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Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs.
A finite element study was then conducted using material properties from the open literature.
The composite coating was able to combine advantageous material properties from both the constituent ceramics.
This fact can have important consequences on the derivation of material properties from experimental data.
The model integrated temperature-dependent material properties from leading candidate thermoelectric materials and experimental time-variant temperature data.
In contrast, trabecular strength due to whey protein treatment significantly reduced when material properties from nanoindentation were introduced.
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The goal of nanosciences in the control of materials properties from the elemental scale (molecules, particles, clusters, etc).
Whatever the nature of the material, Figs. 1 and 2 are general maps summarizing the size and shape effects on the mentioned materials properties from the bulk to the nanoscale.
Different from complex and time-consuming computer simulation process, the universal relation (Eq. 3) can predict the mentioned materials properties from the bulk to sizes of nanostructures higher than ~4 nm.
As we focus on mono- to a few-layer thick chalcogenide thin films and nanosheets to enhance desirable nanoscale or surface effects, it becomes increasingly important to distinguish true intrinsic materials properties from extrinsic environmental effects such as contact metal issues, surface defects such as oxidation or vacancies, and interface-induced impurities or trap sites.
This correlation has been hindered partly by the intrinsic complex composition of the ECM, and by the lack of analytical tools that permit simultaneous assessment of ECM materials properties from the matrix/cellular to the molecular scale.
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