This study investigates the stabilization of water-in-water (W/W) emulsions composed of dextran and poly(ethylene oxide) (PEO) using bis-hydrophilic diblock copolymers composed of a dextran and a POEGMA block. Confocal laser scanning microscopy (CLSM) was employed to assess the stabilization efficacy at different copolymer compositions. Macroscopic phase separation was observed in mixtures of the block copolymers with pure dextran solutions, whereas the block copolymers formed microscopic domains in pure PEO solutions. In the W/W emulsions the block copolymers formed small domains in the PEO phase that adsorbed at the interface stabilizing dispersed dextran droplets. The stability was found to depend on the concentration and composition of the copolymers, the concentration of the dextran and PEO as well as the molecular weight of the latter.

We report the formulation of dextran-based nano-objects via polymerization-induced self-assembly (PISA) in aqueous emulsion, using photoinduced electron/energy transfer RAFT (PET-RAFT) polymerization under visible light irradiation (λ = 500 nm). A dextran macromolecular chain transfer agent (DexCTA), bearing multiple trithiocarbonate groups, was employed as a hydrophilic scaffold for the grafting of poly(methyl methacrylate) (PMMA) chains. Eosin Y was used as a metal-free photocatalyst to mediate polymerization in water under mild conditions. By systematically varying the PMMA graft degree of polymerization (X = 100-400) and solids content (5-15% w/w), we investigated the influence of formulation parameters on nanoparticle morphology and size. High monomer conversion (>99%) was achieved within 2 h of irradiation. Transmission electron microscopy revealed spherical morphologies across all conditions, with increasing particle size as a function of X and solids content. This study highlights the potential of PET-RAFT emulsion PISA as a green and versatile strategy for the design of polymeric nanomaterials.

Machine-learning (ML) models in polymer science typically treat a polymer as a single, perfectly defined molecular graph, even though real materials consist of stochastic ensembles of chains with distributed lengths. This mismatch between physical reality and digital representation limits the ability of current models to capture polymer behaviour. Here we introduce PolySet, a framework that represents a polymer as a finite, weighted ensemble of chains sampled from an assumed molar-mass distribution. This ensemble-based encoding is independent of chemical detail, compatible with any molecular representation and illustrated here in the homopolymer case using a minimal language model. We show that PolySet retains higher-order distributional moments (such as Mz, Mz+1), enabling ML models to learn tail-sensitive properties with greatly improved stability and accuracy. By explicitly acknowledging the statistical nature of polymer matter, PolySet establishes a physically grounded foundation for future polymer machine learning, naturally extensible to copolymers, block architectures, and other complex topologies.

The rapid and unbiased characterization of self-assembled polymeric vesicles in transmission electron microscopy (TEM) images remains a challenge in polymer science. Here, we present a deep learning-powered detection framework based on YOLOv8, enhanced with Weighted Box Fusion, to automate the identification and size estimation of polymer nanostructures. By incorporating multiple morphologies in the training dataset, we achieve robust detection across unseen TEM images. Our results demonstrate that the model provides accurate vesicle detection within 2 seconds-an efficiency unattainable using traditional image analysis software. The proposed framework enables reproducible and scalable nano-object characterization, paving the way for a general AI-driven automation in polymer self-assembly research.

Agriculture today faces the challenge of increasing food production while minimizing the environmental impacts of agricultural practices, particularly the inefficient use of fertilizers. To address this issue, researchers have explored the potential of controlled-release fertilizers (CRFs) capable of releasing nutrients at a controlled rate over an extended period. However, the use of non-biodegradable polymer coatings in many commercial CRFs raises environmental concerns. This study investigates the potential of incorporating Moroccan Ghassoul clay, modified or natural, into fertilizer granules to modulate nutrient release profiles. The unique physicochemical properties of clay minerals potentially grant them the ability to adsorb and release essential nutrients gradually over time. The modification of the ghassoul clay was performed using the pillaring technique, which creates a. complex microstructure that can restrict the transfer of water molecules and nutrients. The characterization of pillared and non-pillared clays was performed using X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR), providing insights into the influence of pillaring on basal spacing and interlayer structure. The morphological analysis was conducted using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). It was found that incorporating pillared clay into fertilizers improved the physical properties of granules compared to those incorporating untreated clay and non-incorporated ones. Nutrient release tests assessed using three phosphatic fertilizers through sand column revealed that the use of untreated clay resulted in faster nutrient release due to its inherent swelling properties, facilitating granule disintegration upon contact with water. In contrast, granules co-granulated with pillared clay exhibited a slower release of nutrients, suggesting increased efficiency under specific conditions. The findings demonstrate the incorporation of clay emerges as an effective strategy for modulating nutrient release rates, whether accelerating or slowing, enabling improved fertilization optimization, and potentially addressing environmental concerns.

Herein, we introduce a sustainable method for latex production via surfactant-free emulsion polymerization (SFEP) carrying out a photoinitiated polymerization (photo-SFEP) under both artificial light and sunlight. We discuss the use of sodium phenyl-2,4,6-trimethylbenzoylphosphinate (TPO-Na) as a water-soluble photoinitiator to in situ prepare polymeric nanoparticles under mild conditions, eliminating the need of conventional surfactants. The methodology exploits the rapid photolysis of TPO-Na, which generates anionic radical species that initiate the polymerization of glycidyl methacrylate (GlyMA), selected as a model monomer. Photo-SFEP was optimized to ensure colloidal stability over several months, even under varying environmental ionic strengths. The structural and colloidal properties of the nanoparticles were thoroughly characterized using dynamic light scattering (DLS), transmission electron microscopy (TEM), and zeta potential measurements, confirming the reproducibility and robustness of the latex dispersions. Our methodology shows promise as a scalable, efficient alternative to conventional emulsion polymerization techniques. Additionally, its versatility was affirmed by extending its application to various vinylic monomers, showcasing its broad potential.

Preparing functional nano-objects by polymerization-induced self-assembly (PISA) from polyelectrolyte-based stabilizers has remained challenging due to strong electrostatic interactions that prevent morphological evolution beyond spherical assemblies. To overcome this limitation, we developed a binary stabilization strategy combining cationic oligochitosan (OCS) and neutral dextran (Dex) stabilizers in a photoinitiated reversible addition-fragmentation chain transfer polymerization-induced self-assembly (photo-RAFT PISA) approach. By fine-tuning the molar fraction of glucosamine units (f GaU) and glucosidic units (f GU), we successfully unlocked morphology transitions, forming complex nanostructures. Our results demonstrate that this dual stabilization mechanism effectively balances electrostatic and steric repulsions, thereby enabling controlled self-assembly in polyelectrolyte-driven PISA. This work provides a scalable and bioinspired strategy for engineering complex polysaccharide-based nanostructures with promising applications in drug delivery and biomaterial design.

Water-in-water (W/W) emulsions are formed by mixing incompatible aqueous polymer solutions. Their stabilization is more challenging in comparison with oil-in-water emulsions due to their extremely low interfacial tension, wide W/W interface and strong sensitivity to dilution. W/W emulsions cease to exist when diluted below the binodal curve, which significantly restricts their use in applications. This work aims to develop original double-hydrophilic grafted and diblock copolymers (DHGC and DHDC, respectively) based on dextran (Dex) and on poly[oligo(ethylene glycol) methyl ether methacrylate] (POEGMA), a hydrophilic and PEO-philic polymer, to stabilize W/W emulsions made of Dex and polyethylene oxide (PEO) phases. A series of grafted and diblock copolymers (Dex-g-POEGMA and Dex-b-POEGMA) was synthesized by photo-mediated reversible addition-fragmentation chain transfer (photo-RAFT) polymerization using multifunctional and monofunctional dextran-based macromolecular chain transfer agents. Key parameters such as the molecular weight of OEGMA monomers, the lengths of Dex and POEGMA parts, and the grafting density were systematically varied. Results showed that Dex/PEO emulsion can be successfully stabilized, for at least one week, depending on the architecture and composition of the copolymers.

In recent years, artificial intelligence (AI) has emerged as a transformative tool for addressing scientific and technical challenges across various disciplines. AI enables data-driven predictions, uncovers hidden patterns, and automates labor-intensive tasks, offering unprecedented opportunities for innovation. However, its rapid rise has been disruptive, and many scientific fields—including polymer science—were not fully prepared for its integration. The complexity of polymer systems, coupled with the traditionally empirical nature of the field, has made AI adoption particularly challenging. Many polymer scientists still face significant barriers, including technical complexity, and a lack of interdisciplinary training. This perspective serves as an entry point for researchers seeking to integrate AI into polymer science by presenting real-world applications, practical tools, and key challenges. Rather than providing an exhaustive review for specialists, it aims to familiarize polymer scientists with AI’s capabilities and encourage further exploration. By lowering entry barriers and fostering interdisciplinary dialogue, this work bridges the gap between conventional polymer research and data-driven innovation, paving the way for future advancements.

We explore the application of artificial intelligence (AI) to predict the morphology of poly(glycerol monomethacrylate)-poly(2-hydroxypropyl methacrylate) (PGMA-PHPMA) diblock copolymer nano-objects prepared via polymerization-induced self-assembly (PISA) in aqueous media. Traditional studies typically map copolymer morphology using two-dimensional (2D) pseudo-phase diagrams, plotting variables such as the mean degree of polymerization (Xn) of the solvophobic block against the copolymer concentration (also known as the solids content). In contrast, our approach utilizes deep neural networks (DNNs) trained on literature data to generate detailed three-dimensional (3D) morphology maps. These maps include the molecular weight of the solvophilic block, providing a comprehensive volumetric view that reveals more complex relationships and transitional morphologies. This advanced modeling not only deepens our understanding of how PGMA molecular weight influences copolymer morphology but also significantly reduces the need for extensive experimental trials. Consequently, it simplifies the creation of accurate pseudo-phase diagrams across a broad range of aqueous PISA formulations. Experimental validation confirms the accuracy of our models, demonstrating the potential of AI to make predictive modeling more accessible to chemists and paving the way for future research on other PISA formulations.

During last few decades, oligochitosan (OCS)-coated nanoparticles have received great interest for nanomedicine, food and environment applications. However, their current formulation techniques are time-consuming with multi-synthesis/purification steps and sometimes require the use of organic solvents, crosslinkers and surfactants. Herein, we report a facile and rapid one-pot synthesis of OCS-based nanoparticles using photo-initiated reversible addition fragmentation chain transfer polymerization-induced self-assembly (Photo-RAFT PISA) under UV-irradiation at room temperature. To achieve this, OCS was first functionalized by a chain transfer agent (CTA) resulting in a macromolecular chain transfer agent (OCS-CTA), which will act as a reactive electrostatic/steric stabilizer. Owing to its UV-sensitivity, OCS-CTA was then used as photo-iniferter to initiate the polymerization of 2-hydroxypropyl methacrylate (HPMA) in aqueous acidic buffer, resulting in OCS-g-PHPMA amphiphilic grafted copolymers which self-assemble into nano-objects. Transmission electron microscopy and light scattering analysis reveal formation of spherical nanostructures.

We explore the application of machine learning (ML) to predict the morphology of poly(glycerol monomethacrylate)-poly(2-hydroxypropyl methacrylate) (PGMA-PHPMA) diblock copolymer nano-objects prepared via polymerization-induced self-assembly (PISA) in aqueous media. Traditional studies typically map copolymer morphology using two-dimensional (2D) pseudo-phase diagrams, plotting variables such as the mean degree of polymerization (Xn) of the solvophobic block against the copolymer concentration (also known as the solids content). In contrast, our approach utilizes deep neural networks (DNNs) trained on literature data to generate detailed three-dimensional (3D) morphology maps. These maps include the molecular weight of the solvophilic block, providing a comprehensive volumetric view that reveals more complex relationships and transitional morphologies that are difficult to capture in 2D representations. This 3D modeling approach enriches our understanding by highlighting critical thresholds and nuanced transitions within the morphology landscape. Such advanced modeling not only deepens our understanding of how PGMA molecular weight influences copolymer morphology but also significantly reduces the need for extensive experimental trials. Consequently, it simplifies the creation of accurate pseudo-phase diagrams across a broad range of aqueous PISA formulations. Experimental validation confirms the accuracy of our models, demonstrating the potential of ML to make predictive modeling more accessible to chemists and paving the way for future research on other PISA formulations. The data set, along with all codes for model training and evaluation, is publicly accessible via both Zenodo and GitHub platforms.

The development of bio-based thermoplastics is rapidly growing due to the depletion of fossil fuel reserves but also in the hope of obtaining polymers with original properties. Herein, we report the controlled synthesis of original homopolymers derived from gallic acid, one of the well-known natural polyphenols that exhibits various health-promoting effects. More precisely, acrylate, methacrylate and acrylamide monomers based on a protected gallic acid are firstly obtained by a rethought multistep scheme. Then, a controlled photo-mediated RAFT (Reversible Addition Fragmentation chain Transfer) polymerization was carried out before a final deprotection. By this way, original polymers bearing a free gallic acid as a lateral substituent on each monomer unit are described. The intrinsic antibacterial and antioxidant properties of these polymers are highlighted and compared with those of gallic acid.

This study aimed at the production of marine bacterial exopolysaccharides (EPS) as biodegradable , nontoxic biopolymers, competing the synthetic derivatives, with detailed structural and conformational analyses using spectroscopy techniques. Twelve marine bacterial bacilli were isolated from the seawater of Mediterranean Sea, Egypt, then screened for EPS production. The most potent isolate was identified genetically as Bacillus para-licheniformis ND2 by16S rRNA gene sequence of similar to 99 % similarity. Plackett-Burman (PB) design identified the optimization conditions of EPS production, which yielded the maximum EPS (14.57 g L-1) with 1.26-fold in-crease when compared to the basal conditions. Two purified EPSs namely NRF1 and NRF2 with average mo-lecular weights ( over bar Mw) of 15.98 and 9.70 kDa, respectively, were obtained and subjected for subsequent analyses. FTIR and UV-Vis reflected their purity and high carbohydrate contents while EDX emphasized their neutral type. NMR identified the EPSs as levan-type fructan composed of beta-(2-6)-glycosidic linkage as a main backbone , HPLC explained that the EPSs composed of fructose. Circular dichroism (CD) suggested that NRF1 and NRF2 had identical structuration with a little variation from the EPS-NR. The EPS-NR showed antibacterial activity with the maximum inhibition against S. aureus ATCC 25923. Furthermore, all the EPSs revealed a proinflammatory action through dose-dependent increment of expression of proinflammatory cytokine mRNAs, IL-6, IL-1 beta and TNF alpha.

Here, a versatile strategy to engineer smart theranostic nanocarriers is reported. The core/shell nanosystem is composed of a superparamagnetic iron oxide (Fe 3− δ O 4 ) nanoparticle (NP) core bearing the biocompatible thermo‐responsive poly(2‐(2‐methoxy)ethyl methacrylate‐oligo(ethylene glycol methacrylate), P(MEO 2 MA x ‐OEGMA 100− x ) copolymer (where x and 100‐ x represent the molar fractions of MEO 2 MA and OEGMA, respectively). Folic acid (FA) is end‐conjugated to the P(MEO 2 MA x ‐OEGMA 100− x ) copolymer, leading to Fe 3 − δ O 4 @P(MEO 2 MA x ‐OEGMA 100− x )‐FA, to facilitate active targeting of NPs to cancer cells. A highly potent hydrophobic anticancer agent doxorubicin (DOX) is incorporated in the thermo‐responsive P(MEO 2 MA x ‐OEGMA y ) brushes via supramolecular interactions to increase its solubility and the assessment of therapeutic potentials. These experiments confirm the magnetic hyperthermia properties of nanocarrier and reveal that only a small amount (10% ± 4%) of DOX is diffused at room temperature, while almost full drug (100%) is released after 52 h at 41 °C. Interestingly, it is found that P(MEO 2 MA 60 ‐OEGMA 40 ) polymers offer to NPs a promising stealth behavior against Human Serum Albumin and Fibrinogen model proteins.

Polymerization-induced self-assembly (PISA) is an emerging platform technology offering many advantages to produce polymeric nano-objects compared to the solvent switch methods. Aqueous dispersion reversible addition fragmentation chain transfer PISA (aqueous dispersion RAFT PISA) has progressively won over many research groups due to its green chemical process and amazing efficiency to produce a wide range of morphologies. This review summarizes the recent works reported in the literature on the aqueous dispersion RAFT PISA. After a general introduction on the PISA process, exhaustive lists of the different RAFT activation techniques, the hydrophilic steric stabilizers, and the monomers used are shown and discussed. Our objective is to provide an overview for experts and a toolbox for nonexperts that aim to explore this robust and efficient route to produce block copolymer-based nano-objects for a specific application.

Since the ancient times, bee products (i.e., honey, propolis, pollen, bee venom, bee bread, and royal jelly) have been considered as natural remedies with therapeutic effects against a number of diseases. The therapeutic pleiotropy of bee products is due to their diverse composition and chemical properties, which is independent on the bee species. This has encouraged researchers to extensively study the therapeutic potentials of these products, especially honey. On the other hand, amid the unprecedented growth in nanotechnology research and applications, nanomaterials with various characteristics have been utilized to improve the therapeutic efficiency of these products. Towards keeping the bee products as natural and non-toxic therapeutics, the green synthesis of nanocarriers loaded with these products or their extracts has received a special attention. Alginate is a naturally produced biopolymer derived from brown algae, the desirable properties of which include biodegradability, biocompatibility, non-toxicity and non-immunogenicity. This review presents an overview of alginates, including their properties, nanoformulations, and pharmaceutical applications, placing a particular emphasis on their applications for the enhancement of the therapeutic effects of bee products. Despite the paucity of studies on fabrication of alginate-based nanomaterials loaded with bee products or their extracts, recent advances in the area of utilizing alginate-based nanomaterials and other types of materials to enhance the therapeutic potentials of bee products are summarized in this work. As the most widespread and well-studied bee products, honey and propolis have garnered a special interest; combining them with alginate-based nanomaterials has led to promising findings, especially for wound healing and skin tissue engineering. Furthermore, future directions are proposed and discussed to encourage researchers to develop alginate-based stingless bee product nanomedicines, and to help in selecting suitable methods for devising nanoformulations based on multi-criteria decision making models. Also, the commercialization prospects of nanocomposites based on alginates and bee products are discussed. In conclusion, preserving original characteristics of the bee products is a critical challenge in developing nano-carrier systems. Alginate-based nanomaterials are well suited for this task because they can be fabricated without the use of harsh conditions, such as shear force and freeze-drying, which are often used for other nano-carriers. Further, conjunction of alginates with natural polymers such as honey does not only combine the medicinal properties of alginates and honey, but it could also enhance the mechanical properties and cell adhesion capacity of alginates.

Health concerns associated with the advent of nanotechnologies have risen sharply when it was found that particles of nanoscopic dimensions reach the cell lumina. Plasma and organelle lipid membranes, which are exposed to both the incoming and the engulfed nanoparticles, are the primary targets of possible disruptions. However, reported adhesion, invagination and embedment of nanoparticles (NPs) do not compromise the membrane integrity, precluding direct bilayer damage as a mechanism for toxicity. Here it is shown that a lipid membrane can be torn by small enough nanoparticles, thus unveiling mechanisms for how lipid membrane can be compromised by tearing from nanoparticles. Surprisingly, visualization by cryo transmission electron microscopy (cryo-TEM) of liposomes exposed to nanoparticles revealed also that liposomal laceration is prevented by particle abundance. Membrane destruction results thus from a subtle particle-membrane interplay that is here elucidated. This brings into a firmer molecular basis the theorized mechanisms of nanoparticle effects on lipid bilayers and paves the way for a better assessment of nanoparticle toxicity.

The morphological evolution of graft copolymer-based nano-objects was monitored by light scattering and electron microscopy during their formulation in water by polymerization induced self-assembly using a photo-mediated reversible addition-fragmentation chain transfer mechanism. The copolymer models used were composed of a dextran backbone bearing poly(2-hydroxypropyl methacrylate) grafts of two degrees of polymerization (X). At a full monomer conversion, unilamellar vesicles (ULVs) and large compound nano-objects (LCNs) were formed when targeting X = 100 and 500, respectively. For X = 100, some spherical, worm-like, then jellyfish-like structures were progressively observed before the ULVs formation. For X = SOO, electron cryotomography revealed an unprecedented intermediate morphology formed from the onset of self-assembly called a multicompartment vesicle (MCV) that fused to form LCN. The formation of MCV was attributed to a local phase separation between dextran and the residual 2-hydroxypropyl methacrylate inducing the appearance of multiple hydrophilic cores.