Chemical potential and banana waffles

The concept of chemical potential is one that seems almost deliberately obscure to many.  I’ve written about this here, and referenced this article.  What you may not realize is that the chemical potential, of water in particular, plays a crucial role in why my banana waffle recipe works so well.  

My waffle recipe starts with an old, peel-getting-brown banana, which I peel and put in a medium bowl with a couple of teaspoons of salt and a tablespoon of brown sugar.  With just a little mashing with a fork to mix with the salt and sugar, the banana basically liquefies in a couple of minutes.  That’s where the chemical potential comes in.  

Chemical potential, \(\mu\), describes how particles tend to diffuse, from regions of high chemical potential (more accurately, high \(\mu/T\)) to regions of low chemical potential \((\mu/T\)). The water molecules in the cells of the banana is already at a higher chemical potential than, e.g., the water vapor in the air around the banana.  That’s why if you let the banana sit around it would eventually dry out, and there is an “osmotic” pressure that pushes out against the cell membranes and cell walls.  Adding salt and sugar to the exterior of the cells lowers the chemical potential for water outside the cells even more (because there is an energetic benefit to the water molecules to form a solution with the salt and sugar - the polar water molecules have an attractive interaction with the ions from the salt, and an attractive interaction via hydrogen bonding with the sugar).  This increases the osmotic pressure, so that water leaks out of the cells (maybe even rupturing the cell membrane, though when people want to encourage that they throw in a little soap, not conducive to good waffles).  Wait a couple of minutes, stir, and then I have yummy banana goo that forms the beginning of my Sunday morning waffle batter.

This is a goofy example of the power of thermodynamics and statistical mechanics.  At room temperature, there are many more microscopic arrangements of the water molecules (in the presence of sugar and salt) with the banana forming liquefied goo than with the water sitting in the cells, and so the liquefaction happens spontaneously once the ingredients are put together.  (Osmosis can even be funny - I highly recommend reading this story of you can find a copy.)

Brief items

With the end of the semester approaching and various grant deadlines, it's been a very busy time.  Here are some items I spotted this week (some new, some old):

• This article from Quanta about the "Einstein tile" is great - I particularly like the animated illustration.  This prompted some fun discussions with colleagues about whether there might be materials with structures like this, and what their properties would be, since they are ordered yet aperiodic yet not quasicrystalline.
• On twitter, I saw a link to this Nature Photonics paper that measures losses in what are designed to be topological photonic structures. The motivation behind such structures is that certain propagating optical modes are expected to be topologically protected from back-scattering.  Instead, the authors find plenty of back-scattering, and they raise the question of how useful topological protection is in practice.  Thought-provoking.
• Also on twitter, I saw this Nature paper, which uses ultrafast optics to look at Floquet effects with sub-optical-cycle timing resolution.   
• Lengthy article in Science about plagiarism and Ranga Dias.
• This article is about making a low-cost (€100) detector for electron microscopy, far cheaper than the hardware supplied by commercial SEM vendors.  I reiterate:  I think it would have enormous impact if someone could develop an SEM that is truly inexpensive (say less than $2000, so that many high schools and community colleges could afford one).  
• I had occasion to re-read the original paper by Little and Parks (1962) on what is now called the Little-Parks effect.  The transition temperature (inferred via the resistance in the transition regime) of a thin-walled superconducting cylinder oscillates with external magnetic field threading the cylinder.  The oscillations are periodic in magnetic flux with a period \(h/2e\), providing key evidence that the current in superconductors is carried by pairs electrons (or holes).  It's cool to see how they made a 1 micron inner-diameter Sn cylinder back before we had all the fancy modern fabrication techniques, reaffirming that GE Varnish is a wonder material. 
• SpaceX is going to try to launch their truly enormous rocket this coming week from Boca Chica, TX.  Like any first test flight, it has a good chance of failure, but if they can get this system to work as envisioned, it will truly be transformative in terms of payload to orbit.  Here's the link to their live webcast that starts Monday morning.

The problems and opportunities of data

We live in a world of "big data", and this presents a number of challenges for how we handle this at research universities.  Until relatively recently, the domain of huge volume/huge throughput scientific data was chiefly that of the nuclear/particle physics community and then the astronomy community.  The particle physicists in particular have been pioneers in how they handle enormous petabyte quantities of data at crazy high rates.

Thanks to advances in technology, though, it is now possible for small university research groups to acquire terabytes of data in an afternoon, thanks to high speed/high resolution video recording, hyperspectral imaging, many GHz bandwidths, etc.  Where things get tricky is, this new volume and pace are demands that researchers, universities, funding agencies, publishers, etc. are generally not equipped to handle, in terms of data stewardship.

I've written before about the responsibilities of various people regarding data stewardship.  Data from sponsored research at universities is "owned" by the universities, because they are held responsible by the funding agencies (e.g. NSF, DOE, DOD) for, among other things, maintaining accessible copies until years after the end of the funding agreements.  The enormous volumes of data that can now be generated are problematic.  With the exception of a small number of universities that host supercomputing centers, most academic institutions just do not have the enterprise-class storage capacity (either locally or contracted via cloud storage services) to meet the ever-growing demand.  Ordering a bunch of 8 TB external hard drives and keeping them in a growing stack on your shelves is not a scalable, sustainable plan.  Universities all over are finding out that providers can't really provide "unlimited" capacity without passing costs along to the research institutions.  Agencies are also often not fans of significant budgeting in proposals for long-term data retention.  It's not clear that anyone has a long-term solution to this that really meets everyone's needs.  Repositories like zenodo are great, but somewhere someone actually has to pay the costs to operate these.

Further, there is a thriving movement toward open science (with data sharing) and FAIR data principles - making sure that data is findable, accessible, interoperable, and resuseable.  In condensed matter physics, this is exemplified by the Materials Genome Initiative and its updated strategic plan.  There is a belief that having this enormous amount of information available (and properly indexed with metadata so that it can be analyzed and used intelligently), combined with machine learning and AI, will lead to accelerated progress in research, design, and discoveries.  

At the same time, in the US there are increasing concerns about data security, and coming regulatory actions about this.   University research administrators are looking very hard at all this, as is the Council on Government Relations, both because of chilling effects across the community and to push to make sure that Congress and agencies don't saddle universities with mutually incompatible and contradictory policies and requirements.  

Meeting all of these needs is going to be a challenge for a long time to come.  If any readers have particular examples of how to meet the needs of very large volume data retention, I'd appreciate the comments.