Microbial Polyhydroxyalkanoates (PHAs): Efficient Replacement of Synthetic Polymers

Muneer, Faizan; Rasul, Ijaz; Azeem, Farrukh; Siddique, Muhammad Hussnain; Zubaır, Muhammad; Nadeem, Habibullah

doi:10.1007/s10924-020-01772-1

Cited by 150 publications

(78 citation statements)

References 200 publications

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“…Since then, several steps ahead have been done in the field, from the identification of other hydroxyalkanoic acid monomers (1974), to the cloning and characterization of the genes involved in PHA biosynthesis (1988) (Choi et al, 2020 ). In 1995, the occurrence of more than 91 PHA monomers had been reported, with this number increasing to up to 150 (R)-hydroxyalkanoic acids at present (Muneer et al, 2020 ). Other important milestones in this field, including the characterization of the first copolymers and the description of the PHA biosynthetic pathways (1990s), up to the elucidation of the first crystal structure of the main PHA biosynthetic enzymes (2017) (Kim et al, 2017 ), have contributed to progresses in PHA production process as well as in modulation of polymer composition (Sudesh et al, 2000 ; Choi et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

In vivo and Post-synthesis Strategies to Enhance the Properties of PHB-Based Materials: A Review

Turco

Santagata

Corrado

et al. 2021

Front. Bioeng. Biotechnol.

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The transition toward “green” alternatives to petroleum-based plastics is driven by the need for “drop-in” replacement materials able to combine characteristics of existing plastics with biodegradability and renewability features. Promising alternatives are the polyhydroxyalkanoates (PHAs), microbial biodegradable polyesters produced by a wide range of microorganisms as carbon, energy, and redox storage material, displaying properties very close to fossil-fuel-derived polyolefins. Among PHAs, polyhydroxybutyrate (PHB) is by far the most well-studied polymer. PHB is a thermoplastic polyester, with very narrow processability window, due to very low resistance to thermal degradation. Since the melting temperature of PHB is around 170–180°C, the processing temperature should be at least 180–190°C. The thermal degradation of PHB at these temperatures proceeds very quickly, causing a rapid decrease in its molecular weight. Moreover, due to its high crystallinity, PHB is stiff and brittle resulting in very poor mechanical properties with low extension at break, which limits its range of application. A further limit to the effective exploitation of these polymers is related to their production costs, which is mostly affected by the costs of the starting feedstocks. Since the first identification of PHB, researchers have faced these issues, and several strategies to improve the processability and reduce brittleness of this polymer have been developed. These approaches range from the in vivo synthesis of PHA copolymers, to the enhancement of post-synthesis PHB-based material performances, thus the addition of additives and plasticizers, acting on the crystallization process as well as on polymer glass transition temperature. In addition, reactive polymer blending with other bio-based polymers represents a versatile approach to modulate polymer properties while preserving its biodegradability. This review examines the state of the art of PHA processing, shedding light on the green and cost-effective tailored strategies aimed at modulating and optimizing polymer performances. Pioneering examples in this field will be examined, and prospects and challenges for their exploitation will be presented. Furthermore, since the establishment of a PHA-based industry passes through the designing of cost-competitive production processes, this review will inspect reported examples assessing this economic aspect, examining the most recent progresses toward process sustainability.

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Section: Introductionmentioning

confidence: 99%

In vivo and Post-synthesis Strategies to Enhance the Properties of PHB-Based Materials: A Review

Turco

Santagata

Corrado

et al. 2021

Front. Bioeng. Biotechnol.

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“…The last group is not economically significant since only few species of bacteria are capable to synthetize PHAs from this source and the process tends to have a low efficiency [ 37 ]. Apart from the carbon, other chemical compounds are required such as nitrogen sources, and some of the most used are (NH 4 ) 2 SO 4 , NH 4 Cl, or NH 4 NO 3 [ 22 ]. Variation in carbon to nitrogen ratios led to a different amount of PHA concentration in bacterial cells [ 38 ], and most of the studies showed that limiting nitrogen concentration while increasing carbon substrates had a positive effect on the PHA production rate [ 39 , 40 ].…”

Section: Pha: Biosynthesis and Propertiesmentioning

confidence: 99%

Advantages of Additive Manufacturing for Biomedical Applications of Polyhydroxyalkanoates

et al. 2021

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In recent years, biopolymers have been attracting the attention of researchers and specialists from different fields, including biotechnology, material science, engineering, and medicine. The reason is the possibility of combining sustainability with scientific and technological progress. This is an extremely broad research topic, and a distinction has to be made among different classes and types of biopolymers. Polyhydroxyalkanoate (PHA) is a particular family of polyesters, synthetized by microorganisms under unbalanced growth conditions, making them both bio-based and biodegradable polymers with a thermoplastic behavior. Recently, PHAs were used more intensively in biomedical applications because of their tunable mechanical properties, cytocompatibility, adhesion for cells, and controllable biodegradability. Similarly, the 3D-printing technologies show increasing potential in this particular field of application, due to their advantages in tailor-made design, rapid prototyping, and manufacturing of complex structures. In this review, first, the synthesis and the production of PHAs are described, and different production techniques of medical implants are compared. Then, an overview is given on the most recent and relevant medical applications of PHA for drug delivery, vessel stenting, and tissue engineering. A special focus is reserved for the innovations brought by the introduction of additive manufacturing in this field, as compared to the traditional techniques. All of these advances are expected to have important scientific and commercial applications in the near future.

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“…These combinations of single monomers can provide PHA polymers with diverse and flexible properties. In addition, the polymer properties can also be tuned by modifying chemical configurations, i.e., the size, shape, and branching of polymers [ 14 , 15 ]. Therefore, the design and synthesis of novel PHAs typically need trial and error experimentation to obtain the structure–property–performance relationships.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

Jiang

Marrone

et al. 2020

Materials

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The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

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Microbial Polyhydroxyalkanoates (PHAs): Efficient Replacement of Synthetic Polymers

Cited by 150 publications

References 200 publications

In vivo and Post-synthesis Strategies to Enhance the Properties of PHB-Based Materials: A Review

In vivo and Post-synthesis Strategies to Enhance the Properties of PHB-Based Materials: A Review

Advantages of Additive Manufacturing for Biomedical Applications of Polyhydroxyalkanoates

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

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