ReviewNature materials2019
Completing the picture through correlative characterization.
Review in Nature materials, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Determination of core and shell radii of individual core-shell magnetic nanoparticles by magnetic force microscopy.Nanoscale advances · 2026Article
- Unary and Binary Dynamic Column Breakthrough Experiments with Carbon Dioxide and Nitrogen Imaged by X-ray Computed Tomography.Langmuir : the ACS journal of surfaces and colloids · 2026Article
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
- A multiscale perspective on cluster-based layered materials: Design and function optimization toward catalytic and energy storage applications.Fundamental research · 2025Review
- Tutorial on In Situ andACS nano · 2024Review
- Machine Learning as a "Catalyst" for Advancements in Carbon Nanotube Research.Nanomaterials (Basel, Switzerland) · 2024Review
- Review
- The Extremely Brilliant Source storage ring of the European Synchrotron Radiation Facility.Communications physics · 2023Review
- Inhibiting weld cracking in high-strength aluminium alloys.Nature communications · 2022Article
- Translating Material Science into Bone Regenerative Medicine Applications: State-of-The Art Methods and Protocols.International journal of molecular sciences · 2022Review
- Damage evolution during fracture by correlative microscopy with hyperspectral electron microscopy and laboratory-based microtomography.Science advances · 2022Article
- Dissecting Biological and Synthetic Soft-Hard Interfaces for Tissue-Like Systems.Chemical reviews · 2022Review
- Cycloidal CT with CNN-based sinogram completion and in-scan generation of training data.Scientific reports · 2022Article
- Project SWAVE 2.0: An overview of the study design for multimodal placental image acquisition and alignment.MethodsX · 2022Article
- The materials tetrahedron has a "digital twin".MRS bulletin · 2022Article
- Robotic fabrication of high-quality lamellae for aberration-corrected transmission electron microscopy.Scientific reports · 2021Article
- Correlative Microscopy: a tool for understanding soil weathering in modern analogues of early terrestrial biospheres.Scientific reports · 2021Article
- High-fidelity and high-resolution phase mapping of granites via confocal Raman imaging.Scientific reports · 2021Article
- Upscaling X-ray nanoimaging to macroscopic specimens.Journal of applied crystallography · 2021Review
- Digital Transformation in Materials Science: A Paradigm Change in Material's Development.Advanced materials (Deerfield Beach, Fla.) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural and manufactured materials rely on complex hierarchical microstructures to deliver a suite of interesting properties. To predict and tailor their performance requires a joined-up knowledge of their multiphase microstructure, interfaces, chemistry and crystallography from the nanoscale to the macroscale. This Perspective reflects on how recent developments in correlative characterization can bring together multiple image modalities and maps of the local chemistry, structure and functionality to form rich multimodal and multiscale correlated datasets. The automated collection and digitization of multidimensional data is an essential part of the picture for developing multiscale modelling and 'big data'-driven machine learning approaches. These are needed to both improve our understanding of existing materials and exploit high-throughput combinatorial synthesis, processing and testing methods to develop materials with bespoke properties.
Identifiers
31209389What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.