Poly-γ-glutamic acid (γ-PGA) is a naturally occurring biopolymer made up of repeating units of l-glutamic acid, d-glutamic acid or both. γ-PGA can exhibit different properties (conformational states, enantiomeric properties and molecular mass). Owing to its biodegradable, non-toxic and non-immunogenic properties, it has been used successfully in the food, medical and wastewater industries. Amongst other novel applications, it has the potential to be used for protein crystallization, as a soft tissue adhesive and a non-viral vector for safe gene delivery. This review focuses on the production, properties and applications of γ-PGA. Each application of γ-PGA utilizes specific properties attributed to various forms of γ-PGA. As a result of its growing applications, more strains of bacteria need to be investigated for γ-PGA production to obtain high yields of γ-PGA with different properties. Many medical applications (especially drug delivery) have exploited α-PGA. As γ-PGA is essentially different from α-PGA (i.e. it does not involve a chemical modification step and is not susceptible to proteases), it could be better utilized for such medical applications. Optimization of γ-PGA with respect to cost of production, molecular mass and conformational/enantiomeric properties is a major step in making its application practical. Analyses of γ-PGA production and knowledge of the enzymes and genes involved in γ-PGA production will not only help increase productivity whilst reducing the cost of production, but also help to understand the mechanism by which γ-PGA is effective in numerous applications.

Poly-γ-glutamic acid: production, properties and applications.

Poly-γ-glutamic acid (γ-PGA) is a naturally occurring biopolymer made from repeating units of l-glutamic acid, d-glutamic acid, or both. Since some bacteria are capable of vigorous γ-PGA biosynthesis from renewable biomass, γ-PGA is considered a promising bio-based chemical and is already widely used in the food, medical, and wastewater industries due to its biodegradable, non-toxic, and non-immunogenic properties. In this review, we consider the properties, biosynthetic pathway, production strategies, and applications of γ-PGA. Microbial biosynthesis of γ-PGA and the molecular mechanisms regulating production are covered in particular detail. Genetic engineering and optimization of the growth medium, process control, and downstream processing have proved to be effective strategies for lowering the cost of production, as well as manipulating the molecular mass and conformational/enantiomeric properties that facilitate screening of competitive γ-PGA producers. Finally, future prospects of microbial γ-PGA production are discussed in light of recent progress, challenges, and trends in this field.

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Microbial synthesis of poly-γ-glutamic acid: current progress, challenges, and future perspectives.

Poly-γ-glutamic acid (γ-PGA) is a naturally occurring biopolymer made from repeating units of l-glutamic acid, d-glutamic acid, or both. Since some bacteria are capable of vigorous γ-PGA biosynthesis from renewable biomass, γ-PGA is considered a promising bio-based chemical and is already widely used in the food, medical, and wastewater industries due to its biodegradable, non-toxic, and non-immunogenic properties. In this review, we consider the properties, biosynthetic pathway, production strategies, and applications of γ-PGA. Microbial biosynthesis of γ-PGA and the molecular mechanisms regulating production are covered in particular detail. Genetic engineering and optimization of the growth medium, process control, and downstream processing have proved to be effective strategies for lower ing the cost of production, as well as manipulating the molecular mass and conformational/enantiomeric properties that facilitate screening of competitive γ-PGA producers. Finally, future prospects of microbial γ-PGA production are discussed in light of recent progress, challenges, and trends in this field.

Microbial synthesis of poly-γ-glutamic acid: current progress, challenges, and future

An Extension to Fuzzy Developed Failure Mode and Effects Analysis (FDFMEA) Application for Aircraft Landing System

Risk assessment based on novel intuitionistic fuzzy-hybrid-modified TOPSIS approach

High-level exogenous glutamic acid-independent production of poly-(γ-glutamic acid) with organic acid addition in a new isolated Bacillus subtilis C10.

Supporting risk management decision making by converting linguistic graded qualitative risk matrices through Interval Type-2 Fuzzy Sets

Layer of protection analysis (LOPA) is a widely used semi-quantitative risk assessment method. It provides a simplified and less precise method to assess the effectiveness of protection layers and the residual risk of an incident scenario. The outcome failure frequency and consequence of that residual risk are intended to be conservative by prudently selecting input data, given that design specification and component manufacturer's data are often overly optimistic. There are many influencing factors, including design deficiencies, lack of layer independence, availability, human factors, wear by testing and maintenance shortcomings, which are not quantified and are dependent on type of process and location. This makes the risk in LOPA usually overestimated. Therefore, to make decisions for a cost-effective system, different sources and types of uncertainty in the LOPA model need to be identified and quantified. In this study, a fuzzy logic and probabilistic hybrid approach was developed to determine the mean and to quantify the uncertainty of frequency of an initiating event and the probabilities of failure on demand (PFD) of independent protection layers (IPLs). It is based on the available data and expert judgment. The method was applied to a distillation system with a capacity to distill 40 tons of flammable n-hexane. The outcome risk of the new method has been proven to be more precise compared to results from the conventional LOPA approach.

A fuzzy logic and probabilistic hybrid approach to quantify the uncertainty in layer of protection analysis

Process safety incidents have large consequences on people, community and environment; hence, it is important to have a learning organization to build on lessons learned from incidents both within and outside the organization. In this study, a web-based collection of process safety incidents is categorized and analyzed in a two-tiered manner to identify proximate causes. Further, a risk-based framework is used to determine deficiencies in the safety management systems. 96 incidents were collected and subjected to the analysis. Even though these incidents are from across the world, they are not statistically representative due to the lack of a universal database of process safety incidents. During the analysis, it was observed that 60% of the incidents resulted in explosions and 16% had equipment malfunction as a proximate cause. In addition, the analysis using the risk-based framework showed that incidents were influenced by a deficiency of safe work practices, operating procedures and conduct of operations. Future work in this area includes the development of tools for continuous monitoring of safety critical systems by leveraging lessons learned from previous incidents.

A web-based collection and analysis of process safety incidents

This paper demonstrates the successful implementation of an artificial neural network to accurately predict the designated thermal radiation distance for jet fire, early pool fire, and late pool fi...

Yizhi Hong

Papers

High-level exogenous glutamic acid-independent production of poly-(γ-glutamic acid) with organic acid addition in a new isolated Bacillus subtilis C10.

Supporting risk management decision making by converting linguistic graded qualitative risk matrices through Interval Type-2 Fuzzy Sets

A fuzzy logic and probabilistic hybrid approach to quantify the uncertainty in layer of protection analysis

A web-based collection and analysis of process safety incidents

Development of Consequent Models for Three Categories of Fire through Artificial Neural Networks