Biodegradation of Biodegradable Polymers in Mesophilic Aerobic Environments.

Finding alternatives to diminish plastic pollution has become one of the main challenges of modern life. A few alternatives have gained potential for a shift toward a more circular and sustainable relationship with plastics. Biodegradable polymers derived from bio- and fossil-based sources have emerged as one feasible alternative to overcome inconveniences associated with the use and disposal of non-biodegradable polymers. The biodegradation process depends on the environment's factors, microorganisms and associated enzymes, and the polymer properties, resulting in a plethora of parameters that create a complex process whereby biodegradation times and rates can vary immensely. This review aims to provide a background and a comprehensive, systematic, and critical overview of this complex process with a special focus on the mesophilic range. Activity toward depolymerization by extracellular enzymes, biofilm effect on the dynamic of the degradation process, CO2 evolution evaluating the extent of biodegradation, and metabolic pathways are discussed. Remarks and perspectives for potential future research are provided with a focus on the current knowledge gaps if the goal is to minimize the persistence of plastics across environments. Innovative approaches such as the addition of specific compounds to trigger depolymerization under particular conditions, biostimulation, bioaugmentation, and the addition of natural and/or modified enzymes are state-of-the-art methods that need faster development. Furthermore, methods must be connected to standards and techniques that fully track the biodegradation process. More transdisciplinary research within areas of polymer chemistry/processing and microbiology/biochemistry is needed.

Photocatalytic and mechanical properties of immobilized nanotubular TiO2 photocatalysts obtained by anodic oxidation: a novel combined analysis.

The photocatalytic and mechanical performance of TiO2 nanotubular coatings obtained by anodic oxidation of commercial titanium, using an NH4F and 3.5% v/v water in ethylene glycol solution as electrolyte was investigated. After the anodization, the coatings were thermally treated at 450 °C for 2 h. The effects of the anodizing voltage (40-80 V) and NH4F concentration (0.06, 0.15, 0.27 M) on the formation of the nanotube arrays were evaluated. Nanotube diameters (57 to 114 nm), wall thicknesses (4 to 13 nm), and lengths (5 to 17 µm) increased with the anodizing voltage and the NH4F concentration. The photocatalysts were characterized by scanning electron microscopy, glancing incidence X-ray diffraction, and UV-Vis diffuse reflectance spectroscopy. The mechanical properties of the photocatalysts were determined: adhesion using the tape test (ASTM D3359) and erosion resistance through a 3 h accelerated test. The photocatalytic activity of the nanotubes under UV irradiation was evaluated using hexavalent chromium (Cr(VI)) in the presence of ethylenediaminetetraacetic acid (EDTA), using a 1.25 EDTA/Cr(VI) molar ratio solution at pH 2. A complete Cr(VI) transformation after 3 h of irradiation was obtained for all samples, with a better performance than that of an immobilized P25 sample. The photocatalyst obtained with 0.27 M NH4F at 40 V presented a good behavior in adherence and erosion resistance, together with a very good photocatalytic activity. This novel analysis, combining photocatalytic and mechanical tests, proved that the new TiO2 nanotubular coatings could be successfully used as immobilized photocatalysts in photoreactors for water treatment.

Genetic algorithm applied to simultaneous parameter estimation in bacterial growth.

Several mathematical models have been developed to understand the interactions of microorganisms in foods and predict their growth. The resulting model equations for the growth of interacting cells include several parameters that must be determined for the specific conditions to be modeled. In this study, these parameters were determined by using inverse engineering and a multi-objective optimization procedure that allows fitting more than one experimental growth curve simultaneously. A genetic algorithm was applied to obtain the best parameter values of a model that permit the construction of the front of Pareto with 50 individuals or phenotypes. The method was applied to three experimental data sets of simultaneous growth of lactic acid bacteria (LAB) and Listeria monocytogenes (LM). Then, the proposed method was compared with a conventional mono-objective sequential fit. We concluded that the multi-objective fit by the genetic algorithm gives superior results with more parameter identifiability than the conventional sequential approach.

Mechanistically Inspired Kinetic Approach to Describe Interactions During Co-Culture Growth of Carnobacterium maltaromaticum and Listeria monocytogenes.

Lactic acid bacteria and Listeria monocytogenes are psychotropic organisms that can grow and compete in food such as lightly preserved fishery products. Predictive microbiology is nowadays one of the leading tools to assess the behavior of bacteria in food and to predict food spoilage. Mathematical models can be used to predict the growth, inactivation or growth probability of bacteria. Currently, the efforts in microbial modeling are oriented towards extrapolation of results beyond experiments in order to predict the growth of interacting microorganisms and develop new food preservation processes. In the present work, a model combining both heterogeneous population and quasi-chemical approaches to describe the different phases of the bacterial growth curve is presented. The model was applied to both monoculture and co-culture cases of lactic acid bacteria, Carnobacterium maltaromaticum H-17, and two Listeria monocytogenes strains in a raw fish extract. It is a highlight that our model includes novel inhibition reactions due to the accumulation of metabolites, and a general equation to take into account the effect of chemical compounds during the lag or physiological adaptation phase of the cells. Our results show that the proposed model can accurately describe the experimental data when the curve shape is a sigmoid, and when it presents a maximum. Besides, the parameters have biological interpretability since the model is mechanistically inspired.

Toughening of Poly(lactic acid) and Thermoplastic Cassava Starch Reactive Blends Using Graphene Nanoplatelets.

Poly(lactic acid) (PLA) was reactively blended with thermoplastic cassava starch (TPCS) and functionalized with commercial graphene (GRH) nanoplatelets in a twin-screw extruder, and films were produced by cast-film extrusion. Reactive compatibilization between PLA and TPCS phases was reached by introducing maleic anhydride and a peroxide radical during the reactive blending extrusion process. Films with improved elongation at break and toughness for neat PLA and PLA-g-TPCS reactive blends were obtained by an addition of GRH nanoplatelets. Toughness of the PLA-g-TPCS-GRH was improved by ~900% and ~500% when compared to neat PLA and PLA-g-TPCS, respectively. Crack bridging was established as the primary mechanism responsible for the improvement in the mechanical properties of PLA and PLA-g-TPCS in the presence of the nanofiller due to the high aspect ratio of GRH. Scanning electron microscopy images showed a non-uniform distribution of GRH nanoplatelets in the matrix. Transmittance of the reactive blend films decreased due to the TPCS phase. Values obtained for the reactive blends showed ~20% transmittance. PLA-GRH and PLA-g-TPCS-GRH showed a reduction of the oxygen permeability coefficient with respect to PLA of around 35% and 50%, respectively. Thermal properties, molecular structure, surface roughness, XRD pattern, electrical resistivity, and color of the films were also evaluated. Biobased and compostable reactive blend films of PLA-g-TPCS compounded with GRH nanoplatelets could be suitable for food packaging and agricultural applications.