Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data - Part B : cycling operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates

Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data – Part A : storage operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing

IIoT based trustworthy demographic dynamics tracking with advanced Bayesian learning

Tracking demographic dynamics for the built environment is important for a smart city. As a kind of ubiquitous Industrial Internet of Things (IIoT) device, portable devices (e.g., mobile phones) afford a great potential to achieve this goal. Tracking the demographic dynamics illuminates two things: populations mobility (where do people go) and the related demographics (who are they). Many past studies have investigated the tracking of population dynamics; however, few of them tried tracking the demographic dynamics. In this context, our study proposed a ubiquitous IIoT based trustworthy approach for built environment demographic dynamics tracking. First, we employed a meta-graph-based data structure to represent users life patterns and projected them into a low-dimension space as uniform features. Then, based on the life-pattern features, we derived a variation-inference-based advanced Bayesian model to infer the demographics. Finally, taking a region in Tokyo as a case study, we compared our methods with baseline methods (heuristic algorithm, deep learning), and the result proved a superior accuracy (the MAPE improved by 0.07 to 0.28) as well as reliability (0.78 Pearson correlation coefficient with survey data)

Excitation of local magnetic moments by tunnelling electrons

The advent of milli-kelvin scanning tunneling microscopes (STM) with inbuilt magnetic fields has opened access to the study of magnetic phenomena with atomic resolution at surfaces. In the case of single atoms adsorbed on a surface, the existence of different magnetic energy levels localized on the adsorbate is due to the breaking of the rotational invariance of the adsorbate spin by the interaction with its environment, leading to energy terms in the meV range. These structures were revealed by STM experiments in IBM Almaden in the early 2000's for atomic adsorbates on CuN surfaces. The experiments consisted in the study of the changes in conductance caused by inelastic tunnelling of electrons (IETS, Inelastic Electron Tunnelling Spectroscopy). Manganese and Iron adatoms were shown to have different magnetic anisotropies induced by the substrate. More experiments by other groups followed up, showing that magnetic excitations could be detected in a variety of systems: e.g. complex organic molecules showed that their magnetic anistropy was dependent on the molecular environment, piles of magnetic molecules showed that they interact via intermolecular exchange interaction, spin waves were excited on ferromagnetic surfaces and in Mn chains, and magnetic impurities have been analyzed on semiconductors. These experiments brought up some intriguing questions: the efficiency of magnetic excitations was very high, the excitations could or could not involve spin flip of the exciting electron and singular-like behavior was sometimes found at the excitation thresholds. These facts called for extended theoretical analysis; perturbation theories, sudden-approximation approaches and a strong coupling scheme successfully explained most of the magnetic inelastic processes. In addition, many-body approaches were also used to decipher the interplay between inelasComment: Review article to appear in Progress of Surface Scienc

Spectrophotometric determination of osmium using molybdate and nile blue in the presence of polyvinyl alcohol

1124-1126A sensitive spectrophotometric method for the determination of osmium(Vlll) has been developed, based on the reaction of osmium(Vlll) with molybdate and nile blue (NB) to form an ion association complex in the presence of polyvinyl alcohol.·The molar absorptivity at 585 nm is 2.81 x 106 dm3 mol-1 cm-1. Beer's law is obeyed over the range 0-1.8 µg of osmium per 25 ml. The method can also be applied for the determination of trace amounts of osmium in some catalysts and metallurgical products

Metal-Responsive Regulation of Enzyme Catalysis using Genetically Encoded Chemical Switches

The design of allosteric regulation in proteins to dynamically control function is a challenge in synthetic biology. To address this challenge, we developed an integrated computational and experimental workflow to incorporate a metal-responsive chemical switch into proteins. Pairs of bipyridinylalanine (BpyAla) residues were genetically encoded into two structurally distinct enzymes, a serine protease and firefly luciferase, so that metal coordination would bias the conformations of these enzymes, leading to reversible control of activity. MD-simulations guided rational BpyAla placement, significantly reducing experimental workload, and cell-free protein synthesis coupled with high-throughput experimentation enabled rapid prototyping of variants. Ultimately, this strategy yielded enzymes with a robust 20-fold dynamic range in response to divalent metals over 24 on/off switches, demonstrating the potential of this approach. We envision that this strategy of genetically encoding chemical switches into enzymes will complement other protein engineering and synthetic biology efforts, enabling new opportunities for applications where precise regulation of protein function is critical

Selection and evaluation of reference genes for expression analysis of Cassi

<div><p><i>Cassia obtusifolia</i>, belonging to legume family, is important in many fields with high pharmaceutical, economic, and ecological values. These interests of <i>C. obtusifolia</i> triggered in-depth and fundamental genetic and molecular research. Therefore, the stable reference gene is necessary for normalization of the gene expression studies. In this study, 10 candidate reference genes were subjected to expression analysis in 12 different tissues and under different stresses by qRT-PCR. The expression stability was evaluated using geNorm, NormFinder, and BestKeeper software. In conclusion, different suitable reference genes were selected in different tissues and under different stress. <i>CYP1</i>, <i>EF1α2</i>, <i>ACT2</i>, <i>UBQ1</i> were the most stable reference genes in all samples. The relative expression levels of <i>WRKY</i> gene were detected to confirm the reliability of the selected reference genes. These results provided suitable reference genes that could be used for normalization in <i>C. obtusifolia</i> tissues and under different stress.</p></div