Breakthrough in Creating Fuel from Sunlight Puts Us Closer to Carbon-Neutral Energy
Since the beginning of life on Earth, plants have been naturally converting sunlight into energy. This photosynthesis process that's effortless for them has been anything but for scientists who have been trying to achieve artificial photosynthesis for the last half a century with the goal of creating a carbon-neutral fuel. Such a fuel could be a gamechanger — rather than putting CO2 back into the atmosphere like traditional fuels do, it would take CO2 out of the atmosphere and convert it into usable energy.
If given the option between a carbon-neutral fuel at the gas station and a fuel that produces carbon dioxide in spades -- and if costs and effectiveness were equal --who wouldn't choose the one best for the planet? That's the endgame scientists are after. A consumer switch to clean fuel could have a huge impact on our global CO2 emissions.
Up until this point, the methods used to make liquid fuel from atmospheric CO2 have been expensive, not efficient enough to really get off the ground, and often resulted in unwanted byproducts. But now, a new technology may be the key to unlocking the full potential of artificial photosynthesis. At the very least, it's a step forward and could help make a dent in atmospheric CO2 reduction.
"It's an important breakthrough in artificial photosynthesis," says Qian Wang, a researcher in the Department of Chemistry at Cambridge University and lead author on a recent study published in Nature about an innovation she calls "photosheets."
The latest version of the artificial leaf directly produces liquid fuel, which is easier to transport and use commercially.
These photosheets convert CO2, sunlight, and water into a carbon-neutral liquid fuel called formic acid without the aid of electricity. They're made of semiconductor powders that absorb sunlight. When in the presence of water and CO2, the electrons in the powders become excited and join with the CO2 and protons from the water molecules, reducing the CO2 in the process. The chemical reaction results in the production of formic acid, which can be used directly or converted to hydrogen, another clean energy fuel.
In the past, it's been difficult to reduce CO2 without creating a lot of unwanted byproducts. According to Wang, this new conversion process achieves the reduction and fuel creation with almost no byproducts.
The Cambridge team's new technology is a first and certainly momentous, but they're far from the only team to have produced fuel from CO2 using some form of artificial photosynthesis. More and more scientists are aiming to perfect the method in hopes of producing a truly sustainable, photosynthetic fuel capable of lowering carbon emissions.
Thanks to advancements in nanoscience, which has led to better control of materials, more successes are emerging. A team at the University of Illinois at Urbana-Champaign, for example, used gold nanoparticles as the photocatalysts in their process.
"My group demonstrated that you could actually use gold nanoparticles both as a light absorber and a catalyst in the process of converting carbon dioxide to hydrocarbons such as methane, ethane and propane fuels," says professor Prashant Jain, co-author of the study. Not only are gold nanoparticles great at absorbing light, they don't degrade as quickly as other metals, which makes them more sustainable.
That said, Jain's team, like every other research team working on artificial photosynthesis including the Cambridge team, is grappling with efficiency issues. Jain says that all parts of the process need to be optimized so the reaction can happen as quickly as possible.
"You can't just improve one [aspect], because that can lead to a decrease in performance in some other aspects," Jain explains.
The Cambridge team is currently experimenting with a range of catalysts to improve their device's stability and efficiency. Virgil Andrei, who is working on an artificial leaf design that was developed at Cambridge in 2019, was recently able to improve the performance and selectivity of the device. Now the leaf's solar-to-CO2 energy conversion efficiency is 0.2%, twice its previous efficiency.
The latest version also directly produces liquid fuel, which is easier to transport and use commercially.
In determining a method of fuel production's efficiency, one must consider how sustainable it is at every stage. That involves calculating whenever excess energy is needed to complete a step. According to Jain, in order to use CO2 for fuel production, you have to condense the CO2, which takes energy. And on the fuel production side, once the chemical reaction has created your byproducts, they need to be separated, which also takes energy.
To be truly sustainable, each part of the conversion system also needs to be durable. If parts need to be replaced often, or regularly maintained, that counts against it. Then you have to account for the system's reuse cycle. If you extract CO2 from the environment and convert it into fuel that's then put into a fuel cell, it's going to release CO2 at the other end. In order to create a fully green, carbon-neutral fuel source, that same amount of CO2 needs to be trapped and reintroduced back into the fuel conversion system.
"The cycle continues, and at each point, you will see a loss in efficiency, and depending on how much you [may also] see a loss in yield," says Jain. "And depending on what those efficiencies are at each one of those points will determine whether or not this process can be sustainable."
The science is at least a decade away from offering a competitive sustainable fuel option at scale. Streamlining a process to mimic what plants have perfected over billions of years is no small feat, but an ever-growing community of researchers using rapidly advancing technology is driving progress forward.
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.