Electrochemical evaluation and phase-related impedance studies on silicon–few layer graphene (FLG) composite electrode systems

Silicon-Few Layer Graphene (Si-FLG) composite electrodes are investigated using a scalable electrode manufacturing method. A comprehensive study on the electrochemical performance and the impedance response is measured using electrochemical impedance spectroscopy. The study demonstrates that the incorporation of few-layer graphene (FLG) results in significant improvement in terms of cyclability, electrode resistance and diffusion properties. Additionally, the diffusion impedance responses that occur during the phase changes in silicon is elucidated through Staircase Potentio Electrochemical Impedance Spectroscopy (SPEIS): a more comprehensive and straightforward approach than previous state-of-charge based diffusion studies

Understanding the limitations of lithium ion batteries at high rates

Commercial lithium ion cells with different power: energy ratios were disassembled, to allow the electrochemical performance of their electrodes to be evaluated. Tests on coin cell half cells included rate tests (continuous and pulsed), resistance measurements, and extended pulse tests. Pulse power tests at high rates typically showed three limiting processes within a 10 s pulse; an instantaneous resistance increase, a solid state diffusion limited stage, and then electrolyte depletion/saturation. On anodes, the third process can also be lithium plating. Most of the cells were rated for a 10 C continuous discharge, and the cathode charging voltage at 10 C was around 4.2 V. For anodes, the maximum charge current to avoid a negative voltage was 3–5 C. Negative anode voltages do not necessarily mean that lithium plating has occurred. However, lithium deposits were observed on all the anodes after 5000 pulse sequences with 10 s pulses at ± 20 C

A comparison of lithium-ion cell performance across three different cell formats

To investigate the influence of cell formats during a cell development programme, lithium-ion cells have been prepared in three different formats. Coin cells, single layer pouch cells, and stacked pouch cells gave a range of scales of almost three orders of magnitude. The cells used the same electrode coatings, electrolyte and separator. The performance of the different formats was compared in long term cycling tests and in measurements of resistance and discharge capacities at different rates. Some test results were common to all three formats. However, the stacked pouch cells had higher discharge capacities at higher rates. During cycling tests, there were indications of differences in the predominant degradation mechanism between the stacked cells and the other two cell formats. The stacked cells showed faster resistance increases, whereas the coin cells showed faster capacity loss. The difference in degradation mechanism can be linked to the different thermal and mechanical environments in the three cell formats. The correlation in the electrochemical performance between coin cells, single layer pouch cells, and stacked pouch cells shows that developments within a single cell format are likely to lead to improvements across all cell formats

Enhancing cycling durability of Li-ion batteries with hierarchical structured silicon–graphene hybrid anodes

Hybrid anode materials consisting of micro-sized silicon (Si) particles interconnected with few-layer graphene (FLG) nanoplatelets and sodium-neutralized poly(acrylic acid) as a binder were evaluated for Li-ion batteries. The hybrid film has demonstrated a reversible discharge capacity of ∼1800 mA h g−1 with a capacity retention of 97% after 200 cycles. The superior electrochemical properties of the hybrid anodes are attributed to a durable, hierarchical conductive network formed between Si particles and the multi-scale carbon additives, with enhanced cohesion by the functional polymer binder. Furthermore, improved solid electrolyte interphase (SEI) stability is achieved from the electrolyte additives, due to the formation of a kinetically stable film on the surface of the Si

Land management strategies can increase oil palm plantation use by some terrestrial mammals in Colombia

While the conservation role of remaining natural habitats in anthropogenic landscapes is clear, the degree to which agricultural matrices impose limitations to animal use is not well understood, but vital to assess species’ resilience to land use change. Using an occupancy framework, we evaluated how oil palm plantations affect the occurrence and habitat use of terrestrial mammals in the Colombian Llanos. Further, we evaluated the effect of undergrowth vegetation and proximity to forest on habitat use within plantations. Most species exhibited restricted distributions across the study area, especially in oil palm plantations. Habitat type strongly influenced habitat use of four of the 12 more widely distributed species with oil palm negatively affecting species such as capybara and naked-tailed armadillo. The remaining species showed no apparent effect of habitat type, but oil palm and forest use probabilities varied among species. Overall, generalist mesocarnivores, white-tailed deer, and giant anteater were more likely to use oil palm while the remaining species, including ocelot and lesser anteater, showed preferences for forest. Distance to nearest forest had mixed effects on species habitat use, while understory vegetation facilitated the presence of species using oil palm. Our findings suggest that allowing undergrowth vegetation inside plantations and maintaining nearby riparian corridors would increase the likelihood of terrestrial mammals’ occurrence within oil palm landscapes

The impact of high-frequency-high-current perturbations on film formation at the negative electrode-electrolyte interface

Long term ageing experimental results show that degradation resulting from coupled DC and AC current waveforms lead to additional degradation of lithium-ion batteries above that experienced through pure DC cycling. More profoundly, such experiments show a dependency of battery degradation on the frequency of AC perturbation. This paper addresses the underlying causality of this frequency dependent degradation. Cell autopsy techniques, namely X-ray photoelectron spectroscopy (XPS) of the negative electrode surface film, show growth of surface film components with the superimposition of an AC waveform. XPS results show that high frequency AC perturbations lead to the increased formation of a passivating film. In order to determine the cause of this increased film formation, a heterogeneous electrochemical model for the LiNiCoAlO2/C6 lithium ion battery coupled with governing equations for the electrical double-layer and solid electrolyte interface film growth is developed. Simulation results suggest that the increased growth of surface film is attributed to frequency dependent heat generation. This is due to ion kinetics in the double layer which are governed by the Poisson-Boltzmann equation. Additional thermal and reference cell relaxation experiments are undertaken that further corroborates the conclusion that heat generation within the battery is a function of the AC excitation frequency through resistive dissipation and the entropy of the cell reaction

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Machine learning for investigating the relative importance of electrodes’ N:P areal capacity ratio in the manufacturing of lithium-ion battery cells

This work studies the impact of the ratio between the areal capacity of Graphite anode to NMC622 cathode for Lithium-ion batteries compared to the electrode characteristics of thickness, mass loading and cathode areal capacity, on their electrochemical properties. The influence of factors on energy capacity and gravimetric capacity at various Crates starting from C/20 up to 10C is quantified by combining experiments obtained via design of experiment techniques, machine learning modelling and explanation techniques. The results highlight that the performance at all Crates is highly affected by all features however their relative importance, and the linearity and nonlinearity of the dependencies is quite unique for each Crate capacity. N:P ratio is showing a relatively smaller effect on electrochemical performance compared to thickness, mass loading of active material and cathode areal capacity. It is also concluded that while the impact of N:P ratio is almost linear at lower Crates, it is nonlinear with a local optimum at medium and high Crates. This study offers a methodology for smart selection of a ratio between anode and cathode aerial capacity for a balanced performance of cells at all Crates

Cross-sectional analysis of lithium ion electrodes using spatial autocorrelation techniques

Join counting, a standard technique in spatial autocorrelation analysis, has been used to quantify the clustering of carbon, fluorine and sodium in cross-sectioned anode and cathode samples. The sample preparation and EDS mapping steps are sufficiently fast for every coating from two Design of Experiment (DoE) test matrices to be characterised. The results show two types of heterogeneity in material distribution; gradients across the coating from the current collector to the surface, and clustering. In the cathode samples, the carbon is more clustered than the fluorine, implying that the conductive carbon component is less well distributed than the binder. The results are correlated with input parameters systematically varied in the DoE e.g. coating blade gap, coating speed, and other output parameters e.g. coat weight, and electrochemical resistance

Interpretable machine learning for battery capacities prediction and coating parameters analysis

Battery manufacturing plays a direct and pivotal role in determining battery performance, which, in turn, significantly affects the applications of battery-related energy storage systems. As a complicated process that involves chemical, mechanical and electrical operations, effective battery property predictions and reliable analysis of strongly-coupled battery manufacturing parameters or variables become the key but challenging issues for wider battery applications. In this paper, an interpretable machine learning framework that could effectively predict battery product properties and explain dynamic effects, as well as interactions of manufacturing parameters is proposed. Due to the data-driven nature, this framework can be easily adopted by engineers as no specific battery manufacturing mechanism knowledge is required. Reliable battery manufacturing dataset particularly for coating (one key stage) collected from a real battery manufacturing chain is adopted to evaluate the proposed framework. Illustrative results demonstrate that three types of battery capacities including cell capacity, gravimetric capacity, and volumetric capacity can be accurately predicted with over 0.98 at the battery early-manufacturing stage. Besides, information regarding how the variations of coating mass, thickness, and porosity affect these battery capacities is effectively identified, while interactions of these coating parameters can be also quantified. The developed framework makes the data-driven model become more interpretable and opens a promising way to quantify the interactions of battery manufacturing parameters and explain how the variations of these parameters affect final battery properties. This could assist engineers to obtain critical insights to understand the underlying complicated battery material and manufacturing behavior, further benefiting smart control of battery manufacturing