Polymers最新文献

The use of adsorbents based on naturally occurring materials to eliminate antibiotics from industrial effluents has attracted remarkable interest owing to the abundance of raw materials and the sustainability of this method. The ciprofloxacin (CIP) removal capacity of a previously synthesized antimicrobial hydrogel based on carboxymethyl chitosan (CMCs)/xanthan gum (XG) was investigated for the first time in this study. CMCs and XG were blended in an equivalent-weight ratio and crosslinked using trimellitic anhydride isothiocyanate (TAI) to synthesize an eco-friendly, low-cost hydrogel, which was characterized using FTIR, SEM, and XRD analyses. The pseudo-second-order model fitted the experimental data well: the experimental q_e (49.59 mg g^-1) was close to the theoretical value (51.81 mg g^-1). The Langmuir isotherm best fitted the adsorption results (R² = 0.999), with a maximum adsorption capacity of 147.06 mg g^-1. The thermodynamic results indicate that adsorption is spontaneous, favorable, and exothermic in nature. The percentages of desorption obtained were 95.72, 94.34, 89.52, 88, and 86.28% after five consecutive cycles. Thus, this hydrogel possesses potential for further testing and application in wastewater remediation.

Objective: This study aimed to obtain a core-shell chitosan-TiO₂ nanoparticle and to investigate its ability to inhibit Candida albicans biofilm formation when added to 3D-printed polymethyl methacrylate (PMMA) denture base resins. Materials and Methods: Ionic gelation was employed to prepare and characterize the nanoparticle, and Atomic Force Microscopy (AFM), Field Emission Scanning Electron Microscopy (FE-SEM), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray diffraction were used to identify the structure and morphology. Nanoparticle was added to 3D-printed denture resins at four different weight percentages (0.25%, 0.5%, 0.75%, and 1%) and antibiofilm activity was determined by carrying out Colony Forming Unite (CFU) counts after exposure to C. albicans. Results: The 0.25 wt.% chitosan-TiO₂ group exhibited a significant reduction in colony-forming units (CFUs) compared to the control (p < 0.05). Although higher nanoparticle concentrations showed improved biofilm formation, this was most likely caused by nanoparticle aggregation, which interfered with surface homogeneity and biofilm resistance. Conclusions: Incorporating a 0.25 wt.% core-shell chitosan-TiO₂ nanoparticle into 3D-printed denture base resin markedly improves its antibiofilm activity against Candida albicans while maintaining the material's integrity.

Heavy metal contamination remains a critical threat to water quality, particularly in effluents associated with industrial activities such as electroplating. This study presents an exploratory proof of concept for a simplified and low-requirement method to fabricate bovine serum albumin (BSA) hydrogels crosslinked with glutaraldehyde (GA) as protein-based adsorbents for Cu²⁺, Ni²⁺, and Co²⁺ removal. Hydrogel slabs were prepared using BSA concentrations of 20% and 25% (w/v) and GA in the 0.6-1.0% (v/v) range, with formulation adjustments guided by handling and aqueous stability. Swelling behavior was monitored for 23 days, and 0.9% (v/v) GA was selected to balance network expansion with hydrogel consistency. FT-IR confirmed preservation of protein functional groups in the crosslinked network, and TGA/DTG demonstrated multi-step thermal behavior consistent with hydrated protein matrices and a stabilizing effect of increased GA content. Metal removal tests at 50-100 ppm (Cu²⁺, Ni²⁺) and 70-100 ppm (Co²⁺) showed rapid removal approaching equilibrium within the first hours and improved performance at higher BSA content, achieving maximum removal percentages of 99.258% for Cu²⁺, 80.733% for Ni²⁺, and 76.070% for Co²⁺. Adsorption behaviors for Cu²⁺ and Co²⁺ aligned with the Langmuir model, while Ni²⁺ was better described by the Freundlich model. Although the scope is intentionally preliminary and limited to controlled synthetic systems, these results support GA-crosslinked BSA hydrogels as promising, easily fabricated adsorbents and establish a foundation for future studies on broader ion selectivity, competitive adsorption, and adsorption-desorption performance.

Theoretical models of polyelectrolyte systems with cross-linked polymer networks are often simplified to linear differential equations by means of the linearized Poisson-Boltzmann approximation, whose validity is traditionally limited to cases where the electrostatic potentials are small. However, the limits of applicability of the linear theory remain debatable in many cases. Moreover, the Poisson-Boltzmann equation is, in principle, not applicable to the description of non-equilibrium systems, particularly those through which an electric current flows. In the present work, a direct comparison is carried out between the exact solution and the approximate solution (i.e., the solution obtained within the framework of the linearization procedure) of the equations describing the contact region between a cross-linked polyelectrolyte network and a low-molecular-mass salt solution. This makes it possible to determine the conditions under which the linear model is applicable, including for the analysis of promising systems in the field of organic electronics. The conclusions obtained in this work are based on basic electrostatics equations and transport equations of low-molecular-mass ions. The proposed approach also makes it possible to obtain a generalized linear differential equation that is not subject to a Boltzmann distribution approximation and is valid for polyelectrolyte systems rather far from thermodynamic equilibrium and even carrying steady electric currents.

Hydrogels have attracted significant interest in multifunctional applications. Among them, self-healing hydrogel stands out for its ability to autonomously repair damage through reversible interactions, yet achieving both rapid self-healing and superior mechanical strength remains challenging. In this study, we report the fabrication of a dual cross-linked hydrogel (PAA-Alg-B) prepared via free radical polymerization of acrylic acid and alginic acid, employing N,N'-methylenebisacrylamide, or vinyl-modified nanocellulose as primary cross-linker, with Fe³⁺ or borax serving as an additional dynamic cross-linker. The resulting borax based hydrogel (PAA-Alg-B) exhibits remarkable fast self-healing efficiency enabled by reversible borate ester bonds and hydrogen bonding. It demonstrates tunable mechanical strength with toughness of 137 kJ/m³ and elongation at break up to 1117%, alongside exceptional swelling capacity (448 g/g). The adsorption studies reveal high removal efficiencies for heavy metals, with maximum capacities of 87.57 mg/g (Cr³⁺), 114.02 mg/g (Ni²⁺), and 99.42 mg/g (Cu²⁺), governed by chemisorption kinetics. The PAA-Alg-B can also be used as a promising solid-state electrolyte and separator for flexible supercapacitors. Protonic modulation via H₂SO₄ soaking significantly enhances ionic conductivity, electrochemical performance, and cycling stability. These findings highlight the potential of natural polymer-based, mechanically robust, self-healing hydrogels for sustainable wastewater treatment and advanced energy storage applications.

This study investigates the wear behavior and multi-technique characterization of 3D printed thermoplastic polyurethane (TPU) intended for friction layers in transmission belts used in pharmaceutical manipulators. Two flexible TPU grades-TPU 51A and TPU 60A-were printed using fused deposition modeling (FDM) with varying printing temperatures (255-265 °C for 51A; 225-235 °C for 60A) and layer counts (three or four layers). Specimens were evaluated for Shore A hardness, wear resistance (mass loss using a Baroid lubricity tester under dry sliding against carton), tensile properties, crystallinity (XRD), chemical structure (FTIR), thermal stability (TGA), and scanning electron microscopy (SEM). The results show that printing parameters significantly influence the mechanical and tribological behavior of the materials. For TPU 51A, increasing the printing temperature to 265 °C and using four layers led to a substantial reduction in cumulative mass loss, although hardness decreased. In contrast, for TPU 60A, higher printing temperature and layer count increased hardness but also resulted in higher wear. Tensile tests indicated that specimens printed with fewer layers exhibited higher yield strength and strain, indicating improved interlayer bonding. XRD analysis confirmed the predominantly amorphous nature of the printed samples, with a reduction in crystallinity compared to the raw filaments. FTIR spectra showed no significant chemical degradation during printing, while thermogravimetric analysis revealed good thermal stability up to approximately 250-260 °C. The results demonstrate that wear behavior is governed by a combination of hardness, interlayer cohesion, and microstructural organization rather than crystallinity alone. Among the investigated conditions, TPU 51A printed at 265 °C with four layers exhibited the most favorable balance between wear resistance and mechanical properties, highlighting its suitability for friction layer applications.

Polymeric electrolyte membranes based on a low equivalent-weight Aquivion^® commercial dispersion (D72-25BS; EW = 720 g eq^-1, Syensqo) were fabricated using a standardized in-house doctor-blade casting technique for application in proton exchange membrane fuel cells (PEMFCs). The low equivalent-weight (EW) Aquivion^® dispersion is a copolymer of tetrafluoroethylene (TFE) and sulfonyl fluoride vinyl ether (SFVE), commonly referred to as a short-side-chain (SSC) ionomer, which exhibits higher ion-exchange capacity (IEC) and proton conductivity than long-side-chain (LSC) perfluorosulfonic membranes. A home-made 30 wt.% Pt/CeO₂ radical scavenger (denoted syn-scavenger) was synthesized via a colloidal method and incorporated into the Aquivion^® membranes to investigate its mitigating effect on chemical degradation induced by peroxide radicals, a role typically associated with Ce-based scavengers. Particularly, the unique aspects of the Pt/CeO₂ scavenger synthesis could be summarized in the following points: (i) the mild aqueous deposition approach enabling highly dispersed Pt species on CeO₂ without the use of organic ligands; and (ii) the tailored redox interaction between Pt and ceria that enhances radical scavenging activity. Two Aquivion^® membranes (denoted Aqu) containing different syn-scavenger loadings (1.0 and 1.5 wt.%) were prepared and compared with a pristine Aquivion^® membrane and a membrane containing commercial CeO₂ (1.0 wt.%). Physicochemical characterization of the scavenger was performed using transmission electron microscopy (TEM), BET surface area analysis, and X-ray diffraction (XRD). The membranes were characterized by micro-Raman spectroscopy, water uptake and hydration number (λ), IEC, and proton conductivity measurements. To assess membrane stability, exsitu chemical oxidative degradation tests were conducted using Fenton's reagent. Overall, the membrane containing 1.0 wt.% syn-scavenger emerged as the most promising candidate, exhibiting favourable chemical-physical properties and the lowest reductions in IEC and proton conductivity following the degradation test.

Polyvinyl alcohol (PVA)-based films are promising biodegradable alternatives to petroleum-derived plastics; however, their high rigidity and moisture sensitivity limit practical applications. In this study, PVA/carnauba wax (CW) films were prepared via solution casting and systematically modified using four plasticizers: glycerol (GLY), sorbitol (SOR), glucose (GLU), and sucrose (SUC), at concentrations of 0.1-0.5% (v/w, relative to PVA). Thermal analysis showed that GLY and SOR effectively reduced the glass transition temperature from 52.35 °C (control) to as low as 49.14 °C (0.2% GLY) and 50.70 °C (0.4% SOR), while SUC and SOR plasticized films exhibited improved thermal stability, with the highest melting temperature observed for 0.3% SUC (80.6 °C). SEM micrographs revealed that GLY at moderate concentrations (0.2-0.3%) produced the most homogeneous film morphology, whereas SUC at higher concentrations led to surface roughness and phase separation. Water contact angle measurements showed increased surface hydrophobicity at low plasticizer contents, with 0.1% GLY and 0.2% GLU exhibiting contact angles above 100° compared to the control film (<90°). Mechanical testing demonstrated that SUC at 0.2% had the highest tensile strength (3.03 MPa) compared to 0.73 MPa (control), while GLY at 0.3% yielded the highest elongation at break (9.26%), compared to 0.62% for the unplasticized film. These results demonstrate that precise control of plasticizer type and concentration enables effective tuning of PVA/CW film properties, offering a viable strategy for designing biodegradable films tailored for packaging and agricultural applications.

This study concerns the analysis of lattice structures printed with EPAX resin for the manufacturing of a motorcycling throttle cam with Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs). The design of the pattern core in the lattice structure is defined parametrically to identify optimal design points (best stiffness to weight ratio in particular). Some geometric parameters used as input in RSM and in the NN analysis include the origin of the lattice structure and its spatial orientation, cell dimensions, and thicknesses. The dataset obtained with this approach is used for an RSM analysis of variance (ANOVA) to highlight the most important inputs. NN analysis is performed on the same RSM dataset to confirm the results. Both methodologies identify in-domain points of optimal design due to the typical non-linear behavior of these structures. The literature and industrial experience already provide numerous references to studies characterizing lattice structures. However, related practical applications are often incomplete and only achieve functional rather than optimal models. The approach described also aims to overcome this limitation. The software used for the design is nTop 5.0.4.

Maintaining consistent part quality remains a critical challenge in industrial additive manufacturing, particularly in laser-based powder bed fusion of polymers (PBF-LB/P), where crystallization-driven thermal instabilities, governed by isothermal crystallization within a narrow sintering window, precipitate defects such as curling, warping, and delamination. In contrast to metal-based systems dominated by melt-pool hydrodynamics, polymer PBF-LB/P requires monitoring strategies capable of resolving subtle spatio-temporal thermal deviations under realistic industrial operating conditions. Although machine learning, particularly convolutional neural networks (CNNs), has demonstrated efficacy in defect detection, a structured evaluation of heterogeneous modeling paradigms and their deployment feasibility in polymer PBF-LB/P remains limited. This study presents a systematic cross-paradigm assessment of unsupervised anomaly detection (autoencoders and generative adversarial networks), supervised CNN classifiers (VGG-16, ResNet50, and Xception), hybrid CNN-LSTM architectures, and physics-informed neural networks (PINNs) using 76,450 synchronized thermal and RGB images acquired from a commercial industrial system operating under closed control constraints. CNN-based models enable frame- and sequence-level defect classification, whereas the PINN component complements detection by providing physically consistent thermal-field regression. The results reveal quantifiable trade-offs between detection performance, temporal robustness, physical consistency, and algorithmic complexity. Pre-trained CNNs achieve up to 99.09% frame-level accuracy but impose a substantial computational burden for edge deployment. The PINN model attains an RMSE of approximately 27 K under quasi-isothermal process conditions, supporting trend-level thermal monitoring. A lightweight hybrid CNN achieves 99.7% validation accuracy with 1860 parameters and a CPU-benchmarked forward-pass inference time of 1.6 ms (excluding sensor acquisition latency). Collectively, this study establishes a rigorously benchmarked, scalable, and resource-efficient deep-learning framework tailored to crystallization-dominated polymer PBF-LB/P, providing a technically grounded basis for real-time industrial quality monitoring.