The node label (e.g. "5 nm") no longer represents a physical dimension and hasn't for some time -- it's simply a continuation of a naming convention. In fact, while we used to scale down transistors by simply reducing the gate width (where the naming convention originated) we now take far more elaborate steps to do so.
Yup, such a blatant copycat attempt. All the way down to the angle the photos are taken from. Good thing for Boosted that you can't just copy a well engineered drivetrain.
The color change of gold and CdSe particles with size are due to two different effects.
CdSe is a semiconductor, and reducing the CdSe nanoparticle size increases its band gap through a process called quantum confinement. For smaller particles, it requires a photon to have higher energy (i.e. smaller wavelength) to be absorbed.
Gold particles derive their color from the scattering mechanism mentioned in the parent comment.
Not that I disagree with what you wrote, I just want to point out that silicon dioxide is probably the most important reason silicon has reached such ubiquity in electronics. No other material has a native oxide as good as SiO2, even though the semiconductors themselves have superior properties than silicon.
I know very little about what's available in Ruby, but I would have used the Pandas library to accomplish this task in python. Their in-memory data structure, a DataFrame, is more than capable of handling those operations.
I'm trying to figure out where something like this fits into the python data ecosystem.
For datasets that fit in memory, Pandas seems like the best bet. Good I/O functions (JSON, CSV), easy slicing (numpy array-like syntax), and some sql-like operations (groupby, join).
For large datasets, you'd need a proper db.
So is Dataset then useful for datasets that cannot fit in memory but aren't too large?
"The manufacturing process for most solar panels involves manufacturing a block of sapphire or other crystalline silicon and then slicing a .2mm-thick sheet off of it with a wafering saw."
Just want to point out that Stanford does have its fair share of "MIT startups".
Here's a small subset:
* Alveo Energy
* Amprius
* Blue River Technology
* C3Nano
* Momentum Machines
* QuantumScape
* Solum
Although if the OP's simple model had a category for startups simultaneously validating customer demand and driving technological advantages, most of these companies would be there.
I still prefer Keynote to JS presentation tools since playing with layout is easier manually than with css (for me).
But one killer feature Keynote lacks is slide inheritance. I love using build-outs and I wish I could edit a parent slide and have the changes propagate to its children. This feature would be much easier to build out with a JS presentation framework.