This research presents a groundbreaking method for transforming 3D-printed metal substrates, specifically Inconel structures created through Laser Powder Bed Fusion (L-PBF), into sophisticated and recyclable Surface-Enhanced Raman Spectroscopy (SERS) platforms. The process involves flame-depositing soot particles onto the metal surfaces using eucalyptus oil, acting as an adlayer and template for the subsequent grafting of TiO2/Ag nanostructures. Unlike conventional SERS substrates, these printed metal structures actively interact with soot particles, forming metal carbides on the surface. The deposition of TiO2 onto these templates ensures robust grafting and preservation of the fractal structure of the soot template on the metal surface. Electroless deposition of silver nanoparticles yields fractally structured TiO2/Ag nanostructures, establishing functionalized metal brushes as effective SERS substrates. Additionally, the incorporation of photocatalytic functionality allows analyte removal from the surface under photochemical conditions, facilitating the recycling and reuse of the SERS substrates. Enhanced photocatalytic activity, achieved through the migration of metals from printed metal structures into fractally ordered TiO2/Ag nanostructures, further enhances the recyclability of these substrates. This research highlights the potential of 3D-printed Inconel metal substrates as the next generation of recyclable SERS platforms, surpassing traditional SERS substrates in terms of sustainability and analytical performance.

Lipase derived from Candida antractica is the most widely used enzyme for catalyzing various reactions. This paper reports the growth and enzyme kinetics of Candida antarctica MTCC-2706 for lipase production, which is one of the fundamental steps in bioprocess design, optimization, and scale-up studies. A hybrid machine learning (ML) aided experimental approach is proposed here to evaluate growth kinetics in which, different ML models were built to predict the growth curves at various substrate concentrations using a substantially smaller set of experimental samples. Comparative assessment of model performances revealed the superiority of gradient boosting regression (GBR) in predicting the growth curves. GBR-based growth kinetics was found to be fitted well with Monod’s model. Further, the activity and enzyme kinetics of lipase produced was investigated by considering the hydrolysis of p-nitrophenyl butyrate. The maximum lipase activity resulted was 24.07 U at 44 hrs. The deviation and R2 values of Monod’s and Michaelis-Menten’s models were 1.4% and 2.25%, and 0.96 and 0.99, respectively. The proposed ML-based approach is found to be efficient in predicting the growth kinetics with reduced experimental effort, time and resources (~50%) as compared to conventional method and its application can be extended to any other microbial processes.

This paper reports a systematic study for evaluating the growth and enzyme kinetics of Candida antarctica MTCC 2706 for the first time. A hybrid machine learning (ML) aided experimental approach is proposed here to evaluate the microbial growth kinetics in which different ML models were built with a smaller set of experimental samples to predict the growth curve at different substrate concentrations and their performance was compared. Gradient boosting regression model was found to predict the growth curves effectively. Further, growth kinetics was evaluated and the data was found to be fitted well with Monod’s model. In addition, the activity of lipase produced in this study was evaluated, and the resulting enzyme kinetics were studied. Further, the statistical significance of models developed was quantified using average absolute deviation percentage (AAD %) and R2 parameters, and robustness of the model parameters is ensured through sensitivity analysis. The results of the proposed ML-based approach were found to be in good agreement with that of the conventional method while successfully reducing the experimental efforts, time and resources by nearly 50%. Also, the application of the proposed method can be extended to any other microbial process.