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Microscopic to Astronomic Knowledge Discovery

Blog Post | Scientific Research

Microscopic to Astronomic Knowledge Discovery

Compared to the unaided eye, humans see 100 million times more with microscopes and 375.5 billion times more with telescopes.

Summary: Human vision has always been limited, but through centuries of innovation—from early lenses to today’s most advanced microscopes and telescopes—we’ve extended our sight to both the atomic level and to distant galaxies. Instruments like cryo-electron microscopes and space telescopes have amplified our ability to explore the microscopic and cosmic, transformed our capacity for discovery.


Limitations in our sense of vision have driven us to invent and share new instruments of knowledge discovery. The unaided human eye can see a 100 micrometer (μm) object, about half the diameter of a human hair. Naked-eye stargazers can see a sufficiently bright celestial object up to 2.5 million light-years away. This was the extent of our vision until around 1600, when glassmakers in the Netherlands started to experiment with shaping lenses. The results of their experiments have given us the astonishing power to see millions and even billions of times more.

Microscopes

Zacharias Janssen developed the first microscope in 1595. It could magnify objects 3 to 10 times their size. By the 1800s, magnification had improved to 1,000 times. A significant advancement occurred in 1931 with the use of the transmission electron microscope (TEM), which could magnify up to 1 million times. TEMs range in cost from $100,000 to $10 million or more, depending on their features. The most advanced TEM, located at Lawrence Berkeley National Laboratory, costs $27 million. This microscope can achieve a resolution of half the width of a hydrogen atom, making it the most powerful microscope in existence.

The scanning electron microscope (SEM), developed in 1937, had lower magnification (approximately 100,000 times) but could produce three-dimensional images. From the 1980s to the present, cryo-electron microscopy (cryo-EM) has increased magnification up to 5 million times; scanning probe microscopes—using methods such as atomic force microscopy (AFM) and scanning tunneling microscopy (STM)—have increased magnification up to about 100 million times.

However, magnification is only marginally meaningful unless paired with resolution, since empty magnification yields no useful details. For true improvement, resolution is critical.

The light microscopes of the 1800s could see 500 times more, at 0.2 μm. In the 1930s, electron microscopes improved resolution to 0.05 nanometers (nm), an increase to 2 million times magnification. Today’s cryo-EM/atomic microscopes have a resolution of 0.001 nm, which is 100 million times that of the unaided human eye.

Telescopes

Hans Lippershey is credited as the inventor of the first telescope, created in 1608. His instrument could magnify 3 times. After learning of the innovation the following year, Galileo built his own version and increased magnification to 30 times, yielding a 10 times improvement in one year. Telescopes have continued to improve in light-gathering power and resolution. In the 1700s and 1800s, innovations by Isaac Newton and others improved both of these factors. The Herschel reflecting telescope, produced in 1789, had 20 times better resolution and over 1,000 times better light-gathering power than the Galileo design. The Great Dorpat Refractor, built by Joseph Fraunhofer and completed in 1824, was the first modern, achromatic, refracting telescope. While the Herschel had a larger aperture, the Dorpat had much higher-quality lenses, yielding sharper and more measurable images.

The Hooker telescope was built in 1917 and offered 3 times resolution and 105 times improvement in light-gathering power over the Dorpat. The next major advancement was the creation of the Hubble telescope in 1990. As a space-based telescope 340 miles above the Earth’s atmosphere, it was 10 times sharper and more stable than its Earth-based counterparts. The James Webb Space Telescope (JWST), launched in 2021, has a much larger mirror (6.5 meter vs. 2.4 meter), giving it vastly greater light-gathering power, and it is optimized for the infrared spectrum.

The Extremely Large Telescope (ELT) is scheduled to go online in 2030. Compared to the JWST, the ELT is 6 times larger, giving it dramatically higher light-gathering power for ground-based observations. The ELT will achieve 14 times sharper resolution (0.005 arcsec vs. JWST’s 0.07 arcsec), especially when using adaptive optics. The JWST retains the edge in overall precision due to its space-based stability and optimized infrared systems, but the ELT will surpass it in spectroscopy, exoplanet imaging, and capturing the detailed structures of distant galaxies.

From the unaided human eye to the ELT, angular resolution will be 12,000 times better and light-gathering power will be 31 million times better. This gives the ELT a combined observational capability approximately 372.5 billion times greater than the unaided human eye. This staggering difference reflects advances in both resolution and light-gathering power, enabling us to study the universe in ways that were unimaginable just a few centuries ago.

Microscopes and telescopes are instruments of knowledge discovery. There has never been a better time to be alive if you want to zoom in and look at an individual 0.05 nm atom or zoom out and look at the edge of the universe, some 46.5 billion light-years away from Earth.

Find more of Gale’s work at his Substack, Gale Winds.

University of Rochester | Scientific Research

Lower-Cost Imaging Sees Through Deep Tissue and Fog

“From helping doctors detect cancer to guiding self-driving cars through traffic, many modern imaging systems rely on near-infrared light, producing a crisp picture when visible light would scatter and yield a blurry picture. But near-infrared systems struggle when light passes through materials like deep tissue or dense fog, succumbing to the same scattering effect where photons deviate from their path. Existing near-infrared imaging systems also rely on specialized detectors made from expensive materials, limiting their affordability and widespread use.

University of Rochester researchers have now developed a lower-cost imaging system that overcomes both challenges. Using inexpensive silicon-based detectors, the system quickly converts near-infrared light to visible light while producing clearer images through these difficult environments. The technology, outlined in a recent Nature Communications paper, uses a technique called time-gating that the laboratory of Robert Boyd, the William F. Krupke Distinguished Professor in Optics, has spent more than a decade refining."”

From University of Rochester.

Rockefeller University | Scientific Research

First Complete Zebra Finch Genome Reveals Hidden Genes

“The zebra finch is one of the best-studied songbirds, and a model for understanding the biology of vocal learning. Now, researchers have produced the first complete genome assembly of the species, revealing thousands of previously hidden genes and chromosome structures.

It is the first songbird genome to capture every chromosome from end to end while distinguishing the DNA inherited from each parent, making it the most complete and accurate bird genome assembled to date. Part of a package of 10 papers being published simultaneously in Cell and Cell Genomics, this study, which appears in Cell, reveals 2,710 previously unknown genes, shows that birds and mammals share an organized centromere architecture, and resolves tiny chromosomes that provide clues to the evolution of vertebrates and vocal learning.”

From Rockefeller University.

New Scientist | Scientific Research

AI Helps Disprove 87-Year-Old Mathematical Conjecture

“A mathematician has cracked an 87-year-old conundrum with the help of AI and announced the solution unceremoniously in a tweet. The finding is the most difficult mathematical problem yet solved by AI, say experts.

Levent Alpöge at Harvard University wrote on X on 19 July that the Jacobian conjecture – which academics have spent decades trying to prove was true – is actually false, giving a tiny, 216-character counterexample as proof…

The Jacobian conjecture – which suggests that a certain type of mathematical function would also work in reverse – was formally set out by Ott-Heinrich Keller in 1939. It was also on an influential list of 18 fiendishly difficult problems for mathematicians to tackle in the 21st century drawn up by Stephen Smale in 1998…

Abhishek Saha at Queen Mary University of London says AI’s recent advances in mathematics, such as the OpenAI model that recently cracked a decades-old conjecture by Paul Erdős, have been surprising, but this latest finding has stepped things up significantly.

'Probably this is the biggest conjecture that AI has played a significant role [in proving or disproving] so far in mathematics,' he says. 'This is a pretty big deal. AI has [made] remarkable progress in the last year.'

The single line of mathematics posted by Alpöge was simple to verify and many mathematicians have already done so, says Saha. Now the big question is how it was done.”

From New Scientist.

Blog Post | Economic Growth

How to Escape the Productivity Slump

Removing policy barriers can unleash a new era of productivity and abundance.

Summary: For the past half-century, much of the developed world has experienced a puzzling slowdown in productivity growth—the rate at which workers and businesses become more efficient over time. While digital technologies have advanced at a remarkable pace, innovation in the physical world has slowed considerably. The problem is not a lack of scientific breakthroughs or a shortage of good ideas. Rather, it is a failure to translate discoveries into products, infrastructure, and services that improve everyday life. This slowdown is largely the result of policy choices. By reforming outdated permitting systems, using innovation incentives such as R&D prizes and Advance Market Commitments, and reducing barriers created by protected local monopolies, we can accelerate the spread of new technologies and usher in a new era of prosperity.


In a previous exploration of the housing affordability crisis, I observed a sobering reality: artificial scarcity is often a policy choice. We have placed arbitrary limits—mostly through local governments—on our ability to build homes, driving up costs and restricting opportunity. But this pattern of self-imposed constraint does not stop at the edges of our neighborhoods. It extends into the institutions and policies that shape economic growth. It is one of the primary reasons why, despite living in an age of extraordinary digital innovation, we remain stuck in a decades-long productivity slump.

Economists often measure technological progress using a concept called Total Factor Productivity (TFP). In simple terms, TFP measures how efficiently an economy turns labor, land, and capital into goods and services. When TFP rises, society discovers better ways to produce more with the same resources.

From the 1920s through the early 1970s, TFP in the United States and much of the developed world grew at more than 2 percent per year. This was the era that gave us commercial aviation, widespread electrification, antibiotics, and the Apollo program. The physical world was transformed in a single generation.

Since the early 1970s, however, productivity growth has slowed dramatically to less than 1 percent in most years. As investor Peter Thiel famously quipped, “We wanted flying cars; instead, we got 140 characters.” Digital technologies have advanced rapidly, while progress in energy, transportation, infrastructure, and advanced manufacturing has been far slower. We can send vast amounts of information across the globe in milliseconds, yet we often struggle to build major infrastructure projects on time or on budget.

A 2020 paper by Nicholas Bloom and co-authors argues that good ideas are getting harder to find – that is, more investment in research and development has become necessary for each new patentable idea. However, more recent research by Teresa Fort and co-authors (currently in working paper form) suggests that this is not the case. The Bloom et al. result may, in fact, be an artifact of focusing on manufacturing firms, which were dominant from about 1970 to 1990. Fort and her co-authors show that patenting and innovation have shifted in recent decades, becoming dominated by firms in information, management, and professional services.

Because manufacturing is a physical process, it is much more likely to be subject to, for example, environmental regulations, whereas an IT firm operates in a much less regulated sector. So, our relative stagnation may not be the result of a scientific drought after all. Universities and research laboratories continue to produce remarkable discoveries. We are not failing at invention; we are failing at diffusion, the process of turning new discoveries into widely used products and services.

The Diffusion Deficit and the Permitting Veto

Innovation does not benefit society until it escapes the laboratory and enters the marketplace. The journey from a peer-reviewed paper to a consumer-ready product is long, expensive, and uncertain. Over time, policymakers have added layer upon layer of regulatory complexity to that journey.

Physical innovation requires physical construction. New technologies need testing facilities, advanced laboratories, semiconductor fabrication plants, energy infrastructure, and transportation networks. Yet building almost anything of significance in the modern West often requires navigating years of environmental reviews, public-comment periods, and multi-agency approvals.

Laws such as the National Environmental Policy Act (NEPA) and state-level counterparts such as the California Environmental Quality Act (CEQA) were originally intended to prevent environmental harm. Over time, however, they have increasingly become tools for the delay of progress. Because these laws frequently allow opponents to challenge projects on procedural grounds, they have contributed to what political scientist Francis Fukuyama calls a “vetocracy”—a system in which many actors can block decisions but few can make them. Average NEPA environmental impact statements now take almost four years to complete, with many extending far beyond a decade. Thankfully, the median is a bit shorter, but still about 2.5 years.

Consider the recent push to reshore semiconductor manufacturing. While the government has allocated billions of dollars in subsidies to build these vital factories, the physical construction is bottlenecked by years of permitting and environmental reviews. A state-of-the-art fabrication plant (commonly called a “fab”) that takes 18 months to build in Taiwan or South Korea can take three to five years just to obtain a permit in the United States.

The result is predictable: projects take longer, cost more, and become less attractive to investors. Even when governments provide subsidies for strategic industries such as semiconductor manufacturing, years of permitting can slow implementation. Time is money, and prolonged regulatory uncertainty discourages investment in capital-intensive industries.

The solution is straightforward, even if politically difficult. Critical infrastructure, advanced manufacturing facilities, and research laboratories should face streamlined approval processes. If projects satisfy clearly defined environmental and safety standards, they should be approved in months rather than years.

Pull Mechanisms: R&D Prizes and Commercialization

Reducing regulatory barriers is only part of the solution. We must also rethink how innovation is encouraged and financed.

In addition to corporate financing, most governments try to support innovation through “push” funding. Researchers receive grants to conduct experiments, purchase equipment, and explore new ideas. This model, some economists argue, can be effective for basic science, especially when commercial applications may be years away.

Commercialization presents a different challenge. Many promising technologies fall into what innovators call the “Valley of Death” – the difficult period between a successful laboratory demonstration and a commercially viable product. At this stage, development costs rise sharply while uncertainty remains high.

That is where “pull” mechanisms become valuable. Instead of paying for research inputs, policymakers reward successful outputs. An Advance Market Commitment (AMC), for example, guarantees that a buyer will purchase a product if it is successfully developed. Rather than funding every possible approach, the sponsor commits to paying for results.

Economist Michael Kremer helped pioneer this approach through vaccine development programs. More recently, Operation Warp Speed demonstrated its effectiveness. The government did more than fund vaccine research; it guaranteed large future purchases for successful vaccines. By reducing market risk, policymakers encouraged firms to accelerate development and manufacturing simultaneously. The result was one of the fastest vaccine-development efforts in history.

Consider other approaches. Throughout history, prizes have also stimulated innovation. The Longitude Prize helped solve a critical navigation problem for maritime trade, while the Ansari X Prize helped launch the private spaceflight industry. Pull mechanisms align private incentives with public goals by rewarding success rather than political connections or grant-writing skill.

Breaking Local Monopolies and Regulatory Capture

When people hear the word “monopoly,” they often think of large technology companies. Yet some of the most significant barriers to innovation exist at the local level.

The electric utility sector provides a clear example of how regulatory design shapes technological adoption. Because most utilities operate as regulated monopolies with government-guaranteed rates of return on capital investments, their business model relies on continuous, large-scale infrastructure growth. 

Put simply, utilities make more money the bigger power plants and power lines they build, so they usually prefer huge projects over things like rooftop solar panels that let people generate their own power without the utility having to build as much infrastructure.

Decentralized energy technologies—such as local battery storage, micro-grids, and advanced management software—directly threaten this model by optimizing the existing grid and reducing the need for new capital projects. As a result, studies from the MIT Energy Initiative and industry financial analysts indicate that utilities frequently leverage legacy regulatory processes to delay or block these decentralized innovations from integrating into the wider network.

Similar dynamics exist elsewhere. State dealership franchise laws frequently restrict direct-to-consumer automobile sales, making it more difficult for new manufacturers to enter the market. Occupational licensing requirements now affect roughly one-fifth of American workers and can create barriers to entry that limit competition and labor mobility.

Innovation depends on what economist Joseph Schumpeter called “creative destruction” – the replacement of older, less efficient business models with better ones. When established interests use regulation to shield themselves from competition, they slow technological adoption and reduce future productivity growth. Encouraging competition and reducing regulatory barriers at the state and local level would help accelerate the diffusion of new ideas throughout the economy.

Choosing Abundance

The productivity slowdown is not an immutable law of nature. It is, at least in part, the consequence of policy choices. Human ingenuity remains as powerful as ever. We have more scientists, more capital, and better tools than any previous generation. The challenge is not generating ideas; it is allowing those ideas to spread.

By streamlining permitting processes, expanding the use of R&D prizes and Advance Market Commitments, and reducing barriers created by protected local monopolies, we can accelerate innovation in the physical world.

An additional one or two percentage points of annual productivity growth may sound insignificant. Yet when compounded over decades, the effects are transformative. Higher productivity means higher incomes, better health outcomes, more abundant energy, and greater opportunities for future generations. The ideas already exist. The question is whether we will allow them to flourish.