Machine Learning for Melting Temperature Predictions and Design in Polyhydroxyalkanoate-Based Biopolymers

Exploring the Future of Polyhydroxyalkanoate Composites with Organic Fillers: A Review of Challenges and Opportunities

Biopolymers from renewable materials are promising alternatives to the traditional petroleum-based plastics used today, although they face limitations in terms of performance and processability. Natural fillers have been identified as a strategic route to create sustainable composites, and natural fillers in the form of waste by-products have received particular attention. Consequently, the primary focus of this article is to offer a broad overview of recent breakthroughs in environmentally friendly Polhydroxyalkanoate (PHA) polymers and their composites. PHAs are aliphatic polyesters obtained by bacterial fermentation of sugars and fatty acids and are considered to play a key role in addressing sustainability challenges to replace traditional plastics in various industrial sectors. Moreover, the article examines the potential of biodegradable polymers and polymer composites, with a specific emphasis on natural composite materials, current trends, and future market prospects. Increased environmental concerns are driving discussions on the importance of integrating biodegradable materials with natural fillers in our daily use, emphasizing the need for clear frameworks and economic incentives to support the use of these materials. Finally, it highlights the indispensable need for ongoing research and development efforts to address environmental challenges in the polymer sector, reflecting a growing interest in sustainable materials across all industries.

Green Synthesis of Bioplastics from Microalgae: A State-of-the-Art Review

The synthesis of conventional plastics has increased tremendously in the last decades due to rapid industrialization, population growth, and advancement in the use of modern technologies. However, overuse of these fossil fuel-based plastics has resulted in serious environmental and health hazards by causing pollution, global warming, etc. Therefore, the use of microalgae as a feedstock is a promising, green, and sustainable approach for the production of biobased plastics. Various biopolymers, such as polyhydroxybutyrate, polyurethane, polylactic acid, cellulose-based polymers, starch-based polymers, and protein-based polymers, can be produced from different strains of microalgae under varying culture conditions. Different techniques, including genetic engineering, metabolic engineering, the use of photobioreactors, response surface methodology, and artificial intelligence, are used to alter and improve microalgae stocks for the commercial synthesis of bioplastics at lower costs. In comparison to conventional plastics, these biobased plastics are biodegradable, biocompatible, recyclable, non-toxic, eco-friendly, and sustainable, with robust mechanical and thermoplastic properties. In addition, the bioplastics are suitable for a plethora of applications in the agriculture, construction, healthcare, electrical and electronics, and packaging industries. Thus, this review focuses on techniques for the production of biopolymers and bioplastics from microalgae. In addition, it discusses innovative and efficient strategies for large-scale bioplastic production while also providing insights into the life cycle assessment, end-of-life, and applications of bioplastics. Furthermore, some challenges affecting industrial scale bioplastics production and recommendations for future research are provided.

Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers

To discover new materials, high-throughput screening (HTS) with machine learning (ML) requires universally available descriptors that can accurately predict the desired properties. For elastomers, experimental and simulation data in current descriptors may not be available for all candidates of interest, hindering elastomer discovery through HTS. To address this challenge, we introduce structure-based multilevel (SM) descriptors of elastomers derived solely from molecular structure that is universally available. Our SM descriptors are hierarchically organized to capture both local soft and hard segment structures as well as the global structures of elastomers. With the SM-Morgan Fingerprint (SM-MF) descriptor, one of our SM descriptors, a machine learning model accurately predicts elastomer toughness with a remarkable accuracy of 0.91. Furthermore, an HTS pipeline is established to swiftly screen elastomers with targeted toughness. We also demonstrate the generality and applicability of SM descriptors by using them to construct HTS pipelines for screening elastomers with a targeted critical strain or Young's modulus. The user-friendliness and low computational cost of SM descriptors make them a promising tool to significantly enhance HTS in the search for novel materials.

Valorization of organic wastes using bioreactors for polyhydroxyalkanoate production: Recent advancement, sustainable approaches, challenges, and future perspectives

Microbial polyhydroxyalkanoates (PHA) are encouraging biodegradable polymers, which may ease the environmental problems caused by petroleum-derived plastics. However, there is a growing waste removal problem and the high price of pure feedstocks for PHA biosynthesis. This has directed to the forthcoming requirement to upgrade waste streams from various industries as feedstocks for PHA production. This review covers the state-of-the-art progress in utilizing low-cost carbon substrates, effective upstream and downstream processes, and waste stream recycling to sustain entire process circularity. This review also enlightens the use of various batch, fed-batch, continuous, and semi-continuous bioreactor systems with flexible results to enhance the productivity and simultaneously cost reduction. The life-cycle and techno-economic analyses, advanced tools and strategies for microbial PHA biosynthesis, and numerous factors affecting PHA commercialization were also covered. The review includes the ongoing and upcoming strategies viz. metabolic engineering, synthetic biology, morphology engineering, and automation to expand PHA diversity, diminish production costs, and improve PHA production with an objective of "zero-waste" and "circular bioeconomy" for a sustainable future.

Biomimetic natural biomaterials for tissue engineering and regenerative medicine: new biosynthesis methods, recent advances, and emerging applications

Biomimetic materials have emerged as attractive and competitive alternatives for tissue engineering (TE) and regenerative medicine. In contrast to conventional biomaterials or synthetic materials, biomimetic scaffolds based on natural biomaterial can offer cells a broad spectrum of biochemical and biophysical cues that mimic the in vivo extracellular matrix (ECM). Additionally, such materials have mechanical adaptability, microstructure interconnectivity, and inherent bioactivity, making them ideal for the design of living implants for specific applications in TE and regenerative medicine. This paper provides an overview for recent progress of biomimetic natural biomaterials (BNBMs), including advances in their preparation, functionality, potential applications and future challenges. We highlight recent advances in the fabrication of BNBMs and outline general strategies for functionalizing and tailoring the BNBMs with various biological and physicochemical characteristics of native ECM. Moreover, we offer an overview of recent key advances in the functionalization and applications of versatile BNBMs for TE applications. Finally, we conclude by offering our perspective on open challenges and future developments in this rapidly-evolving field.

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.