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Srinivas Rangarajan wins NSF CAREER award for catalytic transfer hydrogenation research



Lehigh University computational chemical engineer to advance fundamental understanding of promising approach for safe, streamlined, cost-effective biomass and CO2 conversion

Is it possible to build safe, sustainable chemical plants on a small scale? The kind of plants that could–among other things–convert biomass into biofuel, on the very farms producing those crops?

Possibly. But doing so requires figuring out, in part, a more benign process of hydrogenation, the chemical reaction between molecular hydrogen and other compounds and elements that is used to create new molecules. 

Srinivas Rangarajan, an assistant professor of chemical and biomolecular engineering in Lehigh University’s P.C. Rossin College of Engineering and Applied Science, recently won support from the National Science Foundation’s Faculty Early Career Development (CAREER) program for his proposal to develop novel tools to better understand a promising chemistry called catalytic transfer hydrogenation (CTH).

The prestigious NSF CAREER award is given annually to junior faculty members across the U.S. who exemplify the role of teacher-scholars through outstanding research, excellent education, and the integration of education and research. Each award provides stable support at the level of approximately $500,000 for a five-year period.

When hydrogenation is used to make ultra-low-sulfur diesel, for example, it requires a huge catalytic reactor, temperatures of up to 500 degrees Celsius, and hundreds of pounds of pressure. 

Similar chemistry could be used at a much smaller scale to do things like process biomass, upcycle plastic waste, or manufacture specialty chemicals. But such distributed processing would require reactors that are much smaller and operate at significantly lower temperatures and pressures. 

And that means using a different hydrogen source.

“The problem with molecular hydrogen is that it’s very light,” says Rangarajan. “To transport it, you need to compress it using very high pressures. And to use it, you need very high pressures. But if you can use a different molecule that can carry hydrogen chemically, that molecule could be used as a hydrogen donor. It will decompose or get converted, and in the process, lose hydrogen, which then becomes available for something like biomass conversion.” 

The process of transferring hydrogen atoms from a hydrogen donor to a hydrogen acceptor is called catalytic transfer hydrogenation. “So instead of using a molecular hydrogen, you’re using a liquid molecule that can be shipped very easily from where it’s produced to wherever it’s needed,” he explains. “It can then be used at near-ambient temperatures, and at one atmospheric pressure. So in that sense, CTH could lead to a more compact, safe, modular process.”

While that’s the big-picture view, Rangarajan’s focus is on the molecular level. He will develop a novel computational framework to answer two fundamental questions: How does this hydrogen transfer work exactly at the molecular scale, and what’s the right donor and the right catalyst for a given acceptor? 

“I’m using a model compound called acrolein as my hydrogen acceptor,” he says. “It has functionalities representative of many biomass compounds and many unsaturated chemicals that come up in the chemicals industry, for example, oleochemicals, solvents, and pharmaceuticals. To hydrogenate acrolein, I need to find out what is the right molecule to supply the hydrogen, and what is the right catalyst that can do this chemistry selectively, that is, without making any undesired byproducts.” 

To address possible combinations of donors and catalysts that number in the tens of thousands, Rangarajan will build on his prior work, as well as his recent work at Lehigh, to create a technique called high-throughput kinetic modeling. It incorporates quantum chemistry, optimization, and machine learning to build models that will provide data on how different parameters affect performance.

“This technique allows us to determine how the parameters affect the speed of the reaction and whether the desired product will actually be formed,” he says. “This hasn’t been done before.”  

The ultimate goal is to develop processes that are more energy efficient. The potential impact could be huge, he says, given that 45 million tons of hydrogen are used globally every year to make chemicals and energy carriers like fuels. Alternatives to molecular hydrogen could reduce operating expenses for plants by reducing infrastructure, storage, and transportation costs.

But more immediately, where economies of scale don’t work, “like for a farm looking to process its biomass or a specialty chemical company that doesn’t have large production requirements, the main idea is you’re looking for process intensification,” he says. “Smaller, cost-effective, safe solutions that are energy and carbon efficient.”

The CAREER funding will also support educational outreach around what Rangarajan calls “computational thinking” in both undergraduate and graduate students. He’s developing a range of activities, including research projects in software development, a new course in data science for chemical engineers, and experiential learning opportunities through Lehigh’s Office of Creative Inquiry focused on building interactive data-visualization models. He’ll also assist area high school teachers interested in programming or modeling.

“Computations and data science are becoming more and more mainstream in chemical engineering,” he says. “We’re increasingly using mathematics, mathematical modeling, and data to solve problems in industry and academia. I’m really looking forward to promoting algorithmic thinking in the next generation of scientists and engineers.”   


About Srinivas Rangarajan

Srinivas Rangarajan is an assistant professor of chemical and biomolecular engineering at Lehigh University. He joined the faculty of the P.C. Rossin College of Engineering and Applied Science in January 2017, after his stint as a postdoctoral scholar at the University of Wisconsin, Madison. He received his B.Tech. (2007) from the Indian Institute of Technology, Madras, and PhD (2013) from the University of Minnesota, both in chemical engineering. His industrial experience includes previous employment (2007-2008) at Shell Global Solutions in the Netherlands and India as a senior associate technologist in hydroprocessing.

Research in the Rangarajan group is at the intersection of heterogeneous catalysis, materials science, and process systems engineering. The group develops and applies a variety of computational tools to model and design catalytic systems and materials that are of relevance in energy and environment and are governed by complex chemistries. The spectrum of tools include electronic structure calculations using density functional theory (DFT), microkinetic modeling, optimization, cheminformatics, automated mechanism generation, and machine learning. 

Rangarajan has published over 35 peer-reviewed articles, including a dozen since joining Lehigh, in reputed journals such as ACS Catalysis, Applied Catalysis B, Accounts of Chemical Research, and Nature Communications. His recent awards and honors include the David Smith Graduate Publication Award from the American Institute of Chemical Engineers (CAST division), P.C. Rossin Assistant Professorship, and John Ochs Faculty Achievement Award from the Baker Institute of Creative Inquiry. His group has been supported by grants from the NSF, ACS-PRF (Doctoral New Investigator award), and the Commonwealth of Pennsylvania (PITA).

Related Links:

  • Rossin College Faculty Profile: Srinivas Rangarajan
  • NSF Award Abstract (2045550): CAREER: “Computational design of sustainable hydrogenation systems via a novel combination of…”
  • Lehigh University: Mountaintop Summer Experience
  • NSF: Faculty Early Career Development Program (CAREER)
  • Lehigh University: Rangarajan group

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Invention uses machine-learned human emotions to ‘drive’ autonomous vehicles



FAU College of Engineering and Computer Science receives US utility patent for ‘adaptive mood control in semi or fully autonomous vehicles’

Americans have one of the highest levels of fear in the world when it comes to technology related to robotic systems and self-driving cars. Addressing these concerns is paramount if the technology hopes to move forward.

A researcher from Florida Atlantic University’s College of Engineering and Computer Science has developed new technology for autonomous systems that is responsive to human emotions based on machine-learned human moods. His solution, “Adaptive Mood Control in Semi or Fully Autonomous Vehicles,” has earned a very competitive utility patent from the United States Patent and Trademark Office for FAU.

Adaptive Mood Control provides a convenient, pleasant, and more importantly, trustworthy experience for humans who interact with autonomous vehicles. The technology can be used in a wide range of autonomous systems, including self-driving cars, autonomous military vehicles, autonomous airplanes or helicopters, and even social robots.

“The uniqueness of this invention is that the operational modes and parameters related to perceived emotion are exchanged with adjacent vehicles for achieving objectives of the adaptive mood control module in the semi or fully autonomous vehicle in a cooperative driving context,” said Mehrdad Nojoumian, Ph.D., inventor, and an associate professor in the Department of Computer and Electrical Engineering and Computer Science and director of the Privacy, Security and Trust in Autonomy Lab. “Human-AI/autonomy interaction is at the center of attention by academia and industries. More specifically, trust between humans and AI/autonomous technologies plays a critical role in this domain, because it will directly affect the social acceptability of these modern technologies.”

The patent, titled “Adaptive Mood Control in Semi or Fully Autonomous Vehicles,” uses non-intrusive sensory solutions in semi or fully autonomous vehicles to perceive the mood of the drivers and passengers. Information is collected based on facial expressions, sensors within the handles/seats and thermal cameras among other monitoring devices. Additionally, the adaptive mood control system contains real-time machine-learning mechanisms that can continue to learn the driver’s and passengers’ moods over time. The results are then sent to the autonomous vehicle’s software system allowing the vehicle to respond to perceived emotions by choosing an appropriate mode of operations such as normal, cautious or alert driving mode.

“One of the major issues with the technology of fully or semi-autonomous vehicles is that they may not be able to accurately predict the behavior of other self-driving and human-driving vehicles. This predication is essential to properly navigate autonomous vehicles on roads,” said Stella Batalama, Ph.D., dean, College of Engineering and Computer Science. “Professor Nojoumian’s innovative and cutting-edge technology circumvents this problem by using machine learning algorithms to learn patterns of behaviors of people riding in these vehicles.”


About FAU’s College of Engineering and Computer Science:

The FAU College of Engineering and Computer Science is internationally recognized for cutting edge research and education in the areas of computer science and artificial intelligence (AI), computer engineering, electrical engineering, bioengineering, civil, environmental and geomatics engineering, mechanical engineering, and ocean engineering. Research conducted by the faculty and their teams expose students to technology innovations that push the current state-of-the art of the disciplines. The College research efforts are supported by the National Science Foundation (NSF), the National Institutes of Health (NIH), the Department of Defense (DOD), the Department of Transportation (DOT), the Department of Education (DOEd), the State of Florida, and industry. The FAU College of Engineering and Computer Science offers degrees with a modern twist that bear specializations in areas of national priority such as AI, cybersecurity, internet-of-things, transportation and supply chain management, and data science. New degree programs include Masters of Science in AI (first in Florida), Masters of Science in Data Science and Analytics, and the new Professional Masters of Science degree in computer science for working professionals. For more information about the College, please visit

About Florida Atlantic University:

Florida Atlantic University, established in 1961, officially opened its doors in 1964 as the fifth public university in Florida. Today, the University serves more than 30,000 undergraduate and graduate students across six campuses located along the southeast Florida coast. In recent years, the University has doubled its research expenditures and outpaced its peers in student achievement rates. Through the coexistence of access and excellence, FAU embodies an innovative model where traditional achievement gaps vanish. FAU is designated a Hispanic-serving institution, ranked as a top public university by U.S. News & World Report and a High Research Activity institution by the Carnegie Foundation for the Advancement of Teaching. For more information, visit

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Semiconductor technology mitigates fire risk in electric vehicle batteries



Convergence of semiconductor physics and electrochemistry leads to effective inhibition of dendrite formation using semiconducting passivation layers

Despite rapid development of electric vehicles (EVs), the safety of the lithium-ion (Li-ion) batteries remains a concern as they are as a fire and explosion risk. Among the various approaches to tackle this issue, Korean researchers have used semiconductor technology to improve the safety of Li-ion batteries. A research team from the Korea Institute of Science and Technology (KIST) led by Dr. Joong Kee Lee of the Center for Energy Storage Research has succeeded in inhibiting the growth of dendrites, crystals with multiple branches that cause EV battery fires by forming protective semiconducting passivation layers on the surface of Li electrodes.

When Li-ion batteries are charged, Li ions are transported to the anode (the negative electrode) and are deposited on the surface as Li metal; at this point, tree-like dendrites are formed. These Li dendrites are responsible for the uncontrollable volumetric fluctuations and leads to reactions between the solid electrode and the liquid electrolyte, which causes a fire. Unsurprisingly, this severely degrades battery performance.

To prevent dendrite formation, the research team exposed fullerene (C60), a highly electronic conductive semiconductor material, to plasma, resulting in the formation of semiconducting passivation carbonaceous layers between the Li electrode and the electrolyte. The semiconducting passivation carbonaceous layers allow Li-ions to pass through while blocking electrons due to generation of Schottky barrier, and by preventing electrons and ions from interacting on the electrode surface and inside, they stops the formation of Li crystals and the consequent growth of dendrites.

*fullerene : a particular physical form of carbon in which 60 carbon atoms are connected by single and double bonds in a pentagonal shape to form a soccer ball-like shape

The stability of the electrodes with the semiconducting passivation carbonaceous layers was tested using Li/Li symmetric cells in extreme electrochemical environments where typical Li electrodes remain stable for up to 20 charge/discharge cycles. The newly developed electrodes showed significantly enhanced stability, with Li dendrite growth suppressed for up to 1,200 cycles. Moreover, using a lithium cobalt oxide (LiCoO2) cathode in addition to the developed electrode, approximately 81% of the initial battery capacity was maintained after 500 cycles, representing an improvement of approximately 60% over conventional Li electrodes.

Lead researcher Dr. Joong Kee Lee said, “The effective suppression of dendrite growth on Li electrodes is instrumental for improving battery safety. The technology for developing highly safe Li-metal electrodes proposed in this study provides a blueprint for the development of next-generation batteries that do not pose a fire risk.” As Dr. Lee explains, his team’s next goal is improving the commercial viability of this technology, “We aim to make the fabrication of the semiconducting passivation carbonaceous layers more cost-effective by substituting fullerene with less expensive materials.”


This research was carried out as part of a KIST’s institutional R&D project and a mid-career researcher project. It also received funding as an outstanding new overseas research project from the National Research Foundation of Korea with the support of the Ministry of Science and ICT (MSIT). The results of this study are published in the latest issue of ‘ACS Energy Letters‘ (IF: 19.003, Top 1.852% in JCR), a highly respected international journal in the field of materials science.

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Drone footage reveals social secrets of killer whales



Killer whales have complex social structures including close “friendships”, according to a new study that used drones to film the animals.

The findings show that killer whales spend more time interacting with certain individuals in their pod, and tend to favour those of the same sex and similar age.

The study, led by the University of Exeter and the Center for Whale Research (CWR), also found that the whales become less socially connected as they get older.

“Until now, research on killer whale social networks has relied on seeing the whales when they surface, and recording which whales are together,” said lead author Dr Michael Weiss, of the University of Exeter.

“However, because resident killer whales stay in the social groups into which they’re born, how closely related whales are seemed to be the only thing that explained their social structure.

“Looking down into the water from a drone allowed us to see details such as contact between individual whales.

“Our findings show that, even within these tight-knit groups, whales prefer to interact with specific individuals.

“It’s like when your mom takes you to a party as a kid – you didn’t choose the party, but you can still choose who to hang out with once you’re there.”

Patterns of physical contact – one of the social interactions the study measured – suggest that younger whales and females play a central social role in the group. The older the whale, the less central they became.

The new research built on more than four decades of data collected by CWR on southern resident killer whales, a critically endangered population in the Pacific Ocean.

“This study would not have been possible without the amazing work done by CWR,” said Professor Darren Croft, of Exeter’s Centre for Research in Animal Behaviour.

“By adding drones to our toolkit, we have been able to dive into the social lives of these animals as never before.

“We were amazed to see how much contact there is between whales – how tactile they are.

“In many species, including humans, physical contact tends to be a soothing, stress-relieving activity that reinforces social connection.

“We also examined occasions when whales surfaced together – as acting in unison is a sign of social ties in many species.

“We found fascinating parallels between the behaviour of whales and other mammals, and we are excited about the next stages of this research.”


The start of this drone project – including the purchase of one of the drones used in this study – was made possible by a crowd-funding campaign supported by members of the public, including University of Exeter alumni.

Results from the new study are based on 651 minutes of video filmed over ten days.

The study’s use of drones was conducted under research permits issued by the US National Marine Fisheries Service, and all pilots were licensed under the US Federal Aviation Administration.

The research team included the universities of York and Washington, and the Institute of Biophysics, and the study was partly funded by the Natural Environment Research Council (NERC).

The study, published in the journal Proceedings of the Royal Society B, is entitled: “Age and sex influence social interactions, but not associations, within a killer whale pod.”

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PSMA-targeted radiotracer pinpoints metastatic prostate cancer across anatomic regions



Credit: Images courtesy of Lantheus Holdings, Inc., Billerica, MA.

Reston, VA (Embargoed until 3:00 p.m. EDT, Tuesday, June 15, 2021)–A phase III clinical trial has validated the effectiveness of the prostate-specific membrane antigen (PSMA)-targeted radiotracer 18F-DCFPyL in detecting and localizing recurrent prostate cancer. Approved by the U.S. Food and Drug Administration last month, the radiotracer identified metastatic lesions with high positive predictive values regardless of anatomic region, adding to the evidence that PSMA-targeted radiotracers are the most sensitive and accurate agents for imaging prostate cancer. This study was presented at the Society of Nuclear Medicine and Molecular Imaging (SNMMI) 2021 Annual Meeting.

Prostate cancer patients have high levels of PSMA expression, which makes PSMA an effective target for imaging the disease. In previous studies, the novel positron emission tomography (PET) imaging agent 18F-DCFPyL was found to bind selectively with high affinity to PSMA. To demonstrate the diagnostic performance of 18F-DCFPyL for regulatory approval, a prospective, multicenter study was conducted in 14 sites across the United States and Canada.

The study sought to determine the positive predictive value (the probability that patients with a positive screening test actually have the disease) and detection rate of 18F-DCFPyL PET/computed tomography (CT) by anatomic region, specifically the prostate/prostate bed, pelvic lymph nodes, and regions outside the pelvis. Study participants included men who had rising prostate-specific antigen (PSA) levels after local therapy as well as negative or equivocal conventional imaging results.

Patients were imaged with 18F-DCFPyL PET/CT, then imaged again after 60 days to verify suspected lesions using a composite “standard of truth,” which consisted of histopathology, correlative imaging findings and PSA response. Comparing findings between the 18F-DCFPyL imaging and the “standard of truth,” the positive predictive value and detection rate were measured.

18F-DCFPyL-PET/CT was found to successfully detect and pinpoint metastatic lesions with high positive predictive value, regardless of their location in the body, in men with biochemically recurrent prostate cancer who had negative or equivocal baseline imaging. Higher positive predictive values were observed in extra-pelvic lymph nodes and bone compared to soft tissue regions.

With the recent approval of 18F-DCFPyL (now referred to as piflufolastat F-18) by the FDA, the impact of this research may be realized in the very near future. As these agents become more widely available, patients with newly diagnosed, recurrent, and metastatic prostate cancer may have new therapeutic approaches available to them. The results of the study will be presented at the SNMMI meeting by Steven Rowe, MD, PhD, associate professor of radiology and radiological science at Johns Hopkins University in Baltimore, Maryland.

Abstract 123. “A Phase 3 study of 18F-DCFPyL-PET/CT in Patients with Biochemically Recurrent Prostate Cancer (CONDOR): An Analysis of Disease Detection Rate and Positive Predictive Value (PPV) by Anatomic Region,” Steven Rowe and Michael Gorin, Johns Hopkins, Baltimore, Maryland; Lawrence Saperstein, Yale School of Medicine, New Haven, Connecticut; Frederic Pouliot, Departement de Chirurgie, Division d’Urologie, University of Quebec, Quebec, Canada; David Josephson, Tower Urology, Cedars Sinai Medical Center, Los Angeles, California; Peter Carroll, UCSF, San Francisco, California; Jeffrey Wong, City of Hope, Sierra Madre, California; Austin Pantel, University of Pennsylvania Health System, Philadelphia, Pennsylvania; Morand Piert, University of Michigan, Ann Arbor, Michigan; Kenneth Gage, Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida; Steve Cho, University of Wisconsin-Madison, Madison, Wisconsin; Andrei Iagaru, Stanford University, Stanford, California; Janet Pollard, University of Iowa Hospital, Iowa City, Iowa; Vivien Wong, Jessica Jensen and Nancy Stambler, Progenics Pharmaceuticals, Inc., New York, New York; Michael Morris, Memorial Sloan-Kettering Cancer Center, New York, New York; and Barry Siegel, Washington University School of Medicine, St. Louis, Missouri.


All 2021 SNMMI Annual Meeting abstracts can be found online at

About the Society of Nuclear Medicine and Molecular Imaging

The Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and medical organization dedicated to advancing nuclear medicine and molecular imaging, vital elements of precision medicine that allow diagnosis and treatment to be tailored to individual patients in order to achieve the best possible outcomes.

SNMMI’s members set the standard for molecular imaging and nuclear medicine practice by creating guidelines, sharing information through journals and meetings and leading advocacy on key issues that affect molecular imaging and therapy research and practice. For more information, visit

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