Predicting the state of charge and health of batteries using data-driven machine learning

Machine learning is a specific application of artificial intelligence that allows computers to learn and improve from data and experience via sets of algorithms, without the need for reprogramming. In the field of energy storage, machine learning has recently emerged as a promising modelling approach to determine the state of charge, state of health and remaining useful life of batteries. First, we review the two most studied types of battery models in the literature for battery state prediction: the equivalent circuit and physics-based models. Based on the current limitations of these models, we showcase the promise of various machine learning techniques for fast and accurate battery state prediction. Finally, we highlight the major challenges involved, especially in accurate modelling over length and time, performing in situ calculations and high-throughput data generation. Overall, this work provides insights into real-time, explainable machine learning for battery production, management and optimization in the future

Cationic Polymers based on Fructose and Galactose Moieties for Nucleic Acids Delivery

Cationic polymers and glycopolymers were synthesised using the RAFT technique. Combining cationic polymers with glycopolymers has great potential in targeted nucleic acid delivery.1,2 However, many obstacles prevent the use of cationic glycopolymers as vectors including low success in nucleic acid delivery and high toxicity of the cationic polymer. This project aims to investigate RAFT synthesis of cationic glycopolymers with galactose or fructose carbohydrates, their binding ability with their specific lectins and with negatively charged nucleic acids. The cationic polymer poly(2-(dimethylamino)ethyl methacrylate) (PDMAEMA) was synthesised using RAFT polymerisation. The galactose monomer, 2-(2’,3’,4’,6’-tetra-O-acetyl-β-D-galactosyloxy)ethyl methacrylate (AcGalEMA), and the fructose monomer, 1-O-methacryloyl-2,3:4,5-di-O-isopropylidene--D-fructopyranose (1-O-MAiPFru)3, were polymerised with PDMAEMA to form cationic glycopolymers. Chain extension was confirmed using proton nuclear magnetic spectroscopy (NMR) and gel permeation chromatography (GPC). Gel permeation chromatography was also performed to determine the polydispersity index (uniformity) of the polymers. The protected glycopolymer blocks were modified by deacetylation of the galactose block and acid deprotection of the fructose block. Characterisation of the modified cationic glycopolymers was achieved using proton nuclear magnetic spectroscopy for confirmation of deacetylation/deprotection, and dynamic light scattering to determine the sizes of the diblock copolymers. The zeta potential (ionic charge) of the diblock copolymers was recorded. Aggregation assays between the cationic glycopolymers and plant lectins were assessed. The galactose-containing glycopolymers were conjugated with peanut agglutinin lectin and the fructose-containing glycopolymers were conjugated with lectin from Ulex europaeus. The assays were analysed using dynamic light spectroscopy and ultraviolet-visible spectroscopy. Complexation of the cationic glycopolymer with small interfering RNA (siRNA) was accomplished. The size of the resulting polyplex was recorded with dynamic light spectroscopy. The zeta potential was measured and compared to the zeta potential measurement before complexation with siRNA. Results indicated that RAFT polymerisation was successful in producing diblock polymers of controlled weight and uniform size. The cationic glycopolymers were partially successful in deacetylation/deprotection and highly successful in binding to their specific lectins. The cationic glycopolymer complexed with siRNA; however, further research into the appropriate N:P ratio is necessary. In conclusion, RAFT polymerisation is a suitable technique for the synthesis of cationic glycopolymers for use in nucleic acid delivery. The cationic block of the polymer is able to complex with nucleic acids while the glycopolymer block is able to bind to specific lectins. Further research into carbohydrates specific binding and further modifications to increase nucleic acid delivery efficiency would be beneficial

Coralline Sr/Ca and Mg/Ca thermometer for the northern South China Sea: calibration and primary application high resolution on high-resolution SST reconstructing

利用全谱直读等离子体光谱(ICP2AES) 的分析方法精确分析了南海珊瑚的SrPCa 和MgPCa 比值,结合实测表层海水温度(SST),标定了海南岛南部三亚海域和西沙海域两个滨珊瑚的SrPCa 和MgPCa 温度计。在此基础上,尝试对两个南海北部全新世时期的珊瑚进行SST记录重建。结果显示约540aB.P. (小冰期) 西沙海域夏季月均SST 较现代低约1℃,而约6 500aB.P. (大暖期) 海南岛三亚海域夏季月均SST则高出现代1.0～1.5℃。The method for precisely and simultaneously measuring the coralline Sr/Ca and Mg/Ca ratios was established using inductively coupled plasma atomic emission spectrometry (ICP-AES). Using this method, the high-resolution Sr/Ca and Mg/Ca ratios of two Porites lutea from Sanya, South Hainan Island and Xisha Islands were measured. By comparing to the instrumentally measured sea surface temperatures in these two areas, the coralline Sr/Ca thermometer and the Mg/Ca thermometer were calibrated. These two thermometers can provide SST records with an error bar < 0.2℃, and they are suitable for high-resolution SST reconstructions in these areas. Based on these two thermometers, two short SST records were reconstructed from two Holocene Porites corals of the northern South China Sea. The results indicated that the monthly summer SSTs in Xisha Islands at about 540 years ago (the Little Ice Age) were 1℃ lower than that at present, and the monthly summer SSTs in Sanya, southern Hainan Island at 6 500 years ago (the Megathermal) were about 1℃ to 1 5℃ higher than that at present.published_or_final_versio

Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites

Peer reviewedPublisher PD

Characterization of brain blood flow and the amplitude of low-frequency fluctuations in major depressive disorder: A multimodal meta-analysis.

Background In healthy subjects, there is an association between amplitude of low-frequency fluctuations (ALFF) and regional cerebral blood flow (rCBF). To date, no published meta-analysis has investigated changes in the regional ALFF in medication-free depressed patients. Methods In this study, we aimed to explore whether resting-state rCBF and ALFF changes co-occur in the depressed brain without the potential confound of medication. Using signed differential mapping (SDM), we conducted two meta-analyses, one of rCBF studies and one of ALFF studies, involving medication-free patients with major depressive disorder (MDD). In addition, we conducted a multimodal meta-analysis to identify brain regions that showed abnormalities in both rCBF and ALFF. Results A total of 16 studies were included in this series. We identified abnormalities in resting-state rCBF and ALFF in the left insula in medication-free MDD patients compared with healthy controls (HC). In addition, we observed altered resting-state rCBF in the limbic-subcortical-cortical circuit and altered ALFF in the default mode network (DMN) and some motor-related brain regions. Limitations The analysis techniques, patient characteristics and clinical variables of the included studies were heterogeneous. Conclusions The conjoint alterations in ALFF and rCBF in the left insula may represent core neuropathological changes in medication-free patients with MDD and merit further studying

Approaching Theoretical Performances of Electrocatalytic Hydrogen Peroxide Generation by Cobalt-Nitrogen Moieties

Electrocatalytic oxygen reduction reaction (ORR) has been intensively studied for environmentally benign applications. However, insufficient understanding of ORR 2 e−-pathway mechanism at the atomic level inhibits rational design of catalysts with both high activity and selectivity, causing concerns including catalyst degradation due to Fenton reaction or poor efficiency of H2O2 electrosynthesis. Herein we show that the generally accepted ORR electrocatalyst design based on a Sabatier volcano plot argument optimises activity but is unable to account for the 2 e−-pathway selectivity. Through electrochemical and operando spectroscopic studies on a series of CoNx/carbon nanotube hybrids, a construction-driven approach based on an extended “dynamic active site saturation” model that aims to create the maximum number of 2 e− ORR sites by directing the secondary ORR electron transfer towards the 2 e− intermediate is proven to be attainable by manipulating O2 hydrogenation kinetics

Understanding the nature of "superhard graphite"

Numerous experiments showed that on cold compression graphite transforms into a new superhard and transparent allotrope. Several structures with different topologies have been proposed for this phase. While experimental data are consistent with these models, the only way to solve this puzzle is to find which structure is kinetically easiest to form. Using state-of-the-art molecular-dynamics transition path sampling simulations, we investigate kinetic pathways of the pressure-induced transformation of graphite to various superhard candidate structures. Unlike hitherto applied methods for elucidating nature of superhard graphite, transition path sampling realistically models nucleation events necessary for physically meaningful transformation kinetics. We demonstrate that nucleation mechanism and kinetics lead to $M$-carbon as the final product. $W$-carbon, initially competitor to $M$-carbon, is ruled out by phase growth. Bct-C$_4$ structure is not expected to be produced by cold compression due to less probable nucleation and higher barrier of formation

How to cool lithium ion batteries: optimising cell design using a thermally coupled model

Cooling electrical tabs of the cell instead of the lithium ion cell surfaces has shown to provide better thermal uniformity within the cell, but its ability to remove heat is limited by the heat transfer bottleneck between tab and electrode stack. A two-dimensional electro-thermal model was validated with custom made cells with different tab sizes and position and used to study how heat transfer for tab cooling could be increased. We show for the first time that the heat transfer bottleneck can be opened up with a single modification, increasing the thickness of the tabs, without affecting the electrode stack. A virtual large-capacity automotive cell (based upon the LG Chem E63 cell) was modelled to demonstrate that optimised tab cooling can be as effective in removing heat as surface cooling, while maintaining the benefit of better thermal, current and state-of-charge homogeneity. These findings will enable cell manufacturers to optimise cell design to allow wider introduction of tab cooling. This would enable the benefits of tab cooling, including higher useable capacity, higher power, and a longer lifetime to be possible in a wider range of applications

Super-resolving phase measurements with a multi-photon entangled state

Using a linear optical elements and post-selection, we construct an entangled polarization state of three photons in the same spatial mode. This state is analogous to a ``photon-number path entangled state'' and can be used for super-resolving interferometry. Measuring a birefringent phase shift, we demonstrate two- and three-fold improvements in phase resolution.Comment: 4 pages, 3 figure

A survey of parametric modelling methods for designing the head of a high-speed train

With the continuous increase of the running speed, the head shape of the high-speed train (HST) turns out to be a critical factor for further speed boost. In order to cut down the time used in the HST head design and improve the modelling efficiency, various parametric modelling methods have been widely applied in the optimization design of the HST head to obtain an optimal head shape so that the aerodynamic effect acting on the head of HSTs can be reduced and more energy can be saved. This paper reviews these parametric modelling methods and classifies them into four categories: 2D, 3D, CATIA-based, and mesh deformation-based parametric modelling methods. Each of the methods is introduced, and the advantages and disadvantages of these methods are identified. The simulation results are presented to demonstrate that the aerodynamic performance of the optimal models constructed by these parametric modelling methods has been improved when compared with numerical calculation results of the original models or the prototype models of running trains. Since different parametric modelling methods used different original models and optimization methods, few publications could be found which compare the simulation results of the aerodynamic performance among different parametric modelling methods. In spite of this, these parametric modelling methods indicate more local shape details will lead to more accurate simulation results, and fewer design variables will result in higher computational efficiency. Therefore, the ability of describing more local shape details with fewer design variables could serve as a main specification to assess the performance of various parametric modelling methods. The future research directions may concentrate on how to improve such ability