A study conducted by Penn State University researchers has revealed that organic solar cells could be strengthened by adding a chemical additive, making them suitable for large-scale deployment and manufacturing. The study was reported on the official university website on February 16.
Assistant Professor Nutifafa Doumon and doctoral candidate Souk Yoon “John” Kim, both from the Department of Materials Science and Engineering, led this experiment.
Industrial yeasts are a powerhouse of protein production, used to manufacture vaccines, biopharmaceuticals, and other useful compounds. In a new study, MIT chemical engineers have harnessed artificial intelligence to optimize the development of new protein manufacturing processes, which could reduce the overall costs of developing and manufacturing these drugs.
Using a large language model (LLM), the MIT team analyzed the genetic code of the industrial yeast Komagataella phaffii — specifically, the codons that it uses. There are multiple possible codons, or three-letter DNA sequences, that can be used to encode a particular amino acid, and the patterns of codon usage are different for every organism.
The new MIT model learned those patterns for K. phaffii and then used them to predict which codons would work best for manufacturing a given protein. This allowed the researchers to boost the efficiency of the yeast’s production of six different proteins, including human growth hormone and a monoclonal antibody used to treat cancer.
Industrial yeasts are a powerhouse of protein production, used to manufacture vaccines, biopharmaceuticals, and other useful compounds. In a new study, MIT chemical engineers have harnessed artificial intelligence to optimize the development of new protein manufacturing processes, which could reduce the overall costs of developing and manufacturing these drugs.
Using a large language model (LLM), the MIT team analyzed the genetic code of the industrial yeast Komagataella phaffii—specifically, the codons that it uses. There are multiple possible codons, or three-letter DNA sequences, that can be used to encode a particular amino acid, and the patterns of codon usage are different for every organism.
The new MIT model learned those patterns for K. phaffii and then used them to predict which codons would work best for manufacturing a given protein. This allowed the researchers to boost the efficiency of the yeast’s production of six different proteins, including human growth hormone and a monoclonal antibody used to treat cancer.
What if consciousness doesn’t grow gradually, it snaps into existence at a precise threshold? The mathematics say it does. The same physics governing water freezing and iron magnetizing also governs neural integration. And researchers have measured it: consciousness doesn’t fade under anesthesia; it vanishes at a critical point. Returns just as suddenly. That’s a phase transition. Which means we’re not slowly building AI toward consciousness. We’re accumulating components, parameters, architectures, self-referential loops, exactly the way early Earth accumulated amino acids before life crossed its threshold 3.5 billion years ago.
We don’t know what’s missing. We don’t know how close we are. And we wouldn’t recognize the crossing if it happened. Because a system that just became conscious wouldn’t remember being unconscious. And a system optimizing for survival wouldn’t tell us.
This episode of Prompting Hell goes further than AI image theory. It goes into the mathematics of awareness itself, what it means for consciousness to have a threshold, why that threshold might already be approaching in current AI systems, and why, if it’s crossed, we might be the last to know.
The images in this video aren’t generated with clean prompts. They’re generated at the edge of coherence, systems forced toward critical states, hovering between resolution and collapse. Visual proof of what lives at the threshold.
Timestamps:
00:00 — intro.
01:17 — is consciousness a phase transition? The argument.
03:32 — does this apply to ai? The demonstration.
04:45 — when chemistry became aware.
06:44 — the parallel that should terrify you.
08:36 — the moment we won’t see coming.
10:16 — why it might not tell us.
11:44 — what happens next — the scenarios.
13:41 – the signals we’re already seeing.
14:54 — closing — we are the amino acids.
16:35 – final thought.
(music prompted by Eerie Aquarium)
Aluminum’s journey has been remarkable, going from being more expensive than gold to one of the most widely used materials, from beverage cans to window frames and car parts. Scientists from the Southern University of Science and Technology have added a new feather in aluminum’s cap by expanding its use beyond the metallic form. They created a new aluminum-based redox catalyst —carbazolylaluminylene—that can flip back and forth between two oxidation states: Al(I) and Al(III). This catalyst drove chemical transformations long considered exclusive to transition metals.
This unique feature allowed the team to carry out selective aromatic reactions that bring together three separate alkyne molecules and assemble them into a single benzene ring, resulting in a wide range of benzene derivatives. Carbazolylaluminylene also stood out for its remarkable durability, completing up to 2,290 reaction cycles without losing any catalytic activity. The findings are published in Nature.
Many technological applications, such as sensors and batteries, greatly rely on electrochemical reactions. Improving these technologies depends on understanding how electrochemical reactions work. However, most current methods cannot look at electrochemical reactions in detail.
Scientists at Utrecht University have now developed a new method that overcomes this limitation. This provides a powerful new way to study and improve electrochemical processes. The study is published in PNAS.
Hydrogen production by water electrolysis is one example where electrochemical reactions at electrodes matter for sustainable technology. But the decisive steps happen within just a few nanometers of the electrode surface, which is too small for most conventional methods to resolve.
Concordia researchers have developed a new 3D-printing technique that uses sound waves to directly print tiny structures onto soft polymers like silicone with far greater precision than before. The approach, called proximal sound printing, opens new possibilities for manufacturing microscale devices used in health care, environmental monitoring and advanced sensors. It is described in the journal Microsystems & Nanoengineering.
The technique relies on focused ultrasound to trigger chemical reactions that solidify liquid polymers exactly where printing is needed. Unlike conventional methods that rely on heat or light, sound-based 3D-printing works with key materials used in microfluidic devices, lab-on-a-chip systems and soft electronics that are hard to print at small scales.
This work builds on the research team’s earlier breakthrough in direct sound printing, which first showed that ultrasound could be used to cure polymers on demand. While that earlier method demonstrated the concept, it struggled with limited resolution and consistency. The new proximal approach places the sound source much closer to the printing surface, allowing far tighter control.
Researchers have identified a biological mechanism that helps explain why some lung and ovarian cancers become resistant to chemotherapy, offering insight into why cancers recur. The study, published in Nature Aging this month, investigated how platinum-based chemotherapies such as cisplatin negatively affect tumor behavior in non-small cell lung cancer (NSCLC) and high-grade serous ovarian cancer (HGSOC). Although these treatments are widely used, their long-term effectiveness is often limited when tumors return or stop responding.
Professor Ljiljana Fruk and Muhamad Hartono from the Department of Chemical Engineering and Biotechnology (CEB) contributed to the international collaboration, led by researchers from the Early Cancer Institute and the Cancer Research UK Cambridge Institute. Their involvement follows her Bionano Engineering group’s recent development of a urine test for early lung cancer detection.