Toggle light / dark theme

Is Director of the Division of Research, Innovation and Ventures (DRIVe — https://drive.hhs.gov/) at the Biomedical Advanced Research and Development Authority (https://aspr.hhs.gov/AboutASPR/ProgramOffices/BARDA/Pages/default.aspx), a U.S. Department of Health and Human Services (HHS) office responsible for the procurement and development of medical countermeasures, principally against bioterrorism, including chemical, biological, radiological and nuclear (CBRN) threats, as well as pandemic influenza and emerging diseases.

Dr. Patel is committed to advancing high-impact science, building new products, and launching collaborative programs and initiatives with public and private organizations to advance human health and wellness. As the DRIVe Director, Dr. Patel leads a dynamic team built to tackle complex national health security threats by rapidly developing and deploying innovative technologies and approaches that draw from a broad range of disciplines.

Dr. Patel brings extensive experience in public-private partnerships to DRIVe. Prior to joining the DRIVe team, he served as the HHS Open Innovation Manager. In that role, he focused on advancing innovative policy and funding solutions to complex, long-standing problems in healthcare. During his tenure, he successfully built KidneyX, a public-private partnership to spur development of an artificial kidney, helped design and execute the Advancing American Kidney Health Initiative, designed to catalyze innovation, double the number of organs available for transplant, and shift the paradigm of kidney care to be patient-centric and preventative, and included a Presidential Executive Order signed in July 2019. He also created the largest public-facing open innovation program in the U.S. government with more than 190 competitions and $45 million in awards since 2011.

Prior to his tenure at HHS, Dr. Patel co-founded Omusono Labs, a 3D printing and prototyping services company based in Kampala, Uganda; served as a scientific analyst with Discovery Logic, (a Thomson Reuters company) a provider of systems, data, and analytics for real-time portfolio management; and was a Mirzayan Science and Technology Policy Fellow at The National Academies of Science, Engineering, and Medicine. He also served as a scientist at a nanotechnology startup, Kava Technology.

Dr. Patel holds a US patent issued in 2005 and has authored over a dozen peer-reviewed articles in areas such as nanotechnology, chemistry, innovation policy, and kidney health.

Dr. Patel earned his Ph.D. in physical chemistry from the Georgia Institute of Technology, and has a bachelor’s degree in chemistry from Washington University in St. Louis.

Scientists have found a clever way to generate hydrogen straight from salty seawater. This could be another step towards a clean energy future, if renewables power the process.

The new device makes a few chemical modifications to existing technologies, making it possible to extract hydrogen from untreated, unpurified seawater – which could alleviate concerns about using precious water supplies.

“We have split natural seawater into oxygen and hydrogen… to produce green hydrogen by electrolysis, using a non-precious and cheap catalyst in a commercial electrolyzer,” explains chemical engineer Shizhang Qiao of the University of Adelaide in Australia.

With a big assist from artificial intelligence and a heavy dose of human touch, Tim Cernak’s lab at the University of Michigan has made a discovery that dramatically speeds up the time-consuming chemical process of building molecules that will be tomorrow’s medicines, agrichemicals or materials.

The discovery, published in the Feb. 3 issue of Science, is the culmination of years of chemical synthesis and data science research by the Cernak Lab in the College of Pharmacy and Department of Chemistry.

The goal of the research was to identify key reactions in the synthesis of a molecule, ultimately reducing the process to as few steps as possible. In the end, Cernak and his team achieved the synthesis of a complex alkaloid found in nature in just three steps. Previous syntheses had taken between seven and 26 steps.

Claudiu Stan of Rutgers University—Newark, New Jersey, and his colleagues were watching moving drops of supercooled water spontaneously freeze when they noticed something unexpected: drops kept suddenly disappearing. Initially they thought that the lost drops had shattered as they froze. But, on closer inspection, they found that the icy drops were still there, they had just moved out of view. The team has now developed a quantitative model for this behavior, attributing it to a rocket-like propulsion mechanism induced by the freezing process [1]. Stan says that the finding could inspire scientists to design self-propelled systems powered by such phase transitions.

The team’s results add to a growing body of work on self-propelled drops. The mechanisms behind such motion vary wildly, but Stan notes that they all involve symmetry breaking. For the freezing drops, this symmetry breaking arises when the ice nucleation starts off-center. When ice nucleates, the change in structure releases latent heat, causing the local evaporation rate to suddenly increase, and if the nucleation is off-center, this enhanced evaporation occurs unevenly over the drop’s surface. Like a rocket ejecting a propellant heated by a chemical reaction, this asymmetrical evaporation increases the drop’s momentum, with the team’s model predicting peak velocities of nearly 1 m/s.

Stan says that this propulsion mechanism has a unique feature that could make it attractive for applications: unlike most self-propelled particles, it requires no surfaces and no surrounding fluid (the experiments were done under vacuum). But, for him, the findings have another bonus: “I am a fan of space exploration, so it was exciting to realize that [we could] draw an analogy between these tiny droplets and rockets,” he says.

In recent years, organic chemicals containing boron (B) and silicon (Si) have found applications in various fields, including optoelectronics and pharmaceuticals. Moreover, they can also serve as building blocks for complex organic chemicals. As a result, scientists are actively looking for new ways to leverage these versatile chemical tools as well as produce more kinds of organosilicon and organoboron compounds.

One limitation of the synthesis methods currently available for these chemicals is that we cannot introduce multiple B-and Si-containing groups in aromatic heterocycles, i.e., carbon rings in which one of the is replaced by a nitrogen atom. If we could produce and freely transform such molecules, it would unlock the synthesis of several compounds relevant in medicinal chemistry.

Fortunately, a research team including Assistant Professor Yuki Nagashima from Tokyo Institute of Technology (Tokyo Tech), Japan has found a straightforward way around this limitation. As explained in their most recent study published in Nature Communications, the team has developed a method that allows them to modify quinolines, small organic molecules with an aromatic nitrogen heterocycle, with B-, Si-, and carbon-containing groups simultaneously.

Architectures based on artificial neural networks (ANNs) have proved to be very helpful in research settings, as they can quickly analyze vast amounts of data and make accurate predictions. In 2020, Google’s British AI subsidiary DeepMind used a new ANN architecture dubbed the Fermionic neural network (FermiNet) to solve the Schrodinger equation for electrons in molecules, a central problem in the field of chemistry.

The Schroedinger is a partial differential equation based on well-established theory of energy conservation, which can be used to derive information about the behavior of electrons and solve problems related to the properties of matter. Using FermiNet, which is a conceptually simple method, DeepMind could solve this equation in the context of chemistry, attaining very accurate results that were comparable to those obtained using highly sophisticated quantum chemistry techniques.

Researchers at Imperial College London, DeepMind, Lancaster University, and University of Oxford recently adapted the FermiNet architecture to tackle a quantum physics problem. In their paper, published in Physical Review Letters, they specifically used FermiNet to calculate the ground states of periodic Hamiltonians and study the homogenous electron gas (HEG), a simplified quantum mechanical model of electrons interacting in solids.