The Future of Batteries May Depend on What Happens at the Material Level
Battery technology is often discussed through the products it enables.
Electric vehicles need longer range.
Phones need longer battery life.
Power grids need storage for renewable electricity.
Artificial intelligence data centers may increasingly need batteries for backup power and grid management.
But most of the important competition is taking place somewhere consumers never see.
It is happening inside the battery.
Cathodes, anodes, electrolytes, separators and conductive materials ultimately determine how much energy a battery can store, how quickly it charges, how long it lasts and how safe it is.
The future of batteries may therefore depend less on a completely new battery invention and more on increasingly sophisticated control of materials.
Lithium-Ion Still Has Room to Improve
Lithium-ion batteries dominate portable electronics and electric vehicles for good reason.
The technology has improved enormously.
Costs have fallen.
Energy density has increased.
Manufacturing has reached tremendous scale.
But the basic chemistry involves trade-offs.
Increasing energy density can affect safety.
Faster charging can accelerate degradation.
Some cathode materials depend on expensive or geographically concentrated minerals.
Researchers are therefore improving nearly every component.
Even relatively small changes can matter when manufacturers produce millions of batteries.
The Anode Is Changing
Most lithium-ion batteries use graphite anodes.
Graphite performs reliably, but its storage capacity has physical limits.
Silicon is attractive because it can theoretically hold much more lithium.
The problem is expansion.
Silicon can change volume dramatically as lithium enters and leaves the material during charging.
That expansion can cause cracking and gradually destroy the electrical structure of the electrode.
Researchers are developing silicon-carbon composites, engineered particles and different binders to reduce the problem.
Graphene and other conductive carbon structures are also being investigated as ways to maintain electrical pathways around expanding silicon.
This illustrates a recurring theme in battery development.
The highest-performing material by itself is not always the best battery material.
What matters is how the entire structure behaves over thousands of charge cycles.
Solid-State Batteries Change the Electrolyte
Another major area of research involves solid-state batteries.
Conventional lithium-ion batteries generally use liquid electrolytes to move ions between electrodes.
Solid-state batteries replace that liquid with a solid material.
The potential advantages are significant.
A solid electrolyte could improve safety.
It may also make lithium-metal anodes more practical, increasing energy density.
But solid materials introduce new problems.
The interfaces between layers must remain in close contact.
Defects can create resistance.
Lithium can sometimes form structures that penetrate the electrolyte.
Manufacturing must be consistent across very large areas.
These challenges explain why solid-state batteries have been discussed for years without yet replacing conventional lithium-ion technology at scale.
The concept is compelling.
The materials engineering is difficult.
Sodium Offers a Different Trade-Off
Not every battery needs the maximum possible energy density.
That creates opportunities for other chemistries.
Sodium-ion batteries are particularly interesting because sodium is abundant and widely available.
Sodium ions are larger than lithium ions, which generally makes achieving the same energy density more difficult.
But lower material costs could make sodium attractive for stationary energy storage and selected vehicle applications.
Researchers are also investigating solid-state sodium systems.
A 2026 Chemical Science review highlighted the potential of anode-free solid-state sodium batteries while emphasizing unresolved problems involving electrolytes, interfaces and sodium deposition.
This reinforces the same point.
The chemistry may look promising on paper.
The challenge is controlling materials during real operation.
Battery Manufacturing Is Part of the Materials Problem
An experimental battery cell can be assembled carefully in a laboratory.
Commercial batteries need to be manufactured millions of times with nearly identical performance.
This makes production capability as important as chemistry.
A material that delivers excellent results but requires an expensive or unstable manufacturing process may never become commercially useful.
China currently has a major advantage in battery manufacturing because it has developed dense supply chains for materials, equipment and engineering.
The experience of EnerVenue illustrates this.
The U.S. battery startup originally considered manufacturing in Kentucky but ultimately opened its first commercial factory in Changzhou, China in 2026. The company cited the depth of the local supply chain and engineering workforce as important advantages.
The chemistry is only one part of building a competitive battery industry.
Recycling Is Becoming a Source of Materials
Battery growth also creates a resource problem.
Lithium, nickel, graphite and other materials must come from somewhere.
Mining will remain important.
Recycling can increasingly supplement it.
In September 2026, Nth Cycle announced a $1 billion supply agreement with Glencore involving lithium and other critical minerals produced from recycled batteries.
This changes how we should think about old batteries.
They are not simply waste.
They are concentrated sources of valuable materials.
As more electric vehicles reach the end of their lives, recycling could become an increasingly important component of battery supply chains.
There May Be Several Winning Batteries
People often talk about “the battery of the future.”
There may not be one.
Different applications require different characteristics.
Electric aircraft need extremely high energy density.
Electric vehicles need a balance of energy, cost, safety, charging speed and longevity.
Grid storage can tolerate heavier batteries if they are inexpensive and durable.
Consumer electronics prioritize size.
Industrial equipment may prioritize reliability.
This creates room for lithium-ion, sodium-ion, solid-state and potentially other chemistries to coexist.
Materials will determine where each technology makes sense.
Small Improvements Can Produce Large Changes
Battery breakthroughs are often presented as dramatic announcements.
Commercial progress is usually more incremental.
A better electrolyte improves cycle life.
A new cathode reduces dependence on an expensive metal.
An engineered anode increases energy density.
A conductive additive improves fast charging.
A new manufacturing process lowers cost.
Individually, these advances may not sound revolutionary.
Combined across millions of batteries, they can transform industries.
The future of energy storage therefore may not arrive as one sudden invention.
It may emerge from thousands of improvements in chemistry, interfaces, manufacturing and materials.
The products will get the attention.
But the real competition will continue taking place at the atomic and material level.
Can China Build an Independent AI Technology Stack?
Artificial intelligence depends on far more than artificial intelligence models.
It requires semiconductor manufacturing, processors, memory, networking equipment, data centers, electricity, software and enormous amounts of engineering talent.
That makes the technology unusually dependent on global supply chains.
The United States and China are now trying, in different ways, to reduce that dependence.
China faces the more difficult immediate challenge.
American restrictions have limited access to some of the most advanced AI processors and semiconductor manufacturing equipment.
In response, Chinese technology companies are accelerating an effort to create more of the AI technology stack domestically.
The question is no longer whether China wants an independent AI ecosystem.
It clearly does.
The harder question is how much of that ecosystem China can realistically build itself.
The Chip Problem
Advanced AI models require enormous amounts of computing power.
For much of the global technology industry, Nvidia processors have become the preferred hardware for these workloads.
China’s access to Nvidia’s most advanced chips has been restricted by U.S. export controls.
That has encouraged Chinese companies to develop alternatives.
Huawei is perhaps the most prominent example.
Its Ascend family of AI processors is designed to compete with Nvidia hardware in large-scale AI systems.
Alibaba is also developing its own silicon.
In September 2026, Alibaba introduced the Zhenwu V900, which the company described as its most powerful AI processor to date. Alibaba said the chip provides roughly three times the performance of its previous generation. The company is simultaneously expanding its AI models and data-center capacity.
This illustrates how China’s response is moving beyond simply replacing one imported chip.
Companies are trying to build entire computing systems around domestic processors.
Raw Performance Is Only Part of the Challenge
Producing a capable processor is difficult.
Building an ecosystem around it may be even harder.
Nvidia’s advantage does not come only from semiconductor performance.
Its CUDA software platform has been used by developers for years.
Thousands of applications, libraries and AI tools are optimized around Nvidia hardware.
Developers know how to use it.
Companies have already invested heavily in it.
Replacing that ecosystem requires software as well as silicon.
Huawei has developed its own CANN software platform for Ascend processors.
Chinese cloud companies are also modifying models and frameworks to run efficiently across domestic chips.
This work matters because a processor without a usable software ecosystem has limited value.
China therefore needs to compete at multiple levels simultaneously.
Scaling Can Compensate for Individual Chips
One of the interesting strategies emerging from China involves scale.
If an individual domestic processor cannot match the performance of the best American chip, companies can try connecting larger numbers of processors together.
Huawei has emphasized systems known as SuperPoDs, which combine thousands of AI processors through high-speed networking.
The approach can produce enormous aggregate computing power.
It also creates trade-offs.
Connecting more processors can require more electricity.
Networking becomes more complicated.
Efficiency may be lower.
Huawei’s latest roadmap still trails Nvidia’s most advanced chips in certain measures of individual processor performance, while domestic demand is reportedly strong enough that Huawei is prioritizing the Chinese market for its coming generations.
This demonstrates both the progress and the remaining limitations.
China can build increasingly capable systems.
But doing so efficiently remains important.
Manufacturing Is the Hardest Layer
Designing chips and manufacturing them are different problems.
The world’s most advanced processors depend on a highly specialized supply chain.
Taiwan Semiconductor Manufacturing Company produces many of the leading chips designed by American companies.
ASML in the Netherlands manufactures critical lithography equipment.
Japanese, American, European and Asian firms provide specialized chemicals, software and manufacturing tools.
China has invested heavily in domestic semiconductor manufacturing.
SMIC has made significant progress.
But restrictions on advanced manufacturing equipment make it difficult to match the most advanced production processes available elsewhere.
This is probably one of the largest obstacles to a fully independent Chinese AI stack.
Chip architecture can improve quickly.
Building an entire semiconductor manufacturing ecosystem takes much longer.
Models Are Another Part of the Stack
China’s AI effort extends beyond hardware.
Companies such as Alibaba, DeepSeek, Moonshot and others are developing increasingly capable models.
Alibaba recently said it plans to train an AI model with between five trillion and ten trillion parameters. Its current Qwen3.8-Max model contains roughly 2.4 trillion parameters.
Parameter count alone does not determine model quality.
But the scale of the plans indicates how aggressively Chinese companies are investing.
Open-source models have also played an important role.
If developers can use Chinese models, modify them and deploy them across domestic hardware, China gains more control over another layer of the technology stack.
Infrastructure Matters Too
AI requires data centers.
Alibaba has said it intends to expand its data-center capacity to approximately 20 gigawatts by 2032.
That is an enormous amount of infrastructure.
It also highlights another advantage China may have.
The country has spent decades building power generation, transmission systems, manufacturing capacity and industrial supply chains.
AI increasingly depends on those physical systems.
At the same time, the United States is also trying to expand domestic semiconductor manufacturing and AI infrastructure.
The result is not simply competition between Chinese and American software companies.
It is competition between two increasingly complete technology ecosystems.
Complete Independence May Be Unrealistic
The phrase “technology independence” can be misleading.
The semiconductor industry is one of the most globally integrated industries in the world.
American companies depend on Asian manufacturing.
Chinese firms depend on equipment and technologies developed outside China.
European companies depend on customers in both markets.
Investors remain connected as well.
Despite political efforts to separate technology ecosystems, financial ties between the American and Chinese AI industries remain substantial. Reuters reported that U.S. banks helped facilitate $17.2 billion in Chinese high-tech IPOs during 2026, while Chinese investment in U.S. semiconductor companies also remained significant.
This suggests that complete separation would be difficult and expensive for everyone.
The Goal May Be Resilience Instead
China may not need complete technological independence.
It may only need enough domestic capability that restrictions cannot prevent its AI industry from advancing.
That is a different objective.
A domestic processor does not necessarily have to outperform Nvidia’s best chip.
It needs to be good enough to train and operate competitive models.
Domestic semiconductor manufacturing does not have to replace every foreign supplier.
It needs to reduce the number of critical technologies China cannot obtain elsewhere.
That makes the effort more achievable.
China is making significant progress in processors, models, data centers and software.
Important gaps remain in advanced manufacturing and efficiency.
The final result may not be a completely independent AI ecosystem.
It could be something more practical.
Two partially separate technology systems may emerge, each with its own chips, models, software and infrastructure, while still depending on selected parts of a global supply chain.
That would represent a major change in the way the technology industry has operated for decades.
And it suggests that the future of AI will be shaped not only by which country creates the smartest model, but by which countries can build and sustain the infrastructure required to run them.
Has Quantum Computing Finally Reached Its Most Important Turning Point?
Quantum computing has been described as the future of computing for a surprisingly long time.
Researchers have built larger machines. Technology companies have announced new processors. Governments have invested billions of dollars. Predictions have ranged from revolutionary breakthroughs in drug discovery to the eventual disruption of modern encryption.
Yet practical quantum computing has remained difficult to define.
That may be beginning to change.
The most important developments in quantum computing today are no longer simply about adding more qubits.
The conversation is shifting toward a more demanding question.
Can quantum computers perform calculations that are genuinely useful, beyond the practical reach of classical computers, while producing results we can actually trust?
Recent work suggests researchers are getting closer.
Qubit Counts Are Not Enough
For years, one of the easiest ways to describe progress in quantum computing was to count qubits.
More qubits sounded better.
The problem is that quantum bits are extremely fragile.
Noise, heat, electromagnetic interference and imperfections in hardware can introduce errors into a calculation.
A computer with thousands of unreliable physical qubits is not necessarily more useful than one with fewer but better-controlled qubits.
This is why researchers increasingly focus on logical qubits.
A logical qubit combines multiple physical qubits in a way that allows the system to detect and correct errors.
That process is difficult, but it is essential if quantum computers are ever going to run long and complicated calculations reliably.
In July 2026, IBM and researchers from the University of Chicago announced a demonstration involving 70 logical qubits.
The researchers said the quantum system completed a calculation in approximately 15 minutes that leading classical simulation methods could not practically reproduce. Just as importantly, they developed methods to verify that the result was trustworthy.
That combination is significant.
Speed alone is not enough.
A calculation only becomes useful if we can have confidence in the answer.
Quantum Advantage Is Becoming More Practical
IBM describes 2026 as the year it expects the first examples of quantum advantage integrated with high-performance computing.
This definition is interesting because it does not imagine quantum computers replacing ordinary computers.
Instead, quantum processors become specialized components inside much larger computing systems.
A conventional supercomputer might prepare the data, manage the workflow and perform the calculations it handles best.
A quantum processor might take over one particular part of the problem where quantum techniques provide an advantage.
The result would then return to the classical system.
This is probably a more realistic future than the idea of a completely quantum data center.
We already use specialized processors this way.
Graphics processors accelerated certain calculations without replacing conventional CPUs.
AI accelerators now perform particular machine-learning workloads.
Quantum processors may eventually become another specialized computing resource.
The Problem of Verification
One of the strangest problems in quantum computing appears when the technology begins working as intended.
Suppose a quantum computer solves a calculation that would take an impractical amount of time on a classical computer.
How do you check the answer?
If a classical computer can easily reproduce the result, then the quantum computer may not have demonstrated much of an advantage.
If the classical computer cannot reproduce it, verification becomes difficult.
The recent IBM and University of Chicago experiment specifically addressed this problem.
Researchers designed a computation that included ways of testing whether the quantum system was operating with sufficiently high fidelity even when direct classical reproduction became impractical.
This may prove to be one of the most important parts of the transition toward useful quantum computing.
Businesses and scientists do not simply need answers.
They need answers they can trust.
What Problems Could Quantum Computers Solve?
Quantum computing is unlikely to improve every type of calculation.
The strongest potential applications remain problems that are naturally difficult for conventional computers.
Chemistry is an obvious example.
Atoms and molecules behave according to quantum mechanics.
Simulating complex molecular behavior on a classical computer can become extraordinarily difficult as the number of interacting particles increases.
A sufficiently capable quantum computer could potentially model some of these systems more naturally.
That could affect research into new materials, catalysts, pharmaceuticals and batteries.
Optimization is another area of interest, although researchers are still determining where quantum systems can provide meaningful advantages over improving classical algorithms.
The key word is meaningful.
The industry is moving away from demonstrations designed purely to prove that a quantum device can perform an unusual task.
The next phase is about solving problems scientists or businesses actually care about.
Classical Computing Keeps Improving Too
Quantum researchers face another challenge.
Classical computers do not stand still.
When a quantum computing team announces a new result, researchers specializing in classical algorithms often look for better ways to reproduce it.
Sometimes they succeed.
This competition has repeatedly reduced the apparent advantage of quantum demonstrations.
That is not a failure.
It forces both fields to improve.
It also means quantum advantage is unlikely to be one moment in history when quantum computers suddenly become superior to classical machines.
Advantage will probably emerge problem by problem.
Quantum methods may become useful first in a narrow class of scientific simulations, then expand into other areas as hardware improves.
Error Correction Remains the Big Challenge
Logical qubits are encouraging, but producing them remains expensive.
A reliable logical qubit can require multiple physical qubits plus additional hardware and software for detecting errors.
Large fault-tolerant machines will need dramatically more reliable components and better error-correction systems.
Researchers also need to manufacture these systems consistently.
Quantum computers operate under extremely demanding physical conditions.
Some architectures require temperatures close to absolute zero.
Others use trapped ions, photons or different approaches entirely.
There is still no universal agreement about which hardware architecture will dominate.
This remains an experimental industry.
From Science Project to Computing Platform
The most interesting shift may therefore be psychological.
The quantum industry is beginning to judge itself less by how unusual the hardware is and more by whether the hardware can produce useful computation.
That is a healthy transition.
Technology becomes important when the underlying machinery stops being the main story.
Most people using cloud computing do not know which processor performed their calculation.
Most people using artificial intelligence do not know exactly which data center served the model.
Quantum computing could eventually reach the same point.
Researchers would simply submit a problem to a computing system, and the system would determine whether the classical or quantum resources were best suited to a particular calculation.
We are not there yet.
But the emphasis on logical qubits, verification and integration with high-performance computing suggests that quantum computing may be entering a more mature stage.
The most important milestone may not be the largest quantum computer ever built.
It may be the moment when scientists stop asking whether a quantum computer works and start asking which real problems it should solve.
That turning point appears closer than it has before.
What Happens When Artificial Intelligence Becomes Something We Wear?
Most artificial intelligence today waits for us to ask a question. We open an application.
We type a prompt. We upload a document. The AI responds. Wearable technology could change that relationship.
A device worn on the body can collect information throughout the day. It can understand where someone is, what they are doing and, depending on the device, what they are seeing or how their body is responding.
Combine that information with increasingly capable artificial intelligence and something different becomes possible.
AI stops being something we visit.
It begins traveling with us.
Context Changes Everything
One of the largest limitations of today’s AI systems is context.
An AI assistant may be extremely capable, but it usually knows only what the user tells it or what information it has permission to access.
Wearables can provide another layer.
Smart glasses can potentially see the environment.
A smartwatch measures movement and heart rate.
A smart ring can track sleep and other physiological signals.
More advanced biosensors can monitor additional chemical or physical information.
AI can then interpret those streams of data.
That could make future assistants much more useful because they would understand not only the question but the circumstances surrounding it.
The difference is significant.
Instead of asking an AI what building is in front of you, the system might already know what you are looking at.
Instead of manually recording a workout, a wearable could recognize the activity.
Instead of reviewing a week’s worth of health measurements separately, AI could identify patterns across all of them.
Wearables Are Becoming a Serious Market
Wearable devices are already far beyond the experimental stage.
Researchers writing in Nature Sensors estimate that more than 400 million people own wearables capable of continuously collecting health information.
Smart rings provide an interesting example.
Oura reported selling 3.6 million devices during the 12 months ending June 2026, while revenue increased 74 percent year over year. The company is now pursuing a public offering that could value it at more than $15 billion.
Those numbers suggest consumers are interested in devices that quietly collect useful information without demanding constant attention.
Artificial intelligence could make the information considerably more valuable.
From Measurement to Interpretation
Most early wearables focused on measurement.
How many steps did you take?
What was your heart rate?
How long did you sleep?
Modern devices collect much richer information.
But data alone has limited value.
A person does not necessarily need another graph.
They need to understand what the graph means.
This is where AI could make wearables far more useful.
Artificial intelligence can potentially identify patterns that are difficult for a person to see across thousands of measurements.
A wearable might detect that changes in sleep, resting heart rate and activity occur together.
It might identify unusual behavior.
It could potentially recognize that a person’s normal baseline is changing.
The goal shifts from displaying measurements to explaining them.
Health May Be the Most Important Application
Healthcare is one of the areas where wearable AI could have the greatest impact.
Researchers are developing wearable and implantable biosensors capable of continuously monitoring physiological and biochemical signals.
A March 2026 review in npj Flexible Electronics highlighted the increasing integration of AI with wearable sensors for personalized and preventive health monitoring.
An even more ambitious area involves closed-loop wearable systems.
These devices would not simply collect information.
They could potentially detect a condition, use AI to interpret it and then trigger an appropriate therapeutic response.
Researchers writing in Nature Sensors in July described this as a path toward personalized and partially autonomous healthcare, although they emphasized that safety, clinical validation and human oversight remain essential.
That is an important qualification.
Consumer technology moves quickly.
Medicine requires evidence.
A device being able to measure something does not automatically mean physicians should base treatment decisions on it.
The Gap Between Wellness and Medicine
Wearables increasingly occupy an unusual space.
They are consumer products, but they collect medical-looking information.
A smartwatch might detect an irregular heartbeat.
A ring might monitor temperature changes.
A sensor might continuously measure glucose.
At what point does a wellness device become a medical device?
Regulators are beginning to confront that question.
Nature Biomedical Engineering noted in September that health-monitoring wearables increasingly blur the boundary between wellness products and medical tools, creating challenges for regulatory systems that were not designed for rapidly evolving AI-enabled devices.
Clinical researchers face a similar challenge.
Wearable data can provide continuous measurements outside hospitals and laboratories, something conventional medicine has historically struggled to obtain.
But the measurements need validation before they can become reliable clinical tools.
AI Glasses Add Another Kind of Context
Health sensors understand the body.
Smart glasses understand the environment.
Combining the two could produce something closer to a genuinely context-aware AI assistant.
Glasses could identify objects, provide translations, remember information or help navigate an unfamiliar location.
More experimental systems are going even further.
Research published in Nature Electronics this year described smart glasses capable of measuring electrical signals generated by eye movements, facial muscles and brain activity. Those signals were used to infer user intent and control aspects of the glasses.
This is still research, not a mainstream consumer product.
But it shows how much richer wearable interfaces could become.
Eventually, the interface may not always require a spoken command.
Privacy Becomes Personal
Continuous context creates obvious benefits.
It also creates an obvious problem.
A device that understands more about us must collect more information about us.
Wearable AI could potentially know where we go, what we see, who we speak with, how we sleep and how our bodies change.
That is considerably more intimate than a conventional computer.
Privacy therefore cannot simply be added later.
It may become one of the primary features consumers use to choose between products.
Processing more information directly on the device could help.
If sensitive information can be analyzed locally instead of constantly being transmitted to a cloud server, users may have more control.
Clear recording indicators, explicit permissions and transparent data policies will also matter.
The industry is already discovering that technical capability does not automatically create social acceptance.
Computing Could Become Less Visible
The most interesting outcome may be that wearable AI makes computing less noticeable.
Today, interacting with technology frequently means staring at a screen.
Wearables could move some of that interaction into the background.
A ring monitors without being opened.
Glasses can provide information while someone continues looking at the world.
A sensor can collect data without demanding attention.
AI can interpret information before the user even decides whether it is important.
The smartphone will probably remain with us for a long time.
But the next stage of personal computing may not be another device that replaces it.
It may be a collection of intelligent devices worn throughout the day.
If that happens, the biggest change will not simply be where we put our computers.
It will be the relationship we have with them.
Instead of waiting for us to tell them what is happening, they may increasingly understand the context themselves.
What Quantum Advantage Actually Means and Why It Matters
Quantum computing has spent many years occupying an unusual position in technology.
It is potentially revolutionary, enormously complicated and perpetually described as being just around the corner.
Researchers believe quantum computers could eventually solve certain problems that even the world’s most powerful conventional computers cannot handle efficiently.
Potential applications range from discovering new materials to simulating molecules, improving optimization and understanding complex physical systems.
But there has always been an important question.
When does quantum computing become genuinely useful?
Recent research may be bringing us closer to an answer.
In July 2026, IBM and researchers from the University of Chicago announced an experiment they say demonstrated quantum advantage while also addressing one of the field’s most difficult problems: how to trust the answer produced by a quantum computer when the calculation is too difficult for a conventional computer to verify.
That makes this a good time to ask what quantum advantage actually means.
Quantum Computers Work Differently
Traditional computers process information using bits.
A bit is either a zero or a one.
Quantum computers use quantum bits, or qubits.
Because of properties of quantum mechanics, qubits can represent information in ways conventional bits cannot. Quantum systems can also create relationships between qubits that allow certain calculations to be structured very differently.
That does not mean a quantum computer is simply a faster computer.
For most everyday tasks, a conventional computer remains the better machine.
You probably do not need a quantum computer to browse the internet, create a spreadsheet or run an accounting system.
The potential appears when dealing with certain types of problems whose complexity grows extremely quickly.
Simulating quantum chemistry is one example.
Nature itself operates according to quantum mechanics. Trying to simulate a complicated molecule on a conventional computer can therefore become extraordinarily difficult as the number of interacting particles increases.
A quantum computer may be better suited to the problem because it is itself a quantum system.
What Is Quantum Advantage?
The term “quantum advantage” is sometimes confused with another phrase, “quantum supremacy.”
Both broadly refer to situations where a quantum computer can perform a computation beyond the practical capability of classical computers.
But there is an important distinction in how the technology is now being discussed.
Researchers increasingly want quantum advantage to mean more than completing an artificial benchmark created specifically to make a quantum computer look impressive.
The goal is to perform useful computations that classical computers cannot realistically reproduce.
IBM’s current roadmap describes quantum advantage as workloads in which quantum computation provides a meaningful advantage when integrated with high-performance classical computing.
That last part matters.
The future may not involve quantum computers replacing classical computers.
It may involve the two working together.
The Problem of Trust
Quantum advantage creates a strange problem.
Suppose a quantum computer performs a calculation that would require an impractical amount of time for the world’s best classical computer.
How do we know the quantum computer got the answer right?
Normally we verify a calculation by checking it with another method.
But if the calculation is too difficult for classical computers, direct verification may not be possible.
That is one of the most interesting aspects of IBM and the University of Chicago’s recent work.
Researchers used a technique designed to demonstrate that the quantum computation was operating correctly even when classical computers could no longer reproduce the complete result.
The experiment encoded 70 logical qubits and completed its quantum calculation in approximately 15 minutes. IBM said reproducing the task using leading classical simulation methods would require an infeasible amount of computing time.
Just as importantly, the system included methods for validating the computation.
IBM described this as “trusted quantum computation.”
The distinction is important because an answer that cannot be trusted is not particularly useful, regardless of how difficult the calculation was.
Errors Remain the Central Challenge
Quantum computers are extremely sensitive machines.
Qubits can be affected by noise, temperature, electromagnetic interference and imperfections in the hardware.
As computations become longer and more complicated, small errors can accumulate.
That is why quantum error correction has become one of the central goals of the industry.
Instead of relying directly on individual physical qubits, researchers can combine multiple qubits to create what is known as a logical qubit.
The purpose is to detect and correct errors while preserving the information needed for the computation.
This comes at a cost.
Creating reliable logical qubits can require many physical qubits.
But progress in error correction is important because useful quantum computers will eventually need to perform complicated calculations without errors overwhelming the result.
IBM’s July demonstration is notable partly because it combined logical quantum circuits with verification methods in a computation that researchers argue reached beyond practical classical simulation.
Why Does Any of This Matter?
Quantum computing will become truly interesting when it begins solving problems scientists and businesses actually care about.
Chemistry is one of the most frequently discussed possibilities.
Designing a new battery material, catalyst or pharmaceutical can involve understanding interactions between atoms and electrons.
Those interactions are quantum mechanical.
Conventional computers use approximations because exact simulations can become impossibly complicated.
Quantum computers could eventually allow researchers to model certain systems more accurately.
Materials science offers similar possibilities.
In another 2026 result, IBM and Algorithmiq reported progress using quantum computers to simulate heterogeneous quantum materials while developing methods to validate results beyond straightforward classical verification.
That does not mean quantum computers are about to redesign every battery or discover new drugs next year.
It means researchers are beginning to explore problems closer to the areas where quantum computing could eventually provide practical value.
Quantum Will Probably Work With Classical Computing
One common misconception is that quantum computers will eventually replace conventional computers.
That is unlikely.
Quantum computing is highly specialized.
Classical computers are extremely good at the vast majority of computing tasks.
A more likely future is hybrid computing.
A conventional supercomputer might manage a large problem, prepare data and perform most calculations. A quantum processor could handle the specific portion of the problem where quantum techniques offer an advantage. The result would then return to the classical system.
IBM explicitly describes its quantum roadmap in this way, integrating quantum processors with high-performance computing rather than treating them as replacements.
This resembles other developments in computing.
Graphics processing units did not replace conventional processors. They became specialized components that are exceptionally good at particular workloads.
Quantum processors may eventually play a similar role.
A Milestone, Not the Finish Line
Quantum computing still faces enormous technical challenges.
Machines need to become more reliable.
Error correction must improve.
Useful algorithms need to be developed.
Hardware must scale.
Researchers also need to continue testing quantum advantage claims against improving classical algorithms.
That final point matters because classical computing does not stand still.
When researchers announce that a quantum system has performed a difficult calculation, classical computing experts often discover better ways to simulate part of the experiment.
The competition itself helps both fields improve.
Quantum advantage therefore should not be viewed as a single finish line that the industry crosses once.
It will probably emerge gradually across different types of problems.
The important development is that researchers are moving beyond simply asking whether quantum computers can perform unusual calculations.
They are beginning to ask whether those calculations can be useful, trusted and integrated with the computing systems we already have.
That is a much more meaningful test.
And if quantum computers continue passing it, the technology may finally begin moving from promise toward practical computing.
The Next Chapter of Blockchain May Look Very Different From the Last One
For most people, the word blockchain still brings one thing to mind: cryptocurrency.
That is understandable.
Bitcoin introduced blockchain technology to the world. It was followed by thousands of other cryptocurrencies, decentralized exchanges, NFTs and an entire industry built around digital assets.
The results have been mixed.
Some ideas became important innovations. Others produced extraordinary speculation followed by equally extraordinary collapses.
But blockchain may now be entering a different phase.
Instead of trying to build an entirely separate financial system, developers, banks, asset managers and even central banks are increasingly looking at how blockchain can improve parts of the financial system that already exists.
That could make the next chapter of blockchain considerably less dramatic than the first.
It could also make it more important.
Stablecoins Are Becoming Financial Infrastructure
Stablecoins provide one example of how blockchain is changing.
Unlike Bitcoin and many other cryptocurrencies, stablecoins are designed to maintain a relatively stable value, usually by tracking a conventional currency such as the U.S. dollar.
They originally became popular primarily because crypto traders needed a convenient way to move money between digital assets.
Their role is expanding.
Stablecoins are increasingly being considered for payments, international transfers, financial settlement and transactions involving tokenized assets.
The global stablecoin market reached approximately $311 billion in August 2026, according to CoinDesk Research. Euro-denominated stablecoins also reached a record level.
Perhaps more significant is who is becoming involved.
In September, a group of 21 financial institutions including Goldman Sachs, Bank of America, Citi and Deutsche Bank announced plans to launch a dollar-backed stablecoin in 2027. The group is also considering stablecoins tied to other major currencies.
That is very different from the early days of cryptocurrency.
Banks are no longer simply observing blockchain from the outside.
They are beginning to build with it.
Traditional Assets Are Moving On-Chain
Stablecoins are only part of the story.
Financial institutions are also experimenting with putting traditional assets directly onto blockchain networks.
This process is generally called tokenization.
A government bond, investment fund, stock or other asset can be represented by a digital token. Ownership and transactions can then be recorded using distributed ledger technology.
The underlying investment may remain completely conventional.
The infrastructure changes.
Tokenized real-world assets reached a record $34.7 billion in August 2026, according to CoinDesk Research. Tokenized equities alone reached approximately $4.45 billion.
Those numbers remain small compared with global financial markets.
But the direction is interesting.
Financial institutions are testing whether tokenization can simplify settlement, reduce reconciliation between different systems, automate certain processes and allow assets to move more efficiently.
This begins to look less like cryptocurrency replacing finance and more like blockchain becoming part of financial infrastructure.
Central Banks Are Joining the Experiment
Perhaps nothing illustrates the shift better than the involvement of central banks.
Blockchain originally attracted attention partly because Bitcoin demonstrated that digital value could move without relying on a central bank.
Now central banks themselves are developing blockchain-related infrastructure.
In September 2026, the European Central Bank launched Pontes, a system that allows wholesale transactions involving tokenized assets to settle using central bank money.
The service connects distributed ledger platforms with the Eurosystem’s existing settlement infrastructure.
The ECB also announced that it intends to invest a small portion of its own funds in tokenized securities so it can gain practical experience using the technology.
This does not mean central banks have embraced every aspect of cryptocurrency.
Far from it.
The ECB continues to express concerns about stablecoins, particularly when they could affect financial stability or monetary sovereignty. The Bank for International Settlements has raised similar concerns.
But that distinction is important.
Institutions can be skeptical of particular cryptocurrencies while still believing the underlying technology has useful applications.
Traditional Finance and Crypto Are Beginning to Overlap
For years, traditional finance and crypto were presented almost as competing systems.
That division is becoming harder to maintain.
Consider a future transaction involving a tokenized bond.
The bond could exist on a blockchain.
Payment could arrive through a regulated stablecoin, a tokenized bank deposit or central bank money connected to the blockchain.
A traditional bank might provide custody.
A conventional asset manager might own the investment.
A regulated financial market could supervise the transaction.
At that point, is it a traditional financial transaction or a blockchain transaction?
The answer may increasingly be both.
This blending of systems could be one of the most important developments in blockchain’s evolution.
The technology does not have to replace banks, exchanges or central banks to become useful.
It can become infrastructure that those institutions use.
Regulation May Help Determine What Survives
The next phase of blockchain will also be shaped heavily by regulation.
This is another major difference from its early history.
Cryptocurrency originally developed in an environment where regulation was limited, unclear or inconsistent.
That ambiguity helped experimentation move quickly.
It also created problems.
Consumers lost money. Companies failed. Fraud occurred. Regulators struggled to determine which rules applied to new types of assets.
The industry is gradually moving toward clearer frameworks.
In the United States, the GENIUS Act created federal rules for payment stablecoins in 2025. U.S. regulators provided additional guidance in 2026 about how different stablecoins should be treated.
Europe has implemented its MiCA regulatory framework, although regulators are still debating some of its requirements.
Regulation can slow innovation.
But when financial institutions begin using technology at scale, clarity can also make adoption easier.
Banks generally do not want to build billion-dollar businesses around rules that might suddenly change.
The Technology Could Become Less Visible
The early cryptocurrency industry was extremely visible.
Users had to understand wallets, exchanges, tokens, private keys and blockchain networks.
That may not be how the next generation develops.
A person could eventually own a tokenized investment fund without knowing that blockchain infrastructure is involved.
A company might make an international payment using technology that settles through a distributed ledger without an employee ever seeing a blockchain address.
A bank could move securities between institutions using tokenized systems while the customer continues using the same banking application.
The blockchain becomes invisible.
This happens frequently with successful technology.
Most people do not think about the internet protocols responsible for delivering an email.
They do not think about cloud infrastructure when watching a movie.
They simply use the service.
Blockchain may eventually develop in the same way.
A Quieter but Potentially Bigger Future
The next chapter of blockchain may not produce the same excitement as the first.
There may be fewer headlines about replacing the financial system and more discussions about settlement, custody, interoperability and financial infrastructure.
That sounds less revolutionary.
But it could ultimately affect far more transactions.
Stablecoins are becoming larger.
Traditional assets are being tokenized.
Banks are developing digital currencies.
Central banks are connecting their own payment systems to distributed ledgers.
None of this guarantees that blockchain will replace existing financial infrastructure.
It may not need to.
The more interesting possibility is that blockchain gradually becomes part of that infrastructure.
If that happens, one of the clearest signs of the technology’s success may be that people eventually stop talking about blockchain altogether.
Is Tokenization the Use Case Blockchain Has Been Waiting For?
Blockchain technology has gone through several identities.
First came Bitcoin and the idea of digital money outside the traditional banking system. Then came thousands of cryptocurrencies, decentralized finance, NFTs and a wide variety of blockchain applications.
Some became important. Others attracted enormous attention before disappearing almost as quickly as they arrived.
Now another use of blockchain is gaining momentum, and it looks very different from many of the applications that came before it.
Instead of using blockchain to create new types of assets, financial institutions are increasingly using it to represent assets that already exist.
The process is known as tokenization.
Stocks, bonds, money market funds, private credit, commodities and even real estate can potentially be represented by digital tokens recorded on a blockchain.
The idea is relatively simple.
The implications may not be.
Putting Existing Assets on the Blockchain
A tokenized asset is not necessarily a cryptocurrency.
That distinction is important.
A token can simply represent ownership of something that already exists.
Imagine a bond issued by a company or government. Traditionally, ownership and transactions are recorded through a network of exchanges, custodians, clearing organizations, banks and databases.
With tokenization, a digital token can represent that same financial interest on a blockchain.
The underlying economics of the asset may remain the same.
What changes is the infrastructure used to record, transfer and potentially manage ownership.
That can create some interesting possibilities.
Transactions could settle more quickly. Markets could potentially operate beyond conventional trading hours. Dividends, interest payments and other financial events could be automated through software.
Ownership could also become easier to divide.
A large asset that is difficult to trade might theoretically be divided into smaller digital units that can move between qualified investors more efficiently.
None of these ideas are entirely new.
What is changing is the number of serious financial institutions now experimenting with them.
Tokenization Is Moving Beyond Experiments
For years, tokenization was discussed as something financial markets might adopt eventually.
Increasingly, it is happening now.
According to blockchain analytics platform Dune, the value of tokenized real-world assets across several major categories has more than doubled over the past year, reaching more than $32 billion.
Fixed-income products represent more than half of that market.
That makes sense.
Government bonds and money market instruments are relatively standardized financial products, making them logical candidates for early tokenization.
Franklin Templeton provides one of the clearest examples.
The company’s Franklin OnChain U.S. Government Money Fund launched in 2021 and uses a public blockchain as part of its official system for recording share ownership.
By April 2026, Franklin Templeton said its BENJI tokenized fund platform had reached approximately $1.98 billion in assets.
The interesting part is not simply that the fund uses blockchain.
Its shares can be transferred between eligible investors, distributions can be handled on-chain, and ownership records can operate continuously instead of being confined entirely to conventional market infrastructure.
That begins to show what tokenization could actually change.
The Traditional Financial System Is Getting Involved
Perhaps the strongest indication that tokenization is becoming more than a crypto experiment is the institutions now building the infrastructure.
The Depository Trust & Clearing Corporation, better known as DTCC, sits at the center of the U.S. securities market.
In 2026, DTCC began preparing a tokenization service with participation from more than 50 financial firms, including major banks, asset managers, brokers, custodians and technology companies.
The objective is not to replace financial markets with cryptocurrencies.
It is to explore how regulated securities can operate using blockchain-based infrastructure.
Europe is moving in a similar direction.
In September 2026, the European Central Bank introduced Pontes, a system designed to connect blockchain-based financial markets with central bank money.
The ECB also announced plans to invest a small portion of its own funds in tokenized securities to gain direct experience with the technology.
That is a notable development.
Blockchain began as an attempt to create financial systems that did not require central banks.
Now central banks themselves are experimenting with blockchain infrastructure.
Why Would Markets Change?
The obvious question is why the financial system needs tokenization at all.
Today’s markets already process enormous transaction volumes successfully.
But the infrastructure underneath those markets can be surprisingly complicated.
A single securities transaction can involve brokers, exchanges, clearing organizations, custodians, payment systems and separate databases that must all agree on what happened.
Settlement can take time.
Different systems may operate during different hours.
Moving assets between institutions can require reconciliation across several platforms.
Tokenization could potentially combine some of those processes.
Ownership, transfer and settlement instructions can exist in the same digital environment.
Smart contracts could automate certain functions.
Markets could potentially operate continuously.
A tokenized security could also interact directly with tokenized cash or stablecoins, allowing both sides of a transaction to settle nearly simultaneously.
That could reduce some of the delays and counterparty risks that exist in traditional markets.
Stablecoins May Be Part of the Story
Tokenization also creates an interesting relationship between traditional finance and the cryptocurrency industry.
If assets move on blockchain networks, there needs to be a convenient way to pay for them on those networks.
Stablecoins are one possible solution.
Unlike cryptocurrencies whose prices fluctuate significantly, stablecoins are generally designed to track conventional currencies such as the U.S. dollar.
This means a future transaction could involve a tokenized bond being exchanged for a regulated dollar-backed stablecoin, with both assets moving on blockchain infrastructure.
Alternatively, central banks may provide their own settlement mechanisms.
The ECB’s Pontes initiative is an example of the second approach. It allows certain blockchain-based transactions to settle using central bank euros instead of private stablecoins.
The final financial system may include both.
Technology Does Not Eliminate the Difficult Problems
Tokenization is not a magic solution.
Putting an asset on a blockchain does not automatically make it liquid.
A token representing a building is still ultimately connected to a physical building. Someone must establish legal ownership, handle taxes, manage the property and enforce investor rights.
Financial regulations still apply.
Investors still need protection.
Custody and cybersecurity remain important.
Identity verification does not disappear.
There is also the risk of fragmentation.
If dozens of banks, exchanges and technology companies create separate tokenization systems that cannot communicate with each other, blockchain could reproduce some of the same complexity it is supposed to eliminate.
Technology alone does not solve coordination.
A Different Phase for Blockchain
For much of its history, blockchain has been associated with creating alternatives to the traditional financial system.
Tokenization suggests a different future.
Instead of replacing that system, blockchain may gradually become part of the infrastructure underneath it.
That is a less dramatic story than the idea of cryptocurrencies replacing banks.
But it may ultimately be a more important one.
The success of tokenization will depend on whether it can make markets faster, less expensive, more accessible and easier to operate.
If it cannot provide those advantages, existing financial infrastructure will remain difficult to displace.
If it can, the change may happen gradually and largely behind the scenes.
Investors may continue buying familiar stocks, bonds and funds without thinking very much about the technology recording those transactions.
And that may be the real test for blockchain.
The technology may have reached maturity when people begin using it without needing to know that they are using blockchain at all.
Contemplating 5G’s Impact on the Education Sector
If there was any good to come out of the COVID-10 pandemic, it was the light-bulb realization that 5G technology will stand to revolutionalize the way we work, play, socialize, and even learn.
Here is what you need to know about the latest and greatest version of the internet and how it will cause a huge and much-needed paradigm shift in the education sector.
Enhanced In-Class Learning
With ever-growing expectations for both students and educators, teachers are always looking for ways to maximize learning time. And while the typical 4G-operated school has enough connective strength to satisfy basic online functions and provide a fair share of digital applications throughout the school day, so much time is lost to poor wireless access. Fortunately, a 5G connection moves 100 times faster, a performance that permanently removes latency and makes buffering a bother of the past. This means faster download times and fewer classroom malfunctions all around.
Greater Developments in Virtual and Augmented Reality
It was once widely assumed that the classroom experience could never be replaced by virtual means. That was until 5G-powered VI and AI advancements began to make their mark and relieve many of the educational burdens that hinder the learning experience. One of the main differences that make these innovations superior to the ones fueled by 4G connections is the overall seamless application that removes lagging and extended loading times, ultimately creating a more realistic feel. Also, with greater connective strength to work with, more effective, engaging, and powerful applications can be brought to life.
With the use of virtual and augmented reality, educators can provide both hands-on and in-depth training that can effectively train mechanics, doctors, and especially college and school-aged students. For example, Froggipedia is an AR app developed to make biology class a lot easier and more interactive by walking students through a virtual frog dissection. While being guided through a detailed segment broken down into level-friendly segments, students will remain engaged as they learn about the frog’s internal organs, how those organs function, and how they develop from a mere egg to adulthood.
Massive Gains in Rural Areas
It is no secret that education is inaccessible in many areas of the world. In fact, this massive divide has created a huge gap in educational value in rural areas versus their urban and suburban counterparts. The convenience and affordability of 5G connection provide lasting solutions to equal out learning opportunities for those who have been left behind due to poverty and travel difficulties.
The growing teacher shortage in rural areas is a key thing to look at when it comes to the lack of quality education. With limited instructional hands on deck, these populations are in desperate need of resources to fill the gap and remain up to speed with the more advanced parts of the world. Not to fret, though, as 5G the network has answers for that issue as well. With a greater connection and a stronger reach outward, teachers can reach out to other educators when they require resources or a helping hand.
Also, hybrid learning has been and will continue to be monumental in the gap-solving process. Hybrid, or “blended” learning allows students to learn from a distance in a way that mimics an in-class experience. This style of learning makes it possible for impoverished students to stay educated despite any travel barriers that might keep them from being able to attend in-person courses.
Education From Anywhere
In today’s busy world, having the option to learn on the go is less of a luxury and more of a necessity. This is another invaluable benefit of the worldwide adoption of 5G as time has shown that personal hotspots and public Wifi connections often fall short when it truly counts.
Technology will continue to take off, making way for the next breed of learners, trailblazers, and educational innovators. It’s officially time to gear up for the inevitable 5G future.
How 5G Can Revolutionize The Way We Do Business
As the long, laborious rollout of 5G continues across the U.S., business leaders are contemplating what that might mean. And the expectation is that it will have wide-ranging implications across all sectors. Manufacturing, enabled by all manner of Internet of Things gadgetry, will become more efficient. Healthcare will become more accessible. Operations will improve, no matter the field; waste will be minimized and stakeholders of every stripe will benefit.
Feel free to wonder when it will be widely available. While Verizon continues to lead the other major carriers – AT&T and T-Mobile/Sprint – in the rollout race, Statista reported in mid-November 2022 that 5G was available in 296 U.S. cities, second-most in the world to China (356). But the expectation is that there will be 70 billion connected devices in the world by 2025, many of them powering businesses as they hurtle toward what has been called the Fourth Industrial Revolution, or Industry 4.0.
Fortune Business Insights projects that the global industry 4.0 market, valued at $101.69 billion in 2020, will mushroom to $337.1 billion by 2028. Information Age reporter Andrew Ross wrote in July 2022 that much of that growth will be due to smart manufacturing, and notes that the National Institute of Standards and Technology (NIST) defines that as “fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs.”
Further, Ross wrote that 5G will have a “unique” impact on industry, as it will “enable new operating models” and afford companies the opportunity to “build smart factories that can take advantage of the emerging tech that’s changing the industry.”
Andreas Muller, head of communication and network technology in the corporate sector for research and advance engineering at the German multinational engineering and technology company Robert Bosch GmbH, dug down even further in a piece on that firm’s website. He pointed out that 5G’s speed (20 times faster than 4G), low latency (one millisecond) and reliability (99.999 percent) open up all sorts of possibilities.
One aspect he emphasized was the ability of 5G to streamline production through the use of mobile operating panels and data glasses, and how autonomous vehicles could help resolve supply chain issues.
It is likely that healthcare will be impacted more dramatically than any other sector by 5G’s emergence – and not a moment too soon, given population growth and a looming physician shortage. We already saw during the height of the pandemic how much of a difference technology can make in this space, as telehealth assumed a key role (and will continue to have one, given the fact that 40 percent of those responding to a 2021 McKinsey poll indicated they will still use it.)
The implementation of 5G in healthcare will, according to a recent Business Insider piece, enable clinicians to better manage their time while reducing the waste of supplies (especially of critical medications) and equipment. And that will, in turn, lead to better care and better outcomes.
An example offered in the BI piece was that of a hospital in London that makes use of smart devices and monitoring systems, as well as AR/VR headsets that enable clinicians to collaborate more effectively on particularly challenging cases. In addition, AI-powered heat mapping allows patients to be tracked as they move about the facility.
The conclusion that can be drawn, then, is that while 5G penetration will help all of us as we go about our daily lives, its impact will be much more far-reaching. It will change lives. It might even save some. The possibilities are almost limitless.
3D Printing Has Become a Sporting Proposition
These days, it’s all about the steps – about counting how many one has taken in any given day, via one device or another. Have I reached the exalted threshold of 10,000? Or must I get myself off the couch and trudge around the block? More to the point, have I gone far enough to justify seconds on dessert?
All pertinent questions, and all questions many of us are asking ourselves, now that the wearables have become all the rage. Whether it’s a FitBit or smart watch or some other gadget, some 57 million Americans have taken to wearing devices that track not only their exercise rate but also their vital signs, sleep patterns or caloric intake. It is estimated that the wearable market is currently an industry worth between $25 billion and $33 billion annually, and one that will soar past $63 billion by 2027.
Such is Americans’ obsession with wearables, in fact, that some experts worry that it could lead to mental-health issues – that folks will equate their step count to their self-worth. But in a society that has grown too sedentary, too obese and too prone to chronic conditions like heart disease, cancer and diabetes, every step (whether literal or figurative) would appear to be a positive one.
As Johns Hopkins Medicine cardiologist Seth Martin said on that organization’s website:
“Fitness trackers are a great tool for heart health. Being more active and changing your habits is important, but it can be difficult. Tracking likely helps a lot of people when combined with a clear goal to shoot for.”
Given the popularity of such gadgetry, it should come as no surprise that innovation in the field is ongoing; everybody, it seems, wants to be on the cutting edge. Nor should it be a surprise that 3D printing is playing an increased role in this sector.
Consider the development in 2011 by a team from the University of Arizona’s School of Engineering of a health monitor that never needs to be recharged. This device, labeled “a completely new concept” by Arizona professor Philipp Gutruf, features 3D-printed mesh that can be fitted to different body parts, and which requires no adhesives. Moreover, it is embedded with miniature sensors that enable it to track different biometrics, and draws its power from radio signals – known as “far-field” energy harnessing, according to a UA news release.
Also notable were the developments at two Korean universities of biosensors that use 3D-printed frameworks – one made of a polyvinylidene fluoride (PVDF)/barium titanate (BTO) composite, the other of a sugar scaffold (eventually dissolved) which was injected with a silicone elastomer filler. The first of these sensors was piezoelectric, meaning that it draws its energy from applied stress. One example was when it was fitted into a taekwondo belt, which is constantly subjected to the blows of the competitors in that martial art.
Certainly 3D printers come into play in other aspects of sports, as when it is used to customize equipment that protects athletes from injury and leads to improved performance. Moreover, it has been found that such items reduce the industry’s carbon footprint and enable increased participation by disabled competitors.
But the greatest impact of all is in the day-to-day use of fitness trackers. Everyone seems to have one, and everyone stands to benefit. And those produced by 3D printers seem to have as much promise as any given their durability, flexibility and user friendliness.
