DIGITAL LIFE

How can you tell if an app is listening to you? Here’s how to block this feature

Smartphones incorporate a wide range of sensors and features that enable the use of increasingly sophisticated applications. Microphones, cameras, location services, voice recognition, and virtual assistants are among the tools that millions of people use daily.

Many apps utilize certain phone features to optimize the user experience, learn more about the user, and provide personalized services. However, this same complexity raises privacy concerns.

Key takeaways...Your phone isn’t constantly spying, but it can listen in specific cases. Voice assistants activate only after wake words, but apps with microphone permissions may access audio if misused.

Targeted ads are usually driven by data tracking, not eavesdropping. Search history, location data, and app activity are more commonly used to predict interests than actual voice recordings.

Malware and spyware are the real privacy threats. Malicious apps or advanced tools can hijack your microphone and camera to monitor activity without consent.

You can detect and stop unauthorized listening. Checking microphone access logs, watching indicator lights, restricting permissions, and running security scans help identify and remove suspicious activity.

Think your phone is spying on you? You might be right, especially if you find that your phone is acting strange and it ticks the boxes we're going to discuss in this article. In fact, given the current cybersecurity landscape and all the stories coming up about mobile phone safety, it’s not far-fetched to wonder: Is my phone listening to me? 

You’ve likely heard stories of people swearing that their phones are eavesdropping on their conversations. The most common one is the oddly specific ad that pops up right after you casually mention a product near your smartphone. 

But if anything truly malicious is happening, such as your private conversations being recorded or your data stolen, it’s usually the work of malware, not your phone’s built-in features. 

Still, it’s important to understand the difference and know when to be concerned. We asked our Bitdefender cybersecurity experts these same questions, and here’s what they revealed about why your phone might be listening and how you can stop it. 

How does your phone listen to you? Unless you’re a high-profile government official, political critic, or human rights activist, it’s highly unlikely that you’re the target of 24/7 surveillance or that anyone hacked your phone or deliberately spoofed your microphone to spy on your private conversations. 

That being said, smartphones do listen in certain contexts, mostly through features designed to make your experience more convenient. Let’s take a closer look at the most common ways this happens (and when you should actually be concerned). 

Through voice assistants...When you call out to Siri, Alexa, or Google Assistant, they instantly respond and help you handle everyday tasks like setting reminders, playing music, or managing your schedule. You’re not alone in this, as about half of U.S. adults use virtual assistant apps regularly.  

If you’ve not changed your settings, these assistants will constantly listen in the background for what’s known as “wake words.” For Siri, it’s “Hey Siri,” while Google responds to “Okay Google.” To detect these triggers, your device uses AI to passively listen and process background sounds. This also helps them learn how you speak, recognize patterns, and adapt to different accents. 

Apple and Google explicitly state that their assistants only start recording after hearing the wake words and that they do not save your recordings or audio by default. But if you opt in to allow saved audio, that data may be used to improve its speech recognition technology. 

Via third-party apps...Now, this is where you actually have cause for concern. When you install certain apps on your phone and casually grant them permissions, you might unknowingly be giving malicious actors access to your microphone. If those apps are harmful or run by shady developers, they can use that access to spy on your personal data and sell the information to advertisers. 

Some apps don’t need to be outright malicious, as they might just quietly collect more data than they should. Every “Allow microphone access” or “Allow background activity” click gives them more room to listen in. 

Through spyware like Pegasus...If you’re a high-profile journalist, activist, or politician, you or your family members could be targeted with sophisticated tools like Pegasus, a surveillance software known for hijacking a phone’s camera and microphone for data theft and other cybercrimes. 

Pegasus can be installed in several ways: by clicking on a phishing link, leaving your phone unattended long enough for someone to install it directly, or through zero-day exploits that don’t even require you to do anything. Once you’re targeted, you’re instantly compromised. 

One of the most common questions is whether an app might be listening to conversations through the device's microphone. Although modern operating systems include increasingly stringent security controls, periodically reviewing permissions remains a best practice. Both Android and iPhone offer tools to identify which apps have access to the microphone and to restrict that access when it is not needed.

Here are some useful steps to learn how to block this feature:

1. Check which apps have access to the microphone...The first step is to access your phone’s privacy settings and see which apps have permission to use the microphone. Many people are surprised to discover how many programs have requested this access over time. This check can help you identify apps you may have forgotten about or no longer use.

2. Evaluate whether the permission makes sense...Not all apps need access to audio. Video-calling tools, voice recorders, and virtual assistants require the microphone to function properly. However, other apps may request permissions that do not seem related to their primary purpose. Identifying these instances helps reduce unnecessary risks.

3. Disable unnecessary permissions...Once you have identified the apps, it is a good idea to revoke microphone access for those that do not require it. This change can usually be made with just a few taps in your device’s settings and does not necessarily require you to uninstall the app. 4. Grant permission only while the app is in use

Many systems allow you to choose between different access levels. A particularly useful option is to authorize microphone access only when the app is open. This reduces the possibility of unnecessary access occurring in the background.

5. Pay attention to privacy indicators...Both Android and iPhone display visual indicators when the microphone is in use. Observing these indicators helps detect unexpected behavior. If they appear when no app should be recording audio, it is worth investigating which program is active.

6. Keep your operating system up to date...Security updates often fix vulnerabilities and improve privacy controls. Keeping your phone updated reduces risks and provides access to the latest tools for managing sensitive permissions.

7. Review recently installed apps...If your concerns arose after downloading a new app, that is a good place to start your investigation. Checking an app’s permissions, the developer’s reputation, and reviews from other users provides valuable information.

8. Download apps only from official stores...Official app stores implement security controls that help reduce the presence of malware. Using unknown sources significantly increases the risk of installing apps that request excessive permissions.

9. Remove suspicious apps...If an app exhibits strange behavior, requests excessive permissions, or raises questions about its functionality, the simplest solution is usually to uninstall it and look for a more reliable alternative.

Privacy begins with permissions...Most apps only use the microphone when the user grants permission. Nevertheless, regularly reviewing your privacy settings remains a best practice for digital security.

Knowing which apps have access to your audio—and limiting those permissions when they are not needed—helps you maintain greater control over your personal information.

Your phone does not constantly record your everyday conversations for advertisers, as major operating systems (iOS and Android) and independent research show no proof of continuous secret audio streaming. However, apps may access your microphone or make it feel like they are listening due to specific reasons:

Why it feels like your phone is listening:

• Voice assistants: Built-in assistants like Siri, Google Assistant, or Alexa stay locally active on a tiny chip to listen exclusively for wake words like "Hey Siri" or "OK Google". They only transmit audio to the cloud after hearing that trigger.

• Active features in use: If you open an app like a camera, social media recorder, voice memo tool, or chat app, it legitimately requests microphone access to perform functions you asked for (such as recording a video or sending a voice message).

• Aggressive tracking & coincidence: Instead of listening to your voice, tech platforms build hyper-detailed profiles of you using your search history, location data, website visits, app activity, and data shared by your friends. When you see an ad for something you just spoke about, it is usually the result of smart predictive algorithms and confirmation bias rather than active eavesdropping.

• Malware or spyware: In rare cases, unauthorized or malicious software can compromise a device and hijack hardware permissions.

How to check and stop app listening:

• Look for privacy indicators: Modern iOS and Android devices show a green or orange dot at the top of your screen whenever an app actively uses your microphone or camera.

Review microphone permissions:

• On iPhone: Go to Settings > Privacy & Security > Microphone and toggle off access for any app that does not need it.

• On Android: Go to Settings > Apps > Permission manager > Microphone or check individual app settings to deny unnecessary mic access.

        • Disable Voice Assistants: Turn off "Hey Siri" or "Hey Google" in your device or assistant settings if you do not use voice triggers

 

mundophone

TECH

Building brain-inspired semiconductor chips for smarter, greener data centres

With artificial intelligence (AI) data centres consuming electricity at a staggering and growing rate, identifying a sustainable computing solution to offset these massive community and environmental disruptors is proving critical.

Researchers from the University of Ottawa, Columbia University and Hanyang University believe building brain-inspired computing systems could provide the answer to this global conundrum, with small, fast, energy-efficient and scalable artificial neurons able to support the large-scale computing systems that are now regularly dotting our landscapes.

“The materials and architectures described in this review represent a fundamentally different way to process information. By operating more like a human brain than a traditional processor, these emerging technologies will lead to physical devices that are dramatically more energy-efficient, faster, and adaptive,” explained lead author Dr. Kannan Udaya Mohanan, a Postdoctoral Fellow in Professor Chang-Hyun Kim’s research group at uOttawa.

Building scalable brain-like systems...Professor Kim and his team performed a sweeping, systematic review of years of global advancements in electronic materials, device physics, and circuit integration to chart a new path for "neuromorphic" systems – systems that physically mimic the human brain's processing. Rather than focusing on a single experiment, they evaluated the entire landscape of emerging hardware neurons, distilling complex material science into concrete, real-world technological pathways to help researchers build scalable, brain-like computing systems.

These brain-inspired hardware findings offer a comprehensive, forward-looking roadmap for the scientific community just as the federal government provides new frameworks for data centre developments in Canada.

“Traditional computing is hitting a bottleneck known as the "memory wall," where transferring data between memory and processors causes massive delays and energy loss. We identified the most promising post-CMOS materials and device architectures that can act as artificial neurons, proving that next-generation circuits can process information faster and with a fraction of the energy used by current mainstream chips,” said Chang-Hyun Kim, an Associate Professor in the School of Electrical Engineering and Computer Science and head of the Semiconductor and Microelectronics Laboratory at uOttawa.

Enabling greener technology...Post-CMOS refers to emerging materials and device architectures that move beyond traditional chips. While these brain-inspired technologies can drastically reduce the energy footprint of modern data centres, they offer an equally powerful advantage for personal technology. This is because these novel materials are often lower cost, highly scalable, and support ultra-thin, lightweight and flexible form factors.

“Beyond green AI data centres, this hardware will directly enable smarter, low-power wearable technology for personalized healthcare and seamless human-machine interfaces,” added Kim.

The development of brain-inspired semiconductor chips, known as neuromorphic chips, has emerged as the primary solution for creating smarter and more sustainable data centers. As the adoption of artificial intelligence grows exponentially, traditional computing architecture faces what is known as the "memory wall" bottleneck—a situation where the constant transfer of data between the processor and memory leads to sluggish performance and massive energy waste. In contrast, the human brain is capable of performing incredibly complex tasks while operating with extreme efficiency, consuming only about 20 watts of power.

These new technologies overcome this barrier by unifying processing and storage within a single component, mimicking the behavior of biological neurons and synapses.

Key advancements in neuromorphic technology:

• Post-CMOS materials: Scientists at the University of Ottawa (uOttawa) have identified new materials and circuit architectures capable of functioning as artificial neurons, requiring only a fraction of the electrical power currently consumed.

• Hafnium memristors: Research from the University of Cambridge has led to the development of a stable form of hafnium oxide that operates with currents one million times lower than previous alternatives, with the potential to reduce energy consumption in AI applications by up to 70%.

• Extreme efficiency: Chips based on materials physics and spiking neural networks can process temporal data directly within the hardware, achieving up to 2,000 times greater energy efficiency for specific tasks.

• Biological computing ("wetware"): In an even more radical approach, partnerships involving the startup Cortical Labs have created systems that integrate living, lab-grown human neurons with traditional silicon chips, giving rise to the concept of "biological data centers." Impact beyond data centers... While the immediate focus is on mitigating the sustainability crisis facing large-scale global servers, brain-inspired chips are highly scalable, lightweight, and low-cost. This will enable local, decentralized processing—or "edge computing"—bringing advanced intelligence directly to wearable health devices, smart sensors, and human-machine interfaces, without any reliance on cloud computing.

Would you like to explore any specific aspect of this technology further? I can provide details on how memristors function, explain the differences between spiking neural networks (SNNs) and traditional AI, or share data regarding the implementation of systems incorporating living neurons ("wetware").

University of Ottawa, Columbia University and Hanyang University

 

TECH

Hot and cold climate extremes push UK’s energy system to its limits, new report reveals

The arrival of cooler weather following a summer of heatwaves, is a timely reminder that the UK electricity system must be resilient to the effects of temperature extremes at both ends of the thermometer.

The Royal Meteorological Society has released a new report that highlights how recent weather conditions have influenced electricity generation, demand and the infrastructure that connects them.

Authored by experts from the society, the Met Office and multiple UK universities, the State of the Climate for the UK Energy Sector Report 2025-26 shows that more energy is being provided by weather-dependent renewable sources. But high and low temperatures will continue to put the energy grid’s capacity and infrastructure to the test.

Data from the National Energy System Operator (Neso) shows that renewable energy formed a higher percentage of overall energy generated in the UK over the reporting period (April 2025 – March 2026) than any other year on record. The proportion coming from the combination of solar and wind increased, providing more than 38% of the nation’s electricity. The proportion of gas-powered generation reduced to 25%.

However, the report highlights how extreme weather tests the resilience of the grid. The conditions that lead to very high and low temperatures create different challenges through increasing electricity demand, limiting generation and damaging network infrastructure. The UK’s energy network is having to adapt to cope with increasingly extreme temperatures. In cold conditions, the network is put under strain due to increased heating demand. This requires more energy at short notice, increasing reliance on non-renewable sources.

For those responsible for balancing the electricity system, including Neso, these weather extremes can create operational challenges. This makes it harder to match energy supply with demand.

In early January 2026, strong northerly winds brought cold Arctic air, snow and ice into large parts of the UK. Initially these winds provided up to 20 gigawatts (GW) of power towards the system, a significant proportion of the demand.

But as the freeze reached its peak on the evening of January 6, the wind died down and wind generation fell to less than a quarter of the levels seen in the preceding days. Simultaneously, electricity demand peaked, with the increase in heating needs seeing demand peak an additional 5GW higher than the previous day.

Electricity supply and demand have to be kept in balance in real time. When demand rises or available generation falls, the energy system operator can maintain that balance by using additional generators to increase their output, for example. On this occasion, gas-powered electrical generation filled the gap by providing more than 25GW to the system at it’s peak.

This meant the demand was comfortably met, but this was an expensive strategy that meant the operator was paying up to £700 per megawatt-hour (MWh) for this generation. This was due to the higher cost of turning to gas generation, and the need for additional generation at short notice which costs more than generation that can be planned further in advance. This compares to the usual rate of just over £100 per MWh for the rest of January.

At the other end of the spectrum, hot temperatures are increasingly becoming more of a concern for the sector.

The summer of 2025 was the warmest on record at the time, with temperatures 1.51°C higher than average. During warmer temperatures, the network operator often limits the amount of power that transmission lines carry around the country to prevent components overheating. This reduces the risk of lines expanding more with heat which would make them sag and potentially short out on nearby vegetation or structures beneath.

Nevertheless, several faults in the network were attributed to heat causing infrastructure damage, leading to power cuts across parts of England, especially during two periods of heatwaves that occurred at the end of June and start of July.

In Europe, where temperatures also spiked, nuclear power plants were forced to limit output or shut down altogether because the water used to cool them with, taken from nearby rivers, was too warm. Increased temperatures and below-average rainfall also increased fire risks. The largest area lost to wildfire burning in the UK since satellite records began included a “mega-fire” in Scotland. Several of these fires led to localised power cuts across the country.

Preparing for future extremes...The UK’s new grid upgrade plan from the government, which aims to expand and modernise the British grid system to allow for connections to new generation sources and upgrades of current infrastructure, needs to consider the consequences of extreme temperatures. This includes ensuring that upgraded infrastructure can withstand the extreme heat we are now seeing more regularly in UK summers, as well as ensuring that capacity for transmission of electricity from generation sources to population centres is increased.

Delays to electricity grid upgrades risk higher consumer bills as the operator is required to pay wind farms to stop generating because electricity produced cannot be be carried to where it is needed, replacing it with more local gas-powered generation instead.

The resilience of the UK grid has stood up well to the challenges it faced from the weather in the past year. But maintaining that resilience requires continued investment and adaptation as the climate, energy needs of the nation and profile of energy generation all continue to change.

The Royal Meteorological Society’s annual report has revealed that extreme heat and cold are pushing the UK’s energy system to its limits. The document highlights the severe impact these temperature fluctuations have on energy infrastructure and underscores the urgent need to modernize the sector.

Key impacts on the energy system:

• Transmission constraints due to heat: During heatwaves, the grid operator must limit the amount of electricity flowing through transmission lines to prevent components from overheating. Heat causes cables to expand and sag, creating a risk of short circuits should they come into contact with vegetation.

• Localized blackouts and fires: The summer of 2025 saw temperatures 1.51°C above average. Extreme heat caused physical damage to infrastructure and triggered wildfires—including a "mega-fire" in Scotland—resulting in localized power outages across parts of England.

• Challenges for European nuclear power generation: Extreme heat also forced nuclear power plants in continental Europe to reduce output or shut down entirely, as the river water used for cooling had become too warm.

• Higher costs for consumers: Due to delays in upgrading the transmission grid, the operator is frequently forced to pay wind farms to halt clean energy production—since the power cannot be transported—and replace it with energy from local gas-fired plants, which drives up electricity bills.

Adaptation plans and requirements: The British government’s new grid upgrade plan faces the challenge of ensuring that modernized infrastructure can withstand frequent extreme heat, while also providing greater capacity to transport electricity from new generation sources to urban centers. The regulatory body Ofgem has also launched investigations to assess the performance of the market and the electrical system—managed by the NESO—during recent heat spikes.

Royal Meteorological Society

 

TECH

S&P warns of “off-balance-sheet” AI debt among tech giants

S&P Global believes it is likely that major technology companies carry financial liabilities linked to artificial intelligence—referred to as “AI debt”—that are not fully visible in their public financial disclosures. This warning concerns financing structures used to build data centers and support AI infrastructure companies.

The concern was raised by Mark Habib, global head of digital infrastructure at S&P Global Ratings, during a meeting with journalists in London on October 19. According to Reuters, the analyst acknowledged that the agency may not have visibility into all the financial liabilities taken on by large tech firms, particularly those arising from transactions that are neither disclosed nor directly assessed.

This warning comes at a time when investors are questioning the financial sustainability of the expansion into artificial intelligence. Infrastructure-focused companies, such as CoreWeave, have faced pressure in the debt markets, while major cloud service providers continue to invest heavily in the construction of data centers.

AI financing creates liabilities that are difficult to assess...The issue identified by S&P relates to financing structures that distribute financial commitments across various companies and legal entities.

For instance, a technology firm might enter into long-term contractual commitments to utilize capacity in a data center financed by third parties. It might also provide guarantees for infrastructure projects or participate in ventures involving smaller companies.

While these structures do not necessarily constitute debt incurred directly by the tech company, they can create financial obligations that the firm must honor—even if demand for artificial intelligence services falls short of expectations.

This distinction is significant for the assessment of credit risk. Traditional debt appears on a company’s financial statements, whereas certain contractual commitments and guarantees require further analysis to determine the actual economic exposure.

S&P had already identified this complexity in a report released in August, which highlighted the growing use of debt, lease agreements, guarantees, and special-purpose entities to finance AI infrastructure.

$1.65 trillion in off-balance-sheet AI debt...Kohei Yamada wrote an article for Nikkei Asia titled Five US tech giants’ hidden debts soar to $1.65tn on opaque AI funding in July 2026.

“Hidden debt at U.S. tech giants swelled eightfold in roughly four years to an estimated $1.65 trillion as artificial intelligence investments ballooned., a Nikkei study shows, exceeding actual [reported] debt and making it tougher for investors to assess risk.”

The article is behind a paywall. However, Hedgie posted further details on Twitter.

“A Nikkei investigation found that Alphabet, Microsoft, Amazon, Meta, and Oracle have $1.65 trillion in debt that doesn’t appear on their balance sheets, more than the $1.35 trillion they officially report. These are GPU contracts, data center leases, and joint ventures that don’t count as debt under accounting rules until the facilities go live. Meta’s hidden debt is $420 billion, triple its reported debt. Oracle’s grew 30-fold in four years. All five declined to comment.”

“These companies owe more off their balance sheets than on them, and the accounting rules let them keep it that way until the data centers go live. That’s legal, but it means investors looking at quarterly earnings this week are seeing less than half the picture.”

“But when those data centers start operating, the leases hit the books all at once.”

Hidden debt (off balance sheet)
Meta	~$420B
Oracle	~$273B
Amazon	~$350B
Microsoft	~$350B
Alphabet	~$250B
TOTAL	$1.65 Trillion

S&P estimates that Amazon, Microsoft, Alphabet, Oracle, Meta, and SpaceX will collectively invest more than seven trillion dollars in data centers and other assets related to artificial intelligence between 2025 and 2030.

This figure represents projected capital expenditures for the period, not the total debt held by these companies.

The agency also anticipates that this group of six companies will post negative free cash flows during 2026 and 2027, driven by the scale of investments required to expand computing capacity.

The primary risk lies in the gap between the pace of infrastructure construction and the ability to generate sufficient revenue to justify these investments.

This issue becomes particularly significant given the expansion of projects such as Google’s new data centers in Finland, which entail long-term financial commitments and a guaranteed supply of energy.

S&P does not foresee immediate changes to credit ratings...Despite these concerns, S&P Global does not deem it necessary to immediately revise the financial ratings of the companies analyzed.

In July, the agency had already downgraded Oracle to BBB—the lowest level within the investment-grade category—due to financial pressures linked to AI infrastructure expansion and uncertainty regarding future profitability.

Mark Habib explained that a reassessment might become necessary if these investments cease to be sustainable—whether due to further increases in spending or competition that limits revenue growth. The scale of financial liabilities that are not immediately visible, however, remains to be determined. S&P acknowledges that it lacks sufficient information to calculate the potential magnitude of these additional commitments.

The core issue, therefore, goes beyond simply how much large technology companies are investing in artificial intelligence. It also entails understanding what financial obligations they have taken on to support this expansion—and the extent to which future results will enable them to meet those obligations.

The top five US tech giants (Alphabet, Microsoft, Amazon, Meta, and Oracle) have accumulated $1.65 trillion in off-balance-sheet AI infrastructure obligations, surpassing their $1.35 trillion in recorded on-balance-sheet debt. Across a broader group of major tech companies, hidden commitments reach an estimated $3 trillion.

Key scale and figures:

• The big five hidden debt: Alphabet, Microsoft, Amazon, Meta, and Oracle hold $1.65 trillion in off-balance-sheet liabilities (growing roughly eightfold over four years).

• Meta's exposure: Meta leads with approximately $420 billion in hidden obligations—nearly triple its reported balance-sheet debt—driven by projects like the Hyperion data center joint venture.

• Oracle's growth: Oracle's off-balance-sheet commitments have surged 30-fold since 2022, placing pressure on its credit ratings and default insurance costs.

• Broader industry backstops: Hardware and chip providers have also stepped in; Nvidia has provided backstops and guarantees covering up to $300 billion in debt used by customers to buy its AI chips.

Mechanisms behind the debt:

• Uncommenced leases: Long-term data center and real estate leases that do not legally trigger balance-sheet reporting rules until the facilities actually go live (totaling over $1.2 trillion across providers).

• Take-or-Pay & supply commitments: Multi-year contractual agreements to purchase GPUs, computing power, and energy sources extending decades into the future (such as Alphabet's long-term infrastructure commitments).

• Special purpose vehicles (SPVs) & Joint Ventures: Off-balance-sheet corporate structures (such as private credit or institutional vehicles involving firms like Blue Owl or Blackstone) used to finance data centers and energy plants away from parent-company balance sheets.

Risks to Investors and markets:

• Opaque leverage: Traditional financial metrics capture only half of the actual financial exposure, masking how much capital is tied up in speculative AI expansion.

• Demand shortfalls: If commercial AI revenue fails to scale up as aggressively as projected, uncancelable take-or-pay and lease commitments could convert into massive impairment charges.

• Private credit and refinancing strain: Much of this financing relies on private credit and investment-grade bond markets, which face rising pressure as high interest rates and massive debt supply test borrower solvency.

mundophone

TECH

“Cooling without electricity or water”: DGIST develops “self-cooling film” for thermal management in AI-era electronic devices

A research team led by Professor Seong Kyun Kim of the Department of Physics and Chemistry at DGIST (President Kunwoo Lee) has developed a next-generation “passive cooling film material” that utilizes moisture in the air to cool electronic devices —no water or electricity is required. The technology is drawing attention as a promising, environmentally friendly solution to thermal management challenges in AI data centers, mobile devices, and electric vehicle (EV) batteries, which consume substantial water and electricity.

Recent advances in AI technology have driven the surge in data centers, and securing the electricity and water resources required for their thermal management has emerged as a global challenge. Likewise, mobile devices and EV batteries require technologies that can efficiently dissipate heat within confined spaces. However, conventional active cooling methods that operate pumps or fans require electrical energy and a coolant (water), and their complex systems entail significant maintenance costs.

Accordingly, passive cooling technologies that require no external energy input—particularly, evaporative cooling technologies that utilize airborne moisture—have emerged as an alternative. However, as most conventional hygroscopic materials—that is, materials that absorb moisture—are rigid crystalline solids, they do not adhere firmly to the surfaces of electronic devices, and induce corrosion. These are significant limitations to their commercialization.

Professor Seong Kyun Kim’s research team achieved a major breakthrough in addressing these limitations by combining “nanocellulose,” which possesses excellent moisture storing capacity, with organic ionic molecules with robust hygroscopic properties to synthesize a novel flexible, adhesive film material.

The developed material maintains strong adhesion and flexibility even when completely dry, enabling firm, gap-less adherence to various electronic device surfaces, including metals, semiconductors, and glass. Even in dry environments, it absorbs and stores substantial airborne moisture, which evaporates when the device generates heat during operation, thereby lowering the surface temperature in real time. A key advantage of the material is that it does not induce corrosion despite containing moisture, enabling direct application to device surfaces without any additional coating.

When the research team tested the film under actual conditions of continuous electronic device operation, the developed film material demonstrated exceptional cooling performance, stably reducing the device’s surface temperature by up to 18.2°C (from 72.3°C to 54.1°C) for more than 12 hours.

“The significance of this study goes beyond merely developing an environmentally friendly material. It simultaneously addresses multiple limitations of conventional passive cooling materials, including corrosiveness and insufficient flexibility and adhesion,” explained Professor Seong Kyun Kim of the Department of Physics and Chemistry at DGIST.

This research was led by doctoral student Hyeondo Kim of the Department of Physics and Chemistry, with support from the Ministry of Science and ICT’s Individual Basic Research Program and the DGIST International Joint Research Program. The findings were published in the internationally renowned journal Small Structures in September 2026.

A film has been developed that can cool electronic devices without using electrical energy or separate refrigerants. It is flexible and transparent, and in a dry state adheres well to metal, semiconductor, and glass surfaces, drawing attention as a supplementary cooling material that could help solve thermal management issues in artificial intelligence (AI) data centers and electric vehicle batteries.

Daegu Gyeongbuk Institute of Science and Technology (DGIST) announced on the 6th that a research team led by Professor Kim Sung-kyun of the Department of Chemical Physics has developed a next-generation "passive cooling" film material that autonomously dissipates heat from electronic devices by using moisture in the air. The research results were published on September 17 (local time) in the international journal "Small Structures."

A common challenge for data centers, mobile electronic devices, and electric vehicle batteries, all of which are seeing growing demand, is heat management. Conventional active cooling methods that operate pumps or fans require electrical energy and water as a refrigerant, and their complex equipment results in high maintenance costs.

Among passive cooling technologies that require no external energy input, evaporative cooling, which utilizes moisture in the air, is emerging as an alternative. However, existing hygroscopic materials that readily absorb moisture are often rigid solids, making it difficult for them to closely conform to electronic device surfaces, and they can also cause corrosion of the devices.

Professor Kim's team combined a nanocellulose material with excellent moisture storage capacity and an organic ionic liquid to synthesize a film-type material that is flexible and highly adhesive.

Operating principle of the passive cooling film. The film attached to an electronic device absorbs and stores moisture from the air, then releases it when heat is generated during device operation, continuously cooling the device. Provided by DGIST

The developed film maintains its adhesion and flexibility even when completely dry, allowing it to firmly stick to various surfaces such as metal, semiconductors, and glass. It absorbs and stores moisture from the air, and when the device generates heat, it evaporates the stored moisture, lowering the device surface temperature in real time. Even when it contains moisture, it does not corrode the device, so it can be attached directly to the surface of components without any additional coating process.

When its cooling performance was tested on operating electronic devices, the film stably reduced the device surface temperature by up to 18.2°C, from 72.3°C to 54.1°C, for more than 12 hours.

The research team stated, "These results show that the film can serve as a supplementary cooling device that alleviates the burden on existing electronic cooling systems, which consume large amounts of water and electricity," adding, "It can be used as an eco-friendly, high-efficiency energy-saving solution for the AI era."

Self-cooling films have emerged as a critical breakthrough for managing thermal loads in AI-era electronic devices. As artificial intelligence models demand sustained, high-intensity processing, AI chips and data center architectures generate immense heat densities that push traditional cooling systems beyond their limits. Self-cooling passive films offer an eco-friendly, zero-energy solution to this "thermal wall" without requiring electricity, pumps, or mechanical fans.

Recent breakthroughs utilize two distinct methodologies to solve electronic overheating: hygroscopic evaporative films and passive radiative cooling films.

1. Moisture-absorbing evaporative cooling films...A prominent breakthrough in October 2026 comes from a research team led by Professor Seong Kyun Kim at the Daegu Gyeongbuk Institute of Science and Technology (DGIST).

• The core mechanism: The film operates like a bio-inspired sweat system. It continuously extracts humidity from the surrounding ambient air even when dry. When the device undergoes intense AI workloads and heats up, the trapped water evaporates to absorb and carry the heat away in real time. When the device cools down, the film self-regenerates by re-absorbing moisture from the air.

• Material composition: It is an all-bio-based hydrogel combining nanocellulose (providing structure and immense moisture-holding capability) with hygroscopic organic ionic liquids (attracting water and ensuring flexibility).

• Thermal performance: Under continuous device operation, it consistently reduces surface temperatures by up to 18.2°C (from 72.3°C down to 54.1°C) with continuous cooling lasting over 12 hours.

• Key semiconductor advantage: Unlike historical moisture-based materials that are rigid and cause rust, this hydrogel remains highly adhesive and strictly non-corrosive when dry. It fits perfectly onto glass, semiconductors, and metals with zero air gaps.

2. Metamaterial passive radiative cooling films...Another dominant category includes ultra-thin metamaterial films that use the principles of photonics to reject and dump thermal energy.

• The core mechanism: These films work via a dual-action process. They block incoming ambient radiation/sunlight while simultaneously emitting internal device heat into the mid-infrared spectrum (the "atmospheric window"), effectively shedding heat into the cold sink of deep space with zero input power.

• Material composition: These typically consist of layered polymer structures embedded with nanoparticles (such as silicon dioxide, diamond microparticles, or boron nitride) to enhance both thermal conductivity and broadband infrared emissivity.

• Commercial integration: For example, Moguang New Energy—utilizing tech developed at Nanjing University—mass-produces atomic-layer radiative films for smartphones (adopted by brands like Apple and Xiaomi), which drops internal temperatures by 5°C to 10°C and boosts hardware reliability by up to 40%.

DGIST

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SAMSUNG

Samsung launches affordable 5G phone with 90Hz AMOLED display, 50MP OIS camera and 2TB expandable storage

Samsung has launched the Galaxy F70 5G in India with an Exynos 1330 processor and a 6.7-inch Super AMOLED display. Its 50 MP main camera supports optical image stabilization, and the phone has a 5,000 mAh battery with 25 W charging.

Samsung has expanded its Galaxy F70 series in India with the launch of the Galaxy F70 5G, following the Galaxy F70e 5G in February 2026 and the Galaxy F70 Pro 5G in August 2026.

The Galaxy F70 5G features a 6.7-inch Super AMOLED display with a resolution of 2,340 × 1,080 pixels and a 90Hz refresh rate. Corning Gorilla Glass Victus protects the screen, while an IP54 rating provides resistance to dust and splashes. The phone measures 164.4 × 77.9 × 7.5 mm (6.47 × 3.07 × 0.30 inches) and weighs 192 g (0.423 lb).

Samsung pairs its Exynos 1330 octa-core processor with 4 GB or 6 GB of RAM and 128 GB of storage. A microSD card can expand storage by up to 2 TB. The phone runs Android 15 with Samsung's One UI 7.

The rear camera system consists of a 50 MP main camera with optical image stabilization (OIS), a 5 MP ultrawide camera and a 2 MP macro camera. A 13 MP front camera handles selfies and video calls. Samsung promotes the main camera's stabilization with its "No Shake Cam" branding.

The Galaxy F70 5G has a 5,000 mAh battery, 1,000 mAh less than the Galaxy F70 Pro, and supports 25 W wired charging. Other features include Circle to Search, Google Gemini, Samsung Wallet and Samsung Knox security.

Despite sharing key specifications, including the processor and display, the Galaxy A17 5G (currently available on Amazon), which we have reviewed, offers longer software support, with six generations of Android OS upgrades and six years of security updates. The Galaxy F70 5G is limited to four generations of OS upgrades and five years of security updates.

A stabilized camera and AMOLED display...Samsung’s latest Galaxy F handset offers several hardware and software features for photography, entertainment, and everyday use.

Camera system. A 50MP main camera uses optical image stabilization (OIS) to reduce blur caused by hand movement, a feature the tech giant calls the No Shake Cam. A 5MP ultrawide camera captures wider scenes, while a separate 2MP macro camera handles close-ups. A 13MP front camera handles selfies and video calls.

Display and durability. The phone features a 6.7-inch Full HD+ Super AMOLED screen, Corning Gorilla Glass Victus protection, and an IP54 rating for dust and splash resistance. The device measures 7.5mm thick.

Performance and battery. An Exynos 1330 processor powers the phone, with microSD support for expandable storage. A 5,000mAh battery supports 25W wired charging.

Software and connectivity. Circle to Search lets users search for content directly from their screens, and Google Gemini provides AI assistance. Samsung Wallet supports contactless payments with compatible cards and terminals. Quick Share enables wireless file transfers between supported Galaxy devices and Windows computers.

Several AI functions depend on internet access or account sign-in. Language and regional support can also affect which options are available.

Two configurations and launch discounts in India...Both configurations provide 128GB of internal storage. The 4GB RAM version costs ₹19,999 (about US$211), compared with ₹22,999 (about US$242) for the 6GB model.

Eligible launch bank promotions can lower the entry price to ₹17,999 (about US$190). Shoppers should verify their payment eligibility and final checkout total before relying on the advertised discount.

Sales begin Oct. 20 at noon IST through Flipkart and Samsung India’s website. Bold Black and Vivid Violet are the available colors, and a compatible 25W charging adapter is sold separately. International availability has not been announced

What buyers should check before choosing a Galaxy F70...A newer release is not necessarily a better fit for your daily routine.

Samsung’s earlier announcement identifies a 6,000mAh battery and 120Hz HD+ screen on the F70e, compared with the new handset’s higher-resolution AMOLED panel and optical camera stabilization. The F70e offers greater battery capacity, but comparable battery-life tests are needed to establish which phone lasts longer between charges. Frequent photographers might prefer the stabilized camera, although actual image quality still needs independent testing.

The 6GB model costs ₹3,000 (about US$32) more without adding storage. Buyers who frequently switch between work apps may prefer the extra RAM, but neither configuration’s performance has been established here through hands-on testing. Before paying the premium, consider the apps you use and how long you expect to keep the phone.

Long-term ownership also depends on software support. Samsung’s six-year OS and security-update promise for the F70e should not be assumed to apply to the standard F70. Confirm the exact model’s Android upgrade commitment, security-update frequency, and support end date before purchasing.

Pricing and availability...The Galaxy F70 5G starts at ₹19,999 (approximately $211) for the 4 GB RAM and 128 GB storage variant, while the 6 GB RAM and 128 GB model costs ₹22,999 (approximately $242). Launch offers reduce the starting price to ₹17,999 (approximately $190) on Flipkart. It comes in Vivid Violet and Bold Black, with sales starting in India on October 20. Samsung has not yet announced whether it will launch the smartphone in other countries.

 

by: mundophone

TECH

KAIST develops AI that reconstructs cloud-covered ground in satellite images with photo-like clarity

Satellite images of the Earth are not always easy to use. Clouds can block the view of the ground, and images captured with certain imaging methods look different from ordinary photographs, which makes them difficult to interpret. A KAIST research team has developed an artificial intelligence (AI) model that learns the appearance of diverse terrain and cities. The model can restore cloud-covered images and convert radar images into images that resemble ordinary photographs. It applies what it learned while generating satellite images to a range of image-processing tasks, so it is expected to expand the use of satellite data in disaster response and environmental monitoring.

KAIST (President Choongsik Bae) announced on October 20 that a research team led by Professor Munchurl Kim of the School of Electrical Engineering has developed GeoCore-9B, an AI model that can be used to generate, restore, and convert satellite images. During training, the model’s core image-generating component learns image features by adjusting about 9 billion internal values, known as parameters. GeoCore-9B is pretrained on a large volume of satellite data and can then be adapted to a variety of tasks. For this reason, it is classified as a ‘foundation model,’ a type of AI model that serves as the basis for a wide range of AI technologies.

Satellite images are essential for monitoring changes in forests and cities and for responding to disasters such as floods and wildfires. However, optical images, which record sunlight reflected from the Earth’s surface, cannot clearly capture the ground when clouds are thick or at night. Synthetic aperture radar (SAR) observes the ground by transmitting radio waves and measuring the signals reflected back. It can acquire images of the Earth’s surface at night and in cloudy weather. However, SAR images differ from ordinary photographs in brightness and patterns, which makes them difficult to interpret.

To create a common basis for processing these different types of satellite data, the research team trained the core image-generating model from scratch on Earth observation data. Instead of adapting the core component of a model built to generate ordinary photographs, the team trained a new model on overhead satellite views of terrain, forests, ports, and cities.

The model was trained on 10 million pairs of satellite images from around the world and sentences describing each image. For example, an image of a port was paired with a description stating that ships and warehouses are located there. An image of a rural area was paired with a description stating that a road runs between fields. By learning from this large collection of described images, the AI learned the features of diverse terrain and urban areas.

A key feature of this study is that the AI learned not only what an image contains, but also which location on Earth the image shows and how large an area it covers. In satellite images, objects with similar shapes may be small buildings or large industrial facilities, depending on their actual size. It is therefore important to take into account both where an image was taken and its spatial scale.

The team trained the model using latitude, longitude, and ground sample distance (GSD) together with the descriptive sentences. Latitude and longitude indicate a position on Earth. GSD indicates the size of the ground area represented by a single pixel, the smallest unit of an image. For example, at a GSD of 1 meter, one pixel represents an area of about 1 meter by 1 meter on the ground. Using this information, the AI learned to generate images that match a given location and spatial scale.

The team also applied a training technique called ‘Geospatial Semantic Alignment,’ which helps the model represent the structure of terrain and cities more accurately. In this technique, the generative model learns by referring to features extracted by an AI specialized in satellite imagery. These features serve as a training reference that helps the model learn structural elements of an image, such as how roads connect and where farmland boundaries lie. The specialized AI is used only during training, so it does not add computation when the model generates images.

The researchers then fine-tuned GeoCore-9B separately for cloud removal and for radar image conversion. They used LoRA (Low-Rank Adaptation), a method that keeps the core model fixed and trains only a small number of additional parameters. This approach reduces the burden of retraining an entire large AI model from scratch for each task.

In evaluations using publicly available satellite datasets, GeoCore-9B outperformed existing task-specific models at restoring cloud-covered images. It scored higher on the key metrics that measure brightness, color, and structural similarity to the reference images. In converting radar images into optical images, it also achieved the best results on some of the metrics that assess the quality and fine detail of the generated images.

These results demonstrate that knowledge gained from generating satellite images can also be applied to restoring and converting actual observation images. The model is expected to make greater use of data whose value has been limited by cloud cover or imaging methods. It is also expected to support the assessment of conditions in disaster areas and the monitoring of changes in forests, farmland, and cities. In addition, adapting a single common model to multiple tasks can reduce the cost and time required to develop a separate AI for each task.

The team plans to validate the model’s performance across a wider range of sensors and regions. To apply the model to real-world disaster response, the team also plans to expand its research to accurately predicting changes on the ground, such as floods, building damage, wildfires, and urban expansion. The team will also explore defense applications such as surveillance and reconnaissance.

“By training the AI on where each satellite image was taken and the actual size of the terrain it shows, we made it possible to use a single model for multiple tasks such as cloud removal and image conversion,” said Professor Kim. He added that he expects the work to make satellite data limited by weather or imaging methods more widely usable, which will help with assessing disaster situations and monitoring changes in forests and cities.

Recent artificial intelligence models can reconstruct cloud-covered ground in satellite imagery with high clarity by combining optical data with radar sensing, historical archives, and specialized generative foundation models.

Key AI technologies for cloud removal and ground reconstruction:

• KAIST GeoCore-9B: Developed by researchers as a 9-billion-parameter generative foundation model trained specifically on Earth observation data, this model performs tasks like cloud removal and SAR-to-optical image translation with consistent layout and sharp boundaries.

• Microsoft SpaceEye: Uses Synthetic Aperture Radar (SAR) from the Sentinel-1 mission (which penetrates clouds) paired with historical optical data to generate daily cloud-free predictions of what the ground looks like underneath.

• ClearSKY Vision: Merges optical and radar (SAR) data to reconstruct missing Sentinel-2 data and deliver analysis-ready, cloud-free imagery.

• SenseNet & CMSN: Deep denoising and frequency-domain learning models (such as coarse-to-refine Residual FFT-Convolution and attention-based networks) isolate and remove cloud/haze structures while reconstructing underlying textures.

• Google AlphaEarth Foundations: Functions as a "virtual satellite" that integrates petabytes of multi-modal Earth observation data to map persistent cloud-covered regions (such as parts of Ecuador or Antarctica).

KAIST

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