Development and Application of an Automated Raman Sensor for Bioprocess Monitoring: From the Laboratory to an Algae Production Platform

Investigating the role of extracellular polymeric substances produced by Parachlorella kessleri in Zn(II) bioremediation using atomic force microscopy

Microalgae, such as Parachlorella kessleri, have significant potential for environmental remediation, especially in removing heavy metals like zinc from water. This study investigates how P. kessleri, isolated from a polluted river in Argentina, can remediate zinc. Using atomic force microscopy (AFM), the research examined the interactions between Zn particles and cells grown with different nitrogen sources-nitrate or ammonium. The results showed that cells grown with nitrate produced extracellular polymeric substances (EPS), while those grown with ammonium did not. Raman spectroscopy revealed distinct metabolic responses based on the nitrogen source, with nitrate-grown cells showing altered profiles after zinc exposure. Zinc exposure also changed the surface roughness and nanomechanical properties of the cells, particularly in those producing EPS. AFM force spectroscopy experiments then confirmed strong Zn binding to EPS in nitrate-grown cells, while interactions were weaker in ammonium-grown cells that lacked EPS. Overall, our results elucidate the critical role of EPS in Zn removal by P. kessleri cells and show that Zn remediation is mediated by EPS adsorption. This study underscores the significance of regulating nitrogen sources to stimulate EPS production, offering insights that are essential for subsequent bioremediation applications.

A review on machine learning approaches for microalgae cultivation systems

Microalgae plays a crucial role in biomass production within aquatic environments and are increasingly recognized for their potential in generating biofuels, biomaterials, bioactive compounds, and bio-based chemicals. This growing significance is driven by the need to address imminent global challenges such as food and fuel shortages. Enhancing the value chain of bio-based products necessitates the implementation of an advanced screening and monitoring system. This system is crucial for tailoring and optimizing the cultivation conditions, ensuring the lucrative and efficient production of the final desired product. This, in turn, underscores the necessity for robust predictive models to accurately emulate algae growth in different conditions during the initial cultivation phase and simulate their subsequent processing in the downstream stage. In pursuit of these objectives, diverse mechanistic and machine learning-based methods have been independently employed to model and optimize microalgae processes. This review article thoroughly examines the techniques delineated in the literature for modeling, predicting, and monitoring microalgal biomass across various applications such as bioenergy, pharmaceuticals, and the food industry. While highlighting the merits and limitations of each method, we delve into the realm of newly emerging hybrid approaches and conduct an exhaustive survey of this evolving methodology. The challenges currently impeding the practical implementation of hybrid techniques are explored, and drawing inspiration from successful applications in other machine-learning-assisted fields, we review various plausible solutions to overcome these obstacles.

Pilot SERS Monitoring Study of Two Natural Hypersaline Lake Waters from a Balneary Resort during Winter-Months Period

Water samples from two naturally hypersaline lakes, renowned for their balneotherapeutic properties, were investigated through a pilot SERS monitoring program. Nanotechnology-based techniques were employed to periodically measure the ultra-sensitive SERS molecular characteristics of the raw water-bearing microbial community and the inorganic content. Employing the Pearson correlation coefficient revealed a robust linear relationship between electrical conductivity and pH and Raman and SERS spectral data of water samples, highlighting the interplay complexity of Raman/SERS signals and physicochemical parameters within each lake. The SERS data obtained from raw waters with AgNPs exhibited a dominant, reproducible SERS feature resembling adsorbed β-carotene at submicromole concentration, which could be related to the cyanobacteria-AgNPs interface and supported by TEM analyses. Notably, spurious SERS sampling cases showed molecular traces attributed to additional metabolites, suggesting multiplexed SERS signatures. The conducted PCA demonstrated observable differences in the β-carotene SERS band intensities between the two lakes, signifying potential variations in picoplankton abundance and composition or environmental influences. Moreover, the study examined variations in the SERS intensity ratio I245/I1512, related to the balance between inorganic (Cl--induced AgNPs aggregation) and organic (cyanobacteria population) balance, in correlation with the electrical conductivity. These findings signify the potential of SERS data for monitoring variations in microorganism concentration, clearly dependent on ion concentration and nutrient dynamics in raw, hypersaline water bodies.