Dynamic polydimethylsiloxane based polymer composites for functional materials

Polymer networks with dynamic covalent bonds show properties and functions not achievable with covalently crosslinked systems. Among of the different polymers connected by dynamic covalent bonds, this Thesis is based on polydimethylsiloxane (PDMS) elastomers prepared via acid-catalyzed ring-opening polymerization of cyclic monomer and cross-link. This reaction presents different dynamic equilibrium reactions, such as polymer-oligomer equilibrium and bond exchange reaction. In this Thesis, I have developed three different functional materials based on acid-catalyzed PDMS. In Chapter 1, the basic concepts of dynamic bond chemistry and the state-of-the-art of dynamic covalent polymer networks are described. In Chapter 2, a new PDMS-based elastomer that can self-grow and self-degrow is presented. Chapter 3 describes how the acid-catalyzed PDMS was used to fabricate a strain sensor that could flexibly post-tailor the sensor properties. In the last part (Chapter 4), a gas-flow enhanced relaxation behavior observed in CB/dPDMS composite is described.Polymernetzwerke mit dynamischen kovalenten Bindungen zeigen besondere Eigenschaften und Funktionen, die mit kovalent vernetzten Systemen nicht erreichbar sind. Diese Arbeit befasst sich mit Polydimethylsiloxan (PDMS) -Elastomeren, die durch säurekatalysierte Ringöffnungspolymerisation von cyclischem Monomer und Quervernetzung hergestellt werden. In dieser Reaktion sind verschiedene dynamische Gleichgewichte beteiligt, z.B. das Polymer-Oligomer-Gleichgewicht und die Bindungsaustauschreaktion. In dieser Arbeit wurden drei verschiedene funktionelle Materialien auf der Basis von säurekatalysiertem PDMS entwickelt. In Kapitel 1 werden die Grundkonzepte der dynamischen Bindungschemie und der Stand der Technik dynamischer kovalenter Polymernetzwerke beschrieben. In Kapitel 2 wird ein neues PDMS-basiertes Elastomer vorgestellt, das Selbstwachstum und Selbstabbau zeigt. Kapitel 3 beschreibt, wie ein säurekatalysiertes PDMS verwendet wurde, um einen Dehnungssensor herzustellen, dessen Sensoreigenschaften nachträglich flexibel anpassbar sind. Im letzten Teil (Kapitel 4) wird eine Verbesserung des Relaxationsverhaltens durch einen Gasfluss beschrieben, das in DB/dPDMS-Verbundwerkstoffen beobachtet wird

Self‐Healable and Recyclable Tactile Force Sensors with Post‐Tunable Sensitivity

It is challenging to post‐tune the sensitivity of a tactile force sensor. Herein, a facile method is reported to tailor the sensing properties of conductive polymer composites by utilizing the liquid‐like property of dynamic polymer matrix at low strain rates. The idea is demonstrated using dynamic polymer composites (CB/dPDMS) made via evaporation‐induced gelation of the suspending toluene solution of carbon black (CB) and acid‐catalyzed dynamic polydimethylsiloxane (dPDMS). The dPDMS matrices allow CB to redistribute to change the sensitivity of materials at the liquid‐like state, but exhibit typical solid‐like behavior and thus can be used as strain sensors at normal strain rates. It is shown that the gauge factor of the polymer composites can be easily post‐tuned from 1.4 to 51.5. In addition, the dynamic polymer matrices also endow the composites with interesting self‐healing ability and recyclability. Therefore, it is envisioned that this method can be useful in the design of various novel tactile sensing materials for many applications

Self‐Healable and Recyclable Tactile Force Sensors with Post‐Tunable Sensitivity

It is challenging to post‐tune the sensitivity of a tactile force sensor. Herein, a facile method is reported to tailor the sensing properties of conductive polymer composites by utilizing the liquid‐like property of dynamic polymer matrix at low strain rates. The idea is demonstrated using dynamic polymer composites (CB/dPDMS) made via evaporation‐induced gelation of the suspending toluene solution of carbon black (CB) and acid‐catalyzed dynamic polydimethylsiloxane (dPDMS). The dPDMS matrices allow CB to redistribute to change the sensitivity of materials at the liquid‐like state, but exhibit typical solid‐like behavior and thus can be used as strain sensors at normal strain rates. It is shown that the gauge factor of the polymer composites can be easily post‐tuned from 1.4 to 51.5. In addition, the dynamic polymer matrices also endow the composites with interesting self‐healing ability and recyclability. Therefore, it is envisioned that this method can be useful in the design of various novel tactile sensing materials for many applications

Chemical and Synthetic Biology Approaches for Cancer Vaccine Development

Cancer vaccines have been considered promising therapeutic strategies and are often constructed from whole cells, attenuated pathogens, carbohydrates, peptides, nucleic acids, etc. However, the use of whole organisms or pathogens can elicit unwanted immune responses arising from unforeseen reactions to the vaccine components. On the other hand, synthetic vaccines, which contain antigens that are conjugated, often with carrier proteins, can overcome these issues. Therefore, in this review we have highlighted the synthetic approaches and discussed several bioconjugation strategies for developing antigen-based cancer vaccines. In addition, the major synthetic biology approaches that were used to develop genetically modified cancer vaccines and their progress in clinical research are summarized here. Furthermore, to boost the immune responses of any vaccines, the addition of suitable adjuvants and a proper delivery system are essential. Hence, this review also mentions the synthesis of adjuvants and utilization of biomaterial scaffolds, which may facilitate the design of future cancer vaccines

Reversibly growing crosslinked polymers with programmable sizes and properties

Growth constitutes a powerful method to post-modulate materials’ structures and functions without compromising their mechanical performance for sustainable use, but the process is irreversible. To address this issue, we here report a growing-degrowing strategy that enables thermosetting materials to either absorb or release components for continuously changing their sizes, shapes, compositions, and a set of properties simultaneously. The strategy is based on the monomer-polymer equilibrium of networks in which supplying or removing small polymerizable components would drive the networks toward expansion or contraction. Using acid-catalyzed equilibration of siloxane as an example, we demonstrate that the size and mechanical properties of the resulting silicone materials can be significantly or finely tuned in both directions of growth and decomposition. The equilibration can be turned off to yield stable products or reactivated again. During the degrowing-growing circle, material structures are selectively varied either uniformly or heterogeneously, by the availability of fillers. Our strategy endows the materials with many appealing capabilities including environment adaptivity, self-healing, and switchability of surface morphologies, shapes, and optical properties. Since monomer-polymer equilibration exists in many polymers, we envision the expansion of the presented strategy to various systems for many applications

Study of low-dose PET image recovery using supervised learning with CycleGAN.

PET is a popular medical imaging modality for various clinical applications, including diagnosis and image-guided radiation therapy. The low-dose PET (LDPET) at a minimized radiation dosage is highly desirable in clinic since PET imaging involves ionizing radiation, and raises concerns about the risk of radiation exposure. However, the reduced dose of radioactive tracers could impact the image quality and clinical diagnosis. In this paper, a supervised deep learning approach with a generative adversarial network (GAN) and the cycle-consistency loss, Wasserstein distance loss, and an additional supervised learning loss, named as S-CycleGAN, is proposed to establish a non-linear end-to-end mapping model, and used to recover LDPET brain images. The proposed model, and two recently-published deep learning methods (RED-CNN and 3D-cGAN) were applied to 10% and 30% dose of 10 testing datasets, and a series of simulation datasets embedded lesions with different activities, sizes, and shapes. Besides vision comparisons, six measures including the NRMSE, SSIM, PSNR, LPIPS, SUVmax and SUVmean were evaluated for 10 testing datasets and 45 simulated datasets. Our S-CycleGAN approach had comparable SSIM and PSNR, slightly higher noise but a better perception score and preserving image details, much better SUVmean and SUVmax, as compared to RED-CNN and 3D-cGAN. Quantitative and qualitative evaluations indicate the proposed approach is accurate, efficient and robust as compared to other state-of-the-art deep learning methods

Reversibly growing crosslinked polymers with programmable sizes and properties

Abstract Growth constitutes a powerful method to post-modulate materials’ structures and functions without compromising their mechanical performance for sustainable use, but the process is irreversible. To address this issue, we here report a growing-degrowing strategy that enables thermosetting materials to either absorb or release components for continuously changing their sizes, shapes, compositions, and a set of properties simultaneously. The strategy is based on the monomer-polymer equilibrium of networks in which supplying or removing small polymerizable components would drive the networks toward expansion or contraction. Using acid-catalyzed equilibration of siloxane as an example, we demonstrate that the size and mechanical properties of the resulting silicone materials can be significantly or finely tuned in both directions of growth and decomposition. The equilibration can be turned off to yield stable products or reactivated again. During the degrowing-growing circle, material structures are selectively varied either uniformly or heterogeneously, by the availability of fillers. Our strategy endows the materials with many appealing capabilities including environment adaptivity, self-healing, and switchability of surface morphologies, shapes, and optical properties. Since monomer-polymer equilibration exists in many polymers, we envision the expansion of the presented strategy to various systems for many applications

Catalyst-Free, Mechanically Robust, and Ion-Conductive Vitrimers for Self-Healing Ionogel Electrolytes

Vitrimers have been widely employed in self-healing, recyclable, and shape-shifting materials. However, the application of catalyst-free vitrimers to create self-healable and mechanically robust gel polymer electrolytes (GPEs) remains a challenge, often limiting the potential of vitrimer-based materials. Herein, we utilized a catalyst-free dynamic covalent bond (silyl ether) as a linkage to prepare self-healable and mechanically robust GPEs, which are fully reprocessable. By incorporating polymeric ionic liquids into the dynamically cross-linked networks, both ion conductivity and mechanical properties can be flexibly tuned. The dynamic property of the network was demonstrated through frequency sweep rheology, which revealed a rubbery-like behavior at high frequencies and a liquidlike behavior at low frequencies. This dynamic feature enables self-healing and allows for reprocessing via embedding of such dynamic covalent networks into the GPEs. The GPEs containing 80 wt % of a bis(trifluoro­methansulfonamide) lithium/ionic liquid (LiTFSI/IL) mixture exhibited good ion conductivites of 0.13 mS/cm at 20 °C and 1.88 mS/cm at 80 °C. Furthermore, the elastic modulus of the GPEs could reach a value of 0.24 MPa and was able to persist through electrode-volume expansions during charging/discharging.The tunable dynamic properties, coupled with high ion conductivity and a high modulus, indicate promising applications for this type of dynamic bond in sustainable solid electrolytes