Grand Challenges in global eye health: a global prioritisation process using Delphi method

Background We undertook a Grand Challenges in Global Eye Health prioritisation exercise to identify the key issues that must be addressed to improve eye health in the context of an ageing population, to eliminate persistent inequities in health-care access, and to mitigate widespread resource limitations. Methods Drawing on methods used in previous Grand Challenges studies, we used a multi-step recruitment strategy to assemble a diverse panel of individuals from a range of disciplines relevant to global eye health from all regions globally to participate in a three-round, online, Delphi-like, prioritisation process to nominate and rank challenges in global eye health. Through this process, we developed both global and regional priority lists. Findings Between Sept 1 and Dec 12, 2019, 470 individuals complete round 1 of the process, of whom 336 completed all three rounds (round 2 between Feb 26 and March 18, 2020, and round 3 between April 2 and April 25, 2020) 156 (46%) of 336 were women, 180 (54%) were men. The proportion of participants who worked in each region ranged from 104 (31%) in sub-Saharan Africa to 21 (6%) in central Europe, eastern Europe, and in central Asia. Of 85 unique challenges identified after round 1, 16 challenges were prioritised at the global level; six focused on detection and treatment of conditions (cataract, refractive error, glaucoma, diabetic retinopathy, services for children and screening for early detection), two focused on addressing shortages in human resource capacity, five on other health service and policy factors (including strengthening policies, integration, health information systems, and budget allocation), and three on improving access to care and promoting equity. Interpretation This list of Grand Challenges serves as a starting point for immediate action by funders to guide investment in research and innovation in eye health. It challenges researchers, clinicians, and policy makers to build collaborations to address specific challenge

Bayesian machine learning for efficient minimization of defects in ALD passivation layers

[Image: see text] Atomic layer deposition (ALD) is an enabling technology for encapsulating sensitive materials owing to its high-quality, conformal coating capability. Finding the optimum deposition parameters is vital to achieving defect-free layers; however, the high dimensionality of the parameter space makes a systematic study on the improvement of the protective properties of ALD films challenging. Machine-learning (ML) methods are gaining credibility in materials science applications by efficiently addressing these challenges and outperforming conventional techniques. Accordingly, this study reports the ML-based minimization of defects in an ALD-Al(2)O(3) passivation layer for the corrosion protection of metallic copper using Bayesian optimization (BO). In all experiments, BO consistently minimizes the layer defect density by finding the optimum deposition parameters in less than three trials. Electrochemical tests show that the optimized layers have virtually zero film porosity and achieve five orders of magnitude reduction in corrosion current as compared to control samples. Optimized parameters of surface pretreatment using Ar/H(2) plasma, the deposition temperature above 200 °C, and 60 ms pulse time quadruple the corrosion resistance. The significant optimization of ALD layers presented in this study demonstrates the effectiveness of BO and its potential outreach to a broader audience, focusing on different materials and processes in materials science applications

Direct observation of spin wave focusing by a Fresnel lens

Spin waves are discussed as promising information carrier for beyond complementary metal-oxide semiconductor data processing. One major challenge is guiding and steering of spin waves in a uniform film. Here, we explore the use of diffractive optics for these tasks by nanoscale real-space imaging using x-ray microscopy and careful analysis with micromagnetic simulations. We discuss the properties of the focused caustic beams that are generated by a Fresnel-type zone plate and demonstrate control and steering of the focal spot. Thus, we present a steerable and intense nanometer-sized spin-wave source. Potentially, this could be used to selectively illuminate magnonic devices like nano-oscillators

Self-assembly and characterization of 2D plasmene nanosheets

Freestanding plasmonic nanoparticle (NP) superlattice sheets are novel 2D nanomaterials with tailorable properties that enable their use for broad applications in sensing, anticounterfeit measures, ionic gating, nanophotonics and flat lenses. We recently developed a robust, yet general, two-step drying-mediated approach to produce freestanding monolayer, plasmonic NP superlattice sheets, which are typically held together by holey grids with minimal solid support. Within these superlattices, NP building blocks are closely packed and have strong plasmonic coupling interactions; hence, we termed such freestanding materials plasmene nanosheets. Using the desired NP building blocks as starting material, we describe the detailed fabrication protocol, including NP surface functionalization by thiolated polystyrene and the self-assembly of NPs at the airwater interface. We also discuss various characterization approaches for checking the quality and optical properties of the as-obtained plasmene nanosheets: optical microscopy, spectrophotometry, transmission/scanning electron microscopy (TEM/SEM) and atomic force microscopy (AFM). With regard to different constituent building blocks, the key experimental parameters, including NP concentration and volume, are summarized to guide the successful fabrication of specific types of plasmene nanosheets. This protocol, from initial NP synthesis to the final fabrication and characterization, takes ~33.5 h