Putting the controversy over atomic-molecular theory to rest

Original paper: Einstein, Perrin, and the reality of atoms: 1905 revisited


 

There are many things that we “know” about the world around us. We know that the Earth revolves around the Sun, that gravity makes things fall downward, and that the apparently empty space around us is actually filled with the air that we breathe. We take for granted that these things are true. But how often do we consider whether we have seen evidence that supports these truths instead of trusting our sources of scientific knowledge?

Students in school are taught from an early age that matter is made of atoms and molecules. However, it wasn’t so long ago that this was a controversial belief. In the early 20th century, many scientists thought that atoms and molecules were just fictitious objects. It was only through the theoretical work of Einstein [1] and its experimental confirmation by Perrin [2] in the first decade of the 20th century that the question of the existence of atoms and molecules was put to rest. Today’s paper by Newburgh, Peidle, and Rueckner at Harvard University revisits these momentous developments with a holistic viewpoint that only hindsight can provide. In addition to re-examining Einstein’s theoretical analysis, the researchers also repeat Perrin’s experiments and demonstrate what an impressive feat his measurement was at that time.

In the mid-1800s, the botanist Robert Brown observed that small particles suspended in a liquid bounce around despite being inanimate objects. In an effort to explain this motion, Einstein started his 1905 paper on the motion of particles in a liquid with the assumption that liquids are, in fact, made of molecules. According to his theory, the molecules would move around at a speed determined by the temperature of the liquid: the warmer the liquid, the faster the molecules would move. And if a larger particle were suspended in the liquid, it would be bounced around by the molecules in the liquid.

Einstein knew that a particle moving through a liquid should feel the drag. Anyone who has been in a swimming pool has probably felt this; it is much harder to move through water than through air. The drag should increase with the viscosity, or thickness, of the fluid. Again, this makes sense: it is harder to move something through honey than through water. It is also harder to move a large object through a liquid than a small object, so the drag should increase with the size of the particle.

Assuming that Brownian motion was caused by collisions with molecules, and balancing it with the drag force, Einstein determined an expression for the mean square displacement of a particle suspended in a liquid. This relationship indicates how far a particle moves, on average, from its starting point in a given amount of time. He concluded that it should be given by

$latex \langle \Delta x (\tau) ^2 \rangle = \frac{RT}{3 \pi \eta N_A r} \tau$

where R is the gas constant, T is the temperature, $latex \eta$ is the viscosity of the liquid, $latex N_A$ is Avogadro’s number [3], r is the radius of the suspended particle, and $latex \tau$ is the time between measurements [4]. With this result, Einstein did not claim to have proven that the molecular theory was correct. Instead, he concluded that if someone could experimentally confirm this relationship, it would be a strong argument in favor of the atomistic viewpoint.

A man using a camera lucida to draw a picture of a small statue.
Figure 1: A camera lucida is an optical device allows an observer to simultaneously see an image and drawing surface and is therefore used as a drawing aid. (Source: an illustration from the Scientific American Supplement, January 11, 1879)

This is where Perrin came in. Nearly five years after Einstein’s paper was published, he successfully measured Avogadro’s number using Einstein’s equation, confirming both the relationship and the molecular theory behind it. However, with the resources available at the time, this experiment was a challenge. Perrin had to first learn how to make micron-size spherical particles that were small enough that their Brownian motion could be observed, but still large enough to see in a microscope. In order to measure the particles’ motion, he used a camera lucida attached to a microscope to see the moving particles on a surface where he could trace their outlines and measure their displacements by hand. Perrin obtained a value of $latex N_A = 7.15 \times 10^{23}$ by measuring the displacements of around 200 distinct particles in this way.

Performing this experiment in the 21st century was much simpler than it was for Perrin. Newburgh, Peidle, and Rueckner were able to purchase polystyrene microspheres of various sizes, eliminating the need to synthesize them. They also used a digital camera to record the particle positions over time instead of tracking the particles by hand. Using particles with radii of 0.50, 1.09, and 2.06 microns, they measured values of $latex 8.2 \times 10^{23}$, $latex 6.4 \times 10^{23}$, and $latex 5.7 \times 10^{23}$. Perhaps surprisingly, even with all of their modern advantages, the researchers’ results are not significantly closer to the actual value of $latex N_A = 6.02 \times 10^{23}$ than Perrin’s was a hundred years earlier.

A plot of the average mean square displacement of three different sized particles over time.
Figure 2: Einstein’s relationship predicts that the mean square displacement should be linear in time. By observing this relationship for three different particle sizes, the researchers use the slope to obtain three measurements of Avogadro’s number. (Newburgh et al., 2006)

For those of us who work in the field of soft matter, the existence of Brownian motion and the linear mean square displacement of a particle undergoing such motion are well-known scientific facts. The authors of this paper remind us that, not so long ago, even the existence of molecules was not generally accepted. And, although we often take for granted that these results are correct, first-hand observations can be useful for developing a deeper understanding and appreciation: “…one never ceases to experience surprise at this result, which seems, as it were, to come out of nowhere: prepare a set of small spheres which are nevertheless huge compared with simple molecules, use a stopwatch and a microscope, and find Avogadro’s number.” [5]


[1] A. Einstein, “On a new determination of molecular dimensions,” doctoral dissertation, University of Zürich, 1905.

[2] J. Perrin, “Brownian movement and molecular reality,” translated by F. Soddy Taylor and Francis, London, 1910. The original paper, “Le Mouvement Brownien et la Réalité Moleculaire” appeared in the Ann. Chimi. Phys. 18 8me Serie, 5–114 1909.

[3] Avogadro’s number is the number of atoms or molecules in one mole of a substance.

[4] In 1908, three years after Einstein’s paper, Langevin also obtained the same result using a Newtonian approach. (P. Langevin, “Sur la Theorie du Mouvement Brownien,” C. R. Acad. Sci. Paris 146, 530–533 1908.)

[5] A. Pais, Subtle Is the Lord (Oxford U. P., New York, 1982), pp. 88–92.

The matter of maternal mucus: permeability and preterm birth

Original paper: Probing the potential of mucus permeability to signify preterm birth risk


What is the first thing that comes to mind when you hear the word mucus? For most people, it’s probably the last time they had a cold. Mucus is not usually something we think about unless there’s a problem. However, it is always there, working behind the scenes to make sure that our bodies function smoothly. Mucus lines the digestive, respiratory, and reproductive systems, covering a surface area of about 400 square meters- about 200 times more area than is covered by skin. In addition to providing lubrication and keeping the underlying tissue hydrated, mucus also plays a key role the human immune system. It serves as a selectively permeable membrane that protects against unwanted pathogens while also helping to support and control the body’s microbiome [1].

Mucus is an example of a hydrogel, which is a three-dimensional polymer network that is able to hold a large amount of water. While hydrogels get their structural integrity from this polymer network, the polymer makes up only a small fraction of the material once they are swollen with water [2]. In mucus, this network is made of biopolymer called mucin.

Researchers in the Ribbeck lab at MIT think that mucus is an underappreciated–and understudied–part of the human body. They have developed techniques for characterizing the mucus hydrogel to better understand how it is able to function as a selective filter. In today’s paper, Kathryn Smith-Dupont and coworkers in the Ribbeck lab investigate cervical mucus and try to understand the relationship between mucus permeability, or its ability to be a selective filter, and the risk of preterm birth.

A birth that occurs before 37 weeks of gestation is considered a preterm birth. This can be associated with negative health outcomes for the baby both in infancy and later in life. Preterm birth is the leading cause of death for children 5 years of age and under, and those who survive can face challenges such as learning disabilities and hearing problems [3]. While the causes of preterm birth can be complex and varied, infection in the fluid surrounding the fetus–which is known to trigger preterm birth–is seen in 25-40% of cases. The infecting bacteria are often the same species that are found in the vagina, suggesting that it traveled through the cervical mucus barrier to infect the sterile uterus.

Smith-Dupont and coworkers look for correlations between mucus permeability and preterm birth risk by comparing the cervical mucus in ovulating non-pregnant women with that in pregnant women. Once the pregnant women give birth, their mucus is characterized as low-risk or high-risk depending on whether they had a preterm birth. The cervical mucus in ovulating non-pregnant women is expected to be at its most permeable to facilitate the passage of sperm, whereas in pregnant women the mucus should be less permeable. Whether a microbe makes it through the mucus barrier can be affected by its size, biochemical interaction with the mucin, or a combination of the two.

First, the researchers look at the permeability of the mucus to 1-micrometer spheres. This is comparable in size to both the mucus mesh and bacteria, and is used to see if the structure of the mucin network is hindering transport through the mucus. Next, they look at the permeability of the mucus to nanometer-size peptides (small bio-molecules). These are much smaller than the mucus mesh, so their ability to pass through the mucus is determined by biochemical interactions with the mucus instead of by its structure. By using these two probe sizes, the researchers hope to identify which mechanism is responsible for any differences in the mucus permeability.

msd_2plots_anno2
Figure 1: (a) Examples of trajectories of particles with ballistic (blue), diffusive (green), and subdiffusive (red) behavior. (b) The MSD for each trajectory on a log-log plot. An MSD with a slope of 2 or 1 indicates ballistic and diffusive behavior, respectively. An MSD with a slope smaller than 1 indicates subdiffusive motion.

To quantify the motion of 1 micrometer spheres in the mucus, the researchers track the motion of spheres in each mucus sample and calculate their mean square displacement (MSD). A particle’s mean square displacement describes how far it moves, on average, from its starting point in a given amount of time. The MSD is characterized by

$latex \langle r \left( t \right) ^2\rangle = 4 D_{\alpha} t^{\alpha}$

where $latex \langle r^2 \left( t \right) \rangle$ is how far the particle is from its starting point after t seconds and $latex D_{\alpha}$ describes how quickly the particle moves (called the diffusion coefficient). If a particle is acted on by a constant force, it moves in a straight line known as ballistic motion and $latex \alpha = 2$. This is not how a micrometer-scale particle in a fluid moves because it is being bounced around by random forces from the molecules in the fluid. Instead of moving in a straight line, the particle’s trajectory is a series of small excursions in random directions, and it takes longer to get away from its starting point than if it just moved in a straight line. This type of motion is known as free diffusion, and its MSD is characterized by $latex \alpha = 1$. In mucus, the polymer network gets in the way of the particle’s diffusion, so it can’t diffuse freely. This motion is called subdiffusive, and it has $latex \alpha < 1$. The more the particle’s diffusion is hindered by the polymer network, the lower its value of $latex \alpha$ will be. An example of a trajectory and MSD plot for each type of motion is shown in Figure 1.

mucus_subdiffusion
Figure 2: The diffusion coefficient $latex D_{\alpha}$ (a) and the diffusion exponent $latex \alpha$ (b) from the single particle tracking of 1 micrometer spheres in mucus samples. (Adapted from Smith-Dupont et al., 2017)

To compare the permeability of the mucus samples, the researchers measure $latex \alpha$ and $latex D_{\alpha}$ for each sample, as shown in Figure 2. The mucus from the pregnant women resulted in lower values of $latex \alpha$ and $latex D_{\alpha}$ than in the non-pregnant women, indicating that the network is more restrictive, as expected. However, the small difference between the high-risk and low-risk pregnancy women was not statistically significant [4]. This suggests that the difference in mucus permeability between high-risk and low-risk pregnancies is not primarily caused by differences in the mucus mesh size.

Next, the researchers look at the permeability of the mucus to small, fluorescently labeled peptides. They use a microfluidic device (to learn more about microfluidics, see [5]) to flow a solution of the peptides through the mucus, and observe whether the peptides get trapped or are able to flow through by looking at the fluorescent profile. Figure 3 shows a schematic of the microfluidic device. The ability of a small particle to travel through mucus is controlled by what happens when it comes in contact with part of the network. This interaction is thought to be affected by the charge of the particle, so the researchers investigate the behavior of both positively and negatively charged peptides.

mucus_microfluidic
Figure 3: A schematic of the microfluidic device used to determine the permeability of mucus samples to fluorescently labeled peptides. If the mucus is not permeable to the peptides they get stuck in the mucus, causing enrichment (or buildup) of peptides at the front of the mucus sample. If the mucus is permeable, the peptides penetrate the mucus and are seen throughout the sample. (Adapted from Smith-Dupont et al., 2017)

For both positively and negatively charged peptides, the researchers see a significant difference between low-risk and high-risk mucus, as shown in Figure 4. The mucus from both low-risk and high-risk patients was less permeable to the positively charged peptides than the mucus from the ovulating patients. However, more of the positively charged peptides were able to penetrate into the high-risk mucus than the low-risk mucus. The results for the negatively charged peptide were more dramatic. While the low-risk mucus was not permeable to the negatively charged peptide, the high-risk mucus was as permeable as that from the ovulating patients. This suggests that the biochemical properties of the cervical mucus in low-risk and high-risk patients are primarily responsible for differences in permeability.

mucus_peptides
Figure 4: Fluorescence profiles after 900 seconds for positively and negatively charged peptides through mucus samples. A control shows the profile in fluid with no mucin. (Adapted from Smith-Dupont et al., 2017)

The results in this study help to clarify which properties of cervical mucus cause an increased risk of preterm birth. The researchers considered both structural and biochemical origins for the increased permeability of cervical mucus to harmful pathogens. Structural changes in the mucin network do not appear to be the primary difference between cervical mucus in low-risk and high-risk pregnancies. Instead, biochemical changes in the mucus that affect how the mucus interacts with microbes appear to be the primary cause of its increased permeability in high-risk pregnancies. This understanding could be useful for developing diagnostic tools to determine a woman’s preterm birth risk and, ideally, treatment to reduce her risk.


[1] https://en.wikipedia.org/wiki/Mucous_membrane#cite_note-Sompayrac-3

[2] Ahmed, Enas M. (2015). Hydrogel: Preparation, characterization, and applications: A review. Journal of Advanced Research, 6(2), 105-121.

[3] http://www.who.int/mediacentre/factsheets/fs363/en/

[4] While the difference between high-risk and low-risk pregnant women is not significantly significant, this does not rule out a difference between the two. The sample size is relatively small for this study, with only 14 pregnant women (7 low-risk and 7 high-risk) included, so the lack of statistical significance could also be due to insufficient data.

[5] https://www.nature.com/articles/nature05058.pdf?origin=ppub