Fused deposition modelling/fused filament fabrication (FDM/FFF) enables rapid manufacturing of functional polymer components; however, the reliability of printed parts remains strongly governed by internal architecture, process-induced porosity, and exposure to service fluids. This study quantifies the combined influence of (i) infill pattern (linear, triangular, hexagonal) at 30% density, (ii) infill density (30%, 60%, 100%) for linear infill, and (iii) short-term lubricant exposure on the tensile and microstructural response of FDM-printed polyethylene terephthalate glycol-modified (PETG) and short-carbon-fibre-reinforced PETG (PETG+CF). Specimens were printed following ISO 527-2 and tensile-tested at 5 mm/min. Microstructural analysis coupled quantitative porosity with mechanical response, Young's Modulus, and strain-to-break. At 30% density, PETG with hexagonal infill achieved the highest tensile strength (18.54 ± 0.67 MPa), exceeding linear (16.99 ± 0.52 MPa) and triangular (14.15 ± 0.70 MPa) patterns, while triangular and linear patterns exhibited higher Young's Modulus, indicating topology-driven decoupling of stiffness and strength. Increasing linear infill density raised strength to 31.35 ± 0.33 MPa (PETG) and 38.90 ± 0.28 MPa (PETG+CF) at 100%, consistent with reduced porosity. Seven-day immersion in SAE 15W-40 mineral engine oil reduced PETG strength by ~17% while increasing deformation to failure, whereas PETG+CF showed only minor changes. Overall, the results demonstrate that architecture-aware design, supported by quantitative porosity descriptors, is essential for ensuring the reliable mechanical performance of FDM/FFF-printed PETG-based components exposed to service fluids.

The treatment of ocular cancer faces unique challenges. The complex physiological structures of the eye, such as the corneal barrier and the blood-eye barrier, restrict the efficiency of drug delivery. Traditional chemotherapy drugs, due to their low solubility and poor stability, not only have poor therapeutic effects but also are prone to cause side effects such as ocular irritation. Polymer-based delivery systems, with their controllable chemical structure, good biocompatibility and targeted delivery capabilities, have become key carriers for optimizing the delivery effect of drugs for ocular cancer. The current commonly used machine learning algorithms are difficult to meet the requirements of the refined assessment of the ocular cancer delivery system. Therefore, this paper proposes the LSTM-Adaboost classification algorithm. Firstly, violin graph analysis and correlation analysis are carried out, and then multiple machine learning algorithms are used for testing. The results show that the core evaluation indicators of this algorithm are generally superior to those of ExtraTrees, decision tree, GBDT, Random Forest, CatBoost, AdaBoost and XGBoost algorithms. Its accuracy rate and recall rate both reach 89.3%, and the precision rate and F1 value are both 89.2%. Compared with the suboptimal ExtraTrees algorithm, all indicators were 87.4%, increasing by 1.9, 1.9, 1.8, and 1.8 percentage points respectively. Compared with the decision tree algorithm, which has an accuracy rate and recall rate of 78%, an precision rate of 80%, and an F1 value of 78.7%, and the GBDT algorithm, which has an accuracy rate and recall rate of 81.1%, an precision rate of 81.7%, and an F1 value of 81.4%, its advantages are more significant. The AUC indicator is 94.9%, which is slightly lower than the 95.1% of the ExtraTrees algorithm, but higher than other algorithms. Overall, it still leads. This algorithm provides a reliable method for the refined evaluation of ocular cancer delivery systems and is of great significance for promoting the optimization of drug delivery technologies for ocular cancer treatment.

Sequential enzymatic hydrolysis enables isolation of bioactive functional group-enriched lignin-carbohydrate complex: Insights into structure and α-glucosidase inhibition potential.

Dual-platform chemochromic sensor for low concentration hydrogen gas detection using Y2O3/Pd nanocomposite- incorporated functional polymer: An experiment and DFT approach

Biocompatible thin films are essential for advancing biomedical devices, as they enhance integration with biological tissues, improve device longevity, and reduce complications. The rapid evolution of both medical needs and materials science has led to a diverse array of deposition techniques, each offering unique advantages and challenges for tailoring surface properties without compromising the bulk characteristics of implants and sensors. While laser-based methods-such as pulsed laser deposition (PLD) and Matrix-Assisted Pulsed Laser Evaporation (MAPLE)-are renowned for their precision, ability to preserve complex material stoichiometry, and suitability for low-temperature processing, the broader landscape includes several other important approaches. Physical Vapor Deposition (PVD) techniques, including magnetron sputtering and pulsed electron deposition, are widely used for their ability to create uniform, adherent coatings with controlled thickness and composition, making them suitable for both hard and soft biomedical substrates. Chemical Vapor Deposition (CVD) and its plasma-enhanced variant (PECVD) offer conformal coatings and excellent control over film chemistry, which is particularly valuable for functional polymer and ceramic films. Other methods, such as sol-gel processing, ion beam deposition, and electrophoretic deposition, provide additional flexibility in terms of coating composition, adhesion, and processing temperature, allowing for the fabrication of films with tailored mechanical, chemical, and biological properties. Despite these advances, the field faces ongoing challenges in optimizing film properties for specific clinical applications, ensuring reproducibility, and scaling up production for widespread use. The necessity of this review lies in its comprehensive comparison of laser-based techniques with alternative deposition methods, providing critical insights into their respective strengths, limitations, and suitability for different biomedical scenarios. By synthesizing recent developments and highlighting current gaps, this review aims to guide researchers and clinicians in selecting the most appropriate thin-film deposition strategies to meet the evolving demands of next-generation biomedical devices.

Carbon dioxide (CO2) and carbon monoxide (CO) are among the most abundant, renewable C1 building blocks that have been widely used to synthesize sustainable polymers. However, their markedly different thermodynamic profiles have precluded their simultaneous incorporation into a single polymer. Here we report a novel reaction strategy for the selective construction of formal CO/CO2/butadiene terpolymers in a 1:1:2 molar ratio. This protocol involves the Pd-catalyzed telomerization of CO2 with butadiene to form a lactone-based intermediate (EVP), followed by its catalytic alternating copolymerization with CO, affording functional polyketones bearing pendent unsaturated lactone side groups in high yields. Notably, both the ketone and lactone functionalities within the functional polymers can be quantitatively reduced to hydroxyl groups using sodium borohydride (NaBH4) at room temperature. The obtained multi-hydroxy functionalized polymer exhibits strong adhesion to common substrates (adhesion strength up to 10.13MPa on wood) together with desirable oxidative degradability.

The management of bacterial plant diseases is impeded by biofilm fortifications and the poor foliar affinity of conventional antimicrobials. Supramolecular assemblies have recently emerged as promising biofilm-eradicating agents with enhanced surface adhesion. Yet, supramolecular polymers, although endowed with comparable or even greater potential, remain largely untapped in this arena. Herein, we introduce NOP@CB[8], a flower-like supramolecular polymer self-assembled in water from a de novo designed cationic pyridinium salt (NOP) and cucurbit[8]uril (CB[8]). Acting as a multifunctional agent, NOP@CB[8] disrupts bacterial membranes, perturbs redox equilibrium, disintegrates biofilms, and concurrently enhances foliar affinity. These combined attributes endow NOP@CB[8] with potent in vivo efficacy, exhibiting protective and curative efficacies of 56.1% and 51.2%, respectively, at 200µg mL-1 against rice bacterial leaf blight, thereby surpassing both free NOP (47.9%/43.1%) and thiodiazole copper (TC, 36.2%/33.7%). Remarkably, NOP@CB[8] delivers high control efficacy with uncompromised safety toward both target and non‑target organisms, even demonstrates enhanced safety in zebrafish relative to free NOP. Extending its scope to citrus and kiwifruit cankers, NOP@CB[8] achieves approximately 80% protective and over 60% curative efficacy, consistently outperforming NOP and TC. Together, this study delineates a green alternative for crop protection and a conceptual framework for next-generation functional supramolecular polymers.

This study presents a microwave-assisted hydrolysis (MAH) method for accurately determining the molar mass of functional methacrylic polymers and their protein conjugates synthesized via controlled/living polymerization by a grafting from approach. By cleaving ester side chains, MAH converts polymers into linear poly(methacrylic acid) (PMAA), enabling precise molar mass analysis through aqueous size-exclusion chromatography (ASEC). The method is applied to polyglycerol methacrylate (PGMA), polyethylene glycol methacrylate (P(PEGMA)), and lysozyme-PGMA conjugates, with hydrolysis kinetics evaluated under both conventional and microwave heating. Notably, P(PEGMA) exhibits strong resistance to base-catalyzed hydrolysis due to PEG stabilization; however, microwave irradiation significantly improves conversion, achieving results infeasible with standard heating. Characterization by 1H-NMR, FTIR, and SEC confirms successful hydrolysis and accurate molar mass determination. Calibration using PGMA standards further enhances analytical reliability. The MAH-ASEC approach proves robust, scalable, and broadly applicable, offering a valuable tool for the physicochemical characterization of complex polymeric conjugate systems, particularly in biomedical and materials science contexts.

Research on organic phosphorescence shows great promise, while classical small-molecule phosphorescence systems often face challenges in terms of cost, scalability, and functionalization. Polymer matrices provide powerful and versatile platforms for fabricating advanced functional organic phosphorescent materials. The unique microenvironment within dense polymer networks effectively stabilizes the generated triplet excitons, and the inherent diversity of polymers allows for broad functional integration. This perspective summarizes key construction strategies of polymer matrix-supported organic phosphorescent materials and highlights how these systems enable useful functions, including tunable luminescence, stimulus responsiveness, mechanical flexibility, water solubility, and potential biocompatibility, for targeted applications. Finally, we discuss critical challenges and future directions for further advancement of this rapidly growing field.

Polymer functionalization is rapidly emerging as a transformative strategy for enhancing nanocatalysts by reprogramming the catalytic interface, rather than simply modifying the active phase. This approach leverages the unique tunability of polymers through their chemistry, thickness, permeability, charge density, and ionic/electronic conductivity to stabilize nanophases, regulate local microenvironments, and manage mass transport. These properties significantly improve catalytic activity, selectivity, and long-term durability. This review provides an in-depth examination of key construction strategies for polymer-functionalized nanocatalysts, categorizing them into six primary platforms: neutral functional polymers, ionomers/polyelectrolytes, conductive polymers, crosslinked networks/hydrogels, hybrid polymers, and framework polymers. Additionally, we explore recent advances in electrocatalysis, photocatalysis, and thermocatalysis, addressing challenges such as the trade-off between protection and accessibility, polymer stability under extreme conditions, and the need for standardized reporting of polymer descriptors. By framing polymers as programmable interfacial materials, this review highlights their potential to unlock significant improvements in catalytic performance across various catalytic systems.

Functional polymers: chemistry, light, and applications in memoriam Wolfgang Kern (12th October 1963–29th September 2024)

ABSTRACT Polyvinylidene fluoride (PVDF) is a functional polymer with highly desirable electrical and piezoelectric properties. Optimizing its piezoelectric performance requires modifications to its morphology, composition, and electrical characteristics. Plasticizers are commonly known to improve flexibility in polymer materials and are used to facilitate ion transportation in electrolytes, increasing the conductivity. However, inclusion of plasticizer to enhance piezoelectric behavior, primarily by adding flexibility, facilitating better dipole orientation and consequently inducing higher piezoelectricity, was little explored. This study investigates the effect of the addition of ethylene carbonate (EC) as a plasticizer on the piezoelectric performance of PVDF. The PVDF/EC films using the spin‐coating method were fabricated with different concentrations of EC loading (from 1 to 9 wt.%). The morphology of the prepared samples was investigated using scanning electron microscopy (SEM) and polarized microscopy studies. The crystalline structure and phase changes were analyzed using Fourier transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD), and Raman spectroscopy. The thermal behavior of the samples was analyzed through differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The electrical permittivity under different DC frequencies was investigated using an LCR meter. First of its kind, the incorporation of EC plasticizer demonstrates an enhancement of the β‐phase due to inter‐molecular bonding between the carbonyl group from EC and the hydrogen atom from PVDF, which is crucial for improved dielectric properties. The experiment proves the ability of EC to modify the crystalline structure of PVDF with only polymorphism, which is possible with 2 wt.% of EC in PVDF at 120°C of annealing temperature. These findings establish the potential of plasticizer integration as a cost‐effective methodology for applications in sensors and energy storage devices.

The efficient conversion of dissipated heat into useful electrical energy has emerged as a promising approach for sustainable energy technologies. Ionic thermoelectrics (iTEs) are particularly attractive because they generate substantial thermo-voltages, effectively harvest low-grade heat, and offer advantages such as cost-effectiveness, easy scalability, and remarkable performance. Unlike liquid-state platforms, quasi-solid-state iTEs exhibit properties that are critically governed by the polymer matrix and polymer-ion interactions, which are closely related to the overall device performance. Consequently, the use of functional polymers effectively improves the characteristics and performance of quasi-solid-state iTEs. Therefore, this paper highlights the impact of the polymer matrix on performance and mechanical properties in iTEs from molecular-, micro-, to macro-scale engineering. Particular emphasis is placed on clarifying the role of polymers at various scales to provide an in-depth understanding of performance enhancement. Furthermore, the current challenges and prospective research directions are discussed, offering guidance toward the development of next-generation iTE-based energy harvesting platforms.

ABSTRACT The development of nanomedicines has long relied on scientific intuition within finite chemical space, but machine learning offers a data‐driven approach to explore far broader chemical landscapes. Advances in high‐throughput polymer synthesis and screening now enable the generation of datasets large enough to train predictive models to accurately link polymer structure with biological function. The next challenge is using machine learning to guide iterative development of nanomedicines through active learning. Here we demonstrate an active learning workflow for the design of antiviral polymers. Using molecular descriptors derived from polymer composition and monomer structure, a machine learning model was trained on an experimental dataset of antiviral polymer activity. The trained model was coupled with unsupervised clustering of monomers to explore chemical diversity and predict antiviral activity across a virtual library of up to 500,000 new polymers. By calculating the expected improvement function, active learning identifies optimal candidates for synthesis to efficiently explore chemical space and optimize antiviral activity. This communication highlights how machine learning and active learning can serve as practical, accessible tools for chemists and biologists to accelerate design of functional polymers and future nanomedicines.

Scaling up technologies for creating relief surfaces with controlled wetting properties is a pressing issue for the development of functional polymer materials. There is a need to investigate the patterns of transfer of micro- and nanotextures from metal templates to polymer substrates. The purpose of the study was to determine the influence of the replication method and polymer properties on the accuracy of relief reproduction and the development of water-repellent surface characteristics. Three replication methods were used for this purpose: obtaining polycarbonate film from a solution, reaction moulding using polydimethylsiloxane elastomer, and thermoforming of polyethylene films. The negative copies obtained were used as intermediate forms (polycarbonate) for creating positive replicas from polyethylene and polysiloxane. The hydrophobicity of the materials was evaluated by measuring the contact angles in directions parallel and perpendicular to the texture orientation. The results showed that all methods ensure the reproduction of periodic structures. However, the level of accuracy depends on the viscosity of the medium and the temperature conditions, with the lowest level of accuracy observed for polycarbonate and the highest level observed for polyethylene. Polycarbonate negatives were characterised by high detail of microdefects (up to 5 µm), while polyethylene films showed smoothing of the relief. Silicone positives most fully preserved the geometry of the original textures with defects. The study of surface hydrophobicity showed a significant increase in contact angles on structured surfaces compared to smooth ones, especially with parallel orientation. The maximum contact angles reached 140°C for polysiloxane and 139°C for polyethylene replicas of the structure with regular grooves. The practical significance of the study lay in identifying the optimal materials and methods for creating polymer surfaces with increased water repellency. Polycarbonate is suitable for use as an intermediate material for accurate texture copying, while silicone and polyethylene have potential for use as functional surfaces in protective coatings and liquid-infused porous surfaces.

Two-dimensional (2D) materials offer exceptional electrical, optical, and mechanical properties but face challenges in terms of scalability, stability, and integration. Hybridizing 2D materials with polymers provides an effective route to overcome these limitations by enabling tunable interfaces, mechanical compliance, chemical functionality, and three-dimensional device processability. This review summarizes the fundamental structural configurations of 2D-polymer hybrids, including embedded composites, stacked heterostructures, covalently functionalized interfaces, polymer-encapsulated layers, and fiber-network architecture, and describes how their interfacial interactions dictate charge transport, environmental robustness, and mechanical behavior. We also highlight major fabrication strategies, such as solution dispersion, in situ polymerization, and vapor-phase deposition. Finally, we discuss emerging applications in sensors, optoelectronics, neuromorphic systems, and energy devices, demonstrating how synergistic coupling between 2D materials and functional polymers enables enhanced sensitivity, programmable electronic states, broadband photodetection, and improved electrochemical performance. These insights provide design guidelines for future multifunctional and scalable 2D-polymer hybrid platforms.

Lithography serves as a foundational process in semiconductor fields, enabling high-resolution patterning and transfer. Among various pattern transfer methods, the lift-off process is widely used owing to its material versatility and etch-free advantages. However, conventional lift-off faces several limitations, including solvent-related environmental concerns, low yield, and poor pattern fidelity. To overcome these challenges, we introduce a solvent-free dry lift-off method based on polyvinylidene fluoride (PVDF), a functional polymer with a high thermal expansion coefficient. Thermal shrinkage of PVDF under controlled heating and cooling conditions mechanically interlocks with the resist, enabling spontaneous delamination of the resist structure without the need for solvents or mechanical forces. This method achieves 100% yield and rapid fabrication of high-resolution, high-density patterns at the wafer scale. The process is compatible with both photolithography and electron-beam lithography. We further demonstrate its application in multilayer film-based Fabry-Pérot cavity devices, achieving large-area, uniform structural color patterns. This work establishes a scalable, environmentally friendly spontaneous dry lift-off strategy for next-generation sustainable micro- and nanofabrication.

Surface chemistry effects on electrochemical impedance spectroscopy of biomacromolecule interactions.

ABSTRACT This review article covered the use of electrochemical and optical techniques for 17β‐estradiol(E2) and Progesterone(P4) detection. In this study, many types of nanoparticle‐based electrochemical and optical biosensors for Progesterone(P4) and 17β‐estradiol(E2) detection are critically examined. The sex steroid hormones progesterone(P4) and 17β‐estradiol (E2) are produced by the human ovarian follicle and corpus luteum via the traditional steroidogenesis route throughout the reproductive years before menopause. Cholesterol or acetate is the starting material in this process. These hormones play a major role in foetal development, the early pregnancy, control of the menstrual cycle, and reproductive tissues. Disproportionality of these hormones in the human body may result in undesired side effects such as breast soreness, headache, mood swings, anxiety, infertility, premature puberty, hyperandrogenism, hyperthyroidism, and, probably, ovarian cancer. Progesterone(P4) and 17β‐estradiol(E2) are naturally occurring endocrine‐disrupting chemicals (EDCs) that are excluded by both domestic animals and people. Therefore, the development of effective techniques for the measurement of Progesterone(P4) and 17β‐estradiol(E2) is urgently needed. We reviewed the many research publications on 17β‐estradiol (E2) and Progesterone(P4) that are available in the literature. To achieve highly sensitive detection of 17β‐estradiol(E2) and progesterone(P4), we carefully examined several studies that use a range of interfering agents and nanomaterials. 17β‐estradiol(E2) and progesterone(P4) can be measured analytically using HPLC, gas chromatography‐mass spectrometry, and liquid chromatography mass spectrometry. These techniques are very sensitive and accurate. Nevertheless, they necessitate costly and advanced equipment, intricate, multi‐step sample preparation processes, and lengthy analysis periods. Taking these restrictions into account, electrochemical and optical biosensors based on nanoparticles have been reported for the sensing of 17β‐estradiol (E2) and progesterone(P4) employing biorecognition molecules such as aptamers, functional polymers, or antibodies. Our goal is to consolidate all of the previously published data on 17β‐estradiol(E2) and progesterone(P4) sensing and compare each approach; we primarily concentrated on the principle, observations, detection limit, reaction mechanism, and concentration.

Optical coherence tomography as a viable tool for monitoring curing processes of functional pharmaceutical polymer coatings