Polyurethanes (PU) represent a significant linear economy challenge, as their heterogeneous structures impede the development of energy- and time-efficient recycling pathways. This study presents an intensified "trifecta" solvolysis design that integrates structural insights, thermodynamic optimization, and kinetic principles to achieve rapid deconstruction of diverse PU wastes under rapid and near-ambient conditions. Using model systems, we identified rigidity and physical network density as primary barriers to deconstruction due to reduced polymer swelling and reagent accessibility. Thermodynamic optimization established the co-solvent 1,3-dioxolane as a high-efficiency medium for solvent uptake, and revealed that a methanol volume fraction of XMeOH = 0.10 is critical for ensuring high thermodynamic compatibility and reagent accessibility in rigid segments. Furthermore, kinetic analysis underscored the requirement for an optimum KOH catalyst concentration in order to accelerate carbonyl linkage activation while suppressing water byproduct formation. The intensified trifecta system achieved > 90% solvolysis within short (15-60 min) timescales at remarkably low temperatures (30-70 degrees C) across diverse flexible, rigid, and crosslinked feedstocks. Notably, the process demonstrated high mixedwaste selectivity, achieving 96.9% PU solvolysis in untreated streams with negligible impact on non-target plastics. Finally, we verified this design using gram-scale feedstocks, demonstrating both the robustness of the deconstruction process and the recyclability of the solvent system. This work provides a rigorous engineering pathway for the selective deconstruction of heterogeneous polyurethane waste.

One of the most critical challenges in mechanical plastic recycling is the presence of various materials mixed within the recycled polymer. Even after selective collection, sorting, and cleaning, polymers such as PP, PVC, PS, and PE inevitably remain mixed, causing incompatibility, immiscibility, and inhomogeneity. These issues lead to decreased and inconsistent properties in recycled polymer blends, limiting their use in high-value applications. This study proposes a method for inline, real-time diagnosis of polymer composition during extrusion processes, which are central to large-scale mechanical recycling. The method trains a machine learning model to predict polymer compositions from pressure signals measured during extrusion. The underlying assumption is that extrusion pressure signals reflect both processing conditions and rheological properties of the polymer. Therefore, a change in composition uniquely determines the pressure signal under constant processing conditions. The proposed approach predicts polymer compositions, varying in 10 wt% increments, with 96% accuracy using only 10 s of pressure data. This enables real-time adjustment of processing parameters or product classification in response to continuously varying compositions. Moreover, the method requires only a single pressure sensor, offering a cost-effective and scalable solution for real-time quality control in polymer recycling.

This study utilized upcycled terephthalic acid (uTPA) to induce chain scission of poly(ethylene terephthalate) (PET), enriching carboxyl (-COOH) chain ends, and enhancing chain extension reaction to recover degraded PET, ultimately improving the mechanical recycling performance of PET. We detailed the stoichiometric control of epoxide-carboxyl chain end coupling during epoxy-based chain extension of degraded PET using rheological analysis. The addition of uTPA promoted controlled chain scission and selectively increased the concentration of reactive carboxyl end groups, enabling effective reaction with the epoxide groups of a multifunctional chain extender. A systematic rheological analysis revealed that chain extension efficiency is governed by the epoxideto-chain end molar ratio (R). When R approached 0.06, PET molecular recovery reached its maximum, indicating optimal stoichiometric balance. Additional incorporation of maleic-anhydride-grafted SEBS to the recovered PET produced a homogeneous dispersion of sub-2 mu m elastomer domains, improving elongation at break from 12% to over 400%. These findings demonstrate that uTPA-induced chain end enrichment enables stoichiometric control of reactive moieties, providing a practical route to recover molecular weight and improve the mechanical properties of recycled PET beyond conventional methods. The successful application of uTPA for stoichiometric control highlights a new pathway for creating value from upcycled materials, supporting closed-loop circular economy goals.

We demonstrate that concurrent use of carboxymethyl cellulose (CMC) and cellulose nanofibrils (CNFs) enhances the dispersion and processability of electrode slurries, and elucidate the underlying mechanisms via detailed rheological and microstructural analyses. Sedimentation tests and optical microscopy revealed that slurries containing both dispersants exhibited more uniform dispersion and greater stability than those prepared from CMC or CNFs alone. Dynamic rheological analyses, based on the sequence of physical processes (SPP) framework applied to large-amplitude oscillatory shear (LAOS), demonstrated that combined use of CMC and CNFs effectively suppressed shear-induced aggregation and the resultant stiffening, thereby improving coating processability. Notably, this synergistic effect exhibited a pronounced dependence on the mixing sequence: pre-mixing of CMC with graphite afforded more effective dispersion and stabilization than when CNFs were first mixed with graphite. Based on this sequential dependence and rheological analyses of the dispersant mixture, the improvements in dispersion and stability are attributed to the synergistic effects of steric and electrostatic repulsion from CMC adsorbed onto graphite surfaces, and the stabilization afforded by the CNF-rich matrix. The combined formulation also ensured robust coating and drying performance, minimizing edge defects and enabling homogeneous distribution of the styrene-butadiene rubber (SBR) binder, thereby preventing binder migration.

Conventional viscoelastic characterization of thermoplastic amorphous polymers employs rotational rheometer above the glass transition temperature (Tg) and Dynamic Mechanical Analysis below Tg. This work proposes a Single-Pellet Molding (SPM) method that enables comprehensive viscoelastic characterization across both regimes using a single rotational rheometer. The SPM procedure involves introducing a single polymer pellet into a custom mold seated on the lower plate, compressing the pellet with the upper plate, and molding a cylindrical sample between the parallel plates. This approach offers a critical advantage for thermally and oxidatively sensitive polymers, as sample preparation can be completed in air at temperatures below the trimming threshold without requiring an inert gas atmosphere. Whereas conventional preprocessing (requiring chamber opening for trimming) introduces cooling-induced artifacts to both sample and shaft geometry, that may lead to human error, the SPM method fundamentally eliminates these sources of uncertainty. SPM enables reproducible QC testing across Tg for Cyclo olefin copolymer (COC), addressing recycling limitations critical to smartphone camera lens production scraps. Validation experiments comparing SPM to traditional trimming procedures, using 5 mm diameter parallel plates, demonstrate a 69

Colloidal suspensions exhibit diverse phases from fluid-like to solid-like, which are critical for numerous industrial applications. However, accurately identifying their phases remains a challenge, as they depend on a complex interplay of solid volume fraction, particle size distribution, and interparticle interactions. Near phase boundaries, subtle microstructural changes can induce drastic macroscopic property variations, yet these differences are often indistinguishable through conventional observation. To overcome these limitations and the high computational costs of long-time simulations, we propose a transformer-driven framework based on reference-based data embedding. Unlike standard point cloud models that directly embed positions, our approach utilizes particle stress information as the primary feature while using spatial coordinates solely as a reference to map interparticle relationships. This allows the transformer-driven model to effectively capture structural characteristics at both local and global scales. By training the model exclusively on unambiguous regions far from phase boundaries to prevent mislabeling, we successfully predicted the complete phase diagram, which was further validated through theoretical and statistical analysis. Notably, our methodology significantly alleviates the need to monitor long-term structural convergence, which is typically challenging due to the inherently slow phase evolution in attractive colloidal systems. This framework provides a robust and cost-effective tool for the systematic discovery and reverse engineering of complex soft condensed matter.

In battery manufacturing, electrode slurries are transported through pipelines to the coating step. During pipe-flow transport, slurry microstructures can evolve and degrade electrode quality and manufacturing productivity. Conventional off-line tests inherently rely on sampling, which introduces time delay and can yield biased results because the slurry is spatially non-uniform and prone to inhomogeneity. To address these limitations, we construct a laboratory-scale pipe-flow system and directly probe undiluted electrode slurries using in-line electrochemical impedance spectroscopy (EIS) during flow. The in-line EIS distinguishes dispersant-content variations as small as 0.05 wt

In this study, we investigate the emulsion stabilizing and viscosity-enhancing effects of CO2-derived microalgae (Chlorella sp. HS2) through a simple cell-disruption process using ultrasonication. During ultrasonication, as the ultrasonic energy increases, the particle size of HS2 in suspension decreases, and more intracellular components are released. The suspension with disrupted HS2 particles is then separated by centrifugation into two fractions: a suspension with intracellular extracts and one with residue, distinguishing their performances in the oil-in-water emulsion. These separated fractions exhibit different emulsification and stability effects in oil-in-water emulsions, which are due to significant differences in surface-active protein content and particle size. The emulsion containing the extract fraction shows small droplet sizes and enhanced stability. Interestingly, the emulsion with the cell residue also shows a dramatic reduction in droplet size as particle size decreases. Rheological analysis, which confirms the differences in yield stress, modulus, and viscosity, shows that the disrupted cell debris, as cellulose-protein complexes forms a network structure throughout the emulsion, resulting in increased viscosity and improved emulsion stability. This study demonstrates that the physical microalgae cells via ultrasonication can simultaneously enhance emulsification and stabilization via an increase in viscosity and interfacial tension reduction effects, that are rarely observed under shear mixing.

This study investigates the effects of the molecular weight and degree of substitution (DS) of carboxymethyl cellulose (CMC) on the dispersion and mechanical properties of graphite/Si-based electrodes. By leveraging the interaction between silicon and CMC, which varies with the molecular weight and DS of CMC, the dispersion of silicon within the electrodes is effectively controlled. As the molecular weight of CMC increases, the longer chain length of CMC adsorbed on silicon leads to higher bridging efficiency, resulting in more pronounced bridging flocculation of silicon particles. A critical molecular weight exists at which silicon particles begin to agglomerate; however, increasing the DS of CMC from 0.7 to 0.9 reduces the hydrophobic interaction between CMC molecules, raising the critical molecular weight from approximately 190 kDa to 516 kDa. A trade-off exists between silicon dispersion and the mechanical properties of the electrodes, depending on the molecular weight of CMC. While lower molecular weight CMC at the same DS is beneficial for ensuring silicon dispersion, it also reduces the adhesion strength of the electrodes. However, increasing the DS from 0.7 to 0.9 allows the molecular weight of the binder to be increased up to 516 kDa without causing silicon agglomeration, thereby improving the adhesion strength and significantly enhancing the stability of the electrodes. This study presents a practical strategy to improve the stability of graphite/Si-based electrodes by uniformly dispersing high-content (similar to 15 wt%) silicon particles through Si-CMC interactions, determined by the molecular weight and DS of CMC. We believe that our study will contribute to enhancing the energy density and stability of next-generation silicon-based batteries.

Predicting and mitigating pore clogging is challenging for the sustainable operation of water treatment systems. During transport and filtration through membrane micropores, buoyant contaminants in water gradually deposit on the surface, reducing the membrane's lifespan and performance, and sometimes completely blocking the pores. To alleviate the negative effects of fouling and to ensure sustainable operation, it is necessary to understand the fundamental mechanisms of fouling and to predict the probability of fouling formation under specific geometrical and material conditions. In this study, multiscale simulations are conducted to understand the fundamental mechanisms of particulate fouling at a microscopic level based on a Lagrangian framework incorporating inter-particle hydrodynamic interactions. We investigate both dead-end and cross-flow filtration, considering the direction of the feed stream relative to the unit micropore. The results elucidate the quantitative background of fouling history, which agrees with experimental findings. Depending on the level of hydrodynamic stress specific to the clog location and the nature of inter-particle interactions, deformation or resuspension of the clog is observed, competing with deposition, which leads to a two-way fouling history. Dominant deposition leads to micropore clogging, and to the best of the authors' knowledge, this is the first study to observe complete blockage and subsequent reopening. With this approach, the microscopic backgrounds between permanent and temporary pore blocking are distinguished. This study is expected to provide useful insights for controlling operational conditions to optimize anti-fouling performance in the transport and filtration through micropores.

In this study, we investigate whether the clogging phenomenon in a particulate suspension can be predicted from earlier observations of the system. Our research focuses on a microfluidic model system of polystyrene particles dispersed in a glycerol solution, where the onset of clogging can be controlled by adjusting the solution viscosity and flow rate. The microfluidic system allows for optical observations of the flow channels, providing detailed information on how particles are deposited in the flow passage. Using data collected from this model system, we developed a predictive algorithm based on 3D convolutional neural networks (3D CNN) that estimates the probability of clogging onset in the future based on past video frames of the system. Our results show that the 3D CNN can accurately predict clogging even under experimental conditions not encountered during training. The 3D CNN model with a depth of 9 was able to detect clogging after just 25 min, even though the actual clogging occurred after 118 min. This performance is superior compared to the 2D CNN, which detected clogging in 35 min under the same conditions. The high predictive performance indicates that the evolution of particle positions in the early stages of flow contains the necessary information for predicting clogging onset. Our findings have practical implications for the possibility of data-driven predictive maintenance of flow systems.

In this study, we investigate the volatile organic compounds release characteristics of microalgae (Chlorella sp. HS2) in relation to temperature in an effort to realize an alternative type of biomass for use in the fabrication of eco-friendly polymer composites. The volatile organic compounds (VOCs) generated during the compounding process of the polymer composite is quantified and the VOC generation pathways are discussed. As the microalgae are heated and their content in the composites increases, the intensity of the VOC release increases. At a high temperature of 180 degrees C, a typical polymer processing temperature, microalgae release volatile substances. The VOCs are identified as compounds containing ketone functional groups and benzene derivatives through chromatography and spectrometry analyses. VOCs with ketone functional group can originate from small fatty acids by a complex reaction pathway that includes hydrolysis, oxidation, and degradation, while benzene derivatives are from aromatic amino acids, such as Tyrosine, Phenylalanine, and Tryptophan, contained in the microalgae. The findings demonstrate that microalgae biomass particles produce VOCs during polymer processing, which must be considered during the fabrication and eventual commercialization of polymer composites.

The addition of polymer to the particulate suspension provides several functionalities, such as tuning rheological properties and ensuring uniform dispersion. However, the effect of polymers on particle deposition has not been systematically investigated in the previous literature. In this study, we perform microfluidic observation and image processing analysis of particle deposition in colloid-polymer suspensions, focusing on the interplay between flow characteristics and polymer interactions. Polystyrene particles are selected as model particles, while polyethylene oxide is dissolved in de-ionized water at varying polymer concentrations. Higher polymer concentrations enhance colloidal interactions, as evidenced by zeta potential measurements and depletion interaction analysis. Consequently, in the semi-dilute unentangled regime, increased polymer concentration promotes particle deposition. On the other hand, in the semi-dilute entangled regime, the trend reverses, with higher polymer concentrations leading to reduced particle deposition. In the cavity region, stagnant flow facilitates particle retention, further reinforced by polymer interactions. When the zero-shear viscosity is matched, the deposition behavior in the semi-dilute entangled regime is reversed, indicating that particle motion is primarily governed by flow behavior and particle-polymer interactions. Our findings highlight the role of polymers in deposition dynamics under flow, providing valuable insights for optimizing industrial processes involving complex channel structures.

In this study, we propose an analytical framework to characterize rheological behavior under general superimposed flows, where steady shear is combined with oscillatory shear of arbitrary magnitude and orientation. Building upon the sequence of physical processes approach, the framework remains valid within flow conditions where the newly defined superposition number (Su) is less than or equal to 1. Its applicability was demonstrated through simulations of a model colloidal gel, a representative elastoviscoplastic material with complex rheological responses. The analysis captured rheological evolutions across a broad range of steady shear rates, offering interpretations closely connected to microstructural transitions-from the quiescent state to shear-induced densification and subsequently to rejuvenation and eventual fluidization. Distinct interference effects were observed in parallel superposition, where constructive and destructive interactions between steady and oscillatory shear produced two deltoids in the Cole-Cole plot. In contrast, orthogonal superposition, without such interference, resulted in a single deltoid. Overall, the proposed framework offers a powerful tool for superposition rheology, particularly for materials exhibiting complex structural and rheological transitions driven by flow-induced anisotropy. (c) 2025 Published under an exclusive license by Society of Rheology.

We investigate the drying behavior of Brownian and non-Brownian particle mixtures using a novel mesoscale simulation framework. The framework integrates the lattice Boltzmann method with the smoothed profile method (LBM-SPM) and overdamped Langevin dynamics (LD) to capture the complex interplay between hydrodynamics and particle interactions during evaporation-driven processes. We model bimodal hard-sphere suspensions with a size ratio of 1:50, where Brownian (radius: 0.1 mu m) and non-Brownian (radius: 5 mu m) particles are dispersed in a Newtonian solvent. Under these conditions, we perform simulations to investigate the effects of evaporation rate and flow fields induced by non-Brownian particle dynamics on the microstructural evolution of drying suspensions. Notably, non-Brownian particles reaching the liquid-gas interface hinder the transport of smaller particles, whereas the flow field generated by non-Brownian particle-solvent interactions counteracts this effect by enhancing Brownian particle migration. Furthermore, at higher initial Brownian particle concentrations, a "small-on-top" stratification layer emerges, demonstrating the role of hydrodynamically induced flows in amplifying particle segregation. These findings provide fundamental insights into the drying behavior of particulate suspensions and have direct implications for industrial applications, such as lithium-ion battery manufacturing.

This paper presents a novel algorithm for predicting the kinetic and thermodynamic pathways of colloidal systems. The approach involves constructing a physical point cloud from inter-particle stress information extracted from randomly distributed colloidal particles and embedding it into a graph convolutional network (GCN). In the field of pattern recognition, GCNs are widely utilized to classify arbitrary 3D objects by learning multidimensional relationships within feature spaces defined by spatial coordinates. In contrast, our study constructs a feature space based on the micromechanical stresses imparted on colloidal particles during their self-assembly, rather than relying on spatial information. This enables predictive functionality within the classification task. Using this method, we discover for the first time that the convolution of canonical physical information can predict the self-assembly of colloids by observing only the initial configurations of colloidal particles, whereas conventional pattern recognition techniques using spatial information could only recognize phase transitions near completion. The phases predicted by our model are not limited to liquid-like dispersions and solid–liquid phase separations, where thermodynamic equilibrium differs, but also include sample-spanning gel structures, where only kinetics differ while thermodynamics remain the same. Furthermore, although we train the semantic stress relationships that constitute each phase of the network using same-sized particles with a pre-specified inter-particle interaction, our algorithm demonstrates generalized predictive performance even for suspensions with randomly distributed particle sizes. Our results make it possible to predict the phase behavior of colloidal systems where traditional theoretical approaches have been challenging or impossible due to the inherent complexity of the colloidal system. Given that colloids are characterized by extremely small length scales, long times are required for observable macroscopic changes resulting from self-assembly. Therefore, this study is expected to serve as a highly useful decision-support method for engineering soft matter with desired morphologies.

Throughout its lifecycle, poly(ethylene terephthalate) (PET) undergoes significant degradation from long-term ultraviolet (UV) exposure, moisture, and shear forces, with these effects intensified during mechanical recycling. To characterize molecular degradation, PET samples subjected to UV aging in dry, marine, and freshwater environments, as well as diverse shear rates (90-450 s-1) are investigated using rheological analysis. Rheological analysis effectively distinguishes competing PET degradation effects under UV exposure from shear effects, highlighting crosslinking in dry conditions and hydrolytic chain scission under aqueous environments. Under shear, virgin PET shows an upshift in molecular weight (Mw) due to crosslinking reactions, while recycled PET experiences a drastic reduction in Mw owing to chain scission. When virgin PET goes through even a single shear-processing cycle, its crystallite size decreases by 70%, increasing crystallinity and releasing volatile by-products including aldehydes, acids, and alkanes that further compromise its recyclability. This study reveals that both UV and shear degradation significantly impair the mechanical properties of PET by reducing tensile strength and elongation at break by 55% and 99%, respectively. Linking degradation history to crystallization and mechanical behavior provides molecular-level insights.

Heterogeneous photocatalysis offers substantial potential for sustainable energy conversion, yet its industrial application is constrained by limited durability under stringent photochemical conditions. Achieving high photocatalytic activity often requires harsh reaction conditions, compromising catalyst stability and longevity. Here we propose a strategy involving polymeric stabilization of photocatalytic centres uniquely localized at the gas-liquid interface, substantially enhancing both the catalytic activity and stability. Applied to the photocatalytic conversion of plastic waste into solar hydrogen, this approach maintained its catalytic performance over 2 months under harsh conditions. Using 0.3 wt% dynamically stabilized atomic Pt/TiO2 photocatalysts and concentrated sunlight, we achieved a plastic reforming activity of 271 mmolH2 h-1 m-2. Scaling to 1 m2 under natural sunlight yielded a hydrogen production rate of 0.906 l per day from polyethylene terephthalate waste. Economic analysis and extensive-scale simulations suggest this strategy as a promising pathway for high-performance, durable photocatalysis, advancing renewable energy conversion.

In this study, we investigate the reaction of an epoxy-based chain extender in poly(butylene adipate-coterephthalate) (PBAT) composites incorporating protein-rich microalgae, Chlorella sp. HS2. The chain extension reaction in the PBAT/HS2 system is systematically characterized using rheological and gel permeation chromatography analyses, showing that the addition of HS2 significantly changes reaction kinetics because HS2 induces PBAT degradation and also actively participates in the reaction with the chain extender. Under heat, the chain extender with epoxy functional groups actively reacts with degraded PBAT molecules through thermal decomposition and microalgae incorporation, restoring PBAT, while simultaneously facilitating interfacial bridging between PBAT and HS2. As the content of the chain extender increases, the chain extension reaction leads to a more structural evolution and induces molecular branching, resulting in strong shear thinning and strain hardening behavior. The effect of chain extension improves film processing of PBAT/HS2 composites. Tdie extrusion demonstrated the feasibility of film production, emphasizing the industrial applicability of the newly formulated composites. The findings underscore the crucial role of advanced rheological strategies in characterizing reactive compatibilization between biodegradable polymers and natural fillers.

Processing of electrode slurry, which is highly non-Newtonian fluid, is a critical step in the mass production of lithium-ion batteries (LIBs). While extensional flow plays an important role in the electrode slurry processes such as coating, most previous studies have focused only on the shear rheology, due to the lack of a reliable method to measure the extensional rheological properties of the slurry. Here, it is demonstrated that the extensional rheological properties of the anode slurries can be successfully characterized using the stop-flow-dripping-onto-substrate/capillary break-up rheometry (SF-DoS/CaBER). Using this system, it is observed that the extensional rheology of the anode slurry is significantly affected by the blend ratio of the natural and synthetic graphite, as well as the binder and conductive concentrations. Furthermore, the shear rheology-based model predicts much shorter pinch-off times than those measured experimentally, indicating that the yield-stress of the anode slurry is much larger in extensional flow than in shear flow.