Dynamic Modeling of Intrinsic Self-Healing Polymers Using Deep Learning

Machine Learning in Polymer Research

Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid-state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer-biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine-human interface are shared.

Research Progress of Human Biomimetic Self-Healing Materials

Humans can heal themselves after injury, which inspires researchers to develop bionic self-healing materials. Such materials not only equipped with the self-repair capacities akin to those of the human body, but also emulate the mechanical properties of human organs, including the tensile resilience of muscles, the fatigue resistance of skin, and the elevated modulus typical of cartilage. Based on the design concept of imitating the structure of human organs, the bionic self-healing material perfectly solves the problem of poor mechanical properties of self-healing materials caused by weak bond energy and inter-chain flow. This review discusses various organ-inspired self-healing materials in detail, summarizes their synthetic principles and introduces their fascinating mechanical properties. Finally, the application prospects of bionic self-healing polymer materials, such as bio-strain sensors, self-healing anticorrosive coatings, biomedical detection, etc., are outlined. Considering the excellent comprehensive performance and multi-functions of human biomimetic self-healing polymers, more outstanding sustainable materials will be developed, accelerating research progress in self-healing materials and realizing environmentally friendly products in multiple fields.

Data-Driven Materials Research and Development for Functional Coatings

Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time-consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data-driven approaches are gaining traction in materials science. In this paper, recent advances in data-driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data-driven processes. It is demonstrated how data-driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data-driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data-driven materials R&D for functional coatings, shedding light on its potential and future developments.

Toward an AI Era: Advances in Electronic Skins

Electronic skins (e-skins) have seen intense research and rapid development in the past two decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors that detect physiological and environmental signals have been designed and integrated into functional systems. Recently, researchers have increasingly deployed machine learning and other artificial intelligence (AI) technologies to mimic the human neural system for the processing and analysis of sensory data collected by e-skins. Integrating AI has the potential to enable advanced applications in robotics, healthcare, and human-machine interfaces but also presents challenges such as data diversity and AI model robustness. In this review, we first summarize the functions and features of e-skins, followed by feature extraction of sensory data and different AI models. Next, we discuss the utilization of AI in the design of e-skin sensors and address the key topic of AI implementation in data processing and analysis of e-skins to accomplish a range of different tasks. Subsequently, we explore hardware-layer in-skin intelligence before concluding with an analysis of the challenges and opportunities in the various aspects of AI-enabled e-skins.

Recent advancements in natural polymers-based self-healing nano-materials for wound dressing

The field of wound healing has witnessed remarkable progress in recent years, driven by the pursuit of advanced wound dressings. Traditional dressing materials have limitations like poor biocompatibility, nonbiodegradability, inadequate moisture management, poor breathability, lack of inherent therapeutic properties, and environmental impacts. There is a compelling demand for innovative solutions to transcend the constraints of conventional dressing materials for optimal wound care. In this extensive review, the therapeutic potential of natural polymers as the foundation for the development of self-healing nano-materials, specifically for wound dressing applications, has been elucidated. Natural polymers offer a multitude of advantages, possessing exceptional biocompatibility, biodegradability, and bioactivity. The intricate engineering strategies employed to fabricate these polymers into nanostructures, thereby imparting enhanced mechanical robustness, flexibility, critical for efficacious wound management has been expounded. By harnessing the inherent properties of natural polymers, including chitosan, alginate, collagen, hyaluronic acid, and so on, and integrating the concept of self-healing materials, a comprehensive overview of the cutting-edge research in this emerging field is presented in the review. Furthermore, the inherent self-healing attributes of these materials, wherein they exhibit innate capabilities to autonomously rectify any damage or disruption upon exposure to moisture or body fluids, reducing frequent dressing replacements have also been explored. This review consolidates the existing knowledge landscape, accentuating the benefits and challenges associated with these pioneering materials while concurrently paving the way for future investigations and translational applications in the realm of wound healing.

Towards high performance and durable soft tactile actuators

Soft actuators are gaining significant attention due to their ability to provide realistic tactile sensations in various applications. However, their soft nature makes them vulnerable to damage from external factors, limiting actuation stability and device lifespan. The susceptibility to damage becomes higher with these actuators often in direct contact with their surroundings to generate tactile feedback. Upon onset of damage, the stability or repeatability of the device will be undermined. Eventually, when complete failure occurs, these actuators are disposed of, accumulating waste and driving the consumption of natural resources. This emphasizes the need to enhance the durability of soft tactile actuators for continued operation. This review presents the principles of tactile feedback of actuators, followed by a discussion of the mechanisms, advancements, and challenges faced by soft tactile actuators to realize high actuation performance, categorized by their driving stimuli. Diverse approaches to achieve durability are evaluated, including self-healing, damage resistance, self-cleaning, and temperature stability for soft actuators. In these sections, current challenges and potential material designs are identified, paving the way for developing durable soft tactile actuators.