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Starting from hydrogenated clusters, we have considered different Si/O bonding geometries at the interface.
In the clusters we have detected, several QTLs with major effects could be the primary cause of the cluster.
For each possible pair of clusters, we have to compute potential variability reduction for all attribute layers and to select the smallest value for this pair.
By increasing the number of clusters, gets larger and, therefore, the floor appears at very small (and not practical) values of the probability of decision error (e.g., in the case with eight uniform clusters we have ).
To examine the influence of charge on the structure and stability of the fully planar clusters, we have also studied charged 2D and 3D B12X6 (X = H, F) structures.
Based on a systematic study of gas-phase silicon oxide clusters and the subsequent interesting findings such as the gas-phase favorable composition and distinctive features in reactivity of the different silicon oxide clusters, we have elucidated the mechanism of oxide-assisted nucleation of silicon nanostructures.
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Because we had a relatively small data set of only 42 significant mutation clusters, we had limited statistical power to detect differences between mutations inside and outside of clusters.
To understand what makes a tech cluster we have to first look at what made the Valley a success.
To better understand metalloproteins with Mn-clusters, we have designed artificial four-helix bundles to have one, two, or three dinuclear metal centers able to bind Mn II).
For numbers of rows and lines in cluster, we have r l = n - d, (12).
Then, for the ℓth cluster we have that d ℓ, CH ≥ d ℓ, j, ∀ j ∈ V ℓ V ℓ, p, (15).
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