The macroscopic properties of polymers are significantly influenced by their macromolecular architecture. Linear polymers are characterized by their molar mass distribution, for branched polymers also the number, degree, and distribution of branching points is needed, while for network polymers a detailed insight into the molecular architecture of the polymer network is essential. Predicting the build-up of the macromolecular structure during polymerization is thus essential for predicting the final material properties, as well as their variation during polymerization or processing. Models currently available show limitations in terms of the computational cost and/or lack of generality of their approach. For this reason, the development of a generalization of the method originally developed by Macosko and Miller is presented. This algorithm can calculate the molar mass averages, including for example the z-average molar mass, gelation and the post-gel properties, such as the sol-, pending- and elastic effective mass fractions and crosslink density. Furthermore, the newly developed algorithm can cope with unequal reactivity of functional groups, substitution effects, competing parallel reactions involving the same or other functional groups, and with homo- and copolymerization alike. The algorithm is validated by comparing the results generated with literature for poly (urethane-isocyanurate) systems. The competing formation of urethane (carbamate) and isocyanurate groups is used to illustrate the capabilities of the algorithm, in view of the industrial relevance thereof.

Liquid metal stretchable electronics (LMSE) offer remarkable stretchability, softness, and self-healing properties, making them ideal for smart wearables and soft robotics. A key fabrication method involves spray deposition of LM into patterned structures. However, reliance on manual airbrush techniques has hindered understanding of how spray parameters impact LM deposition, limiting scalability and reliability. This work addresses these challenges by using an innovative automated spray coater (ASC), which provides precise control over deposition. For the first time, the ASC enables a systematic investigation of how spray parameters—such as flow rate, pressure, and spraying distance—affect LMSE properties like reliability, uniformity, and hysteresis. We find that rougher coatings improve yield (nearly 100\%), but compromise long-term reliability. Additionally, finer linewidth patterns (0.25 mm) fail earlier in cyclic testing and show reduced self-healing capabilities compared to wider lines ($>0.5 mm$). The ASC's capabilities are demonstrated through the fabrication of LMSE devices, including a 16-LED array and a large wearable strain sensor (70 × 150 mm) for human motion capture. This work provides crucial insights into LM deposition and highlights relevant applications, advancing the development of scalable and reliable stretchable electronics.

The vulnerability of smart wearables necessitates stretchable sensors capable of recovering their functionality after sustaining damage. Recent research on liquid metal (LM)-based sensors demonstrates the potential of these highly stretchable, conductive, and reliable sensors. Unlike previous studies using silicone-based substrates, this article proposes a self-healing (SH), biocompatible strain sensor based on Galinstan embedded in a Diels-Alder (DA) polymer encapsulant. The novelty of this sensor lies in its ability to restore sensing and mechanical functionalities through numerous damage-healing cycles. This research outlines the fabrication and quasi-static and dynamic characterization of the strain sensor, enabling analysis of its strength, sensitivity, hysteresis, response time, drift, and healing performance. Healing is investigated by repeatedly rupturing the sensor in half, then healing it at 60 (degrees) C for 4 h before recharacterization. On a mechanical level, healing efficiencies of 80% are achieved based on recovered strain, while on a sensor level, the gauge factor (GF) is recovered with 105% efficiency. The degree of hysteresis (DH) for resistance-strain is less than 1%, and the sensing behavior is independent of strain rate. The sensor has a response time of 220 ms with an acceptable drift of 5% over 800 cycles. This article demonstrates the feasibility of recycling the sensor by outlining a method to separate the substrate from the LM and reprocess it. In addition, the sensitivity and biocompatibility of both pristine and healed sensors are validated through case studies, such as tracking finger and knee joint angle bending, highlighting their potential for smart wearable applications. Supplementary video material can be found at https://www.youtube.com/watch?v=SeLYJ6_qT_k

Liquid metal stretchable electronics combine exceptional softness, stretchability, and self-healing capabilities, making them ideal for smart wearables and soft robotics. A key fabrication approach involves pneumatic spray deposition into patterned structures. However, the impact of process parameters on LM deposition remains poorly understood, largely due to reliance on manual airbrushing-limiting both scalability and reliability. This work addresses these challenges with a custom-built automated spray coater, offering precise control over key parameters such as flow rate, pressure, and spray distance. Through systematic process-property analysis, we reveal that rougher coatings improve device yield (approaching 100%) but compromise long-term reliability. Finer linewidths (0.25 mm) fail earlier in cyclic testing and exhibit reduced self-healing compared to wider lines (≥ 0.5 mm). Scalability is demonstrated through the fabrication of a large-area wearable strain sensor (70 × 150 mm) for human motion capture. These findings offer critical insights into process-structure-property relationships, paving the way for reliable, scalable LM-based stretchable electronics.

The study investigates the impact of various metal-based driers on the curing process of commercial artists' oil paints, focusing on drying time, curing kinetics, molecular structure, and mechanical properties of the final paint film. Six custom-made formulations with different metal-based driers (calcium, zirconium, cobalt, cobalt with calcium, and cobalt with manganese) were tested. Results showed that cobalt-containing driers significantly reduced drying time, while calcium and zirconium had a moderate effect. Cobalt combined with calcium further decreased the drying time, whereas manganese slightly increased it. Depending on the drier and/or driers' combination the curing kinetic and thermodynamic are influenced. Cobalt-based driers increase surface polarity and promote inhomogeneous cross-linking density. All driers had an influence on the mechanical properties of the paint films: after three years of natural ageing paint layers containing driers are stiffer. (c) 2025 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Detection of damage serves as the initial phase for autonomous healing or adaptation to damage in resilient robots. While signaling the occurrence of damage proves beneficial, the more critical requirement lies in localizing the damage to enable targeted actions. This article introduces a soft, self- healing damage localization sensor capable of detecting damage at four distinct locations using only a pair of measuring points (electrodes). The sensor comprises four resistive links, forming a resistive circuit, and operates by measuring the equivalent resistance between two fixed terminals. Damage occurring to each link induces a distinct change in the value of the equivalent resistance. The system is initially characterized at the material level and subsequently at the sensor level through multiple damage trials (the sensor can restore functionality through on-demand healing, achieved by applying a temperature of 90 degrees C for 30 min). Finally, the sensor is sandwiched between self-healing layers to form a skin. Out of the 16 damage trials conducted on the sensor, in standalone and embedded configurations, 15 successful localizations were observed. Additionally, reducing the number of electrodes enhances the ease of integration of this technology into various robotic applications, such as the palm of robotic hands.

Reversible polymer network blends leverage the advantageous properties of immiscible polymer backbones. Previous work showed that the phase morphology of blends of a hydrophilic poly(propylene oxide) (PPO) and hydrophobic polydimethylsiloxane (PDMS) cured by the reversible Diels-Alder reaction depends on the mass ratio of the two polymers and the maleimide-to-furan ratio used for the reversible network polymerization. This work studies the competition between the reversible Diels-Alder reaction and the phase separation kinetics and thermodynamics to control the phase formation. A furan-functionalized PPO with a molar mass of 4546 g mol-1 was blended with furan-functionalized PDMS with different molar masses, mass ratios of the polymers, and stoichiometric ratios. At the highest molar mass of 4961 g mol-1, the PDMS and PPO separated quickly into separate layers, creating a barrier against both water and oxygen, respectively. The thickness, morphology, and composition of the layers depend on the composition of the blend. At a lower molar mass of the PDMS, the chemistry of the furan end groups becomes more pronounced, which increases the compatibility of the two polymers, reducing the thermodynamic driving force for phase separation. In addition, the increased concentration of furan and maleimide groups increases the Diels-Alder reaction rates and leads to more cross-linked network blends. Mastering the interplay between the thermodynamics of the blends and the kinetics of the network formation and phase separation by judicious combinations of the network design parameters leads to final blend morphologies ranging from kinetically trapped uniform microstructures to almost completely phase-segregated morphologies. Finally, the solvent extraction time was used as a process parameter of the wet blending process. Slow evaporation of the solvent over the course of 1 week resulted in a near-equilibrium separation of the two immiscible polymers into separate layers with perfect interfacial bonding by the same Diels-Alder chemistry. Manipulation of these factors enables the development of Diels-Alder network blends with a wide range of properties that are suitable for a wide variety of applications. The fastest and most efficient autonomous healing is achieved at higher PPO contents and for the highest PDMS molar masses, while the best barriers against water and oxygen are obtained at the highest cross-link densities.

A kinetic-structural model was developed to quantitatively describe the stress relaxation behavior of covalent adaptable networks based on the reversible Diels-Alder chemistry. The model draws on the analogy between stress relaxation and network de-cross-linking, where residual stress is attributed to elastically active network strands. A recursive network analysis, based on the Macosko-Miller approach, is coupled with a model that incorporates the reversible Diels-Alder reaction kinetics, stress-induced bond activation, and the reduced efficiency of bond exchanges during late-stage relaxation. The model was validated using rheological data from two Diels-Alder-based networks, with cross-linking kinetics and equilibrium conversions used to predict initial stresses. The stress relaxation behavior, including characteristic times and shape parameters across varying temperatures and cross-link densities, was accurately predicted using only two temperature-independent parameters. Beyond predictive capabilities, the model enables the extraction of kinetic and thermodynamic parameters from experimental data, supporting its use in both direct simulation and inverse design. Thanks to its low computational cost, the framework facilitates rapid exploration of compositional and structural scenarios, aiding the design of application-specific materials. This approach offers a robust and efficient tool for bridging the gap between dynamic covalent chemistries and the development of functional materials and their advanced processing.

In this work, a method is proposed to produce monodispersed micron-sized metal-organic-framework (MOF) polymeric composite particles (MOFpp). MOFpp are produced via co-flow microfluidic droplet formation and solvent extraction/evaporation. Disk-shaped MOFpp of 235 mu m diameter and 63 mu m thickness were obtained from zeolitic imidazole framework-8 (ZIF-8) MOF and polyvinyl-formal dissolved in dichloromethane droplets after solvent extraction/evaporation. The MOFpp were characterized by SEM, XRD, ATR-IR, SEM-EDS, TGA, and gravimetric adsorption experiments. The results demonstrated that the ZIF-8 preserved its structural, chemical, and adsorption properties upon formulation.

Herein, we exploit chemical amplification to release –OH groups in dynamic covalent photopolymers on-demand. Via a single photon event, a cascade of reactions occurs, which allows the polymers to flow through thermo-activated transesterification.

Soft robotics has gained increasing interest recently, but challenges persist, including operating at low temperatures, susceptibility to damage, fatigue-induced deterioration, and the need for proprioceptive sensing for autonomous operation and recovery. Addressing these challenges, this paper introduces a novel conductive ionoelastomer with a dendritic microstructure engineered to resist crack propagation and self-repair even at sub-zero temperature, offering opportunities to construct more adaptable soft robotic grippers which can work in complex environments. Silk ionoelastomers, serving as the matrix material, are complemented by dendritic sodium polyacrylate crystalline fibers. This integration significantly enhances strength and Young's modulus, while maintaining low hysteresis (below 24%), high fracture toughness (35.2 kJ m-2), and a fatigue threshold of 754 J m-2. Furthermore, it exhibits exceptional self-healing capabilities at both room temperature and -20 degrees C, enabling its use in flexible sensors with elongation capabilities of up to 500%. Utilizing folding techniques and inherent self-healing properties, this material can be tailored into pneumatic fingers with sensing and damage-detecting functionalities, facilitating the grasping of various objects. Even when exposed to various types of external damage, its pneumatic functionality is fully restored through the self-healing process at room temperature or low temperature, underscoring its resilience and adaptability in practical applications. PSSFIE, featuring a dendritic microstructure, with enhanced strength, modulus, low hysteresis and high fatigue threshold, was developed to addresses soft robotics challenges like low-temperature self-healing, operation, and damage sensing.

More complex 3D structures were manufactured out of reversible covalent polymer networks using multi- material extrusion-based additive manufacturing. The rheological behaviour of reversible polymer networks based on the thermoreversible Diels-Alder reaction was optimized through the addition of different types of nanoclays and carbon black. The extrudability and deposition of the composites improved up to 5 wt% nanoclay, as the viscosity behaviour increased by an order of magnitude in the nozzle and by two orders of magnitude at the print bed temperature, without affecting the reversible gel transition temperature and the healing performance. An electrically conductive composite with 20 wt% carbon black and 1 wt% nanoclay could be printed using nozzle sizes down to 0.3 mm, resulting in higher resolution and accuracy than the pristine polymer networks. The percolating filler network enabled the printing of overhangs and hollow structures without the need for support material with perfect mechanical and electrical isotropy. Multi-material printing combining the electrically conductive and non-conductive composites enabled manufacturing self-healing deformation and force sensors that could recover their sensing performance upon damage healing at 90 degrees C for one hour.

A novel mechanistic model is developed for a vitrifying covalent adaptable polymer network based on the thermoreversible furan-maleimide Diels-Alder (DA) cycloaddition. To account for the effect of diffusion limitations on the reaction rates, a diffusion-controlled encounter pair formation mechanism is introduced, with the related rates of formation and separation calculated using the Williams-Landel-Ferry equation. The kinetic, thermodynamic, and diffusion parameters are optimized using calorimetric data and the variation of the glass transition temperature (T g) with time and/or temperature, leading to a set of parameters that can describe a specific thermosetting system in vitrifying conditions. These parameters are shown to be also valid for a second, chemically similar, reversible network having a comparable T g. Lastly, the parameters obtained are used to simulate time-temperature-transformation (TTT) and continuous-heating-transformation (CHT) diagrams of these systems, including also the vitrified sections. With these results, this model proves to be a versatile tool suitable for the prediction of the effect of diffusion limitations for any time-temperature cure program, aiding in the accurate interpretation of analytical results related to these reversible networks. This is of particular interest for the design and processing of these self-healing and reprocessable materials.

Two dynamic covalent networks based on the Diels-Alder reaction were blended to exploit the properties of the dissimilar polymer backbones. Furan-functionalized polyether amines based on poly(propylene oxide) (PPO) FD4000 and polydimethylsiloxane (PDMS) FS5000 were mixed in a common solvent and reversibly cross-linked with the same bismaleimide DPBM. The morphology of the phase-separated blends is primarily controlled by the concentration of backbones. Increasing the PDMS content of the blends results in a dilute droplet morphology at 25 wt %, with a growing size and concentration of droplets and the formation of two separate PPO- and PDMS-rich layers at 50 wt %. Further increasing the PDMS content to 75 wt % leads to larger droplets and a thicker layer of the secondary phase. The hydrophobic PDMS phase creates a barrier against water, while the more hydrophilic PPO phase enhances the resistance against oxygen diffusion. Lowering the maleimide-to-furan stoichiometric ratio resulted in a decrease in cross-link density and thus more flexible and stretchable encapsulants. Changes in the stoichiometric ratio also affected the phase morphology due to resulting changes in phase separation and network formation kinetics. Lowering the stoichiometric ratio also resulted in enhanced self-healing properties of 96% at room temperature as a consequence of the increased chain mobility in the blended networks. The self-healing blends were used to encapsulate liquid metal circuits to create stretchable strain sensors with a linear electro-mechanical response without much drift or hysteresis, which could be efficiently recovered by 90% after the damage-healing cycles.

Dynamic polymer networks offer a promising solution to key challenges in polymers such as recyclability, processability, and damage repair. However, the trade-off between combining facile processability, fast self-healing, and high creep resistance remains a major obstacle to implementation. To overcome this, two very distinct dynamic covalent chemistries, Diels-Alder and transesterification, is combined in a single network. The resulting dual dynamic networks offer an unprecedented set of properties and control over the relaxation times. The system decouples the relaxation dynamics of the network from the spatial motifs, and the tuning of the ratio between chemistries enables to control of the relaxation dynamics over six orders of magnitude. Taking advantage of this control, the composition and rheological behavior is optimized to drastically improve the resolution for extrusion-based additive manufacturing of dynamic covalent networks. Additionally, two well-defined and separated stress relaxation peaks are observed at compositions close to 50% of each dynamic chemistry, accentuating the double character of the system's relaxation dynamics. This atypical situation, enables to preparation of self-healing materials with negligible creep, and with shape-memory properties solely leveraging the two distinct relaxation dynamics, instead of the glass transition temperature or the melting point.

The rising popularity of soft grippers in industry is due to their impressive adaptability. Yet, this adaptability requires flexibility, which often sacrifices grip firmness and complicates sensor integration. This article introduces two additional innovations, variable stiffness and pneumatic sensing, into a FinRay adaptive gripper. The approach and design for incorporating these innovations are guided by requirements outlined by Festo. Regarding this, a layer-jamming-based variable-stiffness skin broadens gripper applications, manipulating objects of varying hardness and weight, while a pneumatic sensor skin detects contact and loss of contact. Both functionalities rely on the airtightness of the skins, which is compromised if damaged. To address this, both the skins and the gripper were crafted using self-healing polymers. The sensing capability and modulated mechanical performance of the gripper were evaluated experimentally and through simulations, and the self-healing ability was assessed by recharacterization after a damage healing. This work showcases the promising synergy between robotics and self-healing materials, demonstrating mutual reinforcement to a highly efficient gripping system.

The addition of an organo-modified nanoclay to a carbon black-based electrically conductive self-healing composite showed a synergistic improvement of the electrical conductivity and healing ability. The synergistic effect was studied as a function of carbon black (primary) and nanoclay (secondary) filler loadings in a Diels-Alder-based polymer network. The synergistic effect is the greatest when the carbon black particles are organized around the partially exfoliated nanoclay platelets. Too extensive exfoliation and dispersion of the nanoclay by ultrasonication result in a partial loss of electrical conductivity by around 18% and a substantial loss of the healing ability by around 84%, whereas the hybrid composite shows a very poor recovery of only 12% based on the strain at break. Second, the synergy is governed by the compatibility between the organic modifiers of the nanoclays and the backbone chemistry of the polymer network. The incorporation of Cloisite 15A with a hydrophobic modifier in a network based on poly-(propylene oxide) (PPO) results in a greater synergetic improvement of the electrical conductivity and healing behavior than the incorporation of a more hydrophilic nanoclay or the use of Cloisite 15A in a more hydrophilic poly-(ethylene oxide) (PEO) backbone. Finally, the effect of nanoclay as a secondary filler depends also on the chemistry and architecture of the polymer network. By using no platelets at 10 wt % carbon black, CB-filled composites built on PPO- and PEO-based networks show distinctly different electrical conductivities of 6.18 and 15.97 S m-1, respectively. The synergistic effect increases with a decreasing cross-linking density of the polymer network, especially by the improvement of the healing behavior. For instance, introducing Cloisite 15A enhances the mechanical healing efficiency of the PPO-based composite possessing a lower cross-linking density by 44% while diminishing it for the PEO-based composite by 33%. This study provides deep insights into the structure-property relationships that facilitate the optimization of electrically conductive and self-healing composites for a wide variety of applications, in particular for flexible electronics and soft robotic applications.

Herein a new protocol for the multiphosphinylation and-phosphonylation of pyridine and related heterocycles is described to give access to piperidinyl phosphine oxides and - phosphonates. The former are accessible through a straightforward, neat, one-pot reaction which yielded the desired phosphinylated heterocycles moderate to excellent yields (42-99%). These compounds contain a high phosphorus to carbon ratio and were evaluated using TGA and calorimetric measurements for potential flame-retardant applications. The respective phosphonates could be obtained using a second protocol, which employed pyridinium salts, triakly phosphites, formic acid and silica to promote the reaction. These piperidinyl phosphonates were isolated moderate to good yields (38-81%) and provide also an interesting starting point for potential enzyme inhibitors.

Flexible, soft materials are increasingly used for the fabrication of soft robots, as the inherent compliance and shock‐absorbance protect the robot from mechanical impact. Soft universal grippers take full advantage of this adaptability, facilitating effective and safe grasping of various objects. However, due to their predominantly soft material composition, these grippers have limited lifetimes, especially when operating in unstructured and unfamiliar environments. The self‐healing universal gripper (SHUG) is proposed, which can grasp various objects and recover from substantial realistic damages autonomously. It integrates damage detection, heat‐assisted healing, and healing evaluation. Notably, unlike other universal grippers, the entire SHUG can be fully reprocessed and recycled. The gripper's functionality relies on the particle jamming of steel balls enclosed within a self‐healing membrane. Thanks to the thermoreversible covalent Diels–Alder bonds in self‐healing polymer membrane, the gripper is able to recover from macroscopic damages including scratches and punctures. Temperature‐assisted healing is regulated in a closed‐loop manner using an embedded thermocouple and Joule heater. Experimental validation demonstrates the adaptability, resilience, and recyclability of the SHUG.