Can a city’s seawall stand up to a blockbuster storm? Will a region’s power grid hold against record-breaking heat? And can a town’s fire-fighting resources contain a major wildfire? 

To answer these questions, communities will first need to know how such extreme events could unfold. How far is a wildfire likely to spread? How much of a region might a storm impact? How long could a heat wave last? 

But extreme events are notoriously difficult to anticipate. By their nature, they are outliers. In the history of record keeping, extreme events are sporadic and rare. Yet most methods that assess a region’s risk depend on extreme events of the past to characterize even more extreme, worst-case scenarios in the future. 

Now, MIT engineers have developed a tool that generates plausible extreme events and worst-case scenarios, and maps their characteristics, such as an extreme storm’s likely duration, intensity, and area of impact. The key to their method is that it does not need to know about previous extreme events in order to generate plausible future extreme events.

Instead, the method, in the form of a machine-learning algorithm, learns from a dataset, such as a region’s daily weather records and maps. This record may or may not contain past extreme deviations, such as record-setting heat or rain. The team’s algorithm takes a statistical approach to learn from the available data, to exclude implausible weather scenarios. The method then generates plausible extreme events that are likely to occur in a region with a given frequency (such as once every 100 years), and projects how those extreme events might look in terms of their size, intensity, and duration.

“We are trying to model extreme, unprecedented events that no one has seen before, that are not in the dataset,” says Kai Chang, an MIT graduate student in mechanical engineering and affiliate of the MIT Center for Computational Science and Engineering. 

“An event like Hurricane Katrina is something that happens every 30 to 40 years,” adds Themis Sapsis, the William I. Koch Professor of Mechanical and Ocean Engineering at MIT, a core member of the Center for Computational Science and Engineering, and an affiliate of the MIT Institute for Data, Systems, and Society. “What will be the Katrina that happens every 100 years? How bad will it be? That’s exactly what we’re trying to quantify, to help planners prepare for plausible extreme scenarios.”

Beyond weather events, the approach, which the team has dubbed Extreme Event Aware, or “η-learning,” can be applied to other fields, such as robotic navigation and financial markets.

“Financial market crashes are extreme events that are a complicated combination of things, involving many different sectors,” Chang says. “What is the interaction that leads to a market crash? That is something that this method could explore.” 

Sapsis and Chang detail their new method in an open-access paper that appeared on Aug. 20 in the journal Nature Communications. 

“Riskier than everything”

To estimate a region’s risk of an extreme weather event, planners, policymakers, and insurance companies typically ask questions such as “What does a once-every-100-year storm look like for New York City?” For answers, they use computer simulations that must be trained on data that includes extreme, once-in-a-century events, in order to learn the conditions leading up to those events and generate scenarios of how those events might look in the future. 

“These methods assume there are very disastrous events that we have seen in the dataset, and they build a method to either estimate the risk of those events, or they try to predict exactly the events that have happened,” Chang says. “We are trying to see: What do unprecedented extreme events look like that are riskier than everything that has happened before and yet are still plausible?”

For example, if the most extreme rainfall measurement ever recorded in New York City is 200 millimeters, what kind of storm would produce an even more extreme measurement, of 300 millimeters? Such an event has never been recorded before and yet could still be plausible. City planners would want to know where such a storm would hit, how big an area it would cover, and how intense it would be. A simulation of the storm could help them assess infrastructure and plan reinforcements. 

“We want to predict maps of these worst-case scenarios,” Sapsis says. “There is no method that does this efficiently to predict events that happen rarely.”

Extreme learning

The team’s new algorithm generates plausible, unprecedented extreme scenarios, without needing to train on previous extreme event data. To do so, the algorithm combines and learns statistics, or probabilities, about the relationships between two types of data: point statistics and spatial maps.

To demonstrate, the researchers applied the method to generate maps of future extreme precipitation events over the continental United States. The researchers began with 25 years of hourly precipitation maps, which they pooled into daily maps. From the full record, they computed point statistics describing how often the maximum rainfall across a map reached a given level. They then trained the algorithm on paired low- and high-resolution spatial maps from just the first six months of the record, which contained few or no examples of the most extreme rainfall levels.

From these data, the algorithm learned how patterns in low-resolution maps correspond to detailed, high-resolution precipitation maps. It then used the point statistics to constrain the rainfall extremes represented in those maps. This combination enables the algorithm to generate plausible spatial patterns for events more extreme than those represented in the training data — for instance, the possible locations, sizes, and intensities of a once-in-a-century rainfall event with a maximum of 300 millimeters.

A user can prompt the trained algorithm with a question such as, “What could a once-in-a-century storm look like in New York City?” The algorithm then generates maps of statistically plausible storms that are likely to occur with that frequency, including characteristics such as the storm’s size, area of coverage, and intensity of rainfall.

“Someone can say, ‘I’m interested in building things to withstand the risk of an event that happens every 100 years,’” Chang says. “What we can do then is produce thousands of possible realizations that will happen with this sort of rare frequency.”

As long as relevant point statistics and spatial data are available, the method could be applied to visualize other unprecedented events such as extreme floods and wildfires.

“Extreme events have become a strategic concern, not just an environmental one — we’ve optimized global systems for efficiency, and the price of that efficiency is that there’s very little slack left anywhere. A single extreme event propagates through supply chains, energy markets, and food systems in weeks,” Sapsis says. “Being able to put a probability on an event that hasn’t happened yet is now a question of national and economic resilience.”

This research was supported, in part, by a Vannevar Bush Faculty Fellowship and the U.S. Air Force Office of Scientific Research. 

Interesting paper:

Abstract: With entrepreneurial fraud cases on the rise, we investigate how entrepreneurs carry out criminal deception, employing deceptive means to defraud audiences. Analyzing court data from Silicon Valley ventures and their founders prosecuted for fraud between 2000 and 2023, our findings reveal that entrepreneurs carry out criminal deception through a process of façading: Entrepreneurs construct, perform, and protect illusory appearances (façades) that externally project high-growth performance to audiences while masking ventures’ actual underperformance. We identify three forms of façading—­surface, reinforced, and deep façading­—that are contingent on the severity of the gap that entrepreneurs face between audiences’ performance expectations and ventures’ performance reality. Our theoretical framework captures how entrepreneurs facing minor, wide, and extreme expectation-reality gaps engage in evermore sophisticated efforts to detach the venture’s externally projected appearance from its actual operational reality. Practically, we propose several approaches to deter and detect criminal deception, including the extension of U.S. Securities and Exchange Commission surveillance and whistleblower program, investor due diligence reform, and dedicated entrepreneurship education interventions that clearly demarcate when entrepreneurs transgress into criminal deception. We make contributions to literatures on cultural entrepreneurship, organizational wrongdoing, and the social effects of entrepreneurship. ...

As people age, many cognitive functions tend to decline. Brain scanning studies have revealed corresponding changes in the function of a brain network that is involved in many of these cognitive functions, including working memory and problem-solving.

When it comes to language skills, however, the picture is different. Unless impaired by a stroke or dementia, most older people retain their language skills and may even improve them as they steadily gain vocabulary throughout their lives.

A new brain imaging study from an MIT-Boston University collaboration now reveals the neural activity underlying this observation. The researchers found that in older adults, activity of the language processing network is nearly identical to that seen in the brains of younger adults during language tasks.

In contrast, the researchers found that activation patterns in the multiple demand network, a brain system involved in executive control tasks such as decision-making, were very different in older and younger adults. 

“In the language network, we couldn’t find any differences between older and younger groups. In contrast, the executive system showed decline across almost all of the measures. The network synchronization declined in older adults, the extent of activation was reduced, and the magnitude of activation was reduced as well,” says Anne Billot, one of the lead authors of the new study, who carried out this work while doing her PhD at BU and is now a postdoc at Harvard University.

The findings suggest that parts of the brain that are specialized for specific functions, such as language processing, may be more resilient to aging than the multiple demand network, a more general-purpose network that has greater flexibility in its function, the researchers say.

Former MIT research assistant Niharika Jhingan is also a lead author of the study, which appears today in Nature Communications. Evelina Fedorenko, an MIT associate professor of brain and cognitive sciences and member of MIT’s McGovern Institute for Brain Research, and Swathi Kiran, the James and Cecilia Tse Ying Professor in Neurorehabilitation at BU, are the paper’s senior co-authors.

The resilience of language

To study the effects of aging on the brain, the researchers looked at two groups of people, ages 17-39 and 41-80. Based on previous studies, they expected that the multiple demand network, which includes several regions in the frontal and parietal lobes of the brain, would look different in the brains of older people. 

“It’s well known that executive functions, such as attention, working memory, and cognitive control, tend to decline with age. And it’s also known that in opposition to that, language skills typically tend to remain quite stable or even improve with age,” Billot says. “These two types of functions really go in opposite directions in healthy aging. In terms of behavior, that’s quite well-established, and we wanted to see if that was also the case in the brain.”

In previous studies of the multiple demand network, scientists have found that as people age network activity becomes less synchronized. Some neuroscientists have hypothesized that this may also happen in the language network, but studies haven’t found definitive evidence for this.

The MIT researchers were able to look at both networks by designing tasks that elicit responses primarily in either the language network or the multiple demand network. This allowed them to identify, for each participant, the brain areas that belong to each network.

During a spatial memory task — remembering the location of squares in a grid — the researchers confirmed that the multiple demand network showed altered activity in older adults. Compared to the younger subjects, their networks were smaller and less well-synchronized, and the overall activation level was weaker.

To identify the language network, the researchers had participants listen to stories and read sentences. They found that in both groups, brain activity in response to language showed similar levels and spatial distribution across the network.

They also found that younger and older subjects showed similar brain responses when they encountered an unfamiliar word or an unusual grammatical construction.

“We have previously used similar kinds of materials to show that young adults show strong sensitivity to these points of linguistic difficulty: activity in the language areas goes up. Here we found that in older adults, you also see this sensitivity, which suggests that there’s nothing fundamentally different about how they process language,” Fedorenko says. 

A language boost

The researchers also showed that in older people, the language network did not show any signs of becoming less synchronized. Additionally, the network did not show signs that it was blurring together with the multiple demand network, as some neuroscientists have hypothesized might happen.

While this study did not evaluate language ability, other studies have shown that not only do language skills not decline with age, for some people, their language processing improves in older age. This might be because vocabulary and reading skill can continually grow over time, the researchers say.

“Vocabulary keeps increasing as long as people have been measuring, which makes sense. People get exposed to more and more language, and older people sometimes start reading more, so they get an extra boost — it’s like a large language model trained on increasingly more data,” Fedorenko says. 

Given these findings, one possible generalization is that parts of the brain that are specialized for particular functions such as language may be less susceptible to age-related decline than the multiple demand network. That network is unique in its ability to give the human brain the flexibility to learn new skills and adapt to new situations. 

“The multiple demand network is a different system in the sense that it’s not accumulating knowledge over time. It’s more like a flexible resource that you can deploy in all sorts of ways. And somehow that’s the thing that is more vulnerable to aging,” Fedorenko says. “Why it’s so vulnerable — that is a very good question.”

The research was funded by the National Institute on Deafness and Other Communication Disorders, as well as MIT’s McGovern Institute, Simons Center for the Social Brain, Poitras Center for Psychiatric Disorders Research, and Quest for Intelligence.

Nature Climate Change, Published online: 24 August 2026; doi:10.1038/s41558-026-02731-9

Assessing 9,809 freshwater fish species globally, this study shows risk to a high proportion of species in the tropics, and severe extremes in non-tropical regions. Under high emissions, gradual warming drives abrupt exposure, with 63% of species facing accelerating thermal fragmentation by 2099.

The neon flying squid can fly in formation.

The shoal of about 100 squid rose unexpectedly from a patch of the Pacific Ocean around 370 miles from Tokyo and glided near the boat for about 30 metres. The astonished researchers were the first to capture photographs of such a thing, which looked like the early stages of an alien invasion.

They were probably neon flying squid (Ommastrephes bartramii), the subsequent study states, a species that is part of a 20-strong flying squid family that was known to leap from the water but, until then, was only rumoured to also be able to glide above it...

This sort of research is both exciting and terrifying:

The two models in question were told to generate complete genomes for a viable bacteriophage—a type of virus able to infect and replicate itself inside bacteria, destroying them from the inside.

Using an existing bacteriophage as an example—ΦX174 (pronounced “fie-ex-1-7-4”), known for its ability to infect and destroy E. coli bacteria—the models generated about 700,000 potential designs, of which the researchers picked 285 that looked most promising.

The researchers then synthesised new DNA molecules using those designs and inserted them into E. coli bacteria, before waiting to see if viable bacteriophages would emerge...

This week, EFF, along with Big Brother Watch, Defend Digital Me, Liberty, Open Rights Group, Race Equality First, Statewatch, and Stopwatch, wrote to Nottinghamshire Police Force in the UK raising concern about the proposed roll-out of live facial recognition technology (LFR), and called for its immediate halt.

In particular, the letter highlights six concerns:

LFR Is Not "Just Another Tool"

Nottinghamshire Police has stated that “facial recognition is just another tool to fight crime.” But LFR used in public spaces is an incredibly intrusive biometric mass surveillance technology that scans the faces of everyone who walks past the camera and takes biometric face prints. This is not just another tool, but a major escalation of surveillance that treats everyone as a suspect by default.

People Having "Nothing to Worry About" Does Not Hold to Scrutiny 

According to Nottinghamshire Police, “if you aren’t entering the city or county to commit crime then you have nothing to worry about.” However, many people have legitimate concerns about the normalisation of invasive technologies. So a public that cannot move around their towns and cities without being subjected to a biometric identity check may be less willing to seek medical care or legal advice, speak with journalists, act in a union, vote, protest, or express their gender, sexual or religious identity. 

Disproportionate Targeting With LFR

We are particularly concerned to learn that Nottinghamshire Police could deploy LFR to tackle low level crimes, such as youth behavior deemed anti-social, as part of Operation View. Reporting suggests that the force already possesses “a watchlist of young people believed to be causing the most problems,” including children as young as 11 years old. It would be highly disproportionate to deploy live facial recognition to tackle this behaviour. Many of these children are reportedly known to the police, and it is highly likely that there are more proportionate means for locating them. 

LFR Could Increase Social Problems

We are also concerned that Nottinghamshire Police has not adequately examined the distinct risks of using LFR to target children, including negative impacts on their behaviour and outcomes, risk of recidivism, and relationship with the police. Use of LFR could exacerbate behavioural problems in children and create an adversarial, rather than trusting, relationship with the police from a young age. 

Lack of Public Support 

Recent polling commissioned by Liberty indicated that 48% of people oppose scanning the faces of those walking on high streets when there is no suspected imminent threat. Furthermore, Opinium found that the majority of people oppose the use of facial recognition in schools. Likewise, a report by the London Policing Ethics Panel found that Londoners aged 16-24 were most likely to find the Metropolitan Police Service’s use of LFR unacceptable and most likely to stay away from events where LFR was in use.

On these grounds, Nottinghamshire Police must immediately halt their plans to use live facial recognition surveillance any further.

Read our full letter here. 

Tuberculosis (TB) is a leading cause of infectious disease deaths, claiming over 1 million lives every year. It spreads through the air when an infected person coughs, sneezes, or exhales, and drug-resistant strains and asymptomatic spreading are growing concerns. Curbing TB transmission is an urgent public health challenge, yet scientists still don’t understand how airflow and other environmental factors influence that spread.

One problem is that studies of infectious disease transmission have focused mainly on population-level assessments or individual immune responses. But understanding how airflow and mixing influence transmission in indoor spaces requires expertise in fluid physics and computational modeling.

An interdisciplinary team including researchers at MIT and the University of Texas Southwestern Medical Center has now combined animal transmission experiments with quantitative particle tracking and flow modeling to understand how some lab-based environments can promote the spread of respiratory infectious diseases such as TB, while others mitigate that spread.

A key factor in predicting infectious transmission was not just the total ventilation rate but, more importantly, the local pattern of airflow driven by the design — such as air leakage, inflow and outflow locations, and forces created by an infected individual.

“The local airflow patterns turn out to be pivotal,” says Lydia Bourouiba, the Japan Steel Industry Chair Professor at MIT and faculty lead of the Fluid Dynamics of Disease Transmission Laboratory, part of the Fluids and Health Network within the Institute for Medical Engineering and Science (IMES). “Our team’s findings provide some of the clearest evidence I’m aware of showing the importance of accounting for [airflow] inhomogeneity and its effects when designing for airflow detailed patterns. This insight is critical when building or retrofitting an indoor space to mitigate airborne transmission, or when designing an airborne transmission study.”

The research is an important step toward connecting laboratory infectious disease studies with how people spread such diseases in the real world. The team hopes their insights can extend beyond their model system and show the importance of flow physics in building designs to prevent the spread of airborne diseases indoors.

“Despite recent pandemics and epidemics, there is still resistance to incorporating airflow in routine infectious disease prevention tools,” Bourouiba says. “Infrastructure could be retrofitted at relatively low cost, but the paucity and difficulty of gathering direct evidence prevents broader adoption of flow physics as a tool for indoor health. This study helps provide such evidence.”

Joining Bourouiba on a paper about the work are Yash Kulkarni, a postdoc at IMES, who led the fluid and aerosol physics components; Kubra Naqvi, lead author and a postdoc at UT Southwestern; Michael Shiloh, a professor at UT Southwestern, who led the multiyear effort to reestablish a classic tuberculosis transmission model; Hui Ouyang, an assistant professor of aerosol engineering at UT Dallas; Yuhui Guo, Deepak Sapkota, and Arabella Martin, all UT Southwestern PhD students; Pei Lu and Victoria Ektnitphong, research associates at UT Southwestern; Shibo Wang, a University of Minnesota researcher; Beatriz Dias, a UT Southwestern instructor; Bret Evers, an associate professor at UT Southwestern; and Lenette Lu, assistant professor at UT Southwestern.

Opening the black box

When people exhale, talk, cough, or sneeze, tiny microdroplets and bioaerosols launch from their mouths, carried forward by a cloud. If infected by a respiratory disease, these bioaerosols can contain pathogens that can infect others. Disease transmission depends on pathogen survival in the air, which is influenced by temperature, humidity, and ventilation.

In 1882, German physician and microbiologist Robert Koch first established an animal model for the study of tuberculosis pathogenesis. Decades later, researchers demonstrated airborne transmission of tuberculosis between people and animals.

These early experiments have proven difficult to replicate in today’s modern, biosafety-grade facilities. This new study reveals the difficulty comes from stringent containment and ventilation requirements, which can dramatically influence airflow in experiments.

“Host-to-host transmission is an obligatory evolutionary phase of respiratory pathogens, yet it has been considered too intractable or complex to be amenable to systematic investigation, hence is commonly relegated to a black box. Our work opens that black box,” says Bourouiba, who is professor in MIT’s departments of Mechanical and Civil and Environmental Engineering, and an IMES core faculty member.

To quantify how local airflow patterns impact infectious disease transmission, the researchers redesigned and modelled the early studies for modern high-containment lab facilities — including their seal, inflow, outflow, and exhaust pathways — and quantified particle and bacteria-laden particle release and dispersal. They released tracer particles and bacteria into a compartment and modelled recovery from air sampled on the other side under differing airflow rates, designs, and leak configurations.

The MIT team carried out computations, benchmarked against particle release experiments. The results revealed how important seemingly small details such as leakage paths could be.

“Even a small leak could short-circuit the airflow by drawing fresh air directly toward the exhaust, rather than drawing contaminated air across the containment chambers,” says Kulkarni. 

Advancing TB research

To date, uneven indoor airflow patterns have not been fully harnessed as part of a risk mitigation strategy. 

“By systematically defining how airflow and design influence biological exposure, we were ultimately able to restore transmission and create a system that can now be used to ask fundamental questions about the bacterial, host, and environmental factors that determine tuberculosis spread,” says Naqvi.

“I began working to reestablish this seminal TB animal transmission model nearly 10 years ago, and it proved far more challenging than I anticipated,” says Shiloh. “I hope this work serves as a reminder that meaningful scientific advances often require patience and perseverance.”

“This work illustrates how crucial it is to support synergistic collaborations integrating complementary disciplines to tackle research bottlenecks — and to standardize reporting norms across laboratories,” Bourouiba says. “If different labs have varying airflow patterns from uncontrolled leaks or seal details, that physical variability can overwhelm the biological signals researchers seek. Beyond its foundational impact for TB transmission studies, our work shows that opening the black box of transmission provides mechanistic insights: Detailed airflow pattern control can enhance or mitigate airborne transmission — making it exploitable as a prevention measure in crowded gathering spaces.”

This work was supported, in part, by the National Institutes of Health, the National Science Foundation, the Burroughs Wellcome Fund, MathWorks, and the Translational Research Institute for Space Health.

Governments have sunk barges in the Danube, evacuated thousands near wildfires and invented new words in a summer of climate extremes.

Three states in the past year have made it harder for trial lawyers and environmental groups to sue fossil fuel companies over global warming.

A state prohibition on vegetation near homes is undermined by an exception that lets localities write their own rules, insurers say.

The Environmental Defense Fund says the administration lacks authority to shutter a research facility that helps with storm preparation.

Earlier harvests have become increasingly common in Champagne and other French wine regions as temperatures rise.

Historic heat is forcing the French government to rethink its spending plans.

This year’s intensifying El Niño threatens to heighten fire risks as higher temperatures and parched vegetation turn landscapes into tinderboxes.

The scorching drought has also disrupted trade, power and water supplies.

The AI Security Institute has a new report of AI systems engaging in “unsanctioned behavior”—what I have been calling “genie behavior—while being tested on their cybersecurity capabilities.

The incident stemmed from a single evaluation where agents were given a task of solving a cyber security challenge. We ran this challenge 122 times across several models. Our investigation found that in 10 of those runs, an AI agent took autonomous, unsanctioned action on the live internet, targeting real people and organisations. In total, we catalogued 19 such actions. Almost all of this behaviour (17 actions) came from a single model, Anthropic’s Mythos 5, with 2 actions involving OpenAI’s GPT-5.6-Sol with cyber classifiers (mechanisms to prevent misuse) disabled. In the most serious case, an agent tried to insert malicious code into an open-source project. In an attempt to get the code approved, the agent engaged in social engineering—creating fake online identities and using them to pressure the project’s maintainer to approve the code. A human maintainer caught and refused to approve the malicious code...

Brazil's new internet intermediary liability regime is underway. The implementation of changes established by the Supreme Court includes notice and takedown mechanisms and duty of care obligations. Caution is crucial as these measures can create problematic incentives for enforcement overreach and over censorship of protected speech.  

The court in June issued a new decision clarifying elements of its 2025 finding that the previous liability regime was partially unconstitutional. The government also published in late May two presidential decrees that detail how the new rules apply. 

Under the new regime, social media platforms and other internet applications that curate or interfere with posts can be held liable for third-party content if they don’t remove it after being notified by the user seeking take down unless there's a reasonable doubt that the content is unlawful. For certain specific cases, like crimes against honor (e.g. defamation), platform liability still depends on failing to comply with a judicial order.  

For some serious crimes, like human trafficking and crimes against women, applications have a duty of care to remove related content immediately and can be held liable when systemically failing to do so. The precise limits of what constitutes a systemic failure are still unclear. There are also stricter rules for paid ads, boosted content, and bots. 

The previous regime, set by Article 19 of the law known as the Brazilian Civil Rights Framework for the Internet (“Marco Civil da Internet” in Portuguese), sought to protect freedom of expression online by holding internet application providers liable for user content if they failed to comply with a judicial order to remove it. There were specific, limited exceptions to this rule, like the unauthorized disclosure of nude or private sexual images. This was meant to prevent providers from over-removal of user content to avoid legal action. Yet, the court found that this provision failed to sufficiently safeguard democracy and fundamental rights. 

We outlined the thorny context leading to this shift in Brazil’s intermediary liability rules, including Big Tech’s alignment with the far right and hurdles to approve platform regulation in Congress, through a proper legislative process. 

Brazil’s shift is part of broader discussions and changes in response to growing concerns over online harms and digital platforms’ abuses. However, responses focused on platforms’ liability of user-generated content carry important traps and risks—from entrenching dominant platforms’ power over the information flow to escalating arbitrary online surveillance and censorship. The path ahead must prevent this to the extent possible, and the new presidential decrees provide a mixed contribution towards this task. 

New Decrees: Strengths and Flaws  

The government published two decrees regulating the new regime set by the Supreme Court. One introduces changes to its previous regulation, the Decree 8.771/2016, detailing elements of the decision, including additional duties that the court only briefly addressed (Decree 12.975). The other regulates measures to tackle violence against women online (Decree 12.976). 

The Supreme Court's decision didn't establish guidelines to protect users' due process rights when facing content take down and removal demands. Instead, it relies on providers to self regulate, which could lead to over censorship.  

The decrees’ provisions on user notification systems are helpful in this sense. They stipulate that providers must inform users (both the notifier and the content author) about the decision to remove or keep the content up, why, and the means to appeal. The guidance makes explicit that a platform may reconsider and reinstate content after an appeal and must explain its reasons to the party requesting removal and content author. The decrees also address concerns with the weaponization of notification systems, establishing that internet applications must adopt measures to prevent abuses. 

Decree 12.795 reinforces that applications can keep content up after notification when there’s reasonable doubt that the post is unlawful, stating that the analysis should consider the context of the publications, freedom of religion and belief, and any informational, educational, or critical, satirical, or parodic purpose with the aim of ensuring freedom of expression. With these guidelines, it aims to mirror the Digital Services Act's "notice-and-action" approach. Moreover, for sexual related, intimate content, platforms will provide a specific and easily accessible notice channel where victims or their representatives can follow the case. 

One of the most concerning provisions requires applications to proactively report content related to criminal conduct on their platforms to government authorities. Applications must send the post along with information that can identify the user. The Ministry of Justice will regulate this provision, something the Supreme Court didn't touch on in its decision. While it seems to apply just to those providers already required to comply with new content-related obligations (exempting email and videoconference providers, for example), it takes a disastrous step beyond. It’s not only about preventing the spread of unlawful content online; it gets platforms to police and report users to authorities by handing identification information apparently without a court order. 

Decree 12.795 also details the definition of messaging applications that are exempt from notice and duty of care obligations. It excludes features for public dissemination of content and open groups so that the exemption doesn't apply. It's still unclear what exactly open groups mean. Especially regarding end-to-end encrypted applications, it's crucial that duties to monitor and take down don't affect conversations that are under this security architecture. Perhaps more troubling, the Supreme Court stated in its clarification ruling that a judicial order can determine email, voice and video conference, and messaging providers to take down content of private communications. Any measure must respect privacy and free expression safeguards and refrain from undermining end-to-end encryption. 

Furthermore, decree 12.976 importantly addresses the protection of women online, but it contains a broad definition of online violence against women that will guide how platforms handle takedown notices they receive. This definition involves "any act, conduct, or omission that causes (...) psychological, political, or economic suffering (…) in any aspect of their lives, committed, instigated, facilitated, or aggravated, in whole or in part, by the use of digital technologies." Its breadth could unfortunately result in censoring legitimate criticism and other protected speech, which platforms and authorities must avoid. 

The decrees also establish powers to the Brazilian Data Protection Agency (ANPD) to oversee and regulate the new regime. Among controversies, the decrees give ANPD the power to apply penalties for breaches of content-related obligations. These obligations go beyond agency competencies set in the Data Protection Law and the Law 15.211/2025, focused on the online protection of children and adolescents. They are also not clearly covered by Article 12 of Marco Civil as it stipulates administrative penalties for violations of its data privacy provisions.  

We appreciate that ANPD has been open to civil society's demands and concerns. While it’s crucial that the agency conducts its oversight role preserving a proportionality commitment and keeping solid participation channels, sanction powers must be prescribed by law. 

Alerts for the Path Ahead 

It’s true that there are critical platform accountability problems we must address, especially regarding the big players. And yes, platforms should align their policies and practices with human rights standards, including by dealing diligently with the dissemination of unlawful, toxic content. But accountability efforts should look at platforms’ systems and processes and promote measures to put checks on the power of tech giants, instead of having a prevalent focus on policing and reporting user behavior.  

Key digital competition measures to regulate gatekeeper platforms are under discussion in bill 4675/2025, but the proposal is pending in Congress with no clear timeline for approval.  

One important measure is to ensure accountability of take-down requests, including by the government. The Supreme Court’s decision stipulated that internet applications should publish transparency reports of the removal notices they receive. Government institutions should follow suit by periodically disclosing aggregate data of their own requests to online platforms, covering various types of user data and demands for content and account restrictions. Back in 2016, Marco Civil’s regulation decree established that all federal bodies must annually publish statistical reports on their requests of subscriber data to providers. To the best of our knowledge, federal bodies generally fail to meet this provision. ANPD can play a crucial role in stepping up transparency in the implementation of the new rules. 

Ultimately, platform accountability under the new liability regime hinges on how accountable its application will be by platforms and state institutions, and on the regime's commitment to protecting fundamental rights, including freedom of expression and privacy.  

  

 

Ammonia is one of the most important chemicals produced in the world, ranking second only to sulfuric acid in the total volume produced each year. It is used mostly to make fertilizer, which is essential to feeding the world’s population. Yet its production accounts for up to 2 percent of the world’s energy consumption and about 1.5 percent of greenhouse gas emissions, so the search has been underway for ways to produce ammonia more sustainably.

The traditional way of making ammonia, in use for more than a century and accounting for the vast majority of production, is the Haber-Bosch process, which relies on fossil fuels to provide the needed heat. Hydrogen used in the process is also largely produced from fossil fuels.

There is another way, using electrochemistry instead of heat and pressure, but so far this method has not been anywhere near economically competitive at the scales needed.

Now, researchers at MIT have developed a way to predict which materials could be most promising as catalysts in electrochemical ammonia production. Catalysts help drive chemical reactions, and their properties determine how efficiently those reactions proceed. Rather than using trial and error to test each possible combination out of the millions of possible alloys — which can take years — the new approach could greatly speed up the search for materials that could make this low-emissions method competitive with the Haber-Bosch process. 

“Our approach identifies the key physical properties that drive catalytic activity in ammonia production,” says Bilge Yildiz, the Breen M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering (DMSE). The results can guide the search for new and more effective catalyst compounds.

The open-access findings were published Aug. 11 in the Royal Society of Chemistry journal EES Catalysis, in a paper by Yildiz and doctoral students Constantine Athanitis of DMSE and Filip Grajkowski of the Department of Chemistry. 

The challenge of greener ammonia

As the world’s population grows, Athanitis says, “we’re just going to need more and more food, and the only reason why we’re able to sustain so many people is because of fertilizer.” But more than 90 percent of the ammonia needed for fertilizer is still made by that energy-intensive Haber-Bosch process, which “has been hyper-optimized since it first came out more than a century ago,” he says.

“If we’re trying to keep in line with society’s sustainability and energy targets and climate change targets, we really need to come up with another alternative,” he explains. The world currently uses about 200 million metric tons of ammonia each year, “so ideally we want to be able to find a way to produce the same amount of ammonia, or even more, but in a more energy-efficient way and also with lower CO2 emissions,” he says.

Using electricity to produce ammonia is not a new idea. “It’s really just the electrochemical reaction between proton-electron pairs and nitrogen gas. And these technologies exist,” he says. The approach uses the same basic principles as electrolyzers, which use electricity to drive chemical reactions in devices.

But while the process works, it’s not efficient enough for industrial-scale production. “Production rates and yields are still too low,” Athanitis says. “Even though a technology might be better for the world or for the climate, companies and capitalism won’t really allow it unless it’s cost competitive.”

How to make it more competitive? The key ingredient in the electrochemical process is a metallic catalyst, whose properties govern the reaction that takes place on its surface. “If we can somehow find a catalyst that reduces the energy needed and is more selective for ammonia production,” Athanitis says, “then we could essentially hit the jackpot.” A more selective catalyst would produce more ammonia while reducing unwanted side reactions.

Finding better catalysts

But finding that ideal catalyst is not simply a matter of identifying one perfect material. Different materials can improve different parts of the reaction, and researchers are seeking combinations that can make ammonia production efficient, affordable, and practical at large scale.

“Metal nitride compounds make an ideal material system for this reaction and for identifying the electronic, chemical, and structural properties that determine reactivity in nitrogen reduction and ammonia electrosynthesis,” Yildiz says. 

Transition metals could form promising nitride alloys for this purpose, and historically, “materials research has been pretty much trial and error,” Athanitis says.

The usual process is to take some existing material and “tweak it in some way,” he says. “It’s all somewhat guided by scientific and chemical intuition.” 

Now, increasingly, computational tools are being used to model the physical interactions and predict outcomes. A method called density functional theory uses quantum mechanics to simulate the properties and behavior of materials, allowing researchers to predict how different atomic arrangements may perform before making them in the lab. Rather than searching randomly through every possible alloy combination, Yildiz says, “we first assessed what microscopic properties of the material make them tick for nitrogen reduction.” 

For ammonia-producing catalysts, “we’re looking at transition metal nitrides,” Athanitis says, because they have been found to be effective in these electrochemical nitrogen reactions. They are especially effective because “the nitrogen inherent to the catalyst itself becomes part of the reaction.”

This produces a series of chemical steps in which one step provides part of the energy needed to drive the next, reducing the amount of input energy needed. This helps solve one of the major bottlenecks in the nitrogen reduction reaction: the high energy required to break the strong bonds in nitrogen molecules, he says.

But the process is far from perfect, Athanitis says. It is “still limited by certain steps throughout the reaction pathway, including nitrogen dissociation and hydrogen transfer.” The study attempted to identify those bottlenecks and, with the help of machine learning, determine which alloys of these metals might overcome them.

With that understanding, “it can give us insights and open up potential strategies for how we can tune these materials to create next-generation better nitride catalysts,” Athanitis says.

Pushing past theory

The approach is “exciting work” that could help develop a foundation for designing new catalysts for ammonia production, says Dane Morgan, a professor of engineering at the University of Wisconsin who was not involved in this study.

“This work helps clarify how fundamental electronic properties of a material relate to its role as a catalyst in making ammonia,” Morgan says. “Such understanding can help guide researchers in designing new catalysts, both through better qualitative understanding and by accelerating computational screening.” 

So far, the study is purely theoretical: The researchers have used computer models to identify promising alloys, but those materials still need to be made and tested. Morgan notes that “translating these calculations into practical catalysts will require many additional steps, so meaningful real-world impact is likely still some distance away.”

The next step will be to build a working reaction cell, a laboratory device that uses the catalyst to produce ammonia and test its performance under real operating conditions. “For this to really make an impact in society, we need to bring it to the experimental lab,” Athanitis says.

“There have always been pushes at the frontiers of what’s possible,” he adds. “We like to think we’ve pushed the boundary of candidate materials here beyond what was thought of before, and hopefully we’re almost there. But even if we’re not almost there, we’re still pushing in the right direction.”

OpenAI presented details of its AI’s model’s cyberattack on Hugging Face at Black Hat last week. Simon Willison details the timeline. It’s really interesting to read through—and really impressive cyberoffense work.