Tech
How atmospheric water harvesting can be scaled

Water scarcity is a huge global issue. More than 2 billion people lack access to safe drinking water—a situation set to worsen due to climate change, which fuels longer and more severe droughts. As reservoirs shrink, groundwater dries up and rainy seasons become more erratic. Some believe one answer to this crisis lies in the reservoirs of moisture in our skies.
The question is: How close are we to turning air into a dependable water source, and when does it make sense to do so? An article published in Joule explores how atmospheric water harvesting could move from laboratory prototypes to commercial systems by linking thermodynamic limits with a survey of existing products and customer needs. The analysis highlights the gap between what physics makes possible and what the market demands.
Energy paid
Atmospheric water harvesting follows two main routes. Condensation systems cool air to its dew point and collect liquid water. Sorption systems capture vapor in a sorbent and release it with heat. The study builds first-principles models for both routes and calculates the minimum energy required across climates and heat source temperatures. That baseline frames realistic targets for device performance.
Condensation is straightforward but sensitive to climate. At high humidity, conventional refrigeration hardware can deliver continuous, high-volume output. As air gets drier, the energy penalty rises. More input goes to sensible heat, which cools the entire air stream, rather than to the latent heat of condensation. At about 30% relative humidity, the sensible share can approach half of the total, which lowers efficiency and raises cost. In very dry air, dew points can fall below 0°C, frost can form on coils and both heat transfer and water production drop.
Sorption changes the balance. Because the sorbent selects water molecules from the air, the sensible heat fraction is typically lower, often under 30% in dry conditions in the authors’ calculations. Practical performance still depends on a suitable heat source for regeneration and on tight coupling between the sorbent and the heat and mass flows inside the device.
The market scan covers more than 100 participants, their reported energy use and daily output, and financing milestones. Condensation products dominate shipments today, supported by mature heat-pump supply chains and dehumidifier experience. Several vendors list units above 1,000 L per day, yet measured energy use often sits well above the theoretical floor.
The gap stems from multiple irreversibilities and from air-conditioner-style layouts that under-recover heat and moisture and mismatch components. Sorption products are earlier in scale up. Many devices produce under 10 L per day and use non-uniform energy accounting, but investment and technical progress are fast, with strong links to universities and materials advances such as metal-organic frameworks, graphene, and salt-based composites.
How to close the gap to commercialization
A unified platform offers a path to scale. We propose using a heat pump as a common energy backbone. The cold side supplies either direct condensation or enhanced adsorption during uptake, and the hot side drives desorption. A four-way valve alternates beds between adsorption and regeneration for near-continuous operation. Efficiency can improve with multistage heat pumps, tighter sorbent heat-exchanger integration, recovery of condensation heat and selective use of ambient energy.
Economics complete the picture. The analysis uses levelized cost of water and payback period and compares distributed AWH with trucking as distance grows. Longer haul distances improve AWH competitiveness. Priority use cases include emergency and military response, mobile and vehicle-mounted supply, urban bottled-water and beverage replacement, distributed supply for high-rise or modular buildings, and supplemental capacity alongside seawater desalination in some regions.
Progress depends on scenario-first design. Select a target climate, a target customer and a target energy source, then tune materials and systems to that triangle. Standardized energy metrics enable fair comparisons. Closed heat and moisture loops reduce losses and move performance closer to thermodynamic limits. A heat-pump backbone that serves both condensation and sorption on one platform can shorten the path from prototypes to market.
The message we hope readers take away is that better materials or bigger compressors alone will not carry AWH to scale. What closes the gap is alignment: climate conditions with service requirements and energy supply measured against transparent thermodynamic limits and reported on standardized energy bases. If the community coalesces around that yardstick—and if builders embrace heat-pump-centered, climate-adaptive platforms—we believe AWH can move quickly from impressive demonstrations to bankable infrastructure.
This story is part of Science X Dialog, where researchers can report findings from their published research articles. Visit this page for information about Science X Dialog and how to participate.
More information:
He Shan et al, Approaching thermodynamic boundaries and targeting market players for commercial atmospheric water harvesting, Joule (2025). DOI: 10.1016/j.joule.2025.102132
He Shan is a research fellow at the National University of Singapore (NUS). He earned his joint Ph.D. degree in 2025 under the supervision of Prof. Ruzhu Wang at Shanghai Jiao Tong University (SJTU) and NUS. Prior to that, he received his B.S. degree from Chongqing University in 2019. His research focuses on hydrogel-based atmospheric water harvesting and energy management.
Citation:
How atmospheric water harvesting can be scaled (2025, September 30)
retrieved 30 September 2025
from https://techxplore.com/news/2025-09-atmospheric-harvesting-scaled.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.
Tech
Samsung Promo Codes: 30% Off in October 2025

Samsung makes everything from smartphones and gaming monitors, to smart TVs and dishwashers. I’m always looking for a sale (I’m assuming you are, too), and I’ve found the best Samsung promo codes and special offers to help you save big on your most important tech purchases. At WIRED, we often review the South Korean company’s products, especially Samsung’s vast lineup of Galaxy smartphones, and I’ve rounded up a bunch of Samsung coupons for (virtually) every type of shopper.
Get 10% Off With Samsung Promo Code and up to $2,100 Off Phones
Right now, Samsung has some of the best deals I’ve ever seen on their best-selling tech, and they’re about to get even better with limited-time trade-in credits, a special offer program, and bundle deals. Right now, you can get a Samsung promo code for 10% off TVs—all you have to do is register for their email newsletter. The offer is sent to your inbox and is valid through September 30.
Shop Samsung’s best coupons and offers to score major discounts (sometimes up to $2,100) on smartphones, laptops, tablets, TVs, and their latest releases. And when you buy products together that you already need, you can save a ton. This includes up to 54% select Galaxy Buds, watches and tablets when you order select products, like the Galaxy S25 Ultra.
If you’re in the market for a new Samsung phone, you can get a new Galaxy Z Fold7 for $10 less with a trade-in. Feeling nostalgic? The new spin on an old classic, the Galaxy Z Flip7 is $150 off or up to $700 off with trade-in.
Or maybe you want one of the Galaxy S25 Ultra models. Get $200 off a Galaxy S25 Ultra, you’ll get up to $940 off with instant trade-in credits, and a storage upgrade for a limited time.
Unlock a 30% Off Samsung Promo Code With Offer Programs, Plus a $100 Referral Code
One of the hottest Samsung promo codes is a whopping 30% discount for government employees, first responders, military personnel, and educators. Samsung also has offer programs, meaning you can combine your promo code discount with most other offers to increase discounts. Get a pal involved for more savings—when a friend uses your referral code to make a purchase at Samsung.com, they’ll get 5% off their purchase (up to $250 off) and you’ll get up to $100 off per order (with the potential to save $1,000 per calendar year). My insider tip is to sign up for a Samsung Rewards account and download the mobile app for even more perks, including exclusive Samsung coupons, flash sales, and updates on the newest products, like the QLED 8K, select refrigerators, and other home appliances.
Save up to 35% on These Trending Samsung TV Deals
Along with other great tech, Samsung has some seriously nice TVs. The Samsung Frame TV has been trending this year for its stylish ability to blend into your home’s decor. Plus it just feels more elevated than a regular ol’ TV and mount. Some other trending TVs this Summer have been the Q60D, S90C, and the S95D models–not only do they have instant discounts of over up to 35% ($2,100 off). Plus, there are tons of TV and home theater deals at Samsung, including a bundle offer for $7500 off when you buy a Neo QLED 4K TV with a Dolby ATMOS soundbar. If you’re in the market for a new TV, it’s worth checking out Neo QLED AI Smart TVs to score 1 year of ESPN for free (worth $299).
You can also take advantage of their Trade-In Recycling Program for up to $200 off when you trade in your old TV—any brand, any size. When your new one is delivered, Samsung will handle recycling the old one, so you can enjoy your upgrade.
$169 Off With Samsung Promo Code or a $400 Gift Card on Appliances
Although here at WIRED we mostly cover Samsung’s traditional AV tech, they also make top-of-the-line kitchen and home appliances. During the Buy More Save More Event (through December 4), you can get up to 40% off high-tech Samsung kitchen appliances along with free 3-day rush shipping. When you buy any two qualifying Samsung Appliances, you’ll receive a $100 Samsung Prepaid Mastercard; if you buy three qualifying Samsung Appliances, you’ll receive a $300 Samsung Prepaid Mastercard; and when you purchase four or more appliances, you’ll get a Samsung Prepaid Mastercard for $400.
In addition to Buy More Save More discounts, other eye-catching deals include an extra $600 off a top freezer refrigerator when you buy a Bespoke 4-Door Flex Refrigerator with AI Family Hub + AI Vision, and $1,100 off the Bespoke 4-Door French Door Refrigerator with Beverage Center. Special offers also include free installation service, plus Samsung will haul away your old appliances and recycle them, while you get a $50 energy rebate. This futuristic fridge is basically also an iPad, with an AI Family Hub with the large screen and changeable door panels. Plus, there’s AI Vision inside, so you always know what’s inside (and what you need to buy at the store). And the Beverage Center has an internal dispenser or a built-in AutoFill Water Pitcher to get cold, crisp water whenever you want it, whichever way you want.
You can save an extra $170 with code SAVE169 at checkout, plus you’ll get $50 in Samsung Rewards (equal to 10,000 bonus points) with your purchase. And right now, you can get $1,000 off a Bespoke Smart Slide-In Electric Range. This range is straight from a The Jetsons fantasy, with an AI Home LCD display, which is pretty much a kitchen robot helper that gives you personalized recipe recommendations, the ability to search for and follow video recipes, and access your favorite apps so you can see who’s at your door through your video doorbell, and more. There’s also a Smart Oven Camera inside, meaning you can check on meals as they cook from anywhere and even share time-lapse videos to show off your skills.
Stay up to Date on all Things Samsung at WIRED
WIRED also has guides to help determine which Galaxy S24 phone is best for you and how to set up your Samsung Galaxy S25 to ensure you’re getting the most out of its features, as well as advice on which Galaxy S24 series accessories, like cases, chargers, and power banks, are worth the money.
Us nerds here at WIRED also follow CES (sort of the Coachella for tech nerds) for all the updates on tech (almost) no one asked for, and Samsung’s bi-annual Galaxy Unpacked event, where they show off its newest toys. We have a lot of opinions about Samsung’s foldable Galaxy Z Flip6 and Z Fold6 phones. We are also patiently awaiting new releases of Galaxy Tab tablets, a new line of Galaxy Buds Pro 3 wireless earbuds, and a new series of the Galaxy Watch, with a new design and improved sensors for health
Tech
Interrupting encoder training in diffusion models enables more efficient generative AI

A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as variational autoencoders with infinitely many latent variables, reducing computational costs and preventing overfitting. By appropriately interrupting the training of the encoder, this approach enabled development of more efficient generative AI, with broad applicability beyond standard diffusion models.
Diffusion models are among the most widely used approaches in generative AI for creating images and audio. These models generate new data by gradually adding noise (noising) to real samples and then learning how to reverse that process (denoising) back into realistic data. A widely used version, the score-based model, achieves this by the diffusion process connecting the prior to the data with a sufficiently long-time interval. This method, however, has a limitation that when the data differs strongly from the prior, the time intervals of the noising and denoising processes become longer, which causes slowing down sample generation.
Now, a research team from Institute of Science Tokyo (Science Tokyo), Japan, has proposed a new framework for diffusion models that is faster and computationally less demanding. They achieved this by reinterpreting Schrödinger bridge (SB) models, a type of diffusion model, as variational autoencoders (VAEs).
The study was led by graduate student Mr. Kentaro Kaba and Professor Masayuki Ohzeki from the Department of Physics at Science Tokyo, in collaboration with Mr. Reo Shimizu (then a graduate student) and Associate Professor Yuki Sugiyama from the Graduate School of Information Sciences at Tohoku University, Japan. Their findings were published in the Physical Review Research on September 3, 2025.
SB models offer greater flexibility than standard score-based models because they can connect any two probability distributions over a finite time using a stochastic differential equation (SDE). This supports more complex noising processes and higher-quality sample generation. The trade-off, however, is that SB models are mathematically complex and expensive to train.
The proposed method addresses this by reformulating SB models as VAEs with multiple latent variables. “The key insight lies in extending the number of latent variables from one to infinity, leveraging the data-processing inequality. This perspective enables us to interpret SB-type models within the framework of VAEs,” says Kaba.
In this setup, the encoder represents the forward process that maps real data onto a noisy latent space, while the decoder reverses the process to reconstruct realistic samples, and both processes are modeled as SDEs learned by neural networks.
The model employs a training objective with two components. The first is the prior loss, which ensures that the encoder correctly maps the data distribution to the prior distribution. The second is drift matching, which trains the decoder to mimic the dynamics of the reverse encoder process. Moreover, once the prior loss stabilizes, encoder training can be stopped early. This allows us to complete learning faster, reducing the risk of overfitting and preserving high accuracy in SB models.
“The objective function is composed of the prior loss and drift matching parts, which characterizes the training of neural networks in the encoder and the decoder, respectively. Together, they reduce the computational cost of training SB-type models. It was demonstrated that interrupting the training of the encoder mitigated the challenge of overfitting,” explains Ohzeki.
This approach is flexible and can be applied to other probabilistic rule sets, even non-Markov processes, making it a broadly applicable training scheme.
More information:
Kentaro Kaba et al, Schrödinger bridge-type diffusion models as an extension of variational autoencoders, Physical Review Research (2025). DOI: 10.1103/dxp7-4hby
Citation:
Interrupting encoder training in diffusion models enables more efficient generative AI (2025, September 29)
retrieved 29 September 2025
from https://techxplore.com/news/2025-09-encoder-diffusion-enables-efficient-generative.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no
part may be reproduced without the written permission. The content is provided for information purposes only.
Tech
OpenAI Is Preparing to Launch a Social App for AI-Generated Videos

OpenAI is preparing to launch a stand-alone app for its video generation AI model Sora 2, WIRED has learned. The app, which features a vertical video feed with swipe-to-scroll navigation, appears to closely resemble TikTok—except all of the content is AI-generated. There’s a For You–style page powered by a recommendation algorithm. On the right side of the feed, a menu bar gives users the option to like, comment, or remix a video.
Users can create videoclips up to 10 seconds long using OpenAI’s next-generation video model, according to documents viewed by WIRED. There is no option to upload photos or videos from a user’s camera roll or other apps.
The Sora 2 App has an identity verification feature that allows users to confirm their likeness. If a user has verified their identity, they can use their likeness in videos. Other users can also tag them and use their likeness in clips. For example, someone could generate a video of themselves riding a roller coaster at a theme park with a friend. Users will get a notification whenever their likeness is used—even if the clip remains in draft form and is never posted, sources say.
OpenAI launched the app internally last week. So far, it’s received overwhelmingly positive feedback from employees, according to documents viewed by WIRED. Employees have been using the tool so frequently that some managers have joked it could become a drain on productivity.
OpenAI declined to comment.
OpenAI appears to be betting that the Sora 2 app will let people interact with AI-generated video in a way that fundamentally changes their experience of the technology—similar to how ChatGPT helped users realize the potential of AI-generated text. Internally, sources say, there’s also a feeling that President Trump’s on-again, off-again deal to sell TikTok’s US operations has given OpenAI a unique opportunity to launch a short-form video app—particularly one without close ties to China.
OpenAI officially launched Sora in December of last year. Initially, people could only access it via a web page, but it was soon incorporated directly into the ChatGPT app. At the time, the model was among the most state-of-the-art AI video generators, though OpenAI noted it had some limitations. For example, it didn’t seem to fully understand physics and struggled to produce realistic action scenes, especially in longer clips.
OpenAI’s Sora 2 app will compete with new AI video offerings from tech giants like Meta and Google. Last week, Meta introduced a new feed in its Meta AI app called Vibes, which is dedicated exclusively to creating and sharing short AI-generated videos. Earlier this month, Google announced that it was integrating a custom version of its latest video generation model, Veo 3, into YouTube.
TikTok, on the other hand, has taken a more cautious approach to AI-generated content. The video app recently redefined its rules around what kind of AI-generated videos it allows on the platform. It now explicitly bans AI-generated content that’s “misleading about matters of public importance or harmful to individuals.”
Oftentimes, the Sora 2 app refuses to generate videos due to copyright safeguards and other filters, sources say. OpenAI is currently fighting a series of lawsuits over alleged copyright infringements, including a high-profile case brought by The New York Times. The Times case centers on allegations that OpenAI trained its models on the paper’s copyrighted material.
OpenAI is also facing mounting criticism over child safety issues. On Monday, the company released new parental controls, including the option for parents and teenagers to link their accounts. The company also said that it is working on an age-prediction tool that could automatically route users believed to be under the age of 18 to a more restricted version of ChatGPT that doesn’t allow for romantic interactions, among other things. It is not known what age restrictions might be incorporated into the Sora 2 app.
This is an edition of the Model Behavior newsletter. Read previous newsletters here.
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