solutions onto glass substrates overnight at room temperature. The static water contact angle measurements and XPS analysis for the castings film containing 3Ph-C3 and 3Ph-C6 suggested that degree of the segregation amount of 3Ph-C3 was larger than that of 3Ph-C6. The shorter linker length in the phenyl-substituted star-shaped POSS derivative, 3Ph-C3, with its greater predicted solubility in PMMA, exhibited entropy-driven surface segregation.

… the profound influence of the POSS size on the structure–property relationship of HPPs and could be used to predict the property of larger T 14 , T 16 , and T 18 POSS-based HPPs. This …

This review is devoted to experimental studies and modeling in the field of mechanical and physical properties of polymer concretes and polymer-modified concretes. The review analyzes studies carried out over the past two years. The paper examines the properties of polymer concretes based on various polymer resins and presents the advantages and disadvantages of various models developed to predict the mechanical properties of materials. Based on data in the literature, the most promising polymers for use in the field of road surface repair are polymer concretes with poly(meth)acrylic resins. It was found that the most adequate and productive models are the deep machine learning model—using several hidden layers that perform calculations based on input parameters—and the extreme gradient boosting model. In particular, the extreme gradient boosting model showed high R2 values in forecasting (in the range of 0.916–0.981) when predicting damping coefficient and ultimate compressive strength. In turn, among the additives to Portland cement concrete, the most promising are natural polymers, such as mammalian gelatin and cold fish gelatin, and superabsorbent polymers. These additives allow for an improvement in compressive strength of 200% or more. The review may be of interest to engineers specializing in building construction, materials scientists involved in the development and implementation of new materials into production, as well as researchers in the interdisciplinary fields of chemistry and technology.

Solid polymer electrolytes (SPEs) present a viable alternative to organic carbonates typically used as liquid electrolytes in lithium-ion batteries (LIBs). Among various SPEs, poly(vinylidene fluoride) (PVDF)-based SPEs have received significant attention owing to their excellent film forming ability, chemical and thermal stability, mechanical strength, and electrochemical performance. This review focuses on recent innovative strategies in composites, blends, and dielectric engineering to achieve PVDF-based SPEs with enhanced electrochemical performance. It is divided into four primary sections: (1) PVDF-based composite electrolytes, which explores the role of inorganic fillers and nanomaterials in improving ionic conductivity and mechanical properties; (2) PVDF-based blend electrolytes, highlighting the role of polymer blending in optimizing crystallinity, flexibility, and ion transport; (3) dielectric engineering, describing various strategies of manipulating the dielectric properties of PVDF-based SPEs to achieve optimized electrochemical performance; and (4) the emerging role of machine learning (ML) techniques in accelerating the discovery and optimization of SPEs materials by predicting performance and guiding experimental design. Finally, the review concludes with future perspectives and challenges, outlining the potential of PVDF-based SPEs to address current limitations and pave the way for next-generation energy storage applications.

… film made by electrospinning has a good tensile property. After being treated with a … FAS modification. We found that if POSS was not used previously, the WCA of FAS-modified PDA/…

High‐performance polymer fibers, commonly used as reinforced fibers, have garnered significant attention across various scientific and industrial domains due to their extremely high tensile strength and excellent toughness. Notably, poly(p‐phenylene‐2,6‐benzobisoxazole) (PBO) fibers are hailed as the most advanced high‐performance fibers of the twenty‐first century, known for their exceptional mechanical properties, outstanding thermal stability, excellent flame retardancy, and chemical resistance, attracting considerable attention and favor from researchers. This review provides a comprehensive overview of the structure of PBO fibers and two synthesis methods of PBO polymers, detailing the properties of PBO fibers to offer valuable references for researchers in this area. This review highlights various techniques for modifying PBO fibers. Conventional methods can easily damage the structure of PBO fibers, resulting in difficulties in achieving the ideal mechanical properties of the corresponding composites. Surface coating modification can improve the surface roughness and reactivity of PBO fibers without compromising their structure. Additionally, the enhancement of interfacial compatibility for PBO fiber‐reinforced composites via introducing interfacial compatibilizers is analyzed in detail. Finally, the challenges and future prospects of PBO fibers are also discussed. This paper aims to provide theoretical guidance for fabricating PBO fibers and enhancing the interfacial bonding strength with resin matrices, thereby increasing their potential in advanced applications.

… of POSS increases, these blends show two different T g values, suggesting that the addition of POSS does not promote miscibility. The FTIR results indicate that POSS reduces the …

… PUR foam based on the data achieved from their experiment results in different isocyanate indices. They predicted physical properties of modified polyurethane foams by bio-based …

Polyhedral oligomeric silsesquioxanes (POSS) harness their molecularly precise organic–inorganic hybrid cage architecture to deliver hardness, scratch resistance, and programmable functionality for next-generation transparent coatings. Tailoring of solubility, thermal stability, mechanical robustness, electronic characteristics, and interfacial properties is achieved through strategic peripheral modifications enabled by versatile synthetic methodologies—spanning metal catalysis, metal-free routes, and selective bond activation. Advanced integration techniques, including covalent grafting, chemical crosslinking, UV–thermal dual curing, and in situ polymerization, ensure uniform dispersion while optimizing coating–substrate adhesion and network integrity. The resultant coatings exhibit exceptional optical transparency, mechanical durability, tunable electrical performance, thermal endurance, and engineered surface hydrophobicity. These synergistic attributes underpin transformative applications across critical domains: atomic-oxygen-resistant spacecraft shielding, UV-managing agricultural films, flame-retardant architectural claddings, mechanically adaptive foldable displays, and efficiency-enhanced energy devices. Future progress will prioritize sustainable synthesis pathways, emergent asymmetric cage architectures, and multifunctional designs targeting extreme-environment resilience, thereby expanding the frontier of high-performance transparent protective technologies.

Non-isocyanate polyurethane (NIPU) networks physically modified with octa(3-hydroxy-3-methylbutyldimethylsiloxy)POSS (8OHPOSS, 0–10 wt%) were conditioned in environments of different relative humidities (up to 97%) to study water–polymer interactions. The equilibrium sorption isotherms are of Brunauer type III in a water activity range of 0–0.97 and are discussed in terms of the Guggenheim (GAB) sorption model. The study shows that the introduction of 8OHPOSS, even in a large amount (10 wt%), does not hinder the water affinity of the NIPU network despite the hydrophobic nature of POSS; this is attributable to the homogenous dispersion of POSS in the polymer matrix. The shift in the urethane-derived carbonyl bands toward lower wavenumbers with a simultaneous shift in the urethane N-H bending bands toward higher wavenumbers exposes the breakage of polymer–polymer hydrogen bonds upon water uptake due to the formation of stronger water–polymer hydrogen bonds. Upon water absorption, a notable decrease in the glass transition temperature (Tg) is observed for all studied materials. The progressive reduction in Tg with water uptake is driven by plasticization and slaving mechanisms. POSS moieties are thought to impact slaving indirectly by slightly affecting water uptake at very high hydration levels.

Progress in chemistry has been driven by the streamlining of inverse problem-solving methods. In the history of chemistry, several revolutionary technologies have led to leaps forward: the establishment of atomistic theory in the 19th century, structural analysis by spectroscopy in the 20th century, and the development of simulation by theoretical chemistry. Currently, chemistry is about to make a significant leap forward by integrating generative artificial intelligence (AI). In 2016, deep learning techniques were introduced in this domain, leading to explosive development. This paper reviews the development path, including traditional models such as variational autoencoders and more up-to-date models such as large language models and diffusion models. We also discuss how AI can have a real impact on chemistry, including the possibilities and problems associated with synthesizing AI-generated molecules.

Generating new molecules with the desired physical or chemical properties is the key challenge of computational material design. Deep learning techniques are being actively applied in the field of data-driven material informatics and provide a promising way to accelerate the discovery of innovative materials. In this work, we utilize an invertible graph generative model to generate hypothetical promising high-temperature polymer dielectrics. A molecular graph generative model based on the invertible normalizing flow is trained on a data set containing 250k polymer molecular graphs (mostly generated by an RNN-based generative model) to learn the invertible transformations between latent distributions and molecular graph structures. When generating molecular graphs, a sample vector is drawn from the latent space, and then an adjacency tensor and node attribute matrix are generated through two invertible flows in two steps and assembled into a molecular graph. The model has the merits of exact likelihood training and an efficient one-shot generation process. The learned latent space is used to generate polymers with a high glass-transition temperature (Tg) and a wide band gap (Eg) for the application of high-temperature energy storage film capacitors. This work contributes to the efficient design of high-temperature polymer dielectrics by using deep generative models.

Summary This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.

We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The X-LoRA-Gemma model, inspired by biological principles and featuring 7 billion parameters, dynamically reconfigures its structure through a dual-pass inference strategy to enhance its problem-solving abilities across diverse scientific domains. The model is used to first identify molecular engineering targets through a systematic human–AI and AI–AI self-driving multi-agent approach to elucidate key targets for molecular optimization to improve interactions between molecules. Next, a multi-agent generative design process is used that includes rational steps, reasoning and autonomous knowledge extraction. Target properties of the molecule are identified either using a principal component analysis (PCA) of key molecular properties or sampling from the distribution of known molecular properties. The model is then used to generate a large set of candidate molecules, which are analyzed via their molecular structure, charge distribution, and other features. We validate that as predicted, increased dipole moment and polarizability is indeed achieved in the designed molecules. We anticipate an increasing integration of these techniques into the molecular engineering workflow, ultimately enabling the development of innovative solutions to address a wide range of societal challenges. We conclude with a critical discussion of challenges and opportunities of the use of multi-agent generative AI for molecular engineering, analysis and design.

The discovery of novel thermoset shape memory polymers (TSMPs) for additive manufacturing can be accelerated through the use of a deep‐generative algorithm, minimizing the need for laborious traditional laboratory experiments. This study is the first to introduce an innovative approach that uses a deep generative learning model, namely the conditional variational autoencoder (CVAE), to discover novel TSMPs with lower glass transition temperature () and high recovery stress values (). In this study, specific chemical groups, such as epoxy, amine, thiol, and vinyl, are integrated as constraints to generate novel TSMPs while preserving the essential reaction properties. To address the challenges posed by a small dataset, the CVAE model is used with graph‐extracted features. Unlike previous studies focused on single‐polymer systems, this research extends to two‐monomer samples, discovering 22 novel TSMPs. This approach has practical implications in additive manufacturing, biomedical devices, aerospace, and robotics for the discovery of novel samples from limited data.

The design of novel molecules underpins advances in both drug discovery and biomaterials engineering. Traditional approaches, from natural product isolation to high-throughput screening, have delivered important therapeutics but remain costly, inefficient, and limited in exploring the chemical and biomolecular space. While predictive machine learning models improved aspects of discovery, they cannot fully address the complexity of modern precision medicine. Generative artificial intelligence (AI) offers a paradigm shift by enabling de novo molecular creation guided by data-driven optimization. Architectures such as variational autoencoders, generative adversarial networks, normalizing flows, and diffusion models now demonstrate unprecedented capabilities in designing small molecules and macromolecules that satisfy complex physicochemical and biological requirements. This review surveys the rapidly evolving field of generative AI for molecular design. We first introduce the development of generative architectures and optimization strategies, focusing on how sampling, training, and postgeneration techniques improve control over molecular design. We then examine applications across molecular representations, unconstrained and property-constrained design, conformation modeling, and the generation of large biomolecules such as proteins, antibodies, and peptides. Benchmarking datasets, evaluation metrics, and real-world case studies, such as the AI-driven discovery of novel antibiotics demonstrated in vivo efficacy against multidrug-resistant infections, illustrate the growing maturity and translational potential of generative molecular design approaches. Despite rapid advances, generative molecular design still faces critical challenges that point to key future directions. These include integrating physicochemical priors through differentiable physical models, overcoming data scarcity via synthetic augmentation and transfer learning, enabling multimodal fusion of structural, omics, and phenotypic data, deploying autonomous AI agents for adaptive decision-making, and optimizing multiple objectives with uncertainty-aware strategies. Addressing these challenges could lead to more robust, generalizable, and experimentally aligned molecular design systems.

… polymers with T c as low as 0.075 W·m −1 ·K −1 and K exceeding 4.59 GPa, validated by molecular … computationally driven design of multifunctional polymers for extreme environments. …