Search Results (2,212)

The aim of this study was to optimize a set of technological parameters (travel speed, extruder temperature, and extrusion rate) for 3D printing with a PEEK-based composite reinforced with 30 wt.% glass fibers (GFs). For this purpose, both Taguchi and finite element methods (FEM) were utilized. The artificial neural networks (ANNs) were implemented for computer simulation of full-scale experiments. Computed tomography of the additively manufactured (AM) samples showed that the optimal 3D printing parameters were the extruder temperature of 460 °C, the travel speed of 20 mm/min, and the extrusion rate of 4 rpm (the microextruder screw rotation speed). These values correlated well with those obtained by computer simulation using the ANNs. In such cases, the homogeneous micro- and macro-structures were formed with minimal sample distortions and porosity levels within 10 vol.% of both structures. The most likely reason for porosity was the expansion of the molten polymer when it had been squeezed out from the microextruder nozzle. It was concluded that the mechanical properties of such samples can be improved both by changing the 3D printing strategy to ensure the preferential orientation of GFs along the building direction and by reducing porosity via post-printing treatment or ultrasonic compaction. Full article

This review explores the application of Long Short-Term Memory (LSTM) networks, a specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM networks have shown notable effectiveness in modeling sequential data and predicting time-series outcomes, which are essential for understanding complex molecular structures and dynamic processes in polymers. This review delves into the use of LSTM models for predicting polymer properties, monitoring polymerization processes, and evaluating the degradation and mechanical performance of polymers. Additionally, it addresses the challenges related to data availability and interpretability. Through various case studies and comparative analyses, the review demonstrates the effectiveness of LSTM networks in different polymer science applications. Future directions are also discussed, with an emphasis on real-time applications and the need for interdisciplinary collaboration. The goal of this review is to connect advanced machine learning (ML) techniques with polymer science, thereby promoting innovation and improving predictive capabilities in the field. Full article

The demand for polymer composites with improved mechanical and electrical properties is crucial for advanced aerospace, electronics, and energy storage applications. Single-walled carbon nanotubes (SWCNTs) and iron oxide (Fe₃O₄) nanoparticles are key fillers that enhance these properties, yet challenges like orientation, uniform dispersion, and agglomeration must be addressed to realize their full potential. This study focuses on developing SWCNTs/Fe₃O₄ epoxy composites by keeping the SWCNT concentration constant at 0.03 Vol.% and varying with Fe₃O₄ concentrations at 0.1, 0.5, and 1 Vol.% for two different configurations: randomly orientated (R-) and magnetic field-assisted horizontally aligned (A-) SWCNTs/Fe₃O₄ epoxy composites, and investigates the effects of filler concentration, dispersion, and magnetic alignment on the mechanical and electrical properties. The research reveals that both composite configurations achieve an optimal mechanical performance at 0.5 Vol.% Fe₃O₄, while A- SWCNTs/Fe₃O₄ epoxy composites outperformed at all concentrations. However, at 1 Vol.% Fe₃O₄, mechanical properties decline due to nanoparticle agglomeration, which disrupts stress distribution. In contrast, electrical conductivity peaks at 1 Vol.% Fe₃O₄, indicating that the higher density of Fe₃O₄ nanoparticles enhances the conductive network despite the mechanical losses. This study highlights the need for precise control over filler content and alignment to optimize mechanical strength and electrical conductivity in SWCNTs/Fe₃O₄ epoxy nanocomposites. Full article

The present work discusses the influence of the structural architecture of sodium alginate–co-acrylic acid–poly(ethylene) oxide hydrogels, crosslinked through electron beam (e-beam) radiation processing. The most important properties of the hydrogels were studied in detail to identify a correlation between the architecture of the hydrogels and their properties. Furthermore, the effect of sodium alginate (NaAlg) concentration, the amounts of the polymer blend, and the size of the samples on hydrogel properties were investigated. The results show that the hydrogels cross-linked (0.5% and 1% NaAlg) with 12.5 kGy exhibit improved physicochemical properties. High gel fraction levels (exceeding 83.5–93.7%) were achieved. Smaller hydrogel diameter (7 mm) contributed to a maximum swelling rate and degree of 20.440%. The hydrogel network was dependent on the hydrogels’ diameter and the amount of polymer blend used. The hydrogels best suited the first-order rate constants and exhibited a non-Fickian diffusion character with diffusion exponent values greater than 0.5. This study indicates that the cross-linked hydrogel has good properties, particularly because of its high degree of swelling and extensive stability (more than 180 h) in water. These findings show that hydrogels can be effectively applied to the purification of water contaminated with metals, dyes, or even pharmaceuticals, as well as materials with a gradual release of bioactive chemicals and water retention. Full article

The control of the performance of single-walled carbon nanotube (SWCNT) random network-based transistors is of critical importance for their applications in electronic devices, such as complementary metal oxide semiconducting (CMOS)-based logics. In ambient conditions, SWCNTs are heavily p-doped by the H₂O/O₂ redox couple, and most doping processes have to counteract this effect, which usually leads to broadened hysteresis and poor stability. In this work, we coated an SWCNT network with various common polymers and compared their thin-film transistors’ (TFTs’) performance in a nitrogen-filled glove box. It was found that all polymer coatings will decrease the hysteresis of these transistors due to the partial removal of charge trapping sites and also provide the stable control of the doping level of the SWCNT network. Counter-intuitively, polymers with electron-withdrawing functional groups lead to a dramatically enhanced n-branch in their transfer curve. Specifically, SWCNT TFTs with poly (vinylidene fluoride) coating show an n-type mobility up to 61 cm²/Vs, with a decent on/off ratio and small hysteresis. The inverters constructed by connecting two ambipolar TFTs demonstrate high gain but with certain voltage loss. P-type or n-type doping from polymer coating layers could suppress unnecessary n- or p-branches, shift the threshold voltage and optimize the performance of these inverters to realize rail-to-rail switching. Similar devices also demonstrate interesting antiambipolar performance with tunable on and off voltage when tested in a different configuration. Full article

Carbon nanotubes (CNTs) are often regarded as semi-rigid, all-carbon polymers. However, unlike conventional polymers that can form 3D networks such as hydrogels or elastomers through crosslinking in solution, CNTs have long been considered non-crosslinkable under mild conditions. This perception changed with our recent discovery of UV-defluorination-driven direct crosslinking of CNTs in solution. In this study, we further investigate the thermal stability of UV-defluorination-driven crosslinked CNTs, revealing that they are metastable and decompose more readily than either pristine or fluorinated CNTs under Raman laser irradiation. Using Raman spectroscopy under controlled laser power, we examined both single-walled and multi-walled fluorinated CNTs. The results demonstrate that UV-defluorinated CNTs exhibit reduced thermal stability compared to their pristine or untreated fluorinated counterparts. This instability is attributed to the strain on the intertube crosslinking bonds resulting from the curved carbon lattice of the linked CNTs. The metallic CNTs in the crosslinked CNT networks decompose and revert to their pristine state more readily than the semiconducting ones. The inherent instability of crosslinked CNTs leads to combustion at temperatures approximately 100 °C lower than those required for non-crosslinked fluorinated CNTs. This property positions crosslinked CNTs as promising candidates for applications where mechanically robust, lightweight materials are needed, along with feasible post-use removal options. Full article

Polymer fibers are considered ideal transmission media for all-optical networks, but their high intrinsic loss significantly limits their practical use. Quantum dot-doped polymer fiber amplifiers are emerging as a promising solution to this issue and are becoming a significant focus of research in both academia and industry. Based on the properties of CdSe/ZnS quantum dots and PMMA material, this study experimentally explores three fabrication methods for CdSe/ZnS quantum dots-doped PMMA fibers: hollow fiber filling, melt-drawing, and melt extrusion. The advantages and disadvantages of each method and key issues in fiber fabrication are analyzed. Utilizing the CdSe/ZnS quantum dots-doped PMMA fibers that were fabricated, we theoretically analyzed the key factors affecting gain performance, including fiber length, quantum dots doping concentration, and signal light intensity. Under the conditions of 1.5 W power and 445 nm laser pumping, a maximum on-off gain of 16.2 dB was experimentally achieved at 635 nm. Additionally, using a white light LED as the signal source, a broadband on-off gain with a bandwidth exceeding 70 nm and a maximum gain of 12.4 dB was observed in the 580–650 nm range. This research will contribute to the development of quantum dots-doped fiber devices and broadband optical communication technology, providing more efficient solutions for future optical communication networks. Full article

High-oxygen-permeability ionomers (HOPIs) are being actively developed to enhance the performance and durability of high-power polymer electrolyte membrane fuel cells (PEMFCs). While methods for evaluating binder performance are well-established, techniques for assessing binder durability and measuring its degradation in situ during the AST process remain limited. This study examines the distribution of relaxation times (DRT) and Warburg-like response (WLR) methods as in situ analysis techniques during the catalyst-accelerated stress test (AST) process. We conducted catalyst-ASTs (0.6–0.95 V cycling) for 20,000 cycles, monitoring changes using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and linear sweep voltammetry (LSV). Contrary to expectations, during the catalyst-AST, the ion transport resistance of the binder decreased, indicating no binder degradation. Scanning electron microscopy/energy dispersive spectrometer (SEM/EDS) analysis revealed that the degradation rate of the catalyst and the support was relatively higher than that of the binder, leading to a reduction in catalyst layer thickness and improved binder network formation. By applying the DRT method during the catalyst-AST process, we were able to measure the increase in oxygen reduction reaction (ORR) resistance and the decrease in proton transport resistance in situ. This allowed for the real-time detection of the reduction in catalyst layer thickness and improvements in ionomer networks due to catalyst and support degradation. These findings provide new insights into the complex interplay between catalyst degradation and binder performance, contributing to the development of more durable PEMFC components. Full article

The red-billed blue magpie optimizer (RBMO) is employed in this research study to address parameter extraction in polymer exchange membrane fuel cells (PEMFCs), along with three recently implemented optimizers. The sum of squared deviations (SSD) between the simulated and measured stack voltages defines the fitness function of the optimization problem under investigation subject to a set of working constraints. Three distinct PEMFCs stacks models—the Ballard Mark, Temasek 1 kW, and Horizon H-12 units—are used to illustrate the applied RBMO’s feasibility in solving this challenge in comparison to other recent algorithms. The highest percentages of biased voltage per reading for the Ballard Mark V, Temasek 1 kW, and Horizon H-12 are, respectively, +0.65%, +0.20%, and −0.14%, which are negligible errors. The primary characteristics of PEMFC stacks under changing reactant pressures and cell temperatures are used to evaluate the precision of the cropped optimized parameters. In the final phase of this endeavor, the sensitivity of the cropped parameters to the PEMFCs model’s performance is investigated using two machine learning techniques, namely, artificial neural network and Gaussian process regression models. The simulation results demonstrate that the RBMO approach extracts the PEMFCs’ appropriate parameters with high precision. Full article

This study presents a soft-computing-based method for determining polymer pipelines’ creep function parameters (CFPs) and pressure wave speeds (PWSs) through transient flow analysis. To this end, first, a numerical model for transient flow in polymer pipes was developed in the time domain. Then, by considering a pipeline with a specific geometry, 2000 transient flow signals were generated for different CFPs and PWSs. The amplitudes obtained by transforming the time-domain pressure signals to the frequency domain using the fast Fourier transform algorithm are the inputs for an artificial neural network model. The results showed that the proposed approach accurately estimated the creep function of the polymer pipes. Full article

The widespread production and use of multi-fluorinated carbon-based substances for a variety of purposes has contributed to the contamination of the global water supply in recent decades. Conventional wastewater treatment can reduce contaminants to acceptable levels, but the concentrated retentate stream is still a burden to the environment. A selective anion-exchange membrane capable of capture and controlled release could further concentrate necessary contaminants, making their eventual degradation or long-term storage easier. To this end, commercial microfiltration membranes were modified using pore functionalization to incorporate an anion-exchange moiety within the membrane matrix. This functionalization was performed with primary and quaternary amine-containing polymer networks ranging from weak to strong basic residues. Membrane loading ranged from 0.22 to 0.85 mmol/g membrane and 0.97 to 3.4 mmol/g membrane for quaternary and primary functionalization, respectively. Modified membranes exhibited a range of water permeances within approximately 45–131 LMH/bar. The removal of PFASs from aqueous streams was analyzed for both “long-chain” and “short-chain” analytes, perfluorooctanoic acid and perfluorobutyric acid, respectively. Synthesized membranes demonstrated as high as 90% rejection of perfluorooctanoic acid and 50–80% rejection of perfluorobutyric acid after 30% permeate recovery. Regenerated membranes maintained the capture performance for three cycles of continuous operation. The efficiency of capture and reuse can be improved through the consideration of charge density, water flux, and influent contaminant concentration. This process is not limited by the substrate and, thus, is able to be implemented on other platforms. This research advances a versatile membrane platform for environmentally relevant applications that seek to help increase the global availability of safe drinking water. Full article

Although titanium and its alloys are widely used as dental implants, they cannot induce the formation of new bone around the implant, which is a basis for the functional integrity and long-term stability of implants. This study focused on the functionalization of the titanium/titanium oxide surface as the gold standard for dental implants, with electrospun composite fibers consisting of polyvinylpyrrolidone and Ca²⁺ ions. Polymer fibers as carriers of Ca²⁺ ions should gradually dissolve, releasing Ca²⁺ ions into the environment of the implant when it is immersed in a model electrolyte of artificial saliva. Scanning electron microscopy, energy dispersive X-ray spectroscopy and attenuated total reflectance Fourier transform infrared spectroscopy confirmed the successful formation of a porous network of composite fibers on the titanium/titanium oxide surface. The mechanism of the formation of the composite fibers was investigated in detail by quantum chemical calculations at the density functional theory level based on the simulation of possible molecular interactions between Ca²⁺ ions, polymer fibers and titanium substrate. During the 7-day immersion of the functionalized titanium in artificial saliva, the processes on the titanium/titanium oxide/composite fibers/artificial saliva interface were monitored by electrochemical impedance spectroscopy. It can be concluded from all the results that the composite fibers formed on titanium have application potential for the development of osteoinductive and thus more biocompatible dental implants. Full article

A theoretical framework is established for the gel diffusiophoresis of a spherical colloidal particle moving through an uncharged dilute porous polymer gel medium when an electrolyte concentration gradient field is applied. The network of cross-linked polymer segments is treated as a porous skeleton containing an electrolyte solution using the Brinkman–Debye–Bueche model. We derive a general expression for the gel-diffusiophoretic mobility of a charged spherical colloidal particle. Based on this general mobility expression, we farther derive a closed-form approximate expression for the gel-diffusiophoretic mobility of a weakly charged spherical particle correct to the second order of the particle’s zeta potential. The obtained mobility expression depends on the Debye–Hückel parameter and the Brinkmann parameter. Full article

Biofouling poses a significant challenge to the marine industry, and silicone anti-biofouling coatings have garnered extensive attention owing to their environmental friendliness and low surface energy. However, their widespread application is hindered by their low substrate adhesion and weak static antifouling capabilities. In this study, a novel silicone polymer polydimethylsiloxane (PDMS)-based poly(urea-thiourea-imine) (PDMS-PUTI) was synthesized via stepwise reactions of aminopropyl-terminated polydimethylsiloxane (APT-PDMS) with isophorone diisocyanate (IPDI), isophthalaldehyde (IPAL), and carbon disulfide (CS₂). Subsequently, a nanocomposite coating (AgNPs-x/PDMS-PUTI) was prepared by adding silver nanoparticles (AgNPs) to the polymer PDMS-PUTI. The dynamic multiple hydrogen bonds formed between urea and thiourea linkages, along with dynamic imine bonds in the polymer network, endowed the coating with outstanding self-healing properties, enabling complete scratch healing within 10 min at room temperature. Moreover, uniformly dispersed AgNPs not only reduced the surface energy of the coating but also significantly enhanced its antifouling performance. The antibacterial efficiency against common marine bacteria Pseudomonas aeruginosa (P.sp) and Staphylococcus aureus (S.sp) was reduced by 97.08% and 96.71%, respectively, whilst the diatom settlement density on the coating surface was as low as approximately 59 ± 3 diatom cells/mm². This study presents a novel approach to developing high-performance silicone antifouling coatings. Full article

Polymer materials have garnered significant attention due to their exceptional mechanical properties and diverse industrial applications. Understanding the glass transition temperature (T_g) of polymers is critical to prevent operational failures at specific temperatures. Traditional methods for measuring T_g, such as differential scanning calorimetry (DSC) and dynamic mechanical analysis, while accurate, are often time-consuming, costly, and susceptible to inaccuracies due to random and uncertain factors. To address these limitations, the aim of the present study is to investigate the potential of Simplified Molecular Input Line Entry System (SMILES) as descriptors in simple machine learning models to predict T_g efficiently and reliably. Five models were utilized: k-nearest neighbors (KNNs), support vector regression (SVR), extreme gradient boosting (XGBoost), artificial neural network (ANN), and recurrent neural network (RNN). SMILES descriptors were converted into numerical data using either One Hot Encoding (OHE) or Natural Language Processing (NLP). The study found that SMILES inputs with fewer than 200 characters were inadequate for accurately describing compound structures, while inputs exceeding 200 characters diminished model performance due to the curse of dimensionality. The ANN model achieved the highest R² value of 0.79; however, the XGB model, with an R² value of 0.774, exhibited the highest stability and shorter training times compared to other models, making it the preferred choice for T_g prediction. The efficiency of the OHE method over NLP was demonstrated by faster training times across the KNN, SVR, XGB, and ANN models. Validation of new polymer data showed the XGB model’s robustness, with an average prediction deviation of 9.76 from actual T_g values. These findings underscore the importance of optimizing SMILES conversion methods and model parameters to enhance prediction reliability. Future research should focus on improving model accuracy and generalizability by incorporating additional features and advanced techniques. This study contributes to the development of efficient and reliable predictive models for polymer properties, facilitating the design and application of new polymer materials. Full article