Real-Time Sensor Data Profile-Based Deep Learning Method Applied to Open Raceway Pond Microalgal Productivity Prediction.

Microalgal biotechnology holds the potential for renewable biofuels, bioproducts, and carbon capture applications due to unparalleled photosynthetic efficiency and diversity. Outdoor open raceway pond (ORP) cultivation enables utilization of sunlight and atmospheric carbon dioxide to drive microalgal biomass synthesis for production of bioproducts including biofuels; however, environmental conditions are highly dynamic and fluctuate both diurnally and seasonally, making ORP productivity prediction challenging without time-intensive physical measurements and location-specific calibrations. Here, for the first time, we present an image-based deep learning method for the prediction of ORP productivity. Our method is based on parameter profile plot images of sensor parameters, including pH, dissolved oxygen, temperature, photosynthetically active radiation, and total dissolved solids. These parameters can be remotely monitored without physical interaction with ORPs. We apply the model to data we generated during the Unified Field Studies of the Algae Testbed Public-Private-Partnership (ATP3 UFS), the largest publicly available ORP data set to date, which includes millions of sensor records and 598 productivities from 32 ORPs operated in 5 states in the United States. We demonstrate that this approach significantly outperforms an average value based traditional machine learning method (R2 = 0.77 ≫ R2 = 0.39) without considering bioprocess parameters (e.g., biomass density, hydraulic retention time, and nutrient concentrations). We then evaluate the sensitivity of image and monitoring data resolutions and input parameter variations. Our results demonstrate ORP productivity can be effectively predicted from remote monitoring data, providing an inexpensive tool for microalgal production and operational forecasting.

The Minus Approach Can Redefine the Standard of Practice of Drinking Water Treatment.

Chlorine-based disinfection for drinking water treatment (DWT) was one of the 20th century's great public health achievements, as it substantially reduced the risk of acute microbial waterborne disease. However, today's chlorinated drinking water is not unambiguously safe; trace levels of regulated and unregulated disinfection byproducts (DBPs), and other known, unknown, and emerging contaminants (KUECs), present chronic risks that make them essential removal targets. Because conventional chemical-based DWT processes do little to remove DBPs or KUECs, alternative approaches are needed to minimize risks by removing DBP precursors and KUECs that are ubiquitous in water supplies. We present the "Minus Approach" as a toolbox of practices and technologies to mitigate KUECs and DBPs without compromising microbiological safety. The Minus Approach reduces problem-causing chemical addition treatment (i.e., the conventional "Plus Approach") by producing biologically stable water containing pathogens at levels having negligible human health risk and substantially lower concentrations of KUECs and DBPs. Aside from ozonation, the Minus Approach avoids primary chemical-based coagulants, disinfectants, and advanced oxidation processes. The Minus Approach focuses on bank filtration, biofiltration, adsorption, and membranes to biologically and physically remove DBP precursors, KUECs, and pathogens; consequently, water purveyors can use ultraviolet light at key locations in conjunction with smaller dosages of secondary chemical disinfectants to minimize microbial regrowth in distribution systems. We describe how the Minus Approach contrasts with the conventional Plus Approach, integrates with artificial intelligence, and can ultimately improve the sustainability performance of water treatment. Finally, we consider barriers to adoption of the Minus Approach.

Bacteria and Virus Inactivation: Relative Efficacy and Mechanisms of Peroxyacids and Chlor(am)ine.

Peroxyacids (POAs) are a promising alternative to chlorine for reducing the formation of disinfection byproducts. However, their capacity for microbial inactivation and mechanisms of action require further investigation. We evaluated the efficacy of three POAs (performic acid (PFA), peracetic acid (PAA), and perpropionic acid (PPA)) and chlor(am)ine for inactivation of four representative microorganisms (Escherichia coli (Gram-negative bacteria), Staphylococcus epidermidis (Gram-positive bacteria), MS2 bacteriophage (nonenveloped virus), and Φ6 (enveloped virus)) and for reaction rates with biomolecules (amino acids and nucleotides). Bacterial inactivation efficacy (in anaerobic membrane bioreactor (AnMBR) effluent) followed the order of PFA > chlorine > PAA ≈ PPA. Fluorescence microscopic analysis indicated that free chlorine induced surface damage and cell lysis rapidly, whereas POAs led to intracellular oxidative stress through penetrating the intact cell membrane. However, POAs (50 µM) were less effective than chlorine at inactivating viruses, achieving only ∼1-log PFU removal for MS2 and Φ6 after 30 min of reaction in phosphate buffer without genome damage. Results suggest that POAs' unique interaction with bacteria and ineffective viral inactivation could be attributed to their selectivity toward cysteine and methionine through oxygen-transfer reactions and limited reactivity for other biomolecules. These mechanistic insights could inform the application of POAs in water and wastewater treatment.

Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization.

Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimentation. Here, we present a membrane design strategy utilizing machine learning-based Bayesian optimization to precisely identify the optimal combinations of unexplored monomers and their fabrication conditions from an infinite space. We developed ML models to accurately predict water permeability and salt rejection from membrane monomer types (represented by the Morgan fingerprint) and fabrication conditions. We applied Bayesian optimization on the built ML model to inversely identify sets of monomer/fabrication condition combinations with the potential to break the upper bound for water/salt selectivity and permeability. We fabricated eight membranes under the identified combinations and found that they exceeded the present upper bound. Our findings demonstrate that ML-based Bayesian optimization represents a paradigm shift for next-generation separation membrane design.

Differentiating Solutes with Precise Nanofiltration for Next Generation Environmental Separations: A Review.

Selective removal or enrichment of targeted solutes including micropollutants, valuable elements, and mineral scalants from complex aqueous matrices is both challenging and pivotal to the success of water purification and resource recovery from unconventional water resources. Membrane separation with precision at the subnanometer or even subangstrom scale is of paramount importance to address those challenges via enabling "fit-for-purpose" water and wastewater treatment. So far, researchers have attempted to develop novel membrane materials with precise and tailored selectivity by tuning membrane structure and chemistry. In this critical review, we first present the environmental challenges and opportunities that necessitate improved solute-solute selectivity in membrane separation. We then discuss the mechanisms and desired membrane properties required for better membrane selectivity. On the basis of the most recent progress reported in the literature, we examine the key principles of material design and fabrication, which create membranes with enhanced and more targeted selectivity. We highlight the important roles of surface engineering, nanotechnology, and molecular-level design in improving membrane selectivity. Finally, we discuss the challenges and prospects of highly selective NF membranes for practical environmental applications, identifying knowledge gaps that will guide future research to promote environmental sustainability through more precise and tunable membrane separation.