batterydata/paper-abstracts huggingface dataset:
Two-electron transfer chemistry based on earth-abundant Mg and S offers great possibilities of delivering higher energy density than current Li-ion technology. The development of non-nucleophilic electrolytes that reversibly and efficiently plate and strip Mg is believed to be a major obstacle to the implementation of this divalent battery technology. In this study, we present a new type of organic magnesium borate-based electrolyte that primarily comprises tetrakis(hexafluoroisopropyl)borate anions [B(HFP)4]− and solvated cations [Mg4Cl6(DME)6]2+, which was synthesized via a facile in situ reaction of tris(hexafluoroisopropyl)borate [B(HFP)3], MgCl2 and Mg powder in 1,2-dimethoxyethane (DME). Rigorous analyses including NMR, mass spectroscopy and single-crystal XRD were conducted to identify the equilibrium species in the abovementioned solution. The as-prepared Mg-ion electrolyte exhibited unprecedented Mg plating/stripping performance, such as high anodic stability up to 3.3 V (vs. Mg/Mg2+), high ionic conductivity of 5.58 mS cm−1, a low overpotential of 0.11 V for plating processes and Coulombic efficiencies greater than 98%. By virtue of the non-nucleophilic nature of this electrolyte, a fully reversible Mg/S battery was constructed that displayed an extremely low overpotential of 0.3 V and a high discharge capacity of up to 1247 mA h g−1 and yielded a specific energy of approximately 1200 W h kg−1 (10 times higher that of the Chevrel benchmark) based on the weight of active sulfur. More significantly, commonly used sulfur-carbon nanotube (S-CNTs) cathodes with S contents of 80 wt% and S loadings of 1.5 mg cm−2 were demonstrated to withstand more than 100 cycles without obvious capacity decay and to enable fast conversion processes, which achieved a charging current rate of up to 500 mA g−1. Our findings convincingly validate the pivotal role of the newly designed non-nucleophilic Mg-ion electrolyte for practical Mg/S battery chemistry.
The composite with NiS nanorods anchored on reduced graphene oxide (rGO) has been prepared via a two-step hydrothermal method. With the corresponding precursors of hydroxide composites prepared during the first stage, ion exchange (from OH− to S2−) and reduction of GO are performed on subsequently, resulting in NiS/rGO composite. The NiS nanorods uniformly distribute on the surface of graphene, forming a 3D conductive network. The electrochemical performances of the composite are investigated in extenso as electrode materials for supercapacitors, and it exhibits preferable cycling performances and excellent capacitance. These satisfactory electrochemical behaviors can be attributed to the introduction of graphene, which enhances the specific surface area and electronic conductivity of the electrode, producing more active sites for the charging/discharging process and facilitating fast electron transport through the underlying graphene layers.
In this paper, we develop a three-dimensional conductivity model for the active material in the electrodes of a lead-acid battery. We use the model to investigate the influence that this conductivity has on battery capacity. Previous computer models used for this purpose were two-dimensional. Our three-dimensional model allows electrons to move out of a two-dimensional layer to another layer in order to find conductive pathways to an edge thereby allowing the reaction to occur. By using a number of two-dimensional layers, the actual changes in conductivity taking place in the plates can be more accurately modeled. Because of the many layers of our three-dimensional model, the total number of conductive paths is much greater than the previous two-dimensional models. We extend the two-dimensional models to three-dimensions, and compare the model capacity results with those predicted by the Percolation Theory and Effective Medium Theory. We find that the results of the three-dimensional model closely match the predictions of these theories.
Highly robust, flexible, binder-free lithium-ion electrodes were fabricated based on interpenetrative nanocomposites of ultra-long CNTs and V2O5 nanowires. Such robust composite-network architecture provides the electrodes with effective charge transport and structural integrity, leading to high-performance flexible electrodes with high capacity, high rate-capability and excellent cycling stability.
Analogous to the way the Human Genome Project advanced an array of biological sciences by mapping the human genome, the Materials Genome Initiative aims to enhance our understanding of the fundamentals of materials science by providing the information we need to accelerate the development of new materials. This approach is particularly applicable to recently developed classes of nanoporous materials, such as metal–organic frameworks (MOFs), which are synthesized from a limited set of molecular building blocks that can be combined to generate a very large number of different structures. In this Perspective, we illustrate how a materials genome approach can be used to search for high-performance adsorbent materials to store natural gas in a vehicular fuel tank. Drawing upon recent reports of large databases of existing and predicted nanoporous materials generated in silico, we have collected and compared on a consistent basis the methane uptake in over 650000 materials based on the results of molecular simulation. The data that we have collected provide candidate structures for synthesis, reveal relationships between structural characteristics and performance, and suggest that it may be difficult to reach the current Advanced Research Project Agency-Energy (ARPA-E) target for natural gas storage.
There is growing interest in the rational design of electrolytes for multivalent-ion batteries by tuning the molecular-level interactions of solvate species present in the electrolytes. Herein, we report our effort to control Ca-ion speciation in ionic liquid (IL) based electrolytes through the design of alkoxy-functionalized cations. Quantitative analysis reveals that the alkoxy-functionalized ammonium cation (N07+), bearing seven ether oxygen atoms, can effectively displace the bis(trifluoromethanesulfonyl)imide anion (TFSI−) from the Ca2+ ion coordination sphere, facilitating the reversible Ca deposition/stripping process. More importantly, post-analysis of Ca deposits surface chemistry and density functional theory calculations of Ca-ion speciation indicate the formation of an organic-rich, but inorganic-poor solid electrolyte interphase layer, which enables Ca2+ ion diffusion rather than passivating the Ca metal electrode. Finally, as a proof-of-concept, a prototype Ca/V2O5 cell using the optimized IL-based electrolyte ([Ca(BH4)2]0.05[N07TFSI]0.95) is demonstrated for the first time, exhibiting a remarkable initial discharge capacity of 332 mA h g−1 and reversible capacity of 244 mA h g−1.
The oxygen evolution reaction (OER) is a key process in electrochemical energy conversion devices. Understanding the origins of the lattice oxygen oxidation mechanism is crucial because OER catalysts operating via this mechanism could bypass certain limitations associated with those operating by the conventional adsorbate evolution mechanism. Transition metal oxyhydroxides are often considered to be the real catalytic species in a variety of OER catalysts and their low-dimensional layered structures readily allow direct formation of the O–O bond. Here, we incorporate catalytically inactive Zn2+ into CoOOH and suggest that the OER mechanism is dependent on the amount of Zn2+ in the catalyst. The inclusion of the Zn2+ ions gives rise to oxygen non-bonding states with different local configurations that depend on the quantity of Zn2+. We propose that the OER proceeds via the lattice oxygen oxidation mechanism pathway on the metal oxyhydroxides only if two neighbouring oxidized oxygens can hybridize their oxygen holes without sacrificing metal–oxygen hybridization significantly, finding that Zn0.2Co0.8OOH has the optimum activity.
Thermal properties of the component materials are key issues in lithium ion batteries (LIBs). Si-based anodes are one of the most promising materials, but its thermal evolution have received much less attention than its electrochemical performance. In this article, the thermal behavior of various of Si material has been studied by differential scanning calorimetry (DSC). Three kinds of Si-particles, ranging from nano-to micro-sizes was subject to thermal analysis. It has been found that the thermal stability increases with the rise in particle-size. For the nanoparticles of 20 nm, both characteristic peaks of A and B regions in the heating process are stronger than the large-diameter particles. For three kinds of Si particles, the starting temperature of thermal reaction demonstrates a similar trend, gradually becoming lower with the increasing of the lithiation extent. At last, the ex situ XPS has also been conducted to explore the causes of surface state after temperature elevation. In A region, the heating decomposition of SEI with electrolyte, mainly consisting of a variety of esterification compounds, produces high content of lithium carbonate below 180 °C. When lithium in the inner phase of Si particles loses the protection of SEI film, the severe exothermic reaction occurred between lithium and the solvent species.
Residual convolutional neural network (R-CNN) has become a promising method for image recognition in deep learning applications. The application accuracy, as a key indicator, has a close relationship with filter weights in trained R-CNN models. In order to make filters work at full capacity, we find out that lower relevancy between filters in the same layer promotes higher accuracy for R-CNN applications. Furthermore, we propose an improved R-CNN model training method to acquire a higher accuracy and a better generalization ability. In this paper, the main focus is to control the update of filter weights during model training. The key mechanism is achieved through computing the relevancy between filters in the same layer. The relevancy is quantified by a correlation coefficient, e.g., Pearson Correlation Coefficient (PCC). The mechanism takes a larger probability to utilize the updated filter weights with a lower correlation coefficient, and vice versa. In order to validate our proposal, we construct an experiment through PCC on residual networks. The experiment demonstrates that the improved model training method is a promising mean with better generalization ability and higher recognition accuracy (0.52%-1.83%) for residual networks.
Patients with visual agnosia exhibit acquired impairments in visual object recognition, that may or may not involve deficits in low-level perceptual abilities. Here we report a case (patient DM) who after head injury presented with object-recognition deficits. He still appears able to extract 2D information from the visual world in a relatively intact manner; but his ability to extract pictorial information about 3D object-structure is greatly compromised. His copying of line drawings is relatively good, and he is accurate and shows apparently normal mental rotation when matching or judging objects tilted in the picture-plane. But he performs poorly on a variety of tasks requiring 3D representations to be derived from 2D stimuli, including: performing mental rotation in depth, rather than in the picture-plane; judging the relative depth of two regions depicted in line-drawings of objects; and deciding whether a line-drawing represents an object that is ‘impossible’ in 3D. Interestingly, DM failed to show several visual illusions experienced by normals (Muller-Lyer and Ponzo), that some authors have attributed to pictorial depth cues. Taken together, these findings indicate a deficit in achieving 3D intepretations of objects from 2D pictorial cues, that may contribute to object-recognition problems in agnosia.
Undoped lithium iron phosphate (LiFePO4) was prepared and characterized by scanning electron microscope (SEM) and X-ray diffraction (XRD) analysis. The material has a single crystal globular structure with grain-sizes ca. 100–150nm. It was used to prepare composite electrodes containing different amounts of carbon (10, 15 and 20wt.%, respectively) used as cathodes in non-aqueous lithium cells. By increasing the carbon content, an increase in the overall electrochemical performance was observed. Impedance spectroscopy was used to investigate the ohmic and kinetic contributions to the cell overvoltage. It was found that increasing the carbon content leads to a reduction of the cell impedance as a consequence of the reduction of the charge transfer resistance. The poor performance exhibited at very high discharge rates is a direct consequence of the high value of the charge transfer resistance. A further decrease of the charge transfer resistance in high carbon content cathodes (20wt.% carbon) was obtained by improving the powder mixing procedure. The cell performance of well mixed, high carbon content electrodes was better than our previously obtained results in terms of higher capacity retention both for different discharge rates and repeated cycling. For currents larger than a 3C rate, a severe capacity fade affected the electrodes. It was concluded that the electronic contact at the LiFePO4/carbon interface plays a decisive role in material utilization at different discharge rates which affects the capacity fade upon cycling.
Lithium-ion battery packs in hybrid and pure electric vehicles are always equipped with a battery management system (BMS). The BMS consists of hardware and software for battery management including, among others, algorithms determining battery states. The continuous determination of battery states during operation is called battery monitoring. In this paper, the methods for monitoring of the battery state of charge, capacity, impedance parameters, available power, state of health, and remaining useful life are reviewed with the focus on elaboration of their strengths and weaknesses for the use in on-line BMS applications. To this end, more than 350 sources including scientific and technical literature are studied and the respective approaches are classified in various groups.
Lithium-ion batteries are a candidate for the energy storage system onboard low-earth-orbit satellites. Terrestrial experiments are able to capture the performance degradation of cells in orbit, therefore providing the opportunity for lifetime investigations. The lifetime performance of 3Ah commercial Li x Mn2O4-based pouch cells was evaluated in a matrix of different cycling depths-of-discharge (DODs: 0, 20, 40%) and temperatures (25, 45 ° C). Aged cells were disassembled and the electrochemical performance of harvested electrodes investigated with two- and three-electrode pouch cells. The positive electrode had a larger decrease in capacity than the negative electrode. Both the positive and negative electrode contributed to the increase of cell impedance measured at high states-of-charge (SOCs). The data at low SOCs indicated that the increase of cell impedance was associated with the positive electrode, which showed a significant increase in the magnitude of the high-frequency semi-circle. This SOC-dependence was observed for cells cycled for either extended periods of time or at higher temperatures with a 40% DOD swing. Low-current cycling of positive electrodes revealed a change in the second potential plateau, possibly reflecting a structural change of the Li x Mn2O4. This could impact on the electrode kinetics and provide a possible explanation for the SOC-dependent change of the impedance.
Parkinson’s disease (PD) with mild cognitive impairment (PD-MCI) is currently diagnosed based on an arbitrarily predefined standard deviation of neuropsychological test scores, and more objective biomarkers for PD-MCI diagnosis are needed. The purpose of this study was to define possible brain perfusion-based biomarkers of not only mild cognitive impairment, but also risky gene carriers in PD using arterial spin labeling magnetic resonance imaging (ASL-MRI). Fifteen healthy controls (HC), 26 cognitively normal PD (PD-CN), and 27 PD-MCI subjects participated in this study. ASL-MRI data were acquired by signal targeting with alternating radio-frequency labeling with Look–Locker sequence at 3 T. Single nucleotide polymorphism genotyping for rs9468 [microtubule-associated protein tau (MAPT) H1/H1 versus H1/H2 haplotype] was performed using a Stratagene Mx3005p real-time polymerase chain-reaction system (Agilent Technologies, USA). There were 15 subjects with MAPT H1/H1 and 11 subjects with MAPT H1/H2 within PD-MCI, and 33 subjects with MAPT H1/H1 and 19 subjects with MAPT H1/H2 within all PD. Voxel-wise differences of cerebral blood flow (CBF) values between HC, PD-CN and PD-MCI were assessed by one-way analysis of variance followed by pairwise post hoc comparisons. Further, the subgroup of PD patients carrying the risky MAPT H1/H1 haplotype was compared with noncarriers (MAPT H1/H2 haplotype) in terms of CBF by a two-sample t test. A pattern that could be summarized as “posterior hypoperfusion” (PH) differentiated the PD-MCI group from the HC group with an accuracy of 92.6% (sensitivity = 93%, specificity = 93%). Additionally, the PD patients with MAPT H1/H1 haplotype had decreased perfusion than the ones with H1/H2 haplotype at the posterior areas of the visual network (VN), default mode network (DMN), and dorsal attention network (DAN). The PH-type pattern in ASL-MRI could be employed as a biomarker of both current cognitive impairment and future cognitive decline in PD.
In a zinc–bromine redox flow battery, a nonaqueous and dense polybromide phase formed because of bromide oxidation in the positive electrolyte during charging. This formation led to complicated two-phase flow on the electrode surface. The polybromide and aqueous phases led to different kinetics of the Br/Br− redox reaction; poor mixing of the two phases caused uneven redox kinetics on the electrode surface. As the Br/Br− redox reaction was coupled with the zinc deposition reaction, the uneven redox reaction on the positive electrode was accompanied by nonuniform zinc deposition and zinc dendrite formation, which degraded battery stability. A single-flow cell was operated at varying electrolyte circulation rates and current densities. Zinc dendrite formation was observed after cell disassembly following charge–discharge testing. In addition, the flow behavior in the positive compartment was observed by using a transparent version of the cell. At low rate of electrolyte circulation, the polybromide phase clearly separated from the aqueous phase and accumulated at the bottom of the flow frame. In the corresponding area on the negative electrode, a large amount of zinc dendrites was observed after charge–discharge testing. Therefore, a minimum circulation rate should be considered to avoid poor mixing of the positive electrolyte.
Silicon (Si) is one of the most promising next generation anode materials for lithium-ion batteries (LIBs), but the use of Si in LIBs has been rather limited. The main challenge is its large volume change (up to 300%) during battery cycling. This can lead to the fracture of Si, failure at the interfaces between electrode components, and large dimensional change on the cell level. To optimize the Si electrode/battery design, a model that considers the interactions of different cell components is needed. This paper presents the development of a multiphysics microstructure-resolved model (MRM) for LIB cells with a-Si anode. The model considered the electrochemical reactions, Li transports in electrolyte and electrodes, dimensional changes and stresses, property evolution with the structure, and the coupling relationships. Important model parameters, such as the diffusivity, reaction rate constant, and apparent transfer coefficient, were determined by correlating the simulation results to experiments. The model was validated with experimental results in the literature. The use of this model was demonstrated in a parameter study of Si nanowall|Li cells. The specific and volumetric capacities of the cell as a function of the size, length/size ratio, spacing of the nanostructure, and Li+ concentration in electrolyte were investigated.
The aim of the present study was to explore the nature and prevalence of phenotypic variations in Alzheimer's disease (AD). Neuropsychological profiles of a large cross-sectional cohort of patients with a clinical diagnosis of the disease were examined. All tests distinguished the AD group from controls confirming their sensitivity to the presence of early AD. Factor analysis of test scores revealed five factors, reflecting the discrete cognitive domains of memory, language, perceptuospatial abilities, executive skills, and praxis. Cluster analysis revealed distinct performance profiles that could not be accounted for by disease severity. Some patients showed an accentuation of memory impairment relative to other domains, whereas others showed relative sparing. Cognitive deficits other than memory were the salient presenting feature in a relatively high proportion of patients. A subset of the cohort (22%) showed grossly disproportionate impairments in one cognitive domain. The findings emphasise variability in presentation and indicate that distinct phenotypic variations appear to lie on a continuum rather than representing discrete forms of disease.
This paper describes a generic autonomic architecture for use in developing systems for managing hardware faults in mobile robots. The method by which the generic architecture was developed is also described. Using autonomic principles, we focused on how to detect faults within a mobile robot and how specialized algorithms can be deployed to compensate for the faults discovered. We design the foundation of a generic architecture using the elements found in the MAPE-K and IMD architectures. We present case studies that show three different fault scenarios that can occur within the effectors, sensors and power units of a mobile robot. For each case study, we have developed algorithms for monitoring and analyzing data stored from previous tasks completed by the robot. We use the results from the case studies to create and refine a generic autonomic architecture that can be utilized for any general mobile robot setup for fault detection and fault compensation. We then describe a further case study which exercises the generic autonomic architecture in order to demonstrate its utility. Our proposal addresses fundamental challenges in operating remote mobile robots with little or no human intervention. If a fault does occur within the mobile robot during field operations, then having a self-automated strategy as part of its processes may result in the mobile robot continuing to function at a productive level. Our research has provided insights into the shortcomings of existing robot design which is also discussed.
A novel electrode-bipolar plate assembly has been developed and evaluated for application in the vanadium redox flow battery (VRB). It is composed of three parts: a graphite felt (electrode), an adhesive conducting layer (ACL) and a flexible graphite plate (bipolar plate). The ACL connects the electrode with the bipolar plate to an assembly. By the evaluations of cost, resistivity, surface morphology, electrolyte permeation and single cell performance, this novel assembly demonstrates its applicability in VRB as evident in the following outcomes: (1) lowers the cost and area resistivity to about 10% and 40% of the conventional setups, respectively; (2) improves electrical conductivity to 4.97mΩcm as compared to over 100mΩcm of the carbon-plastic composite bipolar plate; (3) attains zero electrolyte permeation; and (4) achieves a higher energy efficiency of 81% at a charge/discharge current density of 40mAcm−2 when employed in a VRB single cell, which is 73% for the conventional setup. All these indicate that the novel electrode-bipolar plate assembly is a promising candidate for VRB applications.