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As the size increases above 10 nm, AF4 becomes superior in terms of resolution and recovery. AF4's applications are flexible for many analytical conditions where a column-based method would be unable to properly separate the desired particles.
where L is the column length and N the number of theoretical plates. [5] The relation between plate number and peak width at the base is given by N = 16 ⋅ ( t R W b ) 2 {\displaystyle N=16\cdot \left({\frac {t_{R}}{W_{b}}}\right)^{2}\,} .
Size-exclusion chromatography, also known as molecular sieve chromatography, [1] is a chromatographic method in which molecules in solution are separated by their shape, and in some cases size. [2] It is usually applied to large molecules or macromolecular complexes such as proteins and industrial polymers . [ 3 ]
A general guide has been provided as a design principle to achieve parameters (e.g., mesh size and physical parameters such as Poisson's ratio that appear in the nearly singular operator) independent convergence rate of the multigrid method applied to such nearly singular systems, [24] i.e., in each grid, a space decomposition based on which ...
A sieve analysis (or gradation test) is a practice or procedure used in geology, civil engineering, [1] and chemical engineering [2] to assess the particle size distribution (also called gradation) of a granular material by allowing the material to pass through a series of sieves of progressively smaller mesh size and weighing the amount of material that is stopped by each sieve as a fraction ...
The colon comes from a general Scala syntax mechanism whereby the apparent infix operator is invoked as a method on the left operand with the right operand passed as an argument, or vice versa if the operator's last character is a colon, here applied symmetrically. Scala also features the tree-like folds using the method list.fold(z)(op). [11]
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Bootstrap aggregating, also called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms.