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A new composite membrane with electrical conductivity and reactive groups (–COOH) was synthesized by copolymerizing pyrrole-3-carboxylic acid and pyrrole onto nylon membrane via the template method.
Cu-nanoparticles were coated on the sidewall of multiwalled carbon nanotubes (MWCNTs) by a facile and effective in situ approach via the template of a polyelectrolyte (polyethylenimine or poly(sodium 4-styrene sulfonate)) noncovalently functionalized on MWCNTs.
Mesostructured SiO2 films were prepared via the template sol-gel pre-doping technique using as precursor material composed of tetraethoxysilane (TEOS), ethanol, distilled water, HCl, and Pluronic 123(P123) (1:8:2:0,5:0,01 molar ratios), and dye or SWNT.
The template used for modeling was identified via the template identification tool (with default parameters), which performs a primary sequence comparison of the query sequence with those in a structural database.
Double-stranded product was captured via the template strand biotins by adding 1 mL streptavidin-coated magnetic beads (MagnaBind Streptavidin, Pierce, 5 mg/mL in 1 M NaCl +0.05% TWEEN-20) and incubating at 25°C for 10 minutes with mixing.
As expected, a variety of zinc complexes are produced in a single step via the template condensation, namely zinc phthalocyanine, zinc meso-tetraphenyltetrabenzoporphyrin and zinc azatetrabenzoporphyrins. Zinc phthalocyanine is virtually insoluble in organic solvents and can be removed easily.
Similar(53)
The above transformations bringing fractional anisotropy and mean diffusivity maps in subject space to the FMRIB58 template analysis space (via the group template) were concatenated and the combined transform applied in a single step to minimize interpolation errors.
The groove depth was adjusted from 150 to 300 nm via fine-tuning of the template.
Spike selection was accomplished by comparing candidate waveforms identified throughout the recording to the ChR2-derived waveform template by transforming candidate waveforms to appear as similar as possible to the template via dynamic time warping.
TFR Unigenes were clustered via the Pavlidis Template Matching (PTM) algorithm [ 24].
For GPU processing, MorphoGraphX uses CUDA (https://developer.nvidia.com/cuda-zone) via the Thrust template library (http://thrust.github.io).
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