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.

No abstract

No abstract

A single Brachionus rotifer can consume thousands of algae cells per hour causing an algae pond to crash within days of infection. Thus, there is a great need to reduce rotifers in order for algal biofuel production to become reality. Copper can selectively inhibit rotifers in algae ponds, thereby protecting the algae crop. Differential toxicity tests were conducted to compare the copper sensitivity of a model rotifer—B. calyciflorus and an alga, C. kessleri. The rotifer LC50 was <0.1 ppm while the alga was not affected up to 5 ppm Cu(II). The low pH of the rotifer stomach may make it more sensitive to copper. However, when these cultures were combined, a copper concentration of 1.5 ppm was needed to inhibit the rotifer as the alga bound the copper, decreasing its bioavailability. Copper (X ppm) had no effect on downstream fatty acid methyl ester extraction.

No abstract

No abstract

Although algae-biofuels have many advantages including high areal productivity, algae can be preyed upon by amoebas, protozoans, ciliates, and rotifers, particularly in open pond systems. Thus, these higher organisms need to be controlled. In this study, Chlorella kessleri was used as the algal culture and Brachionus calyciflorus as the source of predation. The effect of sodium hypochlorite (bleach) was tested with the goal of totally inhibiting the rotifer while causing minor inhibition to the alga. The 24-hr LC(50) for B. calyciflorus in spring water was 0.198 mg Cl/L while the 24-hr LC(50) for C. kessleri was 0.321 mg Cl/L. However, chlorine dissipates rapidly as the algae serves as reductant. Results showed a chlorine dosage between 0.45 to 0.6 mg Cl/L and a dosing interval of two hours created the necessary chlorine concentrations to inhibit predation while letting the algae grow; thus giving algae farmers a tool to prevent pond crashes.

The first step to develop a quantitative structure–activity relationship (QSAR) model is to identify a set of chemicals with known activities/properties, which can be either collected from the published studies or measured experimentally. A key challenge in this process is how to determine which chemicals are used to train a QSAR model, and, of those chemicals, which should be prioritized in experimental trials to ensure that the obtained models have large applicability domains (ADs). In this study, we employ uncertainty-based active learning (AC) to address this challenge. We use the Gaussian process (GP) to develop QSAR models for three public datasets, Koc, solubility, and k •OH, each with a number of chemicals represented by molecular descriptors, in which the GP can offer prediction uncertainty (by means of standard deviation) for the model’s prediction. The training chemicals of each dataset are selected in two different ways: (1) random splitting (RS) and (2) uncertainty-based AC. Uncertainty-based AC iteratively identifies chemicals with the highest uncertainty and selects them for model training. We demonstrate that the chemicals selected by AC are more diverse than those selected by RS and that AC-based QSAR models have better generalizability than those derived from RS. We then use these two types of models to predict the properties of chemicals in the REACH dataset (>300,000 chemicals) and assess their ADs using five different AD determination methods. We demonstrate that the AD of AC-based QSAR models for all AD methods is significantly larger than those of RS-based models (up to 24 times larger). This study provides a novel method to enlarge the AD of QSAR models, which can guide model development and improve the property prediction reliability for more REACH dataset chemicals while minimizing the development cost and time.