Predicting aqueous sorption of organic pollutants on microplastics with machine learning.

Microplastics (MPs) are ubiquitously distributed in freshwater systems and they can determine the environmental fate of organic pollutants (OPs) via sorption interaction. However, the diverse physicochemical properties of MPs and the wide range of OP species make a deeper understanding of sorption mechanisms challenging. Traditional isotherm-based sorption models are limited in their universality since they normally only consider the nature and characteristics of either sorbents or sorbates individually. Therefore, only specific equilibrium concentrations or specific sorption isotherms can be used to predict sorption. To systematically evaluate and predict OP sorption under the influence of both MPs and OPs properties, we collected 475 sorption data from peer-reviewed publications and developed a poly-parameter-linear-free-energy-relationship-embedded machine learning method to analyze the collected sorption datasets. Models of different algorithms were compared, and the genetic algorithm and support vector machine hybrid model displayed the best prediction performance (R2 of 0.93 and root-mean-square-error of 0.07). Finally, comparison results of three feature importance analysis tools (forward step wise method, Shapley method, and global sensitivity analysis) showed that chemical properties of MPs, excess molar refraction, and hydrogen-bonding interaction of OPs contribute the most to sorption, reflecting the dominant sorption mechanisms of hydrophobic partitioning, hydrogen bond formation, and π-π interaction, respectively. This study presents a novel sorbate-sorbent-based ML model with a wide applicability to expand our capacity in understanding the complicated process and mechanism of OP sorption on MPs.

Assessing the algal population dynamics using multiple machine learning approaches: Application to Macao reservoirs.

The quality of reservoir water is important to the health and wellbeing of human and animals. Eutrophication is one of the most serious problems threatening the safety of reservoir water resource. Machine learning (ML) approaches are effective tools to understand and evaluate various environmental processes of concern, such as eutrophication. However, limited studies have compared the performances of different ML models to reveal algal dynamics using time-series data of redundant variables. In this study, the water quality data from two reservoirs in Macao were analyzed by adopting various ML approaches, including stepwise multiple linear regression (LR), principal component (PC)-LR, PC-artificial neuron network (ANN) and genetic algorithm (GA)-ANN-connective weight (CW) models. The influence of water quality parameters on algal growth and proliferation in two reservoirs was systematically investigated. The GA-ANN-CW model demonstrated the best performance in reducing the size of data and interpreting the algal population dynamics data, which displayed higher R-squared, lower mean absolute percentage error and lower root mean squared error values. Moreover, the variable contribution based on ML approaches suggest that water quality parameters, such as silica, phosphorus, nitrogen, and suspended solid have a direct impact on algal metabolisms in two reservoirs' water systems. This study can expand our capacity in adopting ML models in predicting algal population dynamics based on time-series data of redundant variables.

Urinary PART1 and PLA2R1 Could Potentially Serve as Diagnostic Markers for Diabetic Kidney Disease Patients.

Background: Diabetic kidney disease (DKD) is a chronic renal disease which could eventually develop into renal failure. Though albuminuria and estimated glomerular filtration rate (eGFR) are helpful for the diagnosis of DKD, the lack of specific biomarkers reduces the efficiency of therapeutic interventions. Methods: Based on bulk-seq of 56 urine samples collected at different time points (including 11 acquired from DKD patients and 11 from healthy controls), in corporation of scRNA-seq data of urine samples and snRNA-seq data of renal punctures from DKD patients (retrieved from NCBI GEO Omnibus), urine-kidney specific genes were identified by Multiple Biological Information methods. Results: Forty urine-kidney specific genes/differentially expressed genes (DEGs) were identified to be highly related to kidney injury and proteinuria for the DKD patients. Most of these genes participate in regulating glucagon and apoptosis, among which, urinary PART1 (mainly derived from distal tubular cells) and PLA2R1 (podocyte cell surface marker) could be used together for the early diagnosis of DKD. Moreover, urinary PART1 was significantly associated with multiple clinical indicators, and remained stable over time in urine. Conclusion: Urinary PART1 and PLA2R1 could be shed lights on the discovery and development of non-invasive diagnostic method for DKD, especially in early stages.

A Dendrite-Free Tin Anode for High-Energy Aqueous Redox Flow Batteries.

Metal-based aqueous redox flow batteries (ARFBs) such as zinc-based ARFBs have attracted remarkable attention owing to their intrinsic high energy density. However, severe dendrite issues limit their efficiency and lifespan. Here an aqueous metal anode operating between Sn(OH)6 2- (stannate) and metal Sn is presented, providing a reversible four-electron transfer at -0.921 V vs standard hydrogen electrode. In strong contrast to severe Zn dendrites, the Sn(OH)6 2- /Sn electrode shows smooth and dendrite-free morphology, which can be attributed to its intrinsic low-surface-energy anisotropy which facilitates isotropic crystal growth of Sn metal. By coupling with iodide/tri-iodide (I- /I3 - ), the static Sn-I cell demonstrates a stable cycling for 500 cycles (more than 2 months). In contrast, the state-of-the-art Zn anode suffers from serious dendrites and lasts less than 45 cycles (190 h) in Zn-I cells. A stable continuous flow cycling of Sn-I cell achieves a Sn areal capacity of 73.07 mAh cm-2 at an average discharge voltage of 1.3 V for 350 h. The alkaline Sn electrode demonstrates dendrite-free morphology and superior performance in cycle life and areal capacity compared to state-of-the-art Zn metal anodes, offering a promising metal anode for high-energy ARFBs and other metal-based rechargeable aqueous batteries.

Material Design of Aqueous Redox Flow Batteries: Fundamental Challenges and Mitigation Strategies.

Redox flow batteries (RFBs) are critical enablers for next-generation grid-scale energy-storage systems, due to their scalability and flexibility in decoupling power and energy. Aqueous RFBs (ARFBs) using nonflammable electrolytes are intrinsically safe. However, their development has been limited by their low energy density and high cost. Developing ARFBs with higher energy density, lower cost, and longer lifespan than the current standard is of significant interest to academic and industrial research communities. Here, a critical review of the latest progress on advanced electrolyte material designs of ARFBs is presented, including a fundamental overview of their physicochemical properties, major challenges, and design strategies. Assessment methodologies and metrics for the evaluation of RFB stability are discussed. Finally, future directions for material design to realize practical applications and achieve the commercialization of ARFB energy-storage systems are highlighted.

Sorption and desorption characteristics of anionic surfactants to soil sediments.

Surfactants are important environmental chemicals due to their extensive domestic and industrial applications, such as subsurface organic pollution remediation and enhanced oil recovery. However, the interaction of surfactants with subsurface material particularly the desorption behavior of surfactants is less understood. Surfactant desorption is essential to control the fate and transport of surfactants as well as organic pollutants. In this study, the sorption and desorption of linear sodium dodecylbenzene sulfonate (SDBS) and sodium hexadecyl diphenyl oxide disulfonate (DPDS) with two types of soil sediment samples are compared. Sorption of surfactants can be modeled by hydrophobic sorption. Less DPDS sorption is observed at a higher aqueous concentration, which is attributed to the competition between surfactant micelles and sediment organic matter for DPDS sorption. A significant fraction of the sorbed surfactants resists desorption, and this is not a result of surfactant precipitation or desorption kinetics. Surfactant desorption behavior is similar to the irreversible desorption of hydrocarbons from soil with only half of the resistant phase surfactant being readily extracted by heated solvent extraction. The sorption/desorption data are interpreted with a molecular topology and irreversible sorption model. The knowledge of this study can be useful in understanding the environmental fate and transport of these common anionic surfactants. The methodology developed in this study can be expanded to study the sorptive nature of a wider range of surfactants in the environment.

Identification of a new high-molecular-weight Fe-citrate species at low citrate-to-Fe molar ratios: Impact on arsenic removal with ferric hydroxide.

Ferric hydroxide precipitation and flocculation is the most commonly used method for the removal of arsenic in water treatment. However, citrate often interrupts the precipitation of ferric hydroxides and thus affects arsenic removal. To date, the mechanisms controlling the effects of citrate on arsenic removal with ferric hydroxide flocculation and precipitation at very low citrate-to-Fe molar ratios are not well understood. Herein, we report a new mechanism by which citrate inhibits arsenic removal using ferric hydroxide. At a substoichiometric citrate-to-Fe molar ratio of 0.28, citrate forms a high-molecular-weight Fe-citrate (Fe4Cit) species. The optimized structure of the Fe4Cit species was obtained by the density functional theory calculation. To the best of our knowledge, this study is the first to report the formation and to identify the structure of dominant Fe-citrate species at a very low citrate-to-Fe molar ratio.