Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

Nonlinear health evaluation for lithium-ion battery within full-lifespan

Lithium-ion batteries(LIBs), as the first choice for green batteries, have been widely used in energy storage, electric vehicles, 3C devices, and other related fields, and will have greater application prospects in the future. However, one of the obstacles hindering the future development of battery technology is how to accurately evaluate and monitor battery health, which affects the entire lifespan of battery use. It is not enough to assess battery health comprehensively through the state of health(SoH) alone, especially when nonlinear aging occurs in onboard applications. Here, for the first time, we propose a brand-new health evaluation indicator—state of nonlinear aging(SoNA) to explain the nonlinear aging phenomenon that occurs during the battery use, and also design a knee-point identification method and two SoNA quantitative methods. We apply our health evaluation indicator to build a complete LIB full-lifespan grading evaluation system and a ground-to-cloud service framework, which integrates multi-scenario data collection, multi-dimensional data-based grading evaluation, and cloud management functions. Our works fill the gap in the LIBs’ health evaluation of nonlinear aging, which is of great significance for the health and safety evaluation of LIBs in the field of echelon utilization such as vehicles and energy storage. In addition, this comprehensive evaluation system and service framework are expected to be extended to other battery material systems other than LIBs, yet guiding the design of new energy ecosystem.

Enabling dendrite-free charging for lithium batteries based on transport-reaction competition mechanism in CHAIN framework

Worldwide trends in mobile electrification will skyrocket demands for lithium-based battery production,driven by the popularity of electric vehicles.However,both lithium metal batteries and lithium ion batteries face severe safety issues due to dendrite nucleation and growth process.Li deposition is significantly influenced by interfacial factors and charging conditions.In this paper,an electrochemical model considering the internal and external factors is proposed based on Monte Carlo method.The influence of internal solid electrolyte interphase(SEI)porosity,thickness and the external conditions on dendrite growth process is systematically described.The simulation results support that the three factors investigated in this model could synergistically regulate the dendrite growth process.Three competition mechanisms are proposed to tailor lithium deposition for Li-based batteries and numerical solutions for variation pattern of dendrite growth with time are fitted.A three-step process describing kinetic process of lithium deposition is proposed.To achieve dendrite-free charging process,charging strategies and emerging materials design should be considered,including physicochemical materials engineering,artificial SEI,and design for dynamic safety boundary.This work could contribute to the foundation for insights of Li deposition mechanism,which is promising to provide guidelines for next-generation high-energy-density and safe batteries in CHAIN framework.

Novel sediment source fingerprinting quantifying erosion-induced total nitrogen and total phosphorus outputs from an intensive agricultural catchment,North China

Intensive farming is a primary cause of increased sediment and associated nitrogen(N)and phosphorus(P)loads in surface water systems.Determining their contributing sources,pathways and loads present major challenges in the high-intensity agricultural catchments.Herein,we quantify the sediment sources and magnitude of sediment total N and total P from different sources using a novel application of compound-specific stable isotope(CSSI)and fallout radionuclides(FRNs)of^(137)cs and^(210)pbex in an intensive agricultural catchment in North China.Sediment sources from surface and sub-surface soils were estimated from FRNs fingerprint and accounted for 62±7%and 38±7%respectively,while surface soil from land uses that originated from hillslope were identified by CssI fingerprint.Using a novel application of FRNs and CSSI sediment fingerprinting techniques,the dominant sediment source was derived from maize farmland(44±0.1%),followed by channel bank(38±7%).The sedimentation rate(13.55±0.30 t ha^(-1)yr^(-1))was quantifed by the^(137)cs cores(0-60 cm)at the outlet of this catchment.The total N and total P in sediment were both mostly derived from maize farmland and least from channel banks.The channel banks are significant sediment sources but contribute little to the input of sediment N and P for eutrophication.It implies that chemically-applied farmlands are the main hotspots for catchment erosion control and pollution prevention.The novel application of FRNs and CSSI techniques cost-effectively quantified sediment N and P loads from different sources with a single visit to the catchment,enabling rapid assessment for optimizing soil conservation strategies and land management practices.

Quantitative determination of AI-2 quorum-sensing signal of bacteria using high performance liquid chromatography–tandem mass spectrometry

Autoinducer 2（AI-2）, an important bioactive by-product of the Lux S-catalyzed S-ribosylhomocysteine cleavage reaction in the activated-methyl-cycle, has been suggested to serve as a universal intra- and inter-species signaling molecule. The development of reliable and sensitive methods for quantitative determination of AI-2 is highly desired.However, the chemical properties of AI-2 cause difficulty in its quantitative analysis.Herein, we report a high performance liquid chromatography-tandem mass spectrometric method that enables reproducible and sensitive measurement of AI-2 concentrations in complex matrixes. 4,5-Dimethylbenzene-1,2-diamine（DMBDM）, an easy-to-obtain commercial reagent, was used for the derivatization treatment. The assay was linear in the concentration range of 1.0–1000 ng/m L（R^2= 0.999） and had a lower limit of quantification of0.58 ng/m L. The method exhibited several advantages, e.g., high selectivity, wide linear response range, and good sensitivity. Furthermore, the effectiveness of the method was further validated through measuring AI-2 concentrations in the cell-free culture supernatant from Escherichia coli wild type.

Tuning of activated sludge in winter based on respirogram profiles under standard and site temperatures

Respirograms of activated sludge OUR_ x^Tand OUR_x^(20)were measured under site(T) and standard(20°C) temperatures, respectively, and the predicted standard temperature respirogram OUR_( x,cal)^(20)was also calculated using the Arrhenius equation. These respirogram profiles reveal more information than effluent quality. A decrease of OUR_ x ^(20)is a critical alarm signal for the loss of pollutant removal capacity, and a sudden increase of the predicted value OUR_( x,cal)^(20)is an alarm signal for the unrecoverable deterioration of biomass. The sign of OUR_x^(20)–OUR_(x,cal)^(20)can be used for selection of tuning strategies. For example, a negative value of OUR_x^(20)–OUR_( x,cal)^(20)indicates that doubling biomass is difficult,thus strategies such as extending the reaction time with limited available biomass is preferred. The findings in this study elucidated the respiration profile of activated sludge under changes of temperature and can be effectively used for the stable operation of Wastewater Treatment Plants under cold temperatures and seasonal variations.

Distribution of 137Cs and 60Co in plough layer of farmland: Evidenced from a lysimeter experiment using undisturbed soil columns

Radionuclide fallout during nuclear accidents on the land may impair the atmosphere, contaminate farmland soils and crops, and can even reach the groundwater. Previous research focused on the field distribution of deposited radionuclides in farmland soils, but details of the amounts of radionuclides in the plough layer and the changes in their proportional distribution in the soil profile with time are still inadequate. In this study, a lysimeter experiment was conducted to determine the vertical migration of 137Cs and 60Co in brown and aeolian sandy soils, collected from the farmlands adjoining Shidaowan Nuclear Power Plant(NPP) in eastern China, and to identify the factors influencing their migration depths in soil. At the end of the experiment(800 d), >96% of added 137Cs and 60Co were retained in the top 0–20 cm soil layer of both soils;very little 137Cs or 60Co initially migrated to 20–30 cm, but their amounts at this depth increased with time. The migration depth of 137Cs was greater in the aeolian sandy soil than in the brown soil during 0–577 d, but at the end of the experiment, 137Cs migrated to the same depth(25 cm) in both soils. Three phases on the vertical migration rate(v) of 60Co in the aeolian sandy soil can be identified: an initial rapid movement(0–355 d, v = 219 ± 17 mm year-1), followed by a steady movement(355–577 d, v = 150 ± 24 mm year-1) and a very slow movement(577–800 d, v = 107 ± 7 mm year-1). In contrast, its migration rate in the brown soil(v = 133 ± 17 mm year-1) was steady throughout the 800-d experimental period. The migration of both 137Cs and 60Co in the two soils appears to be regulated by soil clay and silt fractions that provide most of the soil surface area, soil organic carbon(SOC), and soil pH, which were manifested by the solid-liquid distribution coefficient of 137Cs and 60Co. The results of this study suggest that most 137Cs and 60Co remained within the top layer(0–20 cm depth) of farmland soils following a simulated NPP accident, and little reached the subsurface(20–30 cm depth). Fixation of radionuclides onto clay minerals may limit their migration in soil, but some could be laterally distributed by soil erosion and taken up by crops, and migrate into groundwater in a high water table level area after several decades.Remediation measures, therefore, should focus on reducing their impact on the farmland soils, crops, and water.

Mechanisms behind the accelerated extracellular electron transfer in Geobacter sulfurreducens DL-1 by modifying gold electrode with self-assembled monolayers

Modification of electrode surface with carboxylic acid terminated alkanethiol self-assembled monolayers （SAMs） has been found to be an effective approach to improve the extracellular electron transfer （EET） of electrochemically active bacteria （EAB） on electrode surface, but the underlying mechanism behind such enhanced EET remains unclear. In this work, the gold electrodes modified by mercapto-acetic acid and mercapto- ethylamine （Au-COOH, Au-NH2） were used as anodes in microbial electrolysis cells （MECs） inoculated with Geobacter sulfurreducens DL- 1, and their electrochemical performance and the bacteria-electrode interactions were investigated. Results showed that the Fe（CN）6^3-/4^- redox reaction occurred on the Au-NH2 with a higher rate and a lower resistance than that on the Au or the Au-COOH. Both the MECs with the Au-COOH and Au-NH2 anodes exhibited a higher current density than that with a bare Au anode. The biofilm formed on the Au-COOH was denser than that on bare Au, while the biofilm on the Au-NH2 had a greater thickness, suggesting a critical role of direct EET in this system. This work suggests that functional groups such as --COOH and-NH2 could promote electrode performance by accelerating the direct EET of EAB on electrode surface.

Purification and molecular weight distribution of a key exopolysaccharide component of Bacillus megaterium TF10

Extracellular polymeric substances（EPS） are organic metabolic compounds excreted by microorganisms. They largely impact microbial aggregate structures and functions.Extracellular polysaccharides（EP） in EPS are responsible for the formation of microbial aggregates. In this work, we successfully separated and characterized EP from EPS of the bacterium Bacillus megaterium TF10. Extraction of EP from EPS was optimized using Sevag＇s reagent. Chemical characteristics, functional groups, and molecular weight（MW） distribution of EP were compared with the harvested EPS and soluble microbial products（SMP）. We found that the polymers of lower MW and free proteins were successfully removed by Sevag＇s reagent. The higher MW components of EPS were predominantly polysaccharides,while the polymers of lower MW tended to secrete to the supernatant and were described as SMP. A part of the proteins in the EP was polysaccharide-bonded. Our results can be further used in elucidating the complex flocculation mechanisms in which EP play a major role.

Approaching the binding between Cu（Ⅱ） and aerobic granules by a modified titration and p-XRF

Interactions between metals and activated sludge can substantially affect the fate and transport of heavy metals in wastewater treatment plants. Therefore, it is important to develop a simple, fast and efficient method to elucidate the interaction. In this study, a modified titration method with a dynamic mode was developed to investigate the binding of Cu（Ⅱ）, a typical heavy metal, onto aerobic granules. The titration results indicated that pH and ionic strength both had a positive effect on the biosorption capacity of the granular sludge. The/-XRF results demonstrated that the distribution of metals on the granular surface was heterogeneous, and Cu showed strong correlations and had the same ＂hot spots＂ positions with other metal ions （e.g., Ca, Mg, Fe etc.）. Ion exchange and complexing were the main mechanisms for the biosorption of Cu（Ⅱ） by aerobic granules. These results would be beneficial for better understanding of Cu（Ⅱ） migration and its fate in wastewater treatment plants.