Tagged: DOE’s Pacific Northwest National Laboratory (US) Toggle Comment Threads | Keyboard Shortcuts

  • richardmitnick 12:03 pm on August 19, 2021 Permalink | Reply
    Tags: "Faster and Cheaper Ethanol-to-Jet-Fuel on the Horizon", DOE’s Pacific Northwest National Laboratory (US), The new PNNL-patented catalyst converts biofuel (ethanol) directly into a versatile “platform” chemical called n-butene.   

    From DOE’s Pacific Northwest National Laboratory (US) : “Faster and Cheaper Ethanol-to-Jet-Fuel on the Horizon” 

    From DOE’s Pacific Northwest National Laboratory (US)

    August 19, 2021
    Karyn Hede

    New catalyst and microchannel reactors improve efficiency and cost.

    1

    A patented process for converting alcohol sourced from renewable or industrial waste gases into jet or diesel fuel is being scaled up at the U.S. Department of Energy’s Pacific Northwest National Laboratory with the help of partners at Oregon State University (US) and the carbon-recycling experts at LanzaTech.

    Two key technologies power the energy-efficient fuel production units.

    A single-step chemical conversion streamlines what is currently a multi-step process. The new PNNL-patented catalyst converts biofuel (ethanol) directly into a versatile “platform” chemical called n-butene. A microchannel reactor design further reduces costs while delivering a scalable modular processing system.


    Watch how a PNNL-patented catalyst, combined with a unique microchannel reactor, can convert ethanol to a useful chemical with multiple commercial uses, including jet fuel. Video by Eric Francavilla; Animation by Mike Perkins | Pacific Northwest National Laboratory.

    The new process would provide a more efficient route for converting renewable and waste-derived ethanol to useful chemicals. Currently, n-butene is produced from fossil-based feedstocks using the energy-intensive cracking—or breaking down—of large molecules. The new technology reduces emissions of carbon dioxide by using renewable or recycled carbon feedstocks. Using sustainably derived n-butene as a starting point, existing processes can further refine the chemical for multiple commercial uses, including diesel and jet fuels, and industrial lubricants.

    “Biomass is a challenging source of renewable energy because of its high cost. Additionally, the scale of biomass drives the need for smaller, distributed processing plants,” said Vanessa Dagle, co-primary investigator of the initial research study, which was published in the journal ACS Catalysis. “We have reduced the complexity and improved efficiency of the process, while simultaneously reducing capital costs. Once modular, scaled processing has been demonstrated, this approach offers a realistic option for localized, distributed energy production.”

    Micro-to-macro jet fuel

    In a leap toward commercialization, PNNL is partnering with long-time collaborators at Oregon State University to integrate the patented chemical conversion process into microchannel reactors built using newly developed 3D printing technology. Also called additive manufacturing, 3D printing allows the research team to create a pleated honeycomb of mini-reactors that greatly increase the effective surface-area-to-volume ratio available for the reaction.

    “The ability to use new multi-material additive manufacturing technologies to combine the manufacturing of microchannels with high-surface-area catalyst supports in one process step, has the potential to significantly reduce the costs of these reactors,” says OSU lead researcher Brian Paul. “We are excited to be partners with PNNL and LanzaTech in this endeavor.”

    3
    Microchannel mini-reactors greatly increase the efficiency of biofuel chemical conversion. Image: Oregon State University.

    “Due to recent advances in microchannel manufacturing methods and associated cost reductions, we believe the time is right to adapt this technology toward new commercial bioconversion applications,” said Robert Dagle, co-primary investigator of the research.

    The microchannel technology would allow commercial-scale bioreactors to be built near agricultural centers where most biomass is produced. One of the biggest impediments to using biomass for fuel is the need to transport it long distances to large, centralized production plants.

    “The modular design reduces the amount of time and risk necessary to deploy a reactor,” said Robert Dagle. “Modules could be added over time as demand grows. We call this scale up by numbering up.”

    The one-fourth commercial-scale test reactor will be produced by 3D printing using methods developed in partnership with OSU and will be operated on the Richland, Wash. campus of PNNL.

    Once the test reactor is completed, PNNL commercial partner LanzaTech will supply ethanol to feed the process. LanzaTech’s patented process converts carbon-rich wastes and residues produced by industries, such as steel manufacturing, oil refining and chemical production, as well as gases generated by gasification of forestry and agricultural residues and municipal waste into ethanol.

    The test reactor will consume ethanol equivalent to up to one-half dry ton biomass per day. LanzaTech has already scaled up the first generation of PNNL technology for jet fuel production from ethanol and formed a new company, LanzaJet, to commercialize LanzaJet™ Alcohol-to-Jet. The current project represents the next step in streamlining that process while providing additional product streams from n-butene.

    “PNNL has been a strong partner in developing ethanol-to-jet technology that LanzaTech spin-off company, LanzaJet, is employing in multiple plants under development,” said Jennifer Holmgren, LanzaTech CEO. “Ethanol can come from a variety of sustainable sources and as such is an increasingly important raw material for sustainable aviation fuel. This project shows great promise for alternate reactor technology which could have benefits for this key pathway to decarbonization of the aviation sector.”

    A tunable process

    Since their early experiments, the team has continued perfecting the process. When ethanol is passed over a solid silver-zirconia-based catalyst supported on a silica, it performs the essential chemical reactions that convert ethanol to either n-butene or, with some modifications to the reaction conditions, butadiene.

    But even more importantly, after prolonged-duration studies, the catalyst remains stable. In a follow-up study [The European Society Journal for Catalysis], the research team showed that if the catalyst loses activity, it can be regenerated by a simple procedure to remove coke―a hard carbon-based coating that can build up over time. An even more efficient, updated catalyst formulation will be used for scale-up.

    “We discovered the concept for this catalyzed system that is highly active, selective, and stable,” said Vanessa Dagle. “By adjusting the pressure and other variables, we can also tune the system to generate either butadiene, a building block for synthetic plastic or rubber or an n-butene, which is suitable for making jet fuels or products such as synthetic lubricant. Since our initial discovery, other research institutions have also begun exploring this new process.”

    In addition to Vanessa Dagle and Robert Dagle, the catalyst development team included PNNL researchers Austin Winkelman, Nicholas Jaegers, Johnny Saavedra-Lopez, Jianzhi Hu, Mark Engelhard, Sneha Akhade, Libor Kovarik, Vassilliki-Alexandra Glezakou, Roger Rousseau and Yong Wang. Senior scientist Susan Habas from the National Renewable Energy Laboratory (US) also contributed. PNNL staff scientists Ward TeGrotenhuis, Richard Zheng and Johnny Saavedra-Lopez contributed to the development of the microchannel technology.

    The chemical conversation research was supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy, within the Chemical Catalysis for Bioenergy (ChemCatBio) Consortium sponsored by the Bioenergy Technology Office (BETO). ChemCatBio is a DOE national-lab-led research and development consortium dedicated to identifying and overcoming catalysis challenges for the conversion of biomass and waste resources into fuels, chemicals, and materials. The public-private, scale-up partnership is being supported by DOE-BETO and the State of Oregon’s University Innovation Research Fund.

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    DOE’s Pacific Northwest National Laboratory (PNNL) (US) is one of the United States Department of Energy National Laboratories, managed by the Department of Energy’s Office of Science. The main campus of the laboratory is in Richland, Washington.

    PNNL scientists conduct basic and applied research and development to strengthen U.S. scientific foundations for fundamental research and innovation; prevent and counter acts of terrorism through applied research in information analysis, cyber security, and the nonproliferation of weapons of mass destruction; increase the U.S. energy capacity and reduce dependence on imported oil; and reduce the effects of human activity on the environment. PNNL has been operated by Battelle Memorial Institute since 1965.

     
  • richardmitnick 6:18 pm on August 1, 2021 Permalink | Reply
    Tags: "Electron Microscopy in the Age of Automation", , , DOE’s Pacific Northwest National Laboratory (US), ,   

    From DOE’s Pacific Northwest National Laboratory (US) : “Electron Microscopy in the Age of Automation” 

    From DOE’s Pacific Northwest National Laboratory (US)

    July 29, 2021
    Kimberlee Papich

    1

    “Many of the greatest challenges of our time, from clean energy to environmental justice, require new approaches to the craft of scientific experimentation. This is exceedingly apparent in the field of electron microscopy. As researchers utilize this powerful window to peer into the atomic machinery behind today’s technologies, they are increasingly inundated with data and constrained by traditional operating models. We must leverage artificial intelligence and machine learning in our scientific instruments if we are to unlock breakthrough discoveries.”

    2
    This JEOL GrandARM 300F scanning transmission electron microscope at PNNL is one of two microscopes at the lab to employ a prototype of the team’s next-generation platform, enabling richer, around-the-clock statistical analysis. Credit: Andrea Starr | Pacific Northwest National Laboratory.

    This is Steven R. Spurgeon’s forward-looking assessment of the present and future state of microscopy and instrumentation in scientific experimentation. Spurgeon, a materials scientist at Pacific Northwest National Laboratory (PNNL), is an international expert in the study of nanomaterials using electron microscopy. At PNNL, he and his colleagues are working to reimagine the discovery and design of new material and chemical systems by applying state-of-the-art computing and data analytics to instrumentation.

    Accordingly, academia and industry are turning to these PNNL electron microscopy science experts for their solutions. PNNL is at the helm of thought leadership in this growing research area and is now bringing advanced technologies to market to accelerate scientific discovery.

    An evolution in scientific experimentation

    Spurgeon and his colleagues are attempting to address a challenge that is ubiquitous throughout multiple industries—experts are deluged with large volumes of data and hampered by outmoded operating models, making knowledge extraction difficult. From new battery development to emerging quantum computing technologies, all domains are grappling with this burden.

    In advanced manufacturing, the opportunity for automation in instrumentation is keenly evident—modernization would have an immediate and transformative impact. In the semiconductor industry, failure analysis is conducted on an immense scale—24 hours a day, 7 days a week. Microscopes and other systems must screen millions of transistors to assure the quality and reliability of microelectronics. Experts are increasingly concerned with how to translate these large data streams into rapid and explainable decisions that ultimately drive down costs.

    The solution they seek requires hardware and software architectures that can emulate the human brain in terms of cognition. This would allow for the evaluation of unique scenarios while tapping into the ability of computers to tirelessly scale analysis to different types and volumes of data.

    Industry-driven technology transfer

    In an October 2020 Nature Materials commentary, a team co-led by Spurgeon shared its vision for electron microscopy infused with the latest advances in data science and artificial intelligence. Fast-forward to present day and this vision is being realized inside PNNL’s Radiological Microscopy Suite. There, researchers have developed a prototype of a next-generation microscope platform, and industry players are taking note.

    PNNL and Japan Electron Optics Laboratory/Integrated Dynamic Electron Solutions (JEOL/IDES), a world leader in electron microscopy, recently signed a licensing and co-development agreement to commercialize the application. Together, they will bring to market the platform’s core concept—applying minimal, or ‘sparse,’ data analytics to perform image classification—an important step toward instrument automation. Technologies developed under this partnership will be further refined and made available to research organizations and private industry. Accessing the platform will allow these experts to process microscopy data without the need for entirely new instrumentation hardware.

    “JEOL/IDES sees the clear need for improvement in the way microscopy data is acquired and analyzed. This doesn’t just mean automated instruments, but smart automated instruments that can acquire data expertly and effectively,” said Tom Isabell, vice president for product management for JEOL/IDES. “We need to develop a new paradigm in which data is acquired efficiently and the vast amounts of data are analyzed intelligently, in turn leading to an even more efficient way to collect further data. PNNL has shown world leadership in taking on this smart microscopy model and JEOL/IDES looks forward to partnering with PNNL to develop and implement these new technologies.”

    The broad application of this platform reflects the intent of PNNL’s Office of Technology Deployment and Outreach and the early work of commercialization manager Jennifer Lee in spearheading the licensing agreement with JEOL/IDES. She was motivated by her interactions with industry partners, where she heard a clear theme—the labor-intensive, manual work involved in processing large volumes of microscopic data was simply too onerous. Industry partners were looking for multi-faceted expertise, not only in materials and electron microscopy science, but especially in data science, inherent to a research entity that could quickly deliver on a solution.

    “At PNNL, we take an industry-driven approach to all of our technology transfer efforts. We work hard to understand the industry’s pain points and bring those concerns back to our scientists to address,” said Lee. “In our work with JEOL/IDES, for example, there was immediate support and palpable expertise for developing an approach that could replicate the human brain’s decision-making capabilities, resulting in the quickest laboratory-directed commercialization effort, from start to finish, yet.”

    Automation meets electron microscopy

    PNNL’s next-generation microscope platform implements a never-before-seen analytics and control architecture. Experts are redesigning the electron microscope’s foundation, leveraging low-level system automation, domain-grounded data pre-processing, and emerging sparse data analytics to rapidly extract statistical information. They’re making significant progress toward the microscope of tomorrow, one that is highly integrated and automated, which can target challenges in energy storage, quantum information science, and more.

    “Steven and his team are addressing an age-old problem in the control and operation of electron microscopes. Their approach has the potential to greatly impact the scientific community by helping researchers conduct richer and more efficient analyses at scale,” explains Sergei V. Kalinin, a corporate fellow at Oak Ridge National Laboratory and a leader in machine learning and automated experiments in electron and scanning probe microscopies not involved in this research.

    To bring the microscopy platform to life, Spurgeon assembled a team from inside and outside PNNL, including fellow materials scientist Matthew Olszta, statistician Sarah Akers, computer scientist Derek Hopkins, and Kevin Fiedler, a mathematician from Washington State University (US). Spurgeon and Olszta’s microscopy expertise was an ideal match for Akers’ few-shot machine learning, which represents a new kind of data analytics that can make decisions using very limited examples. To build a centralized instrument controller, Spurgeon tapped Hopkins, who specializes in hardware/software integration and lab automation. Hopkins and Fiedler designed an architecture to process and analyze incoming images to enable large-area montaging and stage feedback.

    The team’s resulting machine learning work is currently in review in an article led by Akers, titled, Rapid and Flexible Segmentation of Electron Microscopy Data Using Few-Shot Machine Learning, with a more detailed article on the system to follow. Several joint appointments are also in the works for Spurgeon.

    The prototype microscopy system is now being deployed at PNNL on two flagship transmission electron microscopes—a JEOL GrandARM 300F and a JEOL ARM 200CF—with the eventual goal to extend it to other instruments. This unique capability will enable richer, around-the-clock statistical analysis to take advantage of the laboratory’s best-in-class instrumentation.

    Democratizing data-driven analysis

    “The true potential of this work is that it can be extended to many other areas, drawing on PNNL’s expertise across multiple scientific disciplines,” said Spurgeon. “We have the opportunity to move the conversation away from simply buying higher-powered instruments toward more informed modes of operation and analysis. We can think of this as a democratization of best-in-class analysis capabilities.”

    To accelerate this transition, and in support of science, technology, engineering, and mathematics workforce development, the PNNL team recently advised a group of students through The University of Washington (US)’s Data Intensive Research Enabling Clean Technologies (DIRECT) capstone program. The students were tasked with developing a graphical user interface for interacting with the few-shot model. This web-based application allows end users to intuitively process their data and export the results for further use. The students completed a publication, released their codebase, and will present a poster at the Microscopy and Microanalysis Virtual Meeting in early August.

    On the road to the future

    In addition to the team’s publications and the licensing agreement, other upcoming activities speak to broad enthusiasm for the microscopy platform, namely an invited tutorial and four talks planned for the Microscopy & Microanalysis Virtual Meeting. Hosted by the Microscopy Society of America, the annual meeting is open to its 3,000 members and is considered the premiere event covering original microscopy research.

    Cumulatively, these activities are helping propagate the current and future potential of this new platform. This will lead to unlocking experimentation at scale and deriving richer, more meaningful physical models for technologically relevant systems. The team’s work has only just begun, as they plan for the full implementation of the system and build on their machine learning work to increase the power and generalizability of their approach.

    Concluded Spurgeon, “We started with a new approach to classifying data in the microscope, but we’ve grown beyond that to addressing how we as a community approach experimentation. Traditional approaches are very manual and labor-intensive, but, most importantly, they can’t keep pace with the latest generation of hardware. We believe our platform is a first step in that direction. The feedback we’ve received from the scientific community and industry has been very positive, which is extremely gratifying.”

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    DOE’s Pacific Northwest National Laboratory (PNNL) (US) is one of the United States Department of Energy National Laboratories, managed by the Department of Energy’s Office of Science. The main campus of the laboratory is in Richland, Washington.

    PNNL scientists conduct basic and applied research and development to strengthen U.S. scientific foundations for fundamental research and innovation; prevent and counter acts of terrorism through applied research in information analysis, cyber security, and the nonproliferation of weapons of mass destruction; increase the U.S. energy capacity and reduce dependence on imported oil; and reduce the effects of human activity on the environment. PNNL has been operated by Battelle Memorial Institute since 1965.

     
  • richardmitnick 10:26 am on June 17, 2021 Permalink | Reply
    Tags: "An Ally for Alloys", , , “XMAT”—eXtreme environment MATerials—consortium, DOE’s Pacific Northwest National Laboratory (US), , , Stronger materials are key to producing energy efficiently resulting in economic and decarbonization benefits.   

    From DOE’s Pacific Northwest National Laboratory (US) : “An Ally for Alloys” 

    From DOE’s Pacific Northwest National Laboratory (US)

    June 16, 2021
    Tim Ledbetter

    1

    Machine learning techniques have contributed to progress in science and technology fields ranging from health care to high-energy physics. Now, machine learning is poised to help accelerate the development of stronger alloys, particularly stainless steels, for America’s thermal power generation fleet. Stronger materials are key to producing energy efficiently resulting in economic and decarbonization benefits.

    “The use of ultra-high-strength steels in power plants dates back to the 1950s and has benefited from gradual improvements in the materials over time,” says Osman Mamun, a postdoctoral research associate at Pacific Northwest National Laboratory (PNNL). “If we can find ways to speed up improvements or create new materials, we could see enhanced efficiency in plants that also reduces the amount of carbon emitted into the atmosphere.”

    Mamun is the lead author on two recent, related journal articles that reveal new strategies for machine learning’s application in the design of advanced alloys. The articles chronicle the research outcomes of a joint effort between PNNL and the DOE National Energy Technology Lab (US). In addition to Mamun, the research team included PNNL’s Arun Sathanur and Ram Devanathan and NETL’s Madison Wenzlick and Jeff Hawk.

    The work was funded under the Department of Energy’s (US) Office of Fossil Energy via the “XMAT”—eXtreme environment MATerials—consortium, which includes research contributions from seven DOE national laboratories. The consortium seeks to accelerate the development of improved heat-resistant alloys for various power plant components and to predict the alloys’ long-term performance.

    The inside story of power plants

    A thermal power plant’s internal environment is unforgiving. Operating temperatures of more than 650 degrees Celsius and stresses exceeding 50 megapascals put a plant’s steel components to the test.

    “But also, that high temperature and pressure, along with reliable components, are critical in driving better thermodynamic efficiency that leads to reduced carbon emissions and increased cost-effectiveness,” Mamun explains.

    The PNNL–NETL collaboration focused on two material types. Austenitic stainless steel is widely used in plants because it offers strength and excellent corrosion resistance, but its service life at high temperatures is limited. Ferritic-martensitic steel that contains chromium in the 9 to 12 percent range also offers strength benefits but can be prone to oxidation and corrosion. Plant operators want materials that resist rupturing and last for decades.

    Over time, “trial and error” experimental approaches have incrementally improved steel, but are inefficient, time-consuming, and costly. It is crucial to accelerate the development of novel materials with superior properties.

    Models for predicting rupture strength and life

    Recent advances in computational modeling and machine learning, Mamun says, have become important new tools in the quest for achieving better materials more quickly.

    Machine learning, a form of artificial intelligence, applies an algorithm to datasets to develop faster solutions for science problems. This capability is making a big difference in research worldwide, in some cases shaving considerable time off scientific discovery and technology developments.

    The PNNL–NETL research team’s application of machine learning was described in their first journal article, published March 9 in Scientific Reports.

    2
    PNNL’s distinctive capabilities in joining steel to aluminum alloys enable lightweight vehicle technologies for sustainable transportation. Photo by Andrea Starr | Pacific Northwest National Laboratory.

    The paper recounts the team’s effort to enhance and analyze stainless steel datasets, contributed by NETL team members, with three different algorithms. The ultimate goal was to construct an accurate predictive model for the rupture strength of the two types of alloys. The team concluded that an algorithm known as the Gradient Boosted Decision Tree best met the needs for building machine learning models for accurate prediction of rupture strength.

    Further, the researchers maintain that integrating the resulting models into existing alloy design strategies could speed the identification of promising stainless steels that possess superior properties for dealing with stress and strain.

    “This research project not only took a step toward better approaches for extending the operating envelope of steel in power plants, but also demonstrated machine learning models grounded in physics to enable interpretation by domain scientists,” says research team member Ram Devanathan, a PNNL computational materials scientist. Devanathan leads the XMAT consortium’s data science thrust and serves on the organization’s steering committee.

    The project team’s second article was published in npj Materials Degradation’s April 16 edition.

    The team concluded in the paper that a machine-learning-based predictive model can reliably estimate the rupture life of the two alloys. The researchers also described a methodology to generate synthetic alloys that could be used to augment existing sparse stainless steel datasets, and identified the limitations of such an approach. Using these “hypothetical alloys” in machine learning models makes it possible to assess the performance of candidate materials without first synthesizing them in a laboratory.

    “The findings build on the earlier paper’s conclusions and represent another step toward establishing interpretable models of alloy performance in extreme environments, while also providing insights into data set development,” Devanathan says. “Both papers demonstrate XMAT’s thought leadership in this rapidly growing field.”

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    DOE’s Pacific Northwest National Laboratory (PNNL) (US) is one of the United States Department of Energy National Laboratories, managed by the Department of Energy’s Office of Science. The main campus of the laboratory is in Richland, Washington.

    PNNL scientists conduct basic and applied research and development to strengthen U.S. scientific foundations for fundamental research and innovation; prevent and counter acts of terrorism through applied research in information analysis, cyber security, and the nonproliferation of weapons of mass destruction; increase the U.S. energy capacity and reduce dependence on imported oil; and reduce the effects of human activity on the environment. PNNL has been operated by Battelle Memorial Institute since 1965.

     
  • richardmitnick 2:01 pm on June 14, 2021 Permalink | Reply
    Tags: "A Keen Eye Behind the Microscope", , , , DOE’s Pacific Northwest National Laboratory (US), Dongsheng Li’s careful crystal formation work unfolds at the nanoscale with powerful equipment., Her group made many discoveries on particle-mediated growth—especially oriented attachment processes—and solid-state phase transformation., In situ transmission electron microscopy, Li devoted a five-year research grant to using high-powered microscopes to examine the formation of branched nanocrystals less than one-thousandth the width of a human hair., , Women in STEM-Dongsheng Li   

    From DOE’s Pacific Northwest National Laboratory (US) : Women in STEM-Dongsheng Li “A Keen Eye Behind the Microscope” 

    From DOE’s Pacific Northwest National Laboratory (US)

    June 14, 2021
    Allan Brettman

    1
    Dongsheng Li’s careful crystal formation work unfolds at the nanoscale with powerful equipment.

    The experiment was not going well, as experiments often do.

    One researcher’s stomach tied in knots.

    Another researcher, materials scientist Dongsheng Li, reacted calmly.

    Li thought of options and alternate approaches to troubleshoot the experiment, unraveling at Pacific Northwest National Laboratory (PNNL). She thought critically, and headed to another building to try different preparation steps.

    Was the experiment back on track lickety-split? No. But it found its course over time. Li made sure of it. She displayed the skill, experience, perseverance, and mental agility that have characterized her work since arriving in 2013 at PNNL as a staff scientist.

    By May 2015, she received an Early Career Research Program Award from the Department of Energy (US) in the highly competitive program. Li devoted the five-year research grant to using high-powered microscopes to examine the formation of branched nanocrystals less than one-thousandth the width of a human hair. Using in situ transmission electron microscopy and atomic force microscopy, her group made many discoveries on particle-mediated growth—especially oriented attachment processes—and solid-state phase transformation.

    Li’s research has been published in Science [only one link, below] regarding two major breakthroughs stemming from the Early Career Award research. In March 2020, she was appointed as a team leader in PNNL’s Physical Sciences Division.

    Microscopy tools to the rescue

    “She has been able to very effectively use microscopy tools to get atomistic information from material systems that provide insights into the mechanisms that lead to a material’s formation,” said Jim De Yoreo, PNNL chief scientist for materials science, who helped recruit Li to the lab. “Her key strength as a researcher is really knowing her methodology and how to utilize it to answer key questions about the synthesis of a material.”

    Growing up in Jilin Province in northeast China, Li’s gravitation toward science came early. There was little time for sports or music, though today she’s a hiker and swimmer, and occasionally plays piano.

    “I was good at math and chemistry and physics when I was in high school,” Li said. “Because of that, I chose to go for this science direction instead of, I don’t know, politics or music or a totally different direction. Science just kind of made sense to me naturally.”

    Li enrolled at Jilin University [国际教育学院] (CN), a state-supported research university whose chemistry program is among the world’s best, according to U.S. News & World Report’s Best Global Universities. Li earned a bachelor’s degree in applied chemistry and a master’s in inorganic chemistry, supported by “Scholarships for Excellent Students” along the way.

    Destined for research. But where?

    She knew early on in her collegiate career that she’d pursue a PhD. And then? “Something in the scientific field,” she said. “Maybe as a professor, a faculty position. And I knew I wanted to come to the United States, to get my PhD here.”

    She enrolled at Pennsylvania State University (US). As she had moved toward chemistry as an undergrad, the same process of following what made sense naturally led her to materials science at Penn State. “Chemistry and materials science are all connected,” she said.

    While working as a postdoc fellow at the University of California-Riverside (US), Li’s postdoc advisor, Professor David Kasailus, introduced her to De Yoreo at a conference. Li said she wanted to work on De Yoreo’s research team at DOE’s Lawrence Berkeley National Laboratory. When De Yoreo went to work at PNNL, Li followed to continue materials research work.

    “She played an important role at Lawrence Berkeley as well as our early research at PNNL, providing expertise in nanoparticle synthesis and electron microscopy for understanding the science of materials synthesis,” De Yoreo said. “Overall, she takes collaboration seriously. She values working together through timely discussions about the research and following through on the details.”

    2
    Electron microscopy reveals nanocrystals self-assembling into pentagonal polygons. Dongsheng Li led a research team that revealed the secret to why nanoparticles sometimes self-assemble into this five-sided shape, which has special properties and is useful in medical research, electronics, and other applications. (Photo: AAAS)

    Li’s materials science research at LBNL and PNNL has been published in Science regarding two major breakthroughs: Understanding the process of oriented attachment in an iron oxyhydroxide system and describing why and how five-fold twin nanoparticles form [Science].

    In the latter breakthrough, Li and her colleagues used a combination of high-resolution transmission electron microscopy combined with molecular dynamics simulation techniques to probe why the structures form as they do. Nanomaterials with this structure have already been shown to have useful properties, such as light responsiveness. They are deployed in medical research for precisely tagging cancerous tumors for imaging and tracking, and in electronics, where they are valued for their mechanical strength.

    “Natural and synthetic nanoparticles composed of five-fold twinned crystal domains have unique properties,” said Li, who led the research team. “But the formation mechanism of these five-fold twinned nanoparticles has been poorly understood. For the first time, we directly observed five-fold twin formation in real time and determined the mechanism by which they form. Understanding that mechanism is the fundamental knowledge materials scientists need to design new five-fold twinned nanoparticles with optimized properties for desired applications.”

    A new home for powerful equipment

    4
    The Energy Sciences Center is scheduled to open in fall 2021 on PNNL’s main campus in Richland, Wash. (Rendering: Pacific Northwest National Laboratory.)

    “At the center, I will continue to study oriented attachments,” Li said. “What factors control oriented attachments? And how can oriented attachments control crystal structures—not only at a nanoscale but also at the atomic scale?”

    Li said the ESC will eventually be the home of a new transmission electron microscope (TEM), a device that uses a particle beam of electrons to visualize specimens and generate a highly magnified image. TEMs can magnify objects up to 2 million times.

    “It really has some nice capabilities,” Li said, “especially its research laboratories and flexible-use open spaces.”

    De Yoreo also saw how the ESC will help Li as well as other researchers working in basic energy sciences and electron microscopy.

    “It’s designed to have quiet space. That’s essential for high-quality electron microscopy work,” De Yoreo said. “And it will have the ancillary equipment a researcher needs, such as focused ion beams for preparing samples. The atomic force microscopy suite will be in there, which is complementary work that Dongsheng does. Right now, the equipment essential to her work is located in three separate buildings on the PNNL campus. Bringing all of those capabilities together into one high-quality space will advance her research.”

    At the core of bricks, mortar, quiet suites, and advanced equipment, though, is the researcher. PNNL materials scientist Elias Nakouzi knows that, having observed Li in action during a perilous point in that experiment that was not going well.

    “When I first joined PNNL as a postdoc, Dongsheng was the first person to show me, by her example, what it means to be a scientist at a national laboratory,” Nakouzi said. “The way she tried different approaches to troubleshoot the experiment caught my attention. We all have experiments that occasionally do not go too well. When that happens to me, I remember how Dongsheng successfully handled the situation.”

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    DOE’s Pacific Northwest National Laboratory (PNNL) (US) is one of the United States Department of Energy National Laboratories, managed by the Department of Energy’s Office of Science. The main campus of the laboratory is in Richland, Washington.

    PNNL scientists conduct basic and applied research and development to strengthen U.S. scientific foundations for fundamental research and innovation; prevent and counter acts of terrorism through applied research in information analysis, cyber security, and the nonproliferation of weapons of mass destruction; increase the U.S. energy capacity and reduce dependence on imported oil; and reduce the effects of human activity on the environment. PNNL has been operated by Battelle Memorial Institute since 1965.

     
  • richardmitnick 1:02 pm on May 29, 2021 Permalink | Reply
    Tags: "Sidestepping the Thin Data Problem in National Security", A problem is that neural networks trained on data gathered in the presence of a background tend to make predictions that are only valid when the background is the same., DOE’s Pacific Northwest National Laboratory (US), Explainable AI, In national security oftentimes there is not better data., National security, NNSA’s Office of Defense Nuclear Nonproliferation Research and Development, Some isotopes are adrift only when an extremely rare nuclear explosion occurs., Teaching the network to learn how to identify and discount unwanted patterns from its decision-making process., The network often chooses ways of identifying the signal that don’t work when the background is different.   

    From DOE’s Pacific Northwest National Laboratory (US) : “Sidestepping the Thin Data Problem in National Security” 

    From DOE’s Pacific Northwest National Laboratory (US)

    April 26, 2021 [Just now in social media.]
    Tom Rickey

    Learning more from the data you have, thanks to explainable AI.

    1
    Data Analysis. Credit: PNNL.

    (Third in a series about explainable AI and national security at PNNL; see the first and second articles.)

    When a data set is too small to be used to make a decision, the solution is usually obvious: Get more data! That’s the cry of analysts everywhere, whether the need is to confirm the safety of a vaccine or to pinpoint an annoying knock in a car’s engine.

    But one team of artificial intelligence (AI) researchers is moving in another direction. Instead of just fighting for more data, the team at Pacific Northwest National Laboratory (PNNL) accepts the shortcoming and develops new ways around the problem. The approach is paying off, leading to faster, more accurate conclusions.

    The alternative approach is a necessity in national security, said Angie Sheffield, a senior program manager with the U.S. Department of Energy’s National Nuclear Security Administration (NNSA).

    “In national security oftentimes there is not better data. There is not more data. We need new techniques to understand the data we do have, to extract more meaning from the information already in hand,” said Sheffield, who manages the data science portfolio in NNSA’s Office of Defense Nuclear Nonproliferation Research and Development, also known as DNN R&D.

    Such techniques are being developed by scientists like Tom Grimes, a PNNL scientist working on the project along with colleagues Luke Erickson and Kate Gibb, with support from Sheffield’s office.

    “If you have the opportunity to get more data, adding more to the analysis is almost always a good step, of course,” said Grimes. “For instance, doubling the size of a data set from 100,000 to 200,000 images generally makes a large impact on how well your network is able to separate signal from background. But when data are in short supply, sometimes you need to try another approach.”

    The national security arena is one where data are not always abundant. That’s especially true for DNN R&D, which drives the R&D of new capabilities to improve the nation’s ability to detect and monitor nuclear material production and movement, weapons development, and nuclear detonations across the globe.

    For example, some isotopes are adrift only when an extremely rare nuclear explosion occurs. Other signals might occur only when exceedingly uncommon materials processing steps take place. There isn’t much signal of interest—and certainly, no one wants more of a signal that signifies danger—and there is an abundance of background signals that are decidedly uninteresting. These factors can throw off results.

    “A problem is that neural networks trained on data gathered in the presence of a background tend to make predictions that are only valid when the background is the same. The network often chooses ways of identifying the signal that don’t work when the background is different,” said Grimes.

    Explainable AI: Sorting significant from spurious

    Grimes’ solution is to teach a machine learning system to find ways to identify the signals of interest that are not affected by changes in the background. Instead of providing more and more data to learn from every possible scenario, he teaches the network to learn how to identify and discount unwanted patterns from its decision-making process.

    “You want to ignore the irrelevant information and focus on what’s relevant. This approach works well for finding really small signals buried in large backgrounds,” Grimes said.

    The approach came about because of a renewed focus at PNNL on explainable AI, an effort to understand and explain how an AI system makes decisions. The work dovetails with NNSA’s Advanced Data Analytics for Proliferation Detection (ADAPD) program, which aims to detect very faint signals of nuclear proliferation activity. Both ADAPD and PNNL’s work on explainable AI are supported by DNN R&D.

    The analysis of how systems sift such information allowed Grimes to generate insights about what information should be used for decision-making and what information can be discarded without penalty.

    To understand the need to differentiate meaningful signals from less important background signals, consider the challenge a nuclear inspector faces when confronting thousands upon thousands of inputs. It’s a challenge that PNNL scientist Ben Wilson, who leads the Laboratory’s efforts on ADAPD, wrestled with during 12 years as a nuclear safeguards inspector for the International Atomic Energy Agency.

    Inspectors synthesize information from detectors, photographs, shipping manifests, publicly available information, and myriad other sources. The ability to sift reams of complex data and key in on the most relevant details is central to the ability to grasp the status of a nuclear program. For example, two tiny data points—perhaps a seemingly unimportant piece of equipment observed during an inspection, coupled with the slightest discrepancy in declared nuclear material inventory—might open the door to significant inconsistencies about a country’s nuclear activities. Understanding how streams of information add up to a consistent, comprehensive view is what inspectors do, and what afficionados of explainable AI seek to understand and emulate.

    Grimes and colleagues are exploring the benefits of explainable AI to make sure nuclear materials are used only for peaceful purposes. In one experiment, the team challenged an AI network to determine specific properties about the use of an instrument in different settings on the PNNL campus—in an isolated environment, in a laboratory one door over, or in an adjacent building. The different environments translated to highly varied background conditions that were constantly changing.

    Researchers trained the network to automatically identify and use patterns in the data indicative of the instrument and not use ones that also indicated the background. Scientists distinguished the patterns based largely on the timing and consistency of their appearance during a week’s worth of experiments.

    The team found, not surprisingly, that the system’s performance in training was poorer when some patterns—those that indicated both instrument activity and also background activity—were removed from consideration.

    But importantly, even though the system did not perform as well on the training data, the system actually performed better when presented with real data for analysis. When multiple runs on the data were taken into account, the system trained to ignore background signals reduced the error rate of a conventional AI system by about 25 percent.

    “While this improvement seems surprising, it’s actually predicted by theory,” added Grimes. “Focusing more precisely on the true signal in the data you have is key.”

    And that, said Sheffield, is crucial.

    “The national security mission demands the next generation in artificial intelligence—techniques like these are exactly what we need to capitalize on AI to transform national security,” she added. “We need ways to build good models from noisy and insufficient data. That’s not science fiction; we do it by better understanding and exploiting the data we have.”

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    DOE’s Pacific Northwest National Laboratory (PNNL) (US) is one of the United States Department of Energy National Laboratories, managed by the Department of Energy’s Office of Science. The main campus of the laboratory is in Richland, Washington.

    PNNL scientists conduct basic and applied research and development to strengthen U.S. scientific foundations for fundamental research and innovation; prevent and counter acts of terrorism through applied research in information analysis, cyber security, and the nonproliferation of weapons of mass destruction; increase the U.S. energy capacity and reduce dependence on imported oil; and reduce the effects of human activity on the environment. PNNL has been operated by Battelle Memorial Institute since 1965.

     
  • richardmitnick 12:35 pm on May 29, 2021 Permalink | Reply
    Tags: "Energy on Demand-Learning from Nature’s Catalysts", "Janus intermediate", , “Big questions in biocatalysis”-specifically how to control matter and energy., , , , DOE’s Pacific Northwest National Laboratory (US), Enzymes: nature’s catalysts, How natural catalysts churn out specific reactions-over and over-in the blink of an eye., Nitrogenase: an enzyme found in soil-dwelling microorganisms that has a unique ability to break apart nitrogen’s triple bond-one of the strongest bonds in nature., , ,   

    From DOE’s Pacific Northwest National Laboratory (US) : “Energy on Demand-Learning from Nature’s Catalysts” 

    From DOE’s Pacific Northwest National Laboratory (US)

    April 26, 2021 [Just now in social media.]
    Lynne Roeder, PNNL

    New Energy Sciences Center, quantum chemistry to accelerate enzyme research.

    1
    Nitrogenase. Credit: PNNL.

    About 15 years ago, Simone Raugei started simulating chemistry experiments at the molecular level.

    Today, as part of a top-notch research team aided by advanced computing, Raugei and his colleagues stand primed to crack an important hidden code: nature’s intricate method for releasing energy on demand.

    “We want to know how to funnel energy precisely at the right time, in the right spot, to perform the chemical reaction we want—just like enzymes do in nature,” said Raugei, a computational scientist who leads the physical biosciences research at Pacific Northwest National Laboratory (PNNL). “Advances in computing have helped us make tremendous progress in the past five or six years. We now have a critical mass of capabilities and knowledge.”

    The research is part of PNNL’s focus on reinventing chemical conversions, which supports the goals of the U.S. Department of Energy Office of Science, Basic Energy Sciences (BES) program. One of the programs’ many goals is to understand, at an atomic level, how natural catalysts churn out specific reactions-over and over-in the blink of an eye.

    The ability to mimic these natural reactions could profoundly improve the design of new synthetic catalysts for producing cleaner and more efficient energy, industrial processes, and materials.

    Raugei described the BES Physical Biosciences program as the visionary effort that brought together individual research groups and experimentalists to collaborate on “big questions in biocatalysis”—specifically, how to control matter and energy.

    The questions don’t get much bigger than that.

    Enzymes: nature’s catalysts

    At PNNL, Raugei teams closely with fellow computational scientists Bojana Ginovska and Marcel Baer to examine the inner workings of enzymes. Found within every living cell, these miniscule multi-taskers direct all sorts of reactions for different functions.

    Through feedback loops between theory, computer simulations, and experimentation among PNNL and university collaborators, the scientists have made steady progress in uncovering the molecular machinations of several types of enzymes. They are particularly interested in nitrogenase, an enzyme found in soil-dwelling microorganisms, that has a unique ability to break apart nitrogen’s triple bond—one of the strongest bonds in nature. That molecular fracture, which occurs in the buried active core of nitrogenase, produces ammonia.

    In the world of commercial chemistry, ammonia is used to make many valuable products, such as fertilizer. But producing ammonia at an industrial scale takes a lot of energy. Much of that energy is spent trying to break nitrogen’s sturdy triple bonds. Figuring out how nature does it so efficiently is key to designing new synthetic catalysts that improve the production process for ammonia and other commercial products.


    Quantum Chemistry. Credit: PNNL.

    Nitrogenase: cracking the code

    About two years ago, the team of PNNL and university scientists isolated the elusive molecular structure inside nitrogenase—called the Janus intermediate—that represents the ‘point of no return’ in the production of ammonia. The researchers found that two negatively charged hydrogens, called hydrides, form bridges with two iron ions. Those bridges allow four extra electrons to park inside the core cluster of atoms.

    The team’s latest research confirmed the shuffling of electrons within the protein environment, packing in enough energy to break apart the nitrogen bonds and form ammonia. Powerful spectroscopy techniques were used to probe the magnetic interactions between electrons in the enzyme’s metallic core. Those interactions were then correlated with quantum simulations of the enzyme’s transformation to yield the molecular structure of the Janus intermediate.

    “The energetics of the electron delivery are amazing,” said Raugei. “When you think of adding electrons into a tiny cluster of atoms, one electron is difficult, two is harder, three is really hard, and to add the fourth is generally considered impossible. But we found that’s how it happens.”

    Lance Seefeldt, a professor at Utah State University (US) who holds a joint appointment at PNNL, leads the experimental work for the team’s nitrogenase research. Another key collaborator, and the “mastermind behind the spectroscopy measurements” according to Raugei, is Brian Hoffman from Northwestern University (US). The team’s most recent findings about nitrogenase were published in the Journal of the American Chemical Society in December 2020.

    Quantum chemistry collaborations

    Ginovska helps direct the day-to-day activities of the group’s postdoctoral researchers working on the project. She credits Raugei with establishing and maintaining connections among the scientific community to spur progress on enzyme research.

    “As a theoretical hub, we collaborate with universities and other national laboratories for the experimental aspects of the research,” said Ginovska. “We started with nitrogenase and it grew from there. We are now working on several enzymatic systems. All of that work is feeding into the same knowledge base.”

    Karl Mueller, chief science and technology officer for PNNL’s Physical and Computational Sciences Directorate, said nitrogenase is a prime example of the challenging problems that can be tackled at a national laboratory through collaboration between experimental and computational scientists, including university researchers. As the scientists prepare to move into PNNL’s new Energy Sciences Center in the fall of 2021, Raugei is confident the enhanced capabilities and collaborative environment will help the team soon crack the remaining code of how nitrogenase forms ammonia.

    “We know that it has to do with adding hydrogen atoms, but how? There are a multitude of possible pathways and that’s what we’re looking into now,” said Raugei. “This is definitely an application where breakthroughs in quantum computing will accelerate our research and elevate our understanding of complex systems.”

    As the pace of scientific progress speeds forward, nitrogenase is just one example of how the promise of quantum chemistry, quantum computing, and PNNL’s Energy Sciences Center could help answer the next big question in catalysis.

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    DOE’s Pacific Northwest National Laboratory (PNNL) (US) is one of the United States Department of Energy National Laboratories, managed by the Department of Energy’s Office of Science. The main campus of the laboratory is in Richland, Washington.

    PNNL scientists conduct basic and applied research and development to strengthen U.S. scientific foundations for fundamental research and innovation; prevent and counter acts of terrorism through applied research in information analysis, cyber security, and the nonproliferation of weapons of mass destruction; increase the U.S. energy capacity and reduce dependence on imported oil; and reduce the effects of human activity on the environment. PNNL has been operated by Battelle Memorial Institute since 1965.

     
  • richardmitnick 1:57 pm on May 16, 2021 Permalink | Reply
    Tags: "Harvesting Light Like Nature Does", , Big dreams-tiny crystals, Creating a new material that reflects the structural and functional complexity of natural hybrid materials., DOE’s Pacific Northwest National Laboratory (US), , , , POSS-peptoid 2D nanocrystals have the potential to be applied to many different systems from photovoltaics to photocatalysis., POSS-peptoid nanocrystals form a highly efficient light-harvesting system that absorbs exciting light and emits a fluorescent signal., POSS-precise silicate-based cage-like structure, Synthesizing a new class of bio-inspired light-capturing nanomaterials., The scientists created a system that could capture light energy much in the way pigments found in plants do., The sun is the most important energy source we have., , Washington State University (US)   

    From DOE’s Pacific Northwest National Laboratory (US) : “Harvesting Light Like Nature Does” 

    From DOE’s Pacific Northwest National Laboratory (US)

    May 14, 2021

    Synthesizing a new class of bio-inspired light-capturing nanomaterials.

    Inspired by nature, researchers at Pacific Northwest National Laboratory (PNNL), along with collaborators from Washington State University (US), created a novel material capable of capturing light energy. This material provides a highly efficient artificial light-harvesting system with potential applications in photovoltaics and bioimaging.

    The research provides a foundation for overcoming the difficult challenges involved in the creation of hierarchical functional organic-inorganic hybrid materials. Nature provides beautiful examples of hierarchically structured hybrid materials such as bones and teeth. These materials typically showcase a precise atomic arrangement that allows them to achieve many exceptional properties, such as increased strength and toughness.

    PNNL materials scientist Chun-Long Chen, corresponding author of this study, and his collaborators created a new material that reflects the structural and functional complexity of natural hybrid materials. This material combines the programmability of a protein-like synthetic molecule with the complexity of a silicate-based nanocluster to create a new class of highly robust nanocrystals. They then programmed this 2D hybrid material to create a highly efficient artificial light-harvesting system.

    “The sun is the most important energy source we have,” said Chen. “We wanted to see if we could program our hybrid nanocrystals to harvest light energy—much like natural plants and photosynthetic bacteria can—while achieving a high robustness and processibility seen in synthetic systems.” The results of this study were published May 14, 2021, in Science Advances.

    Big dreams, tiny crystals

    Though these types of hierarchically structured materials are exceptionally difficult to create, Chen’s multidisciplinary team of scientists combined their expert knowledge to synthesize a sequence-defined molecule capable of forming such an arrangement. The researchers created an altered protein-like structure, called a peptoid, and attached a precise silicate-based cage-like structure (abbreviated POSS) to one end of it. They then found that, under the right conditions, they could induce these molecules to self-assemble into perfectly shaped crystals of 2D nanosheets. This created another layer of cell-membrane-like complexity similar to that seen in natural hierarchical structures while retaining the high stability and enhanced mechanical properties of the individual molecules.

    “As a materials scientist, nature provides me with a lot of inspiration” said Chen. “Whenever I want to design a molecule to do something specific, such as act as a drug delivery vehicle, I can almost always find a natural example to model my designs after.”

    2
    POSS-peptoid molecules self-assemble into rhomboid-shaped nanocrystals. Illustration: Stephanie King/Pacific Northwest National Laboratory.

    Designing bio-inspired materials

    Once the team successfully created these POSS-peptoid nanocrystals and demonstrated their unique properties including high programmability, they then set out to exploit these properties. They programmed the material to include special functional groups at specific locations and intermolecular distances. Because these nanocrystals combine the strength and stability of POSS with the variability of the peptoid building block, the programming possibilities were endless.

    Once again looking to nature for inspiration, the scientists created a system that could capture light energy much in the way pigments found in plants do. They added pairs of special “donor” molecules and cage-like structures that could bind an “acceptor” molecule at precise locations within the nanocrystal. The donor molecules absorb light at a specific wavelength and transfer the light energy to the acceptor molecules. The acceptor molecules then emit light at a different wavelength. This newly created system displayed an energy transfer efficiency of over 96%, making it one of the most efficient aqueous light-harvesting systems of its kind reported thus far.

    3
    POSS-peptoid nanocrystals form a highly efficient light-harvesting system that absorbs exciting light and emits a fluorescent signal. This system can be used for live cell imaging. Illustration by Chun-Long Chen and Yang Song/Pacific Northwest National Laboratory.

    Demonstrating the uses of POSS-peptoids for light harvesting

    To showcase the use of this system, the researchers then inserted the nanocrystals into live human cells as a biocompatible probe for live cell imaging. When light of a certain color shines on the cells and the acceptor molecules are present, the cells emit a light of a different color. When the acceptor molecules are absent, the color change is not observed. Though the team only demonstrated the usefulness of this system for live cell imaging so far, the enhanced properties and high programmability of this 2D hybrid material leads them to believe this is one of many applications.

    “Though this research is still in its early stages, the unique structural features and high energy transfer of POSS-peptoid 2D nanocrystals have the potential to be applied to many different systems from photovoltaics to photocatalysis,” said Chen. He and his colleagues will continue to explore avenues for application of this new hybrid material.

    Other authors of this study include: James De Yoreo, Mingming Wang, Shuai Zhang, and Xin Zhang from PNNL and Song Yang and Yuehe Lin from Washington State University. Shuai Zhang, James De Yoreo, and Chun-Long Chen are also affiliated with the University of Washington (US). This work was supported by the U.S. Department of Energy Basic Energy Sciences program as part of the University of Washington Center for the Science of Synthesis Across Scales (US), an Energy Frontier Research Centers Community Website (US) located at the University of Washington.

    See the full article here .

    five-ways-keep-your-child-safe-school-shootings

    Please help promote STEM in your local schools.

    Stem Education Coalition

    DOE’s Pacific Northwest National Laboratory (PNNL) (US) is one of the United States Department of Energy National Laboratories, managed by the Department of Energy’s Office of Science. The main campus of the laboratory is in Richland, Washington.

    PNNL scientists conduct basic and applied research and development to strengthen U.S. scientific foundations for fundamental research and innovation; prevent and counter acts of terrorism through applied research in information analysis, cyber security, and the nonproliferation of weapons of mass destruction; increase the U.S. energy capacity and reduce dependence on imported oil; and reduce the effects of human activity on the environment. PNNL has been operated by Battelle Memorial Institute since 1965.

     
c
Compose new post
j
Next post/Next comment
k
Previous post/Previous comment
r
Reply
e
Edit
o
Show/Hide comments
t
Go to top
l
Go to login
h
Show/Hide help
shift + esc
Cancel
%d bloggers like this: