Improved Synthesis and Material Processing of Black Phosphorus for Using as Lithium-ion Battery Anode
Zhang, Jianyu
2020
Abstract
Recent studies show that black phosphorus (BP) is a promising candidate anode material for Li-ion batteries (LIB), exhibiting much higher theoretical capacity (2,596 mAh/g) compared to commercialized graphite anode (372 mAh/g). Application of BP in LIB requires scalable material synthesis procedure, in-depth understanding of degradation mechanism and novel predictive models. In this work, a variety of synthesis, characterization, and modeling methods are developed to optimize the BP quality and promote its electrochemical performance in LIB. BP is not naturally available. High energy mechanical milling (HEMM) is a conventional method which transforms red phosphorus (RP) precursor into BP. To precisely control the quality of BP, the effects of processing time, power, ball-to-powder ratio and atmosphere on BP’s particle size distribution and crystallinity are systematically studied. A multistep milling technique combining planetary and shaking ball millings is developed. The produced BP and BP-graphite (BPG) composite have homogeneous size distribution, coherent bonding connection and high specific surface area. The as-synthesized material is used to fabricate half coin cell to test its electrochemical performance. The optimal sample cell achieves high initial capacity of 2000 mAh/g at 0.1C rate. After 150 cycles, more than 80% capacity is still reversible. Disassembly analysis reveals electrode cracks and particle fractures cause capacity degradation. To address the bulk phase BP’s intrinsic limitation of volume expansion and contraction upon cycling, 2D phosphorene (an analogy of graphene to graphite) is exfoliated from HEMM-synthesized BP particles. The laser-assisted liquid phase exfoliation is found to be superior than existing methods for its low cost, high productivity and significantly promoting phosphorene stability. The as-exfoliated phosphorene is very durable against oxidation and humidity. Which relies on the polycrystalline properties of phosphorene and liquid protective layer. A novel top-down co-exfoliation method to produce phosphorene-graphene heterostructure is developed. An ultrasonication system with moderate processing power is used to do liquid phase exfoliation. Instead of BP crystal, BPG composite from HEMM is directly utilized as precursor. The 2D layered material is found to be ultrathin (~10 nm) and ultrasmall (< 2 μm), relieving the volume expansion issue in LIB. Moreover, phosphorene is chemically bonded to graphene, enabling high electronic conductivity and structural stability. Consequently, the phosphorene-graphene based LIB coin cells deliver state-of-the-art high rate (2,4,6 A/g) performance with high initial capacity (> 1500 mAh/g), long cycling life (> 500 cycles), and high capacity retention ratio (>80%). Galvanostatic Intermittent Titration Technique (GITT) shows fast solid-phase diffusion. Impedance evolution progress is investigated by Electrochemical Impedance Spectroscopy (EIS) test. The improvement doesn’t only come from conventional conversion/alloying reaction between lithium and phosphorus, but also electrode-electrolyte interface pesudocapacitive effect due to high surface area of 2D phosphorene. This effect is quantified by properly designed Cyclic Voltammetry (CV) test. A non-destructive 3D micro-CT rendering is built to track the electrode structural change after battery cycling. A data-driven machine learning framework is proposed to aggregate both cycling-related and material-related features into a predictive model. Which is able to estimate failed/alive batteries and identify important material influencers. 90 in-lab made BP-based coin cells from 16 batches are cycled to extract degradation patterns. Combining the material and electrode properties, the most comprehensive alternative anode database is formed. The insights from modeling can further optimize the material/electrode design.Subjects
Black Phosphorus Lithium-ion Battery Phosphorene Machine Learning
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