## Posts

Showing posts from 2019

### Di-block copolymer analysis: GPC

G el P ermeation C hromatography is a well known method of measuring molecular weight distribution of polymers. For di-block copolymers, a proper calibration curve can be constructed by using Mark-Houwink parameters of homopolymers ( see here ). Usually, di-block copolymers are obtained by synthesis one block first then "grow" the other block from the end of preceding block. The molecular weight distributions of final di-block copolymer and preceding block are accessible by GPC, the molecular weight distribution of the 2nd block, however, is hard to obtain. Here I present a simple method to calculate the molecular weight distribution of 2nd block by GPC data of preceding block and final di-block copolymer. Assuming that we already have the molecular weight distribution of the preceding block, $P_1(n)$, it is generally that the length of the 2nd block "grow" from the depends on the preceding chain length $n$, therefore, the pdf of the 2 blocks should be a joint pdf

### Notes on scipy.optimize.minimize

Problem: evenly distributed points on ellipsoid surfaces. Solution: numerically minimizing energy of charges constrained on the ellipsoid surface. Assuming the ellipsoid satisfies equation: $$\left(\frac{x}{a}\right)^2+\left(\frac{y}{b}\right)^2+\left(\frac{z}{c}\right)^2=1$$ the ratios of 3 axes of the ellipsoid surface are $(a,b,c)^T$. The minimization process is: let $$\mathbf{x}^\prime:=\frac{1}{\sqrt{\left(\frac{x}{a}\right)^2+\left(\frac{y}{b}\right)^2+\left(\frac{z}{c}\right)^2}}(x, y, z)^T$$ then $\mathbf{x}^\prime$ satisfy the ellipsoid equation; minimize the energy function $$u=\sum_i \sum_{j>i} \frac{1}{\sqrt{(x^\prime_i-x^\prime_j)^2+(y^\prime_i-y^\prime_j)^2+(z^\prime_i-z^\prime_j)^2}}$$ the gradient vector is, e.g. the $x$ component of ith particle: $$\partial u/\partial x_i=\sum_{j\ne i} - ((x^{\prime 2}_i/a^2-1)(x^\prime_j-x^\prime_i) + (x^{\prime}_i y^{\prime}_i(y^{\prime}_j-y^{\prime}_i)+x^{\prime}_i z^{\prime}_i (z^{\prime}_j-z^{\prime}_i))/a^2)/d^3$$ w

### Dealing with aggregates: I don't like tedious work

I used to study nanoparticles (NPs) self-assembly in polymer matrices before 2017 by coarse-grained simulation method. Analyzing morphologies of self-assemblies is a tedious work: one needs to repeatedly watch frames from simulation trajectories, finding appropriate view points, counting number of aggregates, measure size of aggregates, etc. Obtaining integrate aggregates under periodic boundary conditions is also a challenging work. In addition, one usually needs to run plenty of simulations under different conditions to find a desired morphology; selecting desired morphologies automatically is even more challenging: most of the morphologies are ill-defined, it is hard to use some common  characterization methods, e.g., $g(r)$ or $S(q)$ of NPs give little differences amongst percolated morphologies. Based on such demands, I designed a co-pilot which can: Automatically clustering the clusters; Remove periodic boundary conditions and make the center-of-mass at $(0,0,0)^T$; Adjust

### CAPVT II -- notes on Wheelock's Latin

First declension nouns and adjectives Endings Singular Plural Nom. -a -ae Gen. -ae -ārum Dat. -ae -īs Acc. -am -ās Abl. -ā -īs Voc. -a -ae