Improvement of cyanobacterial-killing biologically derived substances (BDSs) using an ecologically safe and cost-effective naphthoquinone derivative.

In previous studies, naphthoquinone (NQ) compounds have been shown to be effective, selective, and ecologically safe algicides for controlling harmful algal blooming species (HABs) or winter bloom species, such as Stephanodiscus hantzschii. However, there are no reports on NQ-based algicides for use with cyanobacterial blooming species. In this study, we developed 31 NQ compounds to investigate algicides for mitigating cyanobacterial blooms. In addition, to better apply these compounds in the field, we reduced the number of production steps to develop a cost-effective algicide. In preliminary testing, we screened NQ compounds that showed the best algicidal activity on target cyanobacteria, including Aphanizomenon, Dolichospermum, Microcystis, Oscillatoria, and Nostoc species. The compound NQ 2-0 showed the highest algicidal activity (90%) at a low concentration (≥1µM) on target algae. These were very limiting algicidal effects of 1µM NQ 2-0 observed against non-target algae, such as diatoms (Stephanodiscus hantzschii, Cyclotella meneghiniana, Synedra acus, and Aulacoseira granulata) or green algae (Cosmarium bioculatum and Scenedesmus quadricauda), and the effect did not exceed 15-25% (except against S. quadricauda). NQ 2-0 (1µM) showed no eco-toxicity, as represented by the survival rates of Pseudokirchneriella subcapitata (100%), Daphnia magna (100%), and Danio rerio (100%). Additionally, a chronic eco-toxicity assessment showed no toxicity toward the survival, growth or reproduction of D. magna. Moreover, NQ 2-0 quickly dissipated from field water samples and had a half-life of approximately 3.2 days. These results suggest that NQ 2-0 could be a selective and ecologically safe algicide to mitigate harmful cyanobacterial blooms.

Compositionally Sequenced Interfacial Layers for High-Energy Li-Metal Batteries.

Electrolyte additives with multiple functions enable the interfacial engineering of Li-metal batteries (LMBs). Owing to their unique reduction behavior, additives exhibit a high potential for electrode surface modification that increases the reversibility of Li-metal anodes by enabling the development of a hierarchical solid electrolyte interphase (SEI). This study confirms that an adequately designed SEI facilitates the homogeneous supply of Li+, nonlocalized Li deposition, and low electrolyte degradation in LMBs while enduring the volume fluctuation of Li-metal anodes on cycling. An in-depth analysis of interfacial engineering mechanisms reveals that multilayered SEI structures comprising mechanically robust LiF-rich species, electron-rich P-O species, and elastic polymeric species enabled the stable charge and discharge of LMBs. The polymeric outer SEI layer in the as-fabricated multilayered SEI could accommodate the volume fluctuation of Li-metal anodes, significantly enhancing the cycling stability Li||LiNi0.8Co0.1Mn0.1O2 full cells with an electrolyte amount of 3.6 g Ah-1 and an areal capacity of 3.2 mAh cm-2. Therefore, this study confirms the ability of interfacial layers formed by electrolyte additives and fluorinated solvents to advance the performance of LMBs and can open new frontiers in the fabrication of high-performance LMBs through electrolyte-formulation engineering.

Prediction of Geosmin at Different Depths of Lake Using Machine Learning Techniques.

Geosmin is a major concern in the management of water sources worldwide. Thus, we predicted concentration categories of geosmin at three different depths of lakes (i.e., surface, middle, and bottom), and analyzed relationships between geosmin concentration and factors such as phytoplankton abundance and environmental variables. Data were collected monthly from three major lakes (Uiam, Cheongpyeong, and Paldang lakes) in Korea from May 2014 to December 2015. Before predicting geosmin concentration, we categorized it into four groups based on the boxplot method, and multivariate adaptive regression splines, classification and regression trees, and random forest (RF) were applied to identify the most appropriate modelling to predict geosmin concentration. Overall, using environmental variables was more accurate than using phytoplankton abundance to predict the four categories of geosmin concentration based on AUC and accuracy in all three models as well as each layer. The RF model had the highest predictive power among the three SDMs. When predicting geosmin in the three water layers, the relative importance of environmental variables and phytoplankton abundance in the sensitivity analysis was different for each layer. Water temperature and abundance of Cyanophyceae were the most important factors for predicting geosmin concentration categories in the surface layer, whereas total abundance of phytoplankton exhibited relatively higher importance in the bottom layer.