The Roswell Molecular Electronics Chip uses single molecules as universal sensor elements in a circuit to create a programmable biosensor with real-time, single-molecule sensitivity and unlimited scalability in sensor pixel density. The first molecular electronics chip has been developed, realizing a 50-year-old goal of integrating single molecules into circuits to achieve the ultimate scaling limits of Moore's Law. Developed by Roswell Biotechnologies and a multi-disciplinary team of leading academic scientists, the chip uses single molecules as universal sensor elements in a circuit to create a programmable biosensor with real-time, single-molecule sensitivity and unlimited scalability in sensor pixel density. This innovation, appearing this week in a peer-reviewed article in the Proceedings of the National Academy of Sciences (PNAS), will power advances in diverse fields that are fundamentally based on observing molecular interactions, including drug discovery, diagnostics, DNA sequencing, and proteomics. "Biology works by single molecules talking to each other, but our existing measurement methods cannot detect this," said co-author Jim Tour, PhD, a Rice University chemistry professor and a pioneer in the field of molecular electronics.

Scientists seek stable, high-energy batteries designed for the electric grid. Bringing new sources of renewable energy like wind and solar power onto the electric grid will require specially designed large batteries that can charge when the sun is shining and give energy at night. One type of battery is especially promising for this purpose: The flow battery. Flow batteries contain two tanks of electrically active chemicals that exchange charge and can have large volumes that hold a lot of energy. For researchers working on flow batteries, their chief concern involves finding target molecules that offer the ability to both store a lot of energy and remain stable for long periods of time.

A research team centered at Osaka University, in collaboration with RIKEN, has developed a system that can overcome these difficulties by automatically searching for, focusing on, imaging, and tracking single molecules within living cells. The team showed that this approach could analyze hundreds of thousands of single molecules in hundreds of cells in a short period, providing reliable data on the status and dynamics of molecules of interest. For the development of this method, reported in the journal Nature Communications, the team used an artificial intelligence-based system, involving the training of neural networks to learn to focus correctly on a sample and to automatically search for cells, followed by the tracking of single fluorescently labeled molecules with a total internal reflection fluorescence microscope. The team tested this system on a receptor protein called EGFR, which is more or less free to move along the plasma membrane in which it is expressed depending on whether it has undergone a certain modification. Their findings showed that the system could differentiate between modifying and nonmodifying conditions by tracking the movements of single receptors in membranes.