Abstract Successful spin injection into graphene makes it a competitive contender in the race to become a key material for quantum computation, or the spin-operation-based data processing and sensing. Engineering ferromagnetic metal (FM)/graphene heterojunctions is one of the most promising avenues to realise it, however, their interface magnetism remains an open question up to this day. In any proposed FM/graphene spintronic devices, the best opportunity for spin transport could only be achieved where no magnetic dead layer exists at the FM/graphene interface. Here we present a comprehensive study of the epitaxial Fe/graphene interface by means of X-ray magnetic circular dichroism (XMCD) and density functional theory (DFT) calculations. The experiment has been performed using a specially designed FM 1 /FM 2 /graphene structure that to a large extent restores the realistic case of the proposed graphene-based transistors. We have quantitatively observed a reduced but still sizable magnetic moments of the epitaxial Fe ML on graphene, which is well resembled by simulations and can be attributed to the strong hybridization between the Fe 3 d z2 and the C 2 p z orbitals and the sp -orbital-like behavior of the Fe 3 d electrons due to the presence of graphene.
Abstract The oxygen stacking of O3‐type layered sodium transition metal oxides (O3‐NaTMO 2 ) changes dynamically upon topotactic Na extraction and reinsertion. While the phase transition from octahedral to prismatic Na coordination that occurs at intermediate desodiation by transition metal slab gliding is well understood, the structural evolution at high desodiation, crucial to achieve high reversible capacity, remains mostly uncharted. In this work, the phase transitions of O3‐type layered NaTMO 2 at high voltage are investigated by combining experimental and computational approaches. An OP2‐type phase that consists of alternating octahedral and prismatic Na layers is directly observed by in situ X‐ray diffraction and high‐resolution scanning transmission electron microscopy. The origin of this peculiar phase is explained by atomic interactions involving Jahn–Teller active Fe 4+ and distortion tolerant Ti 4+ that stabilize the local Na environment. The path‐dependent desodiation and resodiation pathways are also rationalized in this material through the different kinetics of the prismatic and octahedral layers, presenting a comprehensive picture about the structural stability of the layered materials upon Na intercalation.
Na Super Ionic Conductor (NASICON) materials are an important class of solid-state electrolytes owing to their high ionic conductivity and superior chemical and electrochemical stability. In this paper, we combine first-principles calculations, experimental synthesis and testing, and natural language-driven text-mined historical data on NASICON ionic conductivity to achieve clear insights into how chemical composition influences the Na-ion conductivity. These insights, together with a high-throughput first-principles analysis of the compositional space over which NASICONs are expected to be stable, lead to the successful synthesis and electrochemical investigation of several new NASICONs solid-state conductors. Among these, a high ionic conductivity of 1.2 mS cm-1 could be achieved at 25 °C. We find that the ionic conductivity increases with average metal size up to a certain value and that the substitution of PO4 polyanions by SiO4 also enhances the ionic conductivity. While optimal ionic conductivity is found near a Na content of 3 per formula unit, the exact optimum depends on other compositional variables. Surprisingly, the Na content enhances the ionic conductivity mostly through its effect on the activation barrier, rather than through the carrier concentration. These deconvoluted design criteria may provide guidelines for the design of optimized NASICON conductors.
Abstract Layered carbides and their analogs with MAX phase (general formula AM n+1 X n ) have emerged as promising candidates for energy storage and conversion applications. One frontier for energy storage is using MAX as an Al‐ion intercalation electrode. Given that many MAXs have Al as the A sites, the structure can potentially serve as a stable host for Al intercalation. Here in this work, 425 ternary MAX Al‐ion battery electrodes are computationally enumerated. Specifically, first principal phase diagram calculations are performed on the combinatorial space of 17 types of typical transition metals, five types of anions (C, N, B, Si, and P), three types of stoichiometries (n = 1, 2, and 3) and two types of layered stackings (α and β). Among all the ternary MAX materials, 44 candidates show reasonable synthetic accessibility, and six with extraordinary performance are predicted to be promising Al‐ion battery electrodes. With the phase stability, and electrochemical performance (average voltage, theoretical capacity, energy density, and Al diffusion barrier), the work provides a comprehensive computational assessment of the great opportunities behind MAX‐based Al‐ion batteries.
Lithium–sulfur (Li-S) batteries with high energy density and low cost are promising for next-generation energy storage. However, their cycling stability is plagued by the high solubility of lithium polysulfide (LiPS) intermediates, causing fast capacity decay and severe self-discharge. Exploring electrolytes with low LiPS solubility has shown promising results toward addressing these challenges. However, here, we report that electrolytes with moderate LiPS solubility are more effective for simultaneously limiting the shuttling effect and achieving good Li-S reaction kinetics. We explored a range of solubility from 37 to 1,100 mM (based on S atom, [S]) and found that a moderate solubility from 50 to 200 mM [S] performed the best. Using a series of electrolyte solvents with various degrees of fluorination, we formulated the S ingle- S olvent, S ingle- S alt, S tandard S alt concentration with M oderate L i PSs so l ubility E lectrolytes (termed S 6 MILE ) for Li-S batteries. Among the designed electrolytes, Li-S cells using fluorinated-1,2-diethoxyethane S 6 MILE (F4DEE-S 6 MILE) showed the highest capacity of 1,160 mAh g −1 at 0.05 C at room temperature. At 60 °C, fluorinated-1,4-dimethoxybutane S 6 MILE (F4DMB-S 6 MILE) gave the highest capacity of 1,526 mAh g −1 at 0.05 C and an average CE of 99.89% for 150 cycles at 0.2 C under lean electrolyte conditions. This is a fivefold increase in cycle life compared with other conventional ether-based electrolytes. Moreover, we observed a long calendar aging life, with a capacity increase/recovery of 4.3% after resting for 30 d using F4DMB-S 6 MILE. Furthermore, the correlation between LiPS solubility, degree of fluorination of the electrolyte solvent, and battery performance was systematically investigated.
Improving Coulombic efficiency (CE) is key to the adoption of high energy density lithium metal batteries. Liquid electrolyte engineering has emerged as a promising strategy for improving the CE of lithium metal batteries, but its complexity renders the performance prediction and design of electrolytes challenging. Here, we develop machine learning (ML) models that assist and accelerate the design of high-performance electrolytes. Using the elemental composition of electrolytes as the features of our models, we apply linear regression, random forest, and bagging models to identify the critical features for predicting CE. Our models reveal that a reduction in the solvent oxygen content is critical for superior CE. We use the ML models to design electrolyte formulations with fluorine-free solvents that achieve a high CE of 99.70%. This work highlights the promise of data-driven approaches that can accelerate the design of high-performance electrolytes for lithium metal batteries.
Abstract In this paper we develop the stability rules for NASICON-structured materials, as an example of compounds with complex bond topology and composition. By first-principles high-throughput computation of 3881 potential NASICON phases, we have developed guiding stability rules of NASICON and validated the ab initio predictive capability through the synthesis of six attempted materials, five of which were successful. A simple two-dimensional descriptor for predicting NASICON stability was extracted with sure independence screening and machine learned ranking, which classifies NASICON phases in terms of their synthetic accessibility. This machine-learned tolerance factor is based on the Na content, elemental radii and electronegativities, and the Madelung energy and can offer reasonable accuracy for separating stable and unstable NASICONs. This work will not only provide tools to understand the synthetic accessibility of NASICON-type materials, but also demonstrates an efficient paradigm for discovering new materials with complicated composition and atomic structure.
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