Controllable deposition of organic metal halide perovskite films with wafer-scale uniformity by single source flash evaporation

Conventional solution-processing techniques such as the spin-coating method have been used successfully to reveal excellent properties of organic-inorganic halide perovskites (OHPs) for optoelectronic devices such as solar cell and light-emitting diode, but it is essential to explore other deposition techniques compatible with large-scale production. Single-source flash evaporation technique, in which a single source of materials of interest is rapidly heated to be deposited in a few seconds, is one of the candidate techniques for large-scale thin film deposition of OHPs. In this work, we investigated the reliability and controllability of the single-source flash evaporation technique for methylammonium lead iodide (MAPbI(3)) perovskite. In-depth statistical analysis was employed to demonstrate that the MAPbI(3) films prepared via the flash evaporation have an ultrasmooth surface and uniform thickness throughout the 4-inch wafer scale. We also show that the thickness and grain size of the MAPbI(3) film can be controlled by adjusting the amount of the source and number of deposition steps. Finally, the excellent large-area uniformity of the physical properties of the deposited thin films can be transferred to the uniformity in the device performance of MAPbI(3) photodetectors prepared by flash evaporation which exhibited the responsivity of 0.2 A/W and detectivity of 3.82x10(11) Jones.

Exploring Cation-Anion Redox Processes in One-Dimensional Linear Chain Vanadium Tetrasulfide Rechargeable Magnesium Ion Cathodes.

For magnesium ion batteries (MIBs) to be used commercially, new cathodes must be developed that show stable reversible Mg intercalation. VS4 is one such promising material, with vanadium and disulfide anions [S2]2- forming one-dimensional linear chains, with a large interchain spacing (5.83 Å) enabling reversible Mg insertion. However, little is known about the details of the redox processes and structural transformations that occur upon Mg intercalation and deintercalation. Here, employing a suite of local structure characterization methods including X-ray photoelectron spectroscopy (XPS), V and S X-ray absorption near-edge spectroscopy (XANES), and 51V Hahn echo and magic-angle turning with phase-adjusted sideband separation (MATPASS) NMR, we show that the reaction proceeds via internal electron transfer from V4+ to [S2]2-, resulting in the simultaneous and coupled oxidation of V4+ to V5+ and reduction of [S2]2- to S2-. We report the formation of a previously unknown intermediate in the Mg-V-S compositional space, Mg3V2S8, comprising [VS4]3- tetrahedral units, identified by using density functional theory coupled with an evolutionary structure-predicting algorithm. The structure is verified experimentally via X-ray pair distribution function analysis. The voltage associated with the competing conversion reaction to form MgS plus V metal directly is similar to that of intermediate formation, resulting in two competing reaction pathways. Partial reversibility is seen to re-form the V5+ and S2- containing intermediate on charging instead of VS4. This work showcases the possibility of developing a family of transition metal polychalcogenides functioning via coupled cationic-anionic redox processes as a potential way of achieving higher capacities for MIBs.S. D. acknowledges DST Overseas Visiting Fellowship in Nano Science and Technology, Government of India (July 2018− June 2019) and EPSRC Programme Grant (EP/M009521/1) for fellowships and funding. This work used the ARCHER UK National Super-computing Service (http://www.archer.ac.uk). This research used resources of the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility, and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under Contract No. DE-SC0012704. The XPS data collection was performed at the EPSRC National Facility for XPS ("HarwellXPS"), operated by Cardiff University and UCL, under Contract No. PR16195. via our membership of the UK's HEC Materials Chemistry Consortium, which is funded by EPSRC (EP/L000202)

High Rate Lithium Ion Battery with Niobium Tungsten Oxide Anode

Highly stable lithium-ion battery cycling of niobium tungsten oxide (Nb16W5O55, NWO) is demonstrated in full cells with cathode materials LiNi0.6Mn0.2Co0.2O2 (NMC-622) and LiFePO4 (LFP). The cells show high rate performance and long-term stability under 5 C and 10 C cycling rates with a conventional carbonate electrolyte without any additives. The degradation of the cell performance is mainly attributed to the increased charge transfer resistance at the NMC side, consistent with the ex situ XRD and XPS analysis demonstrating the structural stability of NWO during cycling together with minimal electrolyte decomposition. Finally, we demonstrate the temperature-dependent performance of this full cell at 10, 25 and 60 °C and confirm, using operando XRD, that the structural change of the NWO material during lithiation/de-lithiation at 60 °C is very similar to its behaviour at 25 °C, reversible and with a low volume change. With the merits of high rate performance and long cycle life, the combination of NWO and a commercial cathode represents a promising, safe battery for fast charge/discharge applications

Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes

ObjectivesMore than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach.MethodsWe enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups.ResultsEighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers (R-squared) were 0.76 and 0.65 for the left and right stroke groups, respectively.ConclusionEstimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke

Reducing time to discovery : materials and molecular modeling, imaging, informatics, and integration

This work was supported by the KAIST-funded Global Singularity Research Program for 2019 and 2020. J.C.A. acknowledges support from the National Science Foundation under Grant TRIPODS + X:RES-1839234 and the Nano/Human Interfaces Presidential Initiative. S.V.K.’s effort was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division and was performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.Peer reviewe

An Experimental and Theoretical Investigation into Mg-ion Battery Electrodes using Nuclear Magnetic Resonance Spectroscopy

This thesis presents a combined experimental and theoretical approach on studying Mg-ion battery electrode materials, where Nuclear Magnetic Resonance (NMR) spectroscopy plays a central role in identifying the local structure and dynamics of the magnesium ions. Density Functional Theory (DFT) techniques are used extensively to (i) calculate and rationalise the observed NMR shifts, (ii) provide insights into the dynamics involved in such electrode materials, and (iii) guide the synthesis of candidate electrode materials. This work begins by a systematic study of 25Mg solid-state NMR in paramagnetic oxides, where the presence of transition metals makes them suitable for applications in high-voltage cathode materials. DFT methods for predicting and rationalising the paramagnetic NMR shifts are developed, with experimental verifications on synthesised samples. Feasibility of using advanced NMR pulse sequences such as Rotor-Assisted Population Transfer and Magic Angle Turning is demonstrated on such systems to afford enhanced resolution and sensitivity. This approach of combined NMR and DFT techniques is then applied to two of magnesium vanadates for high-voltage cathode applications. In particular, DFT-based thermodynamic energies are used to rationally design the synthetic steps leading to the said vanadate materials, followed by DFT prediction of the migration barriers. The prepared material was subject to experimental characterisation using NMR and diffraction techniques, with an initial cycling data in an electrochemical cell. In the final part, a combined experimental and ab initio investigation on Mg3Bi2, a promising Mg-ion battery anode material, is presented. Previous reports on variable-temperature 25Mg NMR spectroscopy is validated by DFT calculations on the migration barrier and defect energetics. Mechanistic insights on the migration mechanism are presented using the hybrid eigenvector-following transition state searching method, where the relativistic effects of heavy bismuth is shown to influence the migration barrier. We show that the defect formation energy of a Mg vacancy is critical in the apparent Mg diffusion barrier, which is heavily influenced by sample preparation conditions

PM deposition on the trichome structure of T.usneoides under flow conditions

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Enhanced efficiency of solid-state NMR investigations of energy materials using an external automatic tuning/matching (eATM) robot.

We have developed and explored an external automatic tuning/matching (eATM) robot that can be attached to commercial and/or home-built magic angle spinning (MAS) or static nuclear magnetic resonance (NMR) probeheads. Complete synchronization and automation with Bruker and Tecmag spectrometers is ensured via transistor-transistor-logic (TTL) signals. The eATM robot enables an automated "on-the-fly" re-calibration of the radio frequency (rf) carrier frequency, which is beneficial whenever tuning/matching of the resonance circuit is required, e.g. variable temperature (VT) NMR, spin-echo mapping (variable offset cumulative spectroscopy, VOCS) and/or in situ NMR experiments of batteries. This allows a significant increase in efficiency for NMR experiments outside regular working hours (e.g. overnight) and, furthermore, enables measurements of quadrupolar nuclei which would not be possible in reasonable timeframes due to excessively large spectral widths. Additionally, different tuning/matching capacitor (and/or coil) settings for desired frequencies (e.g. $^{7}$Li and $^{31}$P at 117 and 122MHz, respectively, at 7.05 T) can be saved and made directly accessible before automatic tuning/matching, thus enabling automated measurements of multiple nuclei for one sample with no manual adjustment required by the user. We have applied this new eATM approach in static and MAS spin-echo mapping NMR experiments in different magnetic fields on four energy storage materials, namely: (1) paramagnetic $^{7}$Li and $^{31}$P MAS NMR (without manual recalibration) of the Li-ion battery cathode material LiFePO$_{4}$; (2) paramagnetic $^{17}$O VT-NMR of the solid oxide fuel cell cathode material La$_{2}$NiO$_{4+δ}$; (3) broadband $^{93}$Nb static NMR of the Li-ion battery material BNb$_{2}$O$_{5}$; and (4) broadband static $^{127}$I NMR of a potential Li-air battery product LiIO$_{3}$. In each case, insight into local atomic structure and dynamics arises primarily from the highly broadened (1-25MHz) NMR lineshapes that the eATM robot is uniquely suited to collect. These new developments in automation of NMR experiments are likely to advance the application of in and ex situ NMR investigations to an ever-increasing range of energy storage materials and systems.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 655444 (O.P.). D.M.H. acknowledges funding from the Cambridge Commonwealth Trusts. J.L. gratefully acknowledges Trinity College, Cambridge (UK) for funding. K.J.G. gratefully acknowledges funding from the Winston Churchill Foundation of the United States and the Herchel Smith Scholarship. M.B. is the CEO of NMR Service GmbH (Erfurt, Germany), which manufactures the eATM device; M.B. acknowledges funding of the Central Innovation Programme for small and medium-sized enterprises (SMEs; Zentrales Innovationsprogramm Mittelstand, ZIM) of the German Federal Ministry of Economic Affairs and Energy (Bundesministerium für Wirtschaft und Energie, BMWi) under the Grant No. KF 2845501UWF. DFT calculations were performed on (1) the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and funding from the Science and Technology Facilities Council and (2) the Center for Functional Nanomaterials cluster, Brookhaven National Laboratory, which is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, under Contract No. DE-AC02-98CH10886