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This is the most popular science of all time! Makes sense that SciShow is covering it. Pop sci is kind of our thing. But I mean something different when I say this video is about the most popular science ever. I mean it's the most popular science according to scientists. …Based on data! Here are the most cited publications of all time, from five big fields of science. And I bet you've never even heard of them. [♪ INTRO] In April of 2025, a paper published in the journal "Nature" ranked academic publications based on how many other papers referenced their work with a citation, going back to the beginning of their databases.
So we went through the list and compiled the number one most cited papers from five different fields. And we're going to count down to the most cited one, starting with materials science. The fifth most cited paper on our list has 52,000 citations. It's all about a legend in the world of materials science: graphene. Yes, one of the most popular publications in the world describes a material you probably know very little about. You're more likely to be familiar with its cousin, graphite.
The stuff in pencils. But graphene is actually much cooler. It's a two dimensional version of graphite. It's literally one atom thick, with its carbon atoms arranged in a flat array of hexagons, like a drawing of a honeycomb pattern. And that shape is so bonkers that back in the 1940s, many scientists didn't believe it could physically exist, since a material that thin would be extremely unstable. But in 2004, researchers managed to make this impossibly thin carbon sheet IRL. That's when graphene really made a name for itself. And it turned out that honeycomb shape was a game changer, making it way more functional.
Materials get people's attention when they have specific properties that make them useful, like their ability to react with light or electricity, their lightness, or their strength. And graphene happens to check a lot of those boxes. Graphene is an incredibly useful material because of its conductivity, thinness, and stability. Let's start with conductivity. That quality gets people, and electrons, going because pretty much anything involving electricity needs a conductor, or a material that electrons can flow through. Usually conductors are metal, since the structure of metals already has plenty of space for its electrons to all move together.
It's like a crowd of marathon runners running along the same race course. But metal has some limitations. And graphene can do the same thing while being made entirely of carbon. The secret is its thin honeycomb atoms. That orientation lets electrons skate across the surface without interference from other atoms. So it's a great conductor. But it's also thin enough to be used in applications like nearly transparent coatings and smartphone screens. And it's strong enough to reinforce the parts of a tennis racket that take the most force when hitting the ball. Because of all the uses graphene has today, the paper has garnered
a lot of attention from scientists over the years. And that's what makes this graphene review the most popular publication in materials science. Now, you might expect that as this list goes on, the papers will get older. They'll have had more time to accumulate citations, after all. But sometimes, a new topic bursts onto the scene with so much enthusiasm behind it that it quickly moves up the ranks and surpasses everything that came before. That's what happened for the next entry in our list: the 2015 paper "Deep Residual Learning for Image Recognition".
This paper has more than 116,000 citations. And while it's not the first publication to describe how deep learning could be used in the context of images, it became a landmark paper in this fast-growing field by proposing a much easier way forward. It suggested an improvement to the way we build image models. And that's wildly popular because we see the world in images. So, naturally, they make up a lot of the world's data. Think about all of the images we take of space to explore it, and the images we take of organs in the human body to diagnose people.
Overall, our brains are pretty good at understanding what's going on in those images. …But not so good at doing that for thousands of images, at least not fast. And that's where computing for images comes in. Image recognition AIs are sort of modeled after the way our brains work, but can interpret images significantly faster. They're layered models known as neural networks, which "learn" patterns from lots and lots of examples. Starting with an image, each layer does a little math and passes information down the line to the next layer, and the next, until it generates the output we want. For example, a label for objects in the image.
And models with lots of layers tend to perform better on tasks like image recognition because of how complex images are. There's so much to learn that having all those layers gives them more opportunity to identify various details. But sometimes, when networks have tons of layers, they can lose track of some information on the way. Each layer of the network is transforming information. With, say, 100 layers, it gets to be like a game of telephone. To address that problem, the most popular paper in computer science proposes grouping the model's layers into chunks.
Now the model can take shortcut paths from one chunk to the next. And a model that uses these shortcuts also still maintains the direct path of information through each layer. We've just added another mechanism to make sure nothing gets lost. They call this method residual learning. And while image recognition isn't its only use, it's especially helpful for complex images that require really deep neural networks. For applications like self-driving cars and identifying tumors on scans, we don't want our neural networks losing any information. And those innovative uses for deep residual learning just keep coming,
which is why this paper is the most popular in all of computer science. Image recognition is all around us. But not all of the top cited papers have their utility right in the title. One example is the most-cited paper in physics, titled "Generalized Gradient Approximation Made Simple". Spoiler alert, it was not made simple. But enough physicists seem to have understood its value, because it's racked up more than 174,000 citations. That's because this paper made it easier to solve the huge problem of describing our tiniest components; the things that make up everything.
We're talking about atoms. They're hard to describe because they're just so dang small! Let's put it this way. When you sit at the top of a slide, we know that gravity will pull you down, even if you don't get a push. You have high potential energy at the top of the slide, and will move toward a place where you have lower energy. But atoms probably don't go down slides like we do. Although how cute would that be?! Little atoms going down the slides at the atom playground? At scales that tiny, matter starts acting …weird. The mass of particles like electrons is almost nothing. So if they were at the top of a teeny tiny slide,
we can't say for sure that they'd get pulled down to the bottom. And we can't really ignore the way electrons in atoms move, because figuring that out is critical to understanding how little things like drugs in our bodies work. So physicists came up with the density functional theory of quantum mechanics. This theory lets us estimate what electrons are doing in an atom, and how much energy they have. And from that, we can figure out how they interact outside the atom as well. Based on the charge of an atom's nucleus and on other atoms nearby, electrons might gather round an atom's nucleus more densely or disperse. And we can figure out when electrons disperse or crowd together using the density functional theory.
But these calculations were not simple, which is probably where this paper's title came from. Each molecule is its own unique environment. So to model electron density accurately, we needed to account for its specific arrangement of atoms. That is, until this paper came along, proposing a more streamlined way to approximate electron density. Now, instead of figuring out the nuances of each environment, we have an equation that depends only on physical constants. That's stuff like the speed of light, that won't change for each situation. So that kind of calculation just got a lot easier to do.
The outputs of these calculations have helped scientists understand how current moves through semiconductors, how to make better pharmaceuticals, even how to make more breathable fabric in your sneakers. It might not seem as flashy as the pop science you're used to, but there's such a wide range of applications that this publication is the most popular physics paper of all time. We're about halfway through our list of popular science. So it's time for a quick ad break. Thanks to our Presidents of Science, binorthedrunkdwarf, Charlie Stanley, and Harry Plumley for supporting this SciShow video!
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And we can only afford to keep up with those standards thanks to our incredible Presidents of Science and other patrons, who support us at patreon.com/SciShow. You might have noticed by now that the science that stands the test of time is the stuff that's incredibly useful. Like an optimized method for studying proteins that's used, almost unchanged from 1970 to this day. This paper has more than 250,000 citations with the catchy title, "Cleavage of structural proteins during the assembly of the head of bacteriophage T4." But, uh, that title isn't what made it so popular.
It racked up the citations because this paper introduces SDS-PAGE, a method used to separate proteins by their mass. Figuring out the size of molecules in our bodies is a major step in characterizing those molecules so doctors and researchers know what they're dealing with. If you've ever done a genetic testing kit, for example, you're looking for a variety of genes. The DNA encoding different genes have different, reliable sizes, which helps professionals identify them. But proteins aren't as straightforward as DNA.
Their charge and shape are a lot more variable. So we need a little extra help to identify their size. That's what sodium dodecyl sulfate, or SDS does. While this method was iterated on for a while, the most popular biology paper of all time is the one that locked it in and demonstrated it on the protein envelope of a virus. This version of SDS-PAGE is now a daily tool for labs diagnosing HIV, among other things. It separates proteins to help clinicians identify those associated with the virus. Which means that lifesaving techniques are the most popular biological science. As they should be.
Finally, the most cited paper ever as of April 2025 is in the field often called "the central science." It's chemistry. This paper, published in 1951, has more than 350,000 citations. That's because it describes a method that has become the basis for many tools that scientists use every day. In labs across the world, researchers are constantly measuring how much protein is in their samples. Those could be samples of soil, blood, or even your protein shake. I mean, you need to know if you're going to meet your macro goals for the day.
Even though the method in this paper, called the Lowry assay, is based on old technology, it's very sensitive and provides consistent results. So we still use it, often to fill in the gaps where newer assays won't work. And when I say "assay," I just mean a lab test that tells you what's in your sample. This assay gets its utility from the Folin Phenol reagent, which is usually clear. But under certain conditions, it can become oxidized, and turn bright blue. The Lowry assay creates those oxidizing conditions by adding copper to a sample of protein. Then, with more protein present, more of the reagent turns blue, creating a more intense blue color in the solution when protein concentration is high.
The day when your science teacher brought out the demonstration of color-changing solutions was always the best part of that class. And it's even better when the color tells you something about a person or environment's health. So even in the days of self-driving cars and virtual doctors' visits, this old and simple chemistry trick is still everyone's favorite thing in science. Maybe the most cited papers in history aren't the most exciting discoveries to the average person. But just like the foundation of a building, the foundations of science aren't always eye-catching and newsworthy.
Instead, they're often solid and reliable, stepping stones for thousands of other scientists to build with. Whether they're glamorous or not, these papers have been integral to creating the world we live in today. So now you can add us to the long list of people citing them. [♪ OUTRO]
