Emerging sutureless wound-closure techniques have led to paradigm shifts in wound management. State-of-the-art biomaterials offer biocompatible and biodegradable platforms enabling high cohesion (toughness) and adhesion for rapid bleeding control as well as robust attachment of implantable devices. Tough bioadhesion stems from the synergistic contributions of cohesive and adhesive interactions. This Review provides a biomacromolecular design roadmap for the development of tough adhesive surgical sealants. We discuss a library of materials and methods to introduce toughness and adhesion to biomaterials. Intrinsically tough and elastic polymers are leveraged primarily by introducing strong but dynamic inter- and intramolecular interactions either through polymer chain design or using crosslink regulating additives. In addition, many efforts have been made to promote underwater adhesion via covalent/noncovalent bonds, or through micro/macro-interlock mechanisms at the tissue interfaces. The materials settings and functional additives for this purpose and the related characterization methods are reviewed. Measurements and reporting needs for fair comparisons of different materials and their properties are discussed. Finally, future directions and further research opportunities for developing tough bioadhesive surgical sealants are highlighted.

The integration of Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) has gathered significant attention across various scientific research domains to facilitate integrated sustainability assessment. Recently, there has been a growing interest in hybrid approaches that combine MCDM with MOO, aiming to enhance the efficacy of the final decisions. However, a critical gap exists in terms of providing clear methodological guidance, particularly when dealing with data uncertainties. To address this gap, this systematic review is designed to develop a generic decision tree that serves as a practical roadmap for practitioners seeking to perform MOO and MCDM in an integrated fashion, with a specific focus on accounting for uncertainties. The systematic review identified the recent studies that conducted both MOO and MCDM in an integrated way. It is important to note that this review does not aim to identify the superior MOO or MCDM methods, but rather it delves into the strategies for integrating these two common methodologies. The prevalent MOO methods used in the reviewed articles were evolution-based metaheuristic methods. TOPSIS and PROMETHEE II are the prevalent MCDM ranking methods. The integration of MOO and MCDM methods can occur either a priori, a posteriori, or through a combination of both, each offering distinct advantages and drawbacks. The developed decision tree illustrated all three paths and integrated uncertainty considerations in each path. Finally, a real-world case study for the pulse fractionation process in Canada is used as a basis for demonstrating the various pathways presented in the decision tree and their application in identifying the optimized processing pathways for sustainably obtaining pulse protein. This study will help practitioners in different research domains use MOO and MCDM methods in an integrated way to identify the most sustainable and optimized system.

Piezoelectric, piezoresistive, and optical fiber sensors have attracted considerable attention in structural health monitoring (SHM) applications for strain/temperature measurements in advanced material structures such as composites. Issues pertaining to the fabrication process and integration of these sensors, however, continue to limit their efficiency in detecting SHM parameters in sensitive applications such as aerospace and medical devices, where accuracy and reliability are prime concerns. This paper provides a critical review of recent developments on sensing materials and their integration methods into composites. For piezoelectric sensors, ferroelectric and piezoelectric characteristics of different material compositions are described and the evolution of ferroelectric properties over a range of temperatures and mechanical loadings is addressed. Fabrication processes and characterization of silicon- and carbon-based piezoresistive materials are presented to assess the electromechanical performance of such sensors. Furthermore, the methods developed for concurrent measurement of strain and temperature using optical fiber sensors are reviewed. The implantation of different sensing mechanisms for detecting physical parameters during and after composite manufacturing processes is described. Since composite delamination due to the embedded sensors is a reoccurring issue, mechanical compatibility of different reported sensors with the base material interface is highlighted. Finally, potential future directions of the reviewed sensing mechanisms are outlined.

Composite materials have been widely used in the aerospace industry and are critical for safe operations. However, the delamination, caused by cyclic loads and corrosive service environment, poses a serious threat to the structural integrity of composite laminates. The acoustic emission technique has been adopted to assess the structural integrity by characterizing damage location, type, and size. This paper proposes and compares data-driven prognostic methods to quantify the delamination area efficiently and accurately. To address the problem of insufficient inspection data, the prediction model adopts the path length across the delamination area as the target value. The delamination area can then be estimated with the predicted path length based on formulated geometric relationships. This solution will augment the model training data sets and consequently avoid the overfitting problem during the training process. Experimental results on composite coupons demonstrate that the proposed ensemble learning based model outperforms other state-of-the-art methods in terms of prediction accuracy and efficiency.

X-ray computed tomography provides qualitative and quantitative structural and compositional information for a broad range of materials. Yet, its contribution to the field of advanced composites such as carbon fiber reinforced polymers is still limited by factors such as low imaging contrast, due to scarce X-ray attenuation features. This article, through a review of the state of the art, followed by an example case study on Micro-computed tomography (CT) analysis of low X-ray absorptive dry and prepreg carbon woven fabric composites, aims to highlight and address some challenges as well as best practices on performing scans that can capture key features of the material. In the case study, utilizing an Xradia Micro-CT-400, important aspects such as obtaining sufficient contrast, an examination of thin samples, sample size/resolution issues, and image-based modeling are discussed. The outcome of an optimized workflow in Micro-CT of composite fabrics can assist in further research efforts such as the generation of surface or volume meshes for the numerical modeling of underlying deformation mechanisms during their manufacturing processes.