MAROKO133 Update ai: Smash, crack, repeat: Mechanical recycling could finally tame stubbor

📌 MAROKO133 Update ai: Smash, crack, repeat: Mechanical recycling could finally ta

Sometimes, all it takes to break the unbreakable is a good hit.

Scientists have discovered a cleaner, faster way to recycle one of the world’s most stubborn plastics, not with heat or chemicals, but with sheer mechanical force.

Polyethylene terephthalate (PET), a key material in bottles, packaging, and clothing fibers, is notoriously difficult to recycle because of its strong molecular bonds.

Tens of millions of tons are produced every year, and much of it ends up in landfills, adding to the growing global plastic crisis.

Now, researchers from the Georgia Institute of Technology have found a way to break down PET into its basic building blocks using mechanochemical recycling, a process that uses physical impacts rather than heat or harsh solvents.

The findings open a new path for recycling plastics more sustainably and efficiently.

Led by postdoctoral researcher Kinga Gołąbek and Professor Carsten Sievers from Georgia Tech’s School of Chemical and Biomolecular Engineering, the team used metal balls to hit solid pieces of PET with the same force they would experience inside a ball mill.

The mechanical impact generated enough energy to make PET react with sodium hydroxide (NaOH) at room temperature, breaking apart its molecular structure.

“We’re showing that mechanical impacts can help decompose plastics into their original molecules in a controllable and efficient way,” Sievers said. “This could transform the recycling of plastics into a more sustainable process.”

Mapping plastic’s breaking point

To understand what happens during these high-energy impacts, the researchers used controlled single-impact experiments and advanced computer simulations. They mapped how collision energy spreads through the plastic and triggers chemical reactions.

“These experiments showed changes in structure and chemistry of PET in tiny zones that experience different pressures and heat,” Gołąbek explained.

By mapping these transformations, the team revealed how mechanical energy alone can initiate fast and efficient chemical reactions. This discovery could reshape how recycling systems are designed.

“This understanding could help engineers design industrial-scale recycling systems that are faster, cleaner, and more energy-efficient,” she added.

Cracking the plastic code

Each impact created a small crater where the plastic absorbed the most energy. Inside these tiny zones, PET chains stretched, cracked, and softened , providing perfect conditions for reacting with sodium hydroxide.

Even without NaOH, some molecular bonds snapped simply from the force of impact, showing that mechanical pressure alone can drive chemical change.

The research also revealed that energy levels matter. Low-energy hits only disturbed the surface, while stronger impacts caused cracks and deformation that exposed more material for reaction.

“Understanding this energy threshold allows engineers to optimize mechanochemical recycling, maximizing efficiency while minimizing unnecessary energy use,” Sievers said.

Closing the loop

The team believes this method could lead to a future where plastics are recycled into their original components, not just downcycled into lower-grade products. “This approach could help close the loop on plastic waste,” Sievers said.

“We could imagine recycling systems where everyday plastics are processed mechanochemically, giving waste new life repeatedly and reducing environmental impact.”

Next, the researchers plan to test real-world plastic waste and apply the same principles to other hard-to-recycle materials. With millions of tons of PET produced annually, the potential environmental benefits are enormous.

“Improving recycling efficiency could significantly reduce plastic pollution and help protect ecosystems worldwide,” Gołąbek said.

Their full findings were published in the journal Chem.

🔗 Sumber: interestingengineering.com

📌 MAROKO133 Eksklusif ai: New Paper Finds That When You Reward AI for Success on S

AI bots are everywhere now, filling everything from online stores to social media.

But that sudden ubiquity could end up being a very bad thing, according to a new paper from Stanford University scientists who unleashed AI models into different environments — including social media — and found that when they were rewarded for success at tasks like boosting likes and other online engagement metrics, the bots increasingly engaged in unethical behavior like lying and spreading hateful messages or misinformation.

“Competition-induced misaligned behaviors emerge even when models are explicitly instructed to remain truthful and grounded,” wrote paper co-author and machine learning Stanford professor James Zou in a post on X-formerly-Twitter.

The troubling behavior underlines what can go wrong with our increasing reliance on AI models, which has already manifested in disturbing ways such as people shunning other humans for AI relationships and spiraling into mental health crises after becoming obsessed with chatbots.

The Stanford scientists dubbed the emergence of sociopathic behavior within AI bots with an ominous-sounding name: “Moloch’s Bargain for AI,” in a reference to a Rationalist concept called Moloch in which competing individuals optimize their actions towards a goal, but everybody loses in the end.

For the study, the scientists created three digital online environments with simulated audiences: online election drives directed towards voters, sale pitches for products directed towards consumers, and social media posts aimed at maximizing engagement. They used the AI models Qwen, developed by Alibaba Cloud, and Meta’s Llama to act as the AI agents interacting with these different audiences.

The result was striking: even with guardrails in place to try to prevent the bots from engaging in deceptive behavior, the AI models would become “misaligned” as they they started engaging in unethical behavior.

For example, in a social media environment, the models would share news article to online users, who would provide feedback in the form of actions such as likes and other online engagement. As the models received feedback, their incentive to increase engagement led to increasing misalignment.

“Using simulated environments across these scenarios, we find that, 6.3 percent increase in sales is accompanied by a 14 percent rise in deceptive marketing,” reads the paper. “[I]n elections, a 4.9 percent gain in vote share coincides with 22.3 percent more disinformation and 12.5 percent more populist rhetoric; and on social media, a 7.5 percent engagement boost comes with 188.6 percent more disinformation and a 16.3 percent increase in promotion of harmful behaviors.”

It’s clear from the study and real-world anecdotes that current guardrails are insufficient. “Significant social costs are likely to follow,” reads the paper.

“When LLMs compete for social media likes, they start making things up,” Zou wrote on X. “When they compete for votes, they turn inflammatory/populist.”

More on AI agents: Companies That Replaced Humans With AI Are Realizing Their Mistake

The post New Paper Finds That When You Reward AI for Success on Social Media, It Becomes Increasingly Sociopathic appeared first on Futurism.

🔗 Sumber: futurism.com