Harnessing a Biocatalyst to Bioremediate the Purification of Alkylglycosides

As the world moves towards net-zero carbon emissions, the development of sustainable chemical manufacturing processes is essential. Within manufacturing, purification by distillation is often used, however this process is energy intensive and methods that could obviate or reduce its use are desirable. Developed herein is an alternative, oxidative biocatalytic approach that enables purification of alkyl monoglucosides (essential bio-based surfactant components). Implementing an immobilised engineered alcohol oxidase, a long-chain alcohol by-product derived from alkyl monoglucoside synthesis (normally removed by distillation) is selectively oxidised to an aldehyde, conjugated to an amine resin and then removed by simple filtration. This affords recovery of the purified alkyl monoglucoside. The approach lays a blueprint for further development of sustainable alkylglycoside purification using biocatalysis and, importantly, for refining other important chemical feedstocks that currently rely on distillation.

Combining model structure identification and hybrid modelling for photo-production process predictive simulation and optimisation

Integrating physical knowledge and machine learning is a critical aspect of developing industrially focused digital twins for monitoring, optimisation, and design of microalgal and cyanobacterial photo-production processes. However, identifying the correct model structure to quantify the complex biological mechanism poses a severe challenge for the construction of kinetic models, while the lack of data due to the time-consuming experiments greatly impedes applications of most data-driven models. This study proposes the use of an innovative hybrid modelling approach that consists of a simple kinetic model to govern the overall process dynamic trajectory and a data-driven model to estimate mismatch between the kinetic equations and the real process. An advanced automatic model structure identification strategy is adopted to simultaneously identify the most physically probable kinetic model structure and minimum number of data-driven model parameters that can accurately represent multiple data sets over a broad spectrum of process operating conditions. Through this hybrid modelling and automatic structure identification framework, a highly accurate mathematical model was constructed to simulate and optimise an algal lutein production process. Performance of this hybrid model for long-term predictive modelling, optimisation, and online self-calibration is demonstrated and thoroughly discussed, indicating its significant potential for future industrial application.

Holistic protocol for callus culture optimization using statistical modelling

Plants endue a key role against illnesses caused by oxidative stress. These attributes are frequently associated with polyphenolic compounds. However, presence and concentration of secondary metabolites are affected by abiotic factors. The in vitro culture techniques can solve these drawbacks. Peppers can be a suitable alternative to obtain polyphenols. Aiming to optimise the callus culture stage from Capsicum baccatum to produce polyphenols, this work evaluated systemically the effects of the explant's origin (root, hypocotyl and cotyledon), growth hormone type (2,4-dichlorophenoxyacetic acid (2,4-D), benzylaminopurine (BAP) and a combination of 2,4-D/BAP at five-to-one ratio) and concentration (0.023-10.000 mg L^-1) on callus culture efficiency parameters using a multilevel factorial design. The root explant in combination with BAP at 1.138 mg L^-1 ensured the optimal values of the assessed responses; ​callus mass (225.03 mg), antioxidant activity (35.95%), total phenols (11.48 mg of GAE/g DE) and flavonoids (15.92 mg of RU/g DE) production.