Science
Saltwater flooding is a serious fire threat for EVs and other devices with lithium-ion batteries
Xinyu Huang, University of South Carolina
Flooding from hurricanes Helene and Milton inflicted billions of dollars in damage across the Southeast in September and October 2024, pushing buildings off their foundations and undercutting roads and bridges. It also caused dozens of electric vehicles and other battery-powered objects, such as scooters and golf carts, to catch fire.
According to one tally, 11 electric cars and 48 lithium-ion batteries caught fire after exposure to salty floodwater from Helene. In some cases, these fires spread to homes.
When a lithium-ion battery pack bursts into flames, it releases toxic fumes, burns violently and is extremely hard to put out. Frequently, firefighters’ only option is to let it burn out by itself.
Particularly when these batteries are soaked in saltwater, they can become “ticking time bombs,” in the words of Florida State Fire Marshall Jimmy Patronis. That’s because the fire doesn’t always occur immediately when the battery is flooded. According to the National Highway Traffic Safety Administration, about 36 EVs flooded by Hurricane Ian in Florida in 2022 caught fire, including several that were being towed after the storm on flatbed trailers.
Many consumers are unaware of this risk, and lithium-ion batteries are widely used in EVs and hybrid cars, e-bikes and scooters, electric lawnmowers and cordless power tools.
I’m a mechanical engineer and am working to help solve battery safety issues for our increasingly electrified society. Here’s what all owners should know about water and the risk of battery fires: https://www.youtube.com/embed/gWkEGEbpqFc?wmode=transparent&start=10 Emergency responders handle EVs that were immersed in saltwater during Hurricane Ian in Florida in 2022, including some that ignited.
The threat of saltwater
The trigger for lithium-ion battery fires is a process called thermal runaway – a cascading sequence of heat-releasing reactions inside the battery cell.
Under normal operating conditions, the probability of a lithium-ion cell going into thermal runaway is less than 1 in 10 million. But it increases sharply if the cell is subjected to electrical, thermal or mechanical stress, such as short-circuiting, overheating or puncture.
Saltwater is a particular problem for batteries because salt dissolved in water is conductive, which means that electric current readily flows through it. Pure water is not very conductive, but the electrical conductivity of seawater can be more than a thousand times higher than that of fresh water.
All EV battery pack enclosures use gaskets to seal off their internal space from the elements outside. Typically, they have waterproof ratings of IP66 or IP67. While these ratings are high, they do not guarantee that a battery will be watertight when it is immersed for a long period of time – say, over 30 minutes.
Battery packs also have various ports to equalize pressure inside the battery and move electrical power in and out. These can be potential pathways for water to leak into the pack enclosure. Inadequate seal ratings and manufacturing defects can also enable water to find its way into the battery pack if it is immersed.
How water leads to fire
All batteries have two terminals: One is marked positive (+), and the other is marked negative (-). When the terminals are connected to a device that uses electricity to do work, such as a light bulb, chemical reactions occur inside the battery that cause electrons to flow from the negative to the positive terminal. This creates an electric current and releases the energy stored in the battery.
Electrons flow between a battery’s terminals because the chemical reactions inside the battery create different electrical potentials between the two terminals. This difference is also known as voltage. When saltwater comes into contact with metal battery terminals with different electrical potentials, the battery can short-circuit, inducing rapid corrosion and electric arcing, and generating excessive current and heat. The more conductive the liquid is that penetrates the battery pack, the higher the shorting current and rate of corrosion.
Rapid corrosion reactions within the battery pack produce hydrogen and oxygen, corroding away materials from metallic terminals on the positive side of the battery and depositing them onto the negative side. Even after the water drains away, these deposited materials can form solid shorting bridges that remain inside the battery pack, causing a delayed thermal runaway. A fire can start days after the battery is flooded.
Even a battery pack that is fully discharged isn’t necessarily safe during flooding. A lithium-ion cell, even at 0% state of charge, still has about a three-volt potential difference between its positive and negative terminals, so some current can flow between them. For a battery string with many cells in a series – a typical configuration in electric cars – residual voltage can still be high enough to drive these reactions.
Many scientists, including me and my colleagues, are working to understand the exact sequence of events that can occur in a battery pack after it is exposed to saltwater and lead to thermal runaway. We also are looking for ways to help reduce fire risks from flooded battery packs.
These could include finding better ways to seal the battery packs; using alternative, more corrosion-resistant materials for the battery terminals; and applying waterproof coatings to exposed terminals inside the battery pack.
What EV owners should know
Electric cars are still very safe to drive and own in most circumstances. However, during extreme situations like hurricanes and flooding, it is very important to keep EV battery packs from becoming submerged in water, particularly saltwater. The same is true for other products that contain lithium-ion batteries.
For EVs, this means evacuating cars out of the affected zone or parking them on high ground before flooding occurs. Smaller objects, like e-bikes and power tools, can be moved to upper floors of buildings or stored on high shelves.
If you own an EV that has been submerged in water for hours to days, particularly in saltwater, public safety experts recommend treating it as a fire hazard and placing it on open ground away from other valuable property. Do not attempt to charge or operate it. Contact the manufacturer for an inspection to assess battery damage.
Often, a flooded electric vehicle will need to be towed away for further inspection. However, since thermal runaway can occur well after submersion, the car should not be moved until it has been professionally assessed.
Xinyu Huang, Associate Professor of Mechanical Engineering, University of South Carolina
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The science section of our news blog STM Daily News provides readers with captivating and up-to-date information on the latest scientific discoveries, breakthroughs, and innovations across various fields. We offer engaging and accessible content, ensuring that readers with different levels of scientific knowledge can stay informed. Whether it’s exploring advancements in medicine, astronomy, technology, or environmental sciences, our science section strives to shed light on the intriguing world of scientific exploration and its profound impact on our daily lives. From thought-provoking articles to informative interviews with experts in the field, STM Daily News Science offers a harmonious blend of factual reporting, analysis, and exploration, making it a go-to source for science enthusiasts and curious minds alike. https://stmdailynews.com/category/science/
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Last Updated on February 10, 2026 by Daily News Staff
Valerie Thomas is a true pioneer in the world of science and technology. A NASA engineer and physicist, she is best known for inventing the illusion transmitter, a groundbreaking device that creates 3D images using concave mirrors. This invention laid the foundation for modern 3D imaging and virtual reality technologies.
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Beneath the Waves: The Global Push to Build Undersea Railways
Undersea railways are transforming transportation, turning oceans from barriers into gateways. Proven by tunnels like the Channel and Seikan, these innovations offer cleaner, reliable connections for passengers and freight. Ongoing projects in China and Europe, alongside future proposals, signal a new era of global mobility beneath the waves.
For most of modern history, oceans have acted as natural barriers—dividing nations, slowing trade, and shaping how cities grow. But beneath the waves, a quiet transportation revolution is underway. Infrastructure once limited by geography is now being reimagined through undersea railways.
Undersea rail tunnels—like the Channel Tunnel and Japan’s Seikan Tunnel—proved decades ago that trains could reliably travel beneath the ocean floor. Today, new projects are expanding that vision even further.
Around the world, engineers and governments are investing in undersea railways—tunnels that allow high-speed trains to travel beneath oceans and seas. Once considered science fiction, these projects are now operational, under construction, or actively being planned.
Undersea Rail Is Already a Reality
Japan’s Seikan Tunnel and the Channel Tunnel between the United Kingdom and France proved decades ago that undersea railways are not only possible, but reliable. These tunnels carry passengers and freight beneath the sea every day, reshaping regional connectivity.
Undersea railways are cleaner than short-haul flights, more resilient than bridges, and capable of lasting more than a century. As climate pressures and congestion increase, rail beneath the sea is emerging as a practical solution for future mobility.
What’s Being Built Right Now
China is currently constructing the Jintang Undersea Railway Tunnel as part of the Ningbo–Zhoushan high-speed rail line, while Europe’s Fehmarnbelt Fixed Link will soon connect Denmark and Germany beneath the Baltic Sea. These projects highlight how transportation and technology are converging to solve modern mobility challenges.
The Mega-Projects Still on the Drawing Board
Looking ahead, proposals such as the Helsinki–Tallinn Tunnel and the long-studied Strait of Gibraltar rail tunnel could reshape global affairs by linking regions—and even continents—once separated by water.
Why Undersea Rail Matters
The future of transportation may not rise above the ocean—but run quietly beneath it.
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Special Education Is Turning to AI to Fill Staffing Gaps—But Privacy and Bias Risks Remain
With special education staffing shortages worsening, schools are using AI to draft IEPs, support training, and assist assessments. Experts warn the benefits come with major risks—privacy, bias, and trust.
Seth King, University of Iowa
In special education in the U.S., funding is scarce and personnel shortages are pervasive, leaving many school districts struggling to hire qualified and willing practitioners.
Amid these long-standing challenges, there is rising interest in using artificial intelligence tools to help close some of the gaps that districts currently face and lower labor costs.
Over 7 million children receive federally funded entitlements under the Individuals with Disabilities Education Act, which guarantees students access to instruction tailored to their unique physical and psychological needs, as well as legal processes that allow families to negotiate support. Special education involves a range of professionals, including rehabilitation specialists, speech-language pathologists and classroom teaching assistants. But these specialists are in short supply, despite the proven need for their services.
As an associate professor in special education who works with AI, I see its potential and its pitfalls. While AI systems may be able to reduce administrative burdens, deliver expert guidance and help overwhelmed professionals manage their caseloads, they can also present ethical challenges – ranging from machine bias to broader issues of trust in automated systems. They also risk amplifying existing problems with how special ed services are delivered.
Yet some in the field are opting to test out AI tools, rather than waiting for a perfect solution.
A faster IEP, but how individualized?
AI is already shaping special education planning, personnel preparation and assessment.
One example is the individualized education program, or IEP, the primary instrument for guiding which services a child receives. An IEP draws on a range of assessments and other data to describe a child’s strengths, determine their needs and set measurable goals. Every part of this process depends on trained professionals.
But persistent workforce shortages mean districts often struggle to complete assessments, update plans and integrate input from parents. Most districts develop IEPs using software that requires practitioners to choose from a generalized set of rote responses or options, leading to a level of standardization that can fail to meet a child’s true individual needs.
Preliminary research has shown that large language models such as ChatGPT can be adept at generating key special education documents such as IEPs by drawing on multiple data sources, including information from students and families. Chatbots that can quickly craft IEPs could potentially help special education practitioners better meet the needs of individual children and their families. Some professional organizations in special education have even encouraged educators to use AI for documents such as lesson plans.
Training and diagnosing disabilities
There is also potential for AI systems to help support professional training and development. My own work on personnel development combines several AI applications with virtual reality to enable practitioners to rehearse instructional routines before working directly with children. Here, AI can function as a practical extension of existing training models, offering repeated practice and structured support in ways that are difficult to sustain with limited personnel.
Some districts have begun using AI for assessments, which can involve a range of academic, cognitive and medical evaluations. AI applications that pair automatic speech recognition and language processing are now being employed in computer-mediated oral reading assessments to score tests of student reading ability.
Practitioners often struggle to make sense of the volume of data that schools collect. AI-driven machine learning tools also can help here, by identifying patterns that may not be immediately visible to educators for evaluation or instructional decision-making. Such support may be especially useful in diagnosing disabilities such as autism or learning disabilities, where masking, variable presentation and incomplete histories can make interpretation difficult. My ongoing research shows that current AI can make predictions based on data likely to be available in some districts.
Privacy and trust concerns
There are serious ethical – and practical – questions about these AI-supported interventions, ranging from risks to students’ privacy to machine bias and deeper issues tied to family trust. Some hinge on the question of whether or not AI systems can deliver services that truly comply with existing law.
The Individuals with Disabilities Education Act requires nondiscriminatory methods of evaluating disabilities to avoid inappropriately identifying students for services or neglecting to serve those who qualify. And the Family Educational Rights and Privacy Act explicitly protects students’ data privacy and the rights of parents to access and hold their children’s data.
What happens if an AI system uses biased data or methods to generate a recommendation for a child? What if a child’s data is misused or leaked by an AI system? Using AI systems to perform some of the functions described above puts families in a position where they are expected to put their faith not only in their school district and its special education personnel, but also in commercial AI systems, the inner workings of which are largely inscrutable.
These ethical qualms are hardly unique to special ed; many have been raised in other fields and addressed by early-adopters. For example, while automatic speech recognition, or ASR, systems have struggled to accurately assess accented English, many vendors now train their systems to accommodate specific ethnic and regional accents.
But ongoing research work suggests that some ASR systems are limited in their capacity to accommodate speech differences associated with disabilities, account for classroom noise, and distinguish between different voices. While these issues may be addressed through technical improvement in the future, they are consequential at present.
Embedded bias
At first glance, machine learning models might appear to improve on traditional clinical decision-making. Yet AI models must be trained on existing data, meaning their decisions may continue to reflect long-standing biases in how disabilities have been identified.
Indeed, research has shown that AI systems are routinely hobbled by biases within both training data and system design. AI models can also introduce new biases, either by missing subtle information revealed during in-person evaluations or by overrepresenting characteristics of groups included in the training data.
Such concerns, defenders might argue, are addressed by safeguards already embedded in federal law. Families have considerable latitude in what they agree to, and can opt for alternatives, provided they are aware they can direct the IEP process.
By a similar token, using AI tools to build IEPs or lessons may seem like an obvious improvement over underdeveloped or perfunctory plans. Yet true individualization would require feeding protected data into large language models, which could violate privacy regulations. And while AI applications can readily produce better-looking IEPs and other paperwork, this does not necessarily result in improved services.
Filling the gap
Indeed, it is not yet clear whether AI provides a standard of care equivalent to the high-quality, conventional treatment to which children with disabilities are entitled under federal law.
The Supreme Court in 2017 rejected the notion that the Individuals with Disabilities Education Act merely entitles students to trivial, “de minimis” progress, which weakens one of the primary rationales for pursuing AI – that it can meet a minimum standard of care and practice. And since AI really has not been empirically evaluated at scale, it has not been proved that it adequately meets the low bar of simply improving beyond the flawed status quo.
But this does not change the reality of limited resources. For better or worse, AI is already being used to fill the gap between what the law requires and what the system actually provides.
Seth King, Associate Profess of Special Education, University of Iowa
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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