Engineering Transition Metal Layers for Long Lasting Anionic Redox in Layered Sodium Manganese Oxide

Oxygen-redox-based-layered cathode materials are of great importance in realizing high-energy-density sodium-ion batteries (SIBs) that can satisfy the demands of next-generation energy storage technologies. However, Mn-based-layered materials (P2-type Na-poor Nay[AxMn1−x]O2, where A = alkali ions) still suffer from poor reversibility during oxygen-redox reactions and low conductivity. In this work, the dual Li and Co replacement is investigated in P2-type-layered NaxMnO2. Experimentally and theoretically, it is demonstrated that the efficacy of the dual Li and Co replacement in Na0.6[Li0.15Co0.15Mn0.7]O2 is that it improves the structural and cycling stability despite the reversible Li migration from the transition metal layer during de-/sodiation. Operando X-ray diffraction and ex situ neutron diffraction analysis prove that the material maintains a P2-type structure during the entire range of Na+ extraction and insertion with a small volume change of ≈4.3%. In Na0.6[Li0.15Co0.15Mn0.7]O2, the reversible electrochemical activity of Co3+/Co4+, Mn3+/Mn4+, and O2-/(O2)n- redox is identified as a reliable mechanism for the remarkable stable electrochemical performance. From a broader perspective, this study highlights a possible design roadmap for developing cathode materials with optimized cationic and anionic activities and excellent structural stabilities for SIBs.</p

Unlocking the hidden chemical space in cubic-phase garnet solid electrolyte for efficient quasi-all-solid-state lithium batteries

Garnet-type Li7La3Zr2O12 (LLZO) solid electrolytes (SE) demonstrates appealing ionic conductivity properties for all-solid-state lithium metal battery applications. However, LLZO (electro)chemical stability in contact with the lithium metal electrode is not satisfactory for developing practical batteries. To circumvent this issue, we report the preparation of various doped cubic-phase LLZO SEs without vacancy formation (i.e., Li = 7.0 such as Li7La3Zr0.5Hf0.5Sc0.5Nb0.5O12 and Li7La3Zr0.4Hf0.4Sn0.4Sc0.4Ta0.4O12). The entropy-driven synthetic approach allows access to hidden chemical space in cubic-phase garnet and enables lower solid-state synthesis temperature as the cubic-phase nucleation decreases from 750 to 400 ??C. We demonstrate that the SEs with Li = 7.0 show better reduction stability against lithium metal compared to SE with low lithium contents and identical atomic species (i.e., Li = 6.6 such as Li6.6La3Zr0.4Hf0.4Sn0.4Sc0.2Ta0.6O12). Moreover, when a Li7La3Zr0.4Hf0.4Sn0.4Sc0.4Ta0.4O12 pellet is tested at 60 ??C in coin cell configuration with a Li metal negative electrode, a LiNi1/3Co1/3Mn1/3O2-based positive electrode and an ionic liquid-based electrolyte at the cathode|SE interface, discharge capacity retention of about 92% is delivered after 700 cycles at 0.8 mA/cm2 and 60 ??C

Unexpected discovery of low-cost maricite NaFePO_4 as a high-performance electrode for Na-ion batteries

Battery chemistry based on earth-abundant elements has great potential for the development of cost-effective, large-scale energy storage systems. Herein, we report, for the first time, that maricite NaFePO_4 can function as an excellent cathode material for Na ion batteries, an unexpected result since it has been regarded as an electrochemically inactive electrode for rechargeable batteries. Our investigation of the Na re-(de)intercalation mechanism reveals that all Na ions can be deintercalated from the nano-sized maricite NaFePO_4 with simultaneous transformation into amorphous FePO_4. Our quantum mechanics calculations show that the underlying reason for the remarkable electrochemical activity of NaFePO_4 is the significantly enhanced Na mobility in the transformed phase, which is ~ one fourth of the hopping activation barrier. Maricite NaFePO_4, fully sodiated amorphous FePO_4, delivered a capacity of 142 mA h g^(−1) (92% of the theoretical value) at the first cycle, and showed outstanding cyclability with a negligible capacity fade after 200 cycles (95% retention of the initial cycle)

High-energy and durable lithium metal batteries using garnet-type solid electrolytes with tailored lithium-metal compatibility

Lithium metal batteries using solid electrolytes are considered to be the next-generation lithium batteries due to their enhanced energy density and safety. However, interfacial instabilities between Li-metal and solid electrolytes limit their implementation in practical batteries. Herein, Li-metal batteries using tailored garnet-type Li7-xLa3-aZr2-bO12 (LLZO) solid electrolytes is reported, which shows remarkable stability and energy density, meeting the lifespan requirements of commercial applications. We demonstrate that the compatibility between LLZO and lithium metal is crucial for long-term stability, which is accomplished by bulk dopant regulating and dopant-specific interfacial treatment using protonation/etching. An all-solid-state with 5 mAh cm(-2) cathode delivers a cumulative capacity of over 4000 mAh cm(-2) at 3 mA cm(-2), which to the best of our knowledge, is the highest cycling parameter reported for Li-metal batteries with LLZOs. These findings are expected to promote the development of solid-state Li-metal batteries by highlighting the efficacy of the coupled bulk and interface doping of solid electrolytes. Lithium-metal batteries (LMBs) have attracted intense interest but the instability issues limit its practical deployment. Here, the authors report a durable LMB with high energy density using a garnet-type solid electrolyte with a tailored Li-metal compatibility

Cell Mates

Catalog for the exhibition Cell Mates held at the Seton Hall University Walsh Gallery, June 3 - July 18, 2013. Curated by Jeanne Brasile and Lisbeth Murray. Includes the essays The Art of Science and the Science of Art by Jeanne Brasile and Incubating Hybrid Art by Lisbeth Murray. Includes color illustrations

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease