The performance of Li+ ion batteries (LIBs) is hindered by steep Li+ ion concentration gradients in the electrodes. Although thick electrodes (≥300 µm) have the potential for reducing the proportion of inactive components inside LIBs and increasing battery energy density, the Li+ ion concentration gradient problem is exacerbated. Most understanding of Li+ ion diffusion in the electrodes is based on computational modeling because of the low atomic number (Z) of Li. There are few experimental methods to visualize Li+ ion concentration distribution of the electrode within a battery of typical configurations, for example, coin cells with stainless steel casing. Here, for the first time, an interrupted in situ correlative imaging technique is developed, combining novel, full-field X-ray Compton scattering imaging with X-ray computed tomography that allows 3D pixel-by-pixel mapping of both Li+ stoichiometry and electrode microstructure of a LiNi0.8Mn0.1Co0.1O2 cathode to correlate the chemical and physical properties of the electrode inside a working coin cell battery. An electrode microstructure containing vertically oriented pore arrays and a density gradient is fabricated. It is shown how the designed electrode microstructure improves Li+ ion diffusivity, homogenizes Li+ ion concentration through the ultra-thick electrode (1 mm), and improves utilization of electrode active materials.
Solid‐state lithium batteries will revolutionize the lithium‐ion battery and energy storage applications if certain key challenges can be resolved. The formation of lithium‐protrusions (dendrites) that can cause catastrophic short‐circuiting is one of the main obstacles, and progresses by a mechanism that is not yet fully understood. By utilizing X‐ray computed tomography with nanoscale resolution, the 3D morphology of lithium protrusions inside short‐circuited solid electrolytes has been obtained for the first time. Distinguishable from adjacent voids, lithium protrusions partially filled cracks that tended to propagate intergranularly through the solid electrolyte, forming a large waved plane in the shape of the grain boundaries. Occasionally, the lithium protrusions bifurcate into flat planes in a transgranular mode. Within the cracks themselves, lithium protrusions are preferentially located in regions of relatively low curvature. The crack volume filled with lithium in two samples is 82.0% and 83.1%, even though they have distinctly different relative densities. Pre‐existing pores in the solid electrolyte, as a consequence of fabrication, can also be part‐filled with lithium, but do not have a significant influence on the crack path. The crack/lithium‐protrusion behavior qualitatively supports a model of propagation combining electrochemical and mechanical effects.
Octopus (Optics Clustered to OutPut Unique Solutions) celebrated in June 2020 its 10th birthday. Based at Harwell, near Oxford, Octopus is an open access, peer reviewed, national imaging facility that offers successful U.K. applicants supported access to single molecule imaging, confocal microscopy, several flavours of superresolution imaging, light sheet microscopy, optical trapping and cryoscanning electron microscopy. Managed by a multidisciplinary team, Octopus has so far assisted >100 groups of U.K. and international researchers. Cross‐fertilisation across fields proved to be a strong propeller of success underpinned by combining access to top‐end instrumentation with a strong programme of imaging hardware and software developments. How Octopus was born, and highlights of the multidisciplinary output produced during its 10‐year journey are reviewed below, with the aim of celebrating a myriad of collaborations with the U.K. scientific community, and reflecting on their scientific and societal impact.
We investigate the mechanism of direct CO2 hydrogenation to methanol on Pd (111), (100) and (110) surfaces using density functional theory (DFT), providing insight into the reactivity of CO2 on Pd-based catalysts. The initial chemisorption of CO2, forming a partially charged CO2δ-, is weakly endothermic on a Pd (111) surface, with an adsorption energy of 0.06 eV, and slightly exothermic on Pd (100) and (110) surfaces, with adsorption energies of -0.13 and -0.23 eV, respectively. Based on Mulliken analysis, we attribute the low stability of CO2δ- on the Pd (111) surface to a negative charge that accumulates on the surface Pd atoms interacting directly with the CO2δ- adsorbate. For the reaction of the adsorbed species on the Pd surface, HCOOH hydrogenation to H2COOH is predicted to be the rate determining step of the conversion to methanol in all cases, with activation barriers of 1.35, 1.26, and 0.92 eV on Pd (111), (100) and (110) surfaces, respectively.
We report a detailed Density Functional Theory (DFT) based investigation of the structure and stability of bulk and surface structures for the Group 10-12 elements Pd, Cu and Zn, considering the effect of the choice of exchange-correlation density functionals and computation parameters. For the initial bulk structures, the lattice parameter and cohesive energy are calculated, which are then augmented by calculation of surface energies and work functions for the lower-index surfaces. Of the 22 density functionals considered, we highlight the mBEEF density functional as providing the best overall agreement with experimental data. The optimal density functional choice is applied to the study of higher index surfaces for the three metals, and Wulff constructions performed for nanoparticles with a radius of 11nm, commensurate with nanoparticle sizes commonly employed in catalytic chemistry.
There is need for effective and affordable vaccines against SARS-CoV-2 to tackle the ongoing pandemic. In this study, we describe a protein nanoparticle vaccine against SARS-CoV-2. The vaccine is based on the display of coronavirus spike glycoprotein receptor-binding domain (RBD) on a synthetic virus-like particle (VLP) platform, SpyCatcher003-mi3, using SpyTag/SpyCatcher technology. Low doses of RBD-SpyVLP in a prime-boost regimen induce a strong neutralising antibody response in mice and pigs that is superior to convalescent human sera. We evaluate antibody quality using ACE2 blocking and neutralisation of cell infection by pseudovirus or wild-type SARS-CoV-2. Using competition assays with a monoclonal antibody panel, we show that RBD-SpyVLP induces a polyclonal antibody response that recognises key epitopes on the RBD, reducing the likelihood of selecting neutralisation-escape mutants. Moreover, RBD-SpyVLP is thermostable and can be lyophilised without losing immunogenicity, to facilitate global distribution and reduce cold-chain dependence. The data suggests that RBD-SpyVLP provides strong potential to address clinical and logistic challenges of the COVID-19 pandemic.
We present Parameter Quantification Network (PQ-Net), a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems. The network is tested against simulated and experimental datasets of increasing complexity with the last one being an X-ray diffraction computed tomography dataset of a multi-phase Ni-Pd/CeO2-ZrO2/Al2O3 catalytic material system consisting of ca. 20,000 diffraction patterns. It is shown that the network predicts accurate scale factor, lattice parameter and crystallite size maps for all phases, which are comparable to those obtained through full profile analysis using the Rietveld method, also providing a reliable uncertainty measure on the results. The main advantage of PQ-Net is its ability to yield these results orders of magnitude faster showing its potential as a tool for real-time diffraction data analysis during in situ/operando experiments.