Lawrence Berkeley Research Articles

(Invited) Hydrogen Materials Advanced Research Consortium R&D Activities

The Hydrogen Materials—Advanced Research Consortium (HyMARC) is a National Lab-led consortium working to advance and develop hydrogen storage materials that meet DOE targets for energy density, kinetics and cost for a wide range of applications. The goal of HyMARC is to enable a doubling of the hydrogen storage density compared with state-of-the-art compressed storage systems. DOE’s Hydrogen and Fuel Cell Technologies Office launched HyMARC in 2015, bringing together world-class National Lab capabilities in materials modelling, characterization and synthesis; and fostering collaboration with industry and academia to overcome R&D challenges in materials-based hydrogen storage. The consortium is led by teams at Sandia National Laboratories and the National Renewable Energy Laboratory and includes groups at Lawrence Livermore, Lawrence Berkeley, and Pacific Northwest National Laboratories. HyMARC is working to develop foundational understanding of phenomena governing thermodynamics and kinetics of hydrogen release and uptake in all classes of hydrogen storage materials, including metal hydrides, adsorbents, and hydrogen carriers. It seeks to accelerate materials development by addressing critical R&D gaps in hydrogen storage—leveraging recent advances in predictive multiscale modeling, machine learning, high-resolution in situ characterization, and novel materials synthesis techniques to realize their goal to enable twice the energy density for hydrogen storage in the coming years. This presentation will describe HyMARC’s ongoing R&D activities and past accomplishments which have pushed the bar forward for materials-based hydrogen storage.

(Invited) Development and Understanding of Advanced Materials for Hydrogen Storage and Delivery

Continued efforts to advance hydrogen storage and delivery require development and validation of efficient materials platforms coupled with mechanistic understanding of storage and conversion of hydrogen or its carriers. The Hydrogen Materials Advanced Research Consortium (HyMARC) is working with the research community to provide cutting-edge materials development advanced characterization, and theoretical modeling in support of these efforts, leveraging multiple U.S. Department of Energy national labs and their user facilities. Within this program, Berkeley Lab is developing stable and efficient solutions to hydrogen storage involving nanoscale encapsulation, and boosts to the conversion efficiency for liquid organic carriers through the development of single-atom catalyst platforms. The Molecular Foundry and The Advanced Light Source at Berkeley Lab provide deep insight into our materials advances through first-principles modeling of atomic and electronic structure, and X-ray spectroscopy of evolving materials systems at high temperatures and pressures.

Interfacial Properties of a PEM Water Electrolysis Pt Electrode Probed by Operando Tender Ambient Pressure X-Ray Photoelectron Spectroscopy

The probe of the chemical, structural, and electronic properties at the electrode-electrolyte interfaces under operational conditions is critical for the mechanistic study and optimization of all the electrochemical systems and has therefore been at the center of numerous studies [1]. Soft X-ray Ambient pressure X-ray photoelectron spectroscopy (APXPS) has been known as a promising in-situ/operando technique, but its applications in the probe of a realistic solid/liquid interface under ambient conditions has been limited by the short inelastic mean free path (IMFP) of photoelectrons in the liquid medium. Here, inspired by prior work [2,3] and taking advantage of the synchrotron tender X-ray source (Beamline 9.3.1) at the Advanced Light Source of the Lawrence Berkeley National Laboratory [4], the IMFP can be significantly increased. A larger IMFP enables interfacial characterization under higher background pressures and through liquid overlayer greater than 30 nm in thickness. We have designed and built a graphene sealed operando electrolysis electrochemical cell that is compatible with an existing tender X-ray APXPS setup for the investigation of electrode-electrolyte interfaces under operational conditions. The present setup will be utilized to probe various systems involving solid/liquid interfaces, including but not limited to the interactions of ions, the solvation of salts, and electrocatalysis.In this work, the utilization of tender X-rays (2000 – 6000 eV) facilitates investigations of the electrode-electrolyte interface with a liquid-layer thickness of tens of nanometers, which can be preserved within the sandwiched region between the topmost graphene and the Pt nanoparticle loaded proton exchange membrane (PEM). In addition, the environmental water vapor pressure within the analysis chamber was kept at the water vapor pressure at 298 K (~15 Torr). The setup allows the study of a water splitting electrolyzer under real working conditions, where the electrocatalytic performance and the operando photoemission spectroscopy can be simultaneous investigated. As a proof-of-concept, Pt nanoparticle-catalyzed oxygen evolution reaction (OER) was investigated under operando conditions. The electrochemistry and detailed analysis of the photoemission spectra demonstrating the evolution of the chemical, structural, and electronic properties at the H2O/Pt interface under the influence of external electrical field will be presented.

Membrane-Electrode-Assembly Design Parameters and Their Impact on CO2 Reduction

CO2 electrolyzers that operate at high current densities and selectivities are essential for commercializing CO2 electroreduction and for a sustainable, CO2-neutral green energy paradigm. As part of this, membrane-electrode assemblies (MEAs) are critical components that enable industrial-scale electrochemical CO2 conversion to value-added fuels and chemicals. They consist of humidified gaseous feeds at one or both electrodes and a solid, ion-conducting polymer (ionomer) as the electrolyte. Such assemblies can overcome the experimentally observed ohmic and mass-transfer limitations characteristic of aqueous gas-diffusion electrode (GDE) and planar systems, respectively, which limit their maximum achievable current densities and render these systems impractical for industrial implementation.Foundational design parameters that characterize the ionomer-based catalyst layers unique to MEA systems include the ionomer-to-catalyst (I:Cat) ratio, the catalyst loading, and the catalyst layer thickness. In this talk, the results of a comprehensive experimental study on a Ag-cathode CO2 reduction (CO2R) MEA will be reported, where we systematically analyzed the effects of these design parameters on CO2R MEA performance and selectivity. An optimum, intermediate I:Cat ratio was uncovered and rationalized based on electrochemically-active surface area and ionomer distribution arguments. The effect of decreasing the catalyst loading and/or thickness corroborates previous modeling work, resulting in an optimum range for catalyst layer utilization for CO2R. The effect of MEA anolyte/exchange solution concentration on system performance is also explored, yielding an efficient Ag-cathode CO2R MEA that operates at 200 to 1000 mA/cm2, at CO FEs of 78 to 91%, and with an area-specific resistance of 1 Ω*cm2.The scientific and experimental design learnings from our Ag-cathode MEA studies are subsequently applied to a Cu-cathode MEA, which has farther reaching and impactful applications due to its ability to produce value-added chemical precursors like ethylene and ethanol. The Cu-MEA cathodic I:Cat ratio and catalyst loading are fixed based on settings from the optimized Ag-MEA and additional catalyst layer parameters and operating conditions are examined. This systematic exploration provides deeper insights into the underlying interrelationships between physical phenomena in the Cu-MEA system. Overall, these studies provide a fundamental understanding of the relationships between critical design parameters and inherent operating and materials tradeoffs for practical CO2 reduction devices. Acknowledgements The authors gratefully acknowledge Lawrence Berkeley National Laboratory’s Laboratory Directed Research and Development (LDRD) Grant for funding.

Cable Design and Development for the High-Temperature Superconductor Cable Test Facility Magnet

A large bore “High-Temperature Superconductor Cable Test Facility Magnet” for testing advanced cables and inserts in high transverse field is in its design phase. This magnet will be the core component of a facility for developing conductors and accelerator magnets operating above 15 T, an enabling technology for next-generation fusion devices using magnetic confinement of plasma and for future energy frontier colliders. The procurement of Nb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Sn conductor, fabrication of cables, winding of coils, and assembly of the dipole magnet will be done at Lawrence Berkeley National Laboratory (LBNL) and the test pit and cryostat will be constructed at Fermi National Accelerator Laboratory. This article will present the conductor element of the LBNL project, specifically cable design parameters (based on the Bruker OST RRP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\bigcirc \!\!\!\! {\hbox{R}}}$</tex-math></inline-formula> Nb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Sn superconducting wire) and the development phase cable fabrication experience. Challenges of the cable fabrication will be discussed. The wire and cable planned for this magnet are similar to those under study for the Future Circular Collider and other large facility magnets. The successful fabrication of the development cable has positive implications for these other projects.

Polaris-LAMP: Multi-Modal 3-D Image Reconstruction With a Commercial Gamma-Ray Imager

The Polaris-LAMP multi-modal 3-D gamma-ray imager is a radiation mapping and imaging platform which uses a commercial off-the-shelf (COTS) detector integrated with a contextual sensor localization and mapping platform. The integration of these systems enables a free-moving radiation imaging capability with proximity mapping, coded-aperture, and Compton imaging modalities, which can create 3-D reconstruction of photon sources from tens of keV to several MeV. Gamma-ray events are recorded using a segmented cadmium zinc telluride (CZT) detector (Polaris-H Quad by H3D Inc., Ann Arbor, MI, USA), while scene data are derived from a contextual sensor and computation package developed by Lawrence Berkeley National Laboratory which includes GPS, laser ranging, and inertial measurement sensors. An onboard computer uses these inputs to create rapidly updating pose (10 Hz) and 3-D scene estimates using a simultaneous localization and mapping (SLAM) algorithm. The precise gamma-ray event location and timing resolution of the Polaris CZT sensor enables Compton imaging above several hundred keV, while photon sources at lower images are localized using coded-aperture imaging techniques. The multi-modal imaging concept enables imaging of diverse radiation sources spanning from the 59-keV emission of <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">241</sup> Am to the 1.1 and 1.3 MeV lines of <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">60</sup> Co. This work focuses on the description of the operational principles of the detector system and demonstrating the 3-D imaging performance in a variety of source detection and mapping scenarios. As a proof of concept, we demonstrate mapping complex environments, including both point source and distributed-source environments using proximity, coded-aperture, and Compton imaging modalities. Furthermore, we show the successful use of the system to perform measurements in high-background environments through analysis of arrays of uranium hexafluoride cylinders at the Paducah UF6 project site.

Introduction to the Department of Energy Leadership Computing Facilities

Did you ever say to yourself "Boy I wished I had a bigger computer." Do you look at your computation and see that cloud computing is not a viable solution? Do you think your problem could benefit from *really* high performance computing but don't know where to start in trying to access and use it? Acoustics researchers typically fail to think "big" with respect to their simulations—finding ways to reduce and limit their problems to fit a single workstation, a small cluster, a small HPC, or the cloud. The Department of Energy (DOE) Leadership Computing Facilities (LCF) are a set of the highest performance publicly accessible computing facilities in the U.S. Summit at Oak Ridge National Laboratory and Perlmutter at Lawrence Berkeley National Laboratory are #2 and #5 of the Top500 HPC list. Two Exascale computers: Frontier at Oak Ridge and Aurora at Argonne National Laboratory are set to arrive in the next couple of years. This presentation will introduce the DOE LCF and several of the allocation programs by which researchers and industry can gain access and training to use these incredible facilities.

Hidden physicists

Like me, my friend Vincent Kargatis earned a PhD in high-energy astrophysics and worked at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. Also like me, Kargatis left Goddard in the late 1990s to pursue a career outside of astronomy—in his case, computer programming. Now, 23 years later, he manages a team that ensures the quality of software produced by Oracle Utilities.Since I joined Physics Today in 1997, I’ve been obliged to keep up with developments in physics and to nurture contacts with physicists and their close professional relatives. Still, neither Kargatis nor I think of ourselves as physicists any longer.Debbie Bard leads the data science engagement group at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory. Before then, she worked at Imperial College London and SLAC. She earned a PhD in experimental particle physics at Edinburgh University in Scotland.Bard’s team helps researchers who need supercomputers for their data analysis and simulations. The job entails performing two translations: from what scientists need into what NERSC’s systems engineers can provide and from what NERSC’s hardware and software can do into what the scientists can implement.“My work is very far from actual physics,” Bard told me by email. “I’m mainly a manager of people and projects these days. But if someone asks me what I do, I still answer ‘physicist.’ It’s pretty deeply ingrained in me.”I learned about Bard from Spotlight on Hidden Physicists, an ongoing series of articles in Radiations, the magazine of Sigma Pi Sigma, the physics honor society. Each article is about a person with a physics degree who, like Bard, Kargatis, and me, went on to do other things. The range of other things is wide. Among the 37 hidden physicists are a jazz singer, a Disney animator, a Wall Street analyst, and a theologian.Kargatis qualifies as a hidden physicist. Indeed, people who work in computer software of one kind or another form the largest group in the Radiations series. Computer software is also the private-sector profession that employs the most PhD physicists, according to surveys conducted by the American Institute of Physics’s Statistical Research Center. (AIP publishes Physics Today and hosts Sigma Pi Sigma.)Sigma Pi Sigma’s Spotlight on Hidden Physicists succeeds in showing the variety of rewarding careers that people with a bachelor’s, master’s, or PhD in physics choose for themselves. High school seniors contemplating what subject to major in can be justifiably confident that a physics degree opens doors to careers beyond physics itself.But when the professional physics community contemplates hidden physicists, it should be careful. Although Bard continues to think of herself as a physicist, Kargatis does not. When he and I get together, we talk mostly about music, food, and wine and occasionally about physics. He is not a prime candidate for becoming a Physics Today subscriber or for rejoining the American Astronomical Society.Of the physicists in the private sector, how many still consider themselves to be physicists? I don’t know, but my hunch is that the answer has to do with whether they continue to do physics. This past May Google announced its intention to spend billions of dollars to build a quantum computer that can perform large-scale, error-free calculations by 2029. Out of curiosity, I looked up Google’s jobs site. To accomplish that ambitious goal, the company sought to recruit just six additional physicists.Two other companies in physics-flavored industries, IBM and Tesla, had no postings for physicists. That’s not to say either company had no jobs for which physicists were qualified. Tesla is currently looking for someone to do computer-aided engineering and computational fluid dynamics. Some physicists meet the position’s requirements, but so, too, do engineers and applied mathematicians.If “hidden physicist” is intended to mean someone outside academia who identifies as a physicist and who does physics, some adjustment might be needed. There could be fewer of them than expected.© 2021 American Institute of Physics.

An adaptive approach to machine learning for compact particle accelerators

Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

The Rankine–Kirchhoff approximations for moist thermodynamics

Quarterly Journal of the Royal Meteorological SocietyVolume 147, Issue 740 p. 3493-3497 LETTER TO THE EDITOR The Rankine–Kirchhoff approximations for moist thermodynamics David M. Romps, Corresponding Author David M. Romps romps@berkeley.edu orcid.org/0000-0001-7649-5175 Department of Earth and Planetary Science, University of California, Berkeley, California Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California Correspondence D. M. Romps, Department of Earth and Planetary Science, University of California, Berkeley, 307 McCone Hall, CA, USA. Email: romps@berkeley.eduSearch for more papers by this author David M. Romps, Corresponding Author David M. Romps romps@berkeley.edu orcid.org/0000-0001-7649-5175 Department of Earth and Planetary Science, University of California, Berkeley, California Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California Correspondence D. M. Romps, Department of Earth and Planetary Science, University of California, Berkeley, 307 McCone Hall, CA, USA. Email: romps@berkeley.eduSearch for more papers by this author First published: 01 September 2021 https://doi.org/10.1002/qj.4154Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume147, Issue740October 2021 Part APages 3493-3497 RelatedInformation

A lightweight data transmission reduction method based on a dual prediction technique for sensor networks

AbstractAn essential design concern in a resource‐constraint sensor network is optimizing data transmission for each sensor node (SN) to prolong the network lifetime. Many research works cited that the dual prediction technique remains the most efficient technique for data reduction. A large amount of redundant data is usually transmitted across the network, leading to collisions, loss of data, and energy dissipation. This article proposes a data transmission reduction method (DTRM) to solve these problems, implemented on the cluster heads and operates in rounds. DTRM is lightweight in processing, has low complexity costs, and needs a limited memory footprint, but it is robust and effective. It can be combined with any form of cluster‐based data aggregation. We have incorporated the proposed DTRM with the data aggregation‐adaptive frame method (DA‐AFM), implemented on the SNs within the clusters. DA‐AFM can eliminate temporal redundancies and correlations in the sensor's time‐series readings. This helps the SN take fewer readings, which improves the efficiency of reducing data transmission and decreases the amount of energy spent during sensing. The proposed DTRM approach decreases the average transmission rates of data while maintaining data quality. This study is evaluated on real data obtained from the Intel Berkeley Lab and compared with three recent data reduction techniques focused on prediction. The results show that DTRM consumes up to 70% less energy while preserving the expected quality of data and reducing transmission.

Commissioning of the SLAC Linac Coherent Light Source II electron source

For the Linac Coherent Light Source II (LCLS-II) project at SLAC, a 1.3 GHz superconducting rf (SRF) linac is being constructed that will generate 4 GeV electron bunches at a high repetition rate to drive x-ray free electron lasers. The LCLS-II electron source, which comprises the first three meters of the electron injector, includes two normal-conducting, continuous-wave rf cavities: a one-cell, 185.7 MHz gun and a two-cell, 1.3 GHz buncher. It also includes a gun load-lock system that allows photocathodes to be changed under vacuum. The components in this beam-line section were designed and built by Lawrence Berkeley National Laboratory based on experience from their advanced photoinjector experiment program. In combination with the SLAC UV laser system, the electron source is designed to produce beam rates up to 1 MHz with average currents up to $30\text{ }\text{ }\ensuremath{\mu}\mathrm{A}$ initially. The source was installed in mid-2018, well in advance of the SRF linac, which is now nearing completion. The source was commissioned over a two-year period, and this paper presents results including electron beam and dark current characterization.

Simultaneous measurement of organic scintillator response to carbon and proton recoils

Background: Organic scintillators are widely used for neutron detection in both basic nuclear physics and applications. While the proton light yield of organic scintillators has been extensively studied, measurements of the light yield from neutron interactions with carbon nuclei are scarce.Purpose: Demonstrate a new approach for the simultaneous measurement of the proton and carbon light yield of organic scintillators. Provide new carbon light yield data for the EJ-309 liquid and EJ-204 plastic organic scintillators.Method: A 33-MeV $^{2}\mathrm{H}^{+}$ beam from the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory was impinged upon a 3-mm-thick Be target to produce a high-flux, broad-spectrum neutron beam. The double time-of-flight technique was extended to simultaneously measure the proton and carbon light yields of the organic scintillators, wherein the light output associated with the recoil particle was determined using $np$ and $n\mathrm{C}$ elastic scattering kinematics.Results: The proton and carbon light yield relations of the EJ-309 liquid and EJ-204 plastic organic scintillators were measured over a recoil energy range of approximately 0.3 to 1 MeV and 2 to 5 MeV, respectively, for EJ-309, and 0.2 to 0.5 MeV and 1 to 4 MeV, respectively, for EJ-204.Conclusions: These data provide new insight into the ionization quenching effect in organic scintillators and key input for simulation of the response of organic scintillators for both basic science and a broad range of applications.

Assessment of the Second-Ionization Potential of Lawrencium: Investigating the End of the Actinide Series with a One-Atom-at-a-Time Gas-Phase Ion Chemistry Technique.

Experiments were performed at the Lawrence Berkeley National Laboratory 88-Inch Cyclotron facility to investigate the electron-transfer reduction reaction of dipositive Lr (Z = 103) with O2 gas. Ions of 255Lr were produced in the fusion-evaporation reaction 209Bi(48Ca,2n) 255Lr and were studied with a novel gas-phase ion chemistry technique. The produced 255Lr2+ ions were trapped and O2 gas was introduced, such that the charge-exchange reaction to reduce 255Lr2+ to 255Lr1+ was observed and the reaction rate constant was determined to be k = 1.5(7) × 10-10 cm3/mol/s. The observation that this reaction proceeds establishes the lower limit on the second ionization potential of Lr to be 13.3(3) eV. This gives further support that the actinide series terminates with Lr. Additionally, this result can be used to better interpret the situation concerning the placement of Lu and Lr on the periodic table within the current framework of the actinide hypothesis. The success of this experimental approach now identifies unique opportunities for future gas-phase reaction studies on actinide and super heavy elements.

Global trends in research on carbon footprint of buildings during 1971-2021: a bibliometric investigation.

The increasing issue of global warming has received tremendous attention from researchers around the world as researchers are actively publishing their findings related to environmental issues such as greenhouse gas emissions, carbon footprint, and air quality. In this bibliometric review, Scopus database was accessed to retrieve publications from 1971 to 2021, related to carbon footprint of buildings which is significantly associated with global warming and air quality. The results suggested that 41% of publications were published in close access journals requiring nominal subscription fee and/or institutional permissions for access to articles. Only 1% of publications were in press for publication, while 99% of them were online available. The trend of publications on carbon footprint has increased after 2002 and is also increasing in recent years as the topic is widely studied in many fields such as environmental sciences, engineering, materials sciences, earth and planetary sciences, chemical engineering, and energy. Approximately 97% publications were peer-reviewed journal articles. The authors, i.e., Aresta, M., Lin, T.P., and Persily, A.K., published highest number of publications among all on topic of carbon footprint. However, other authors, i.e., Cai, W., Chen, Z., Ma, M. Paik, I., and Pomponi, F., have published two publications each on carbon footprint of buildings. The funding for research on carbon footprint of buildings is mainly received from the National Institute of Standards and Technology, Lawrence Berkeley National Laboratory, and Tianjin University. However, the National Taiwan University, George Mason University, and Universita degi Studi di Bari hold 3% share in total number of publications on carbon footprint of buildings. As China and the USA are countries with highest share in global carbon footprint, both countries also have highest contribution in research on carbon footprint, followed by South Korea, the UK, Japan, Italy, Germany, Taiwan, etc. The study also concluded that, due to its wider readability and understanding, most of the publications were in the English language.

Study of the Synchrotron Photoionization Oxidation of Alpha-Angelica Lactone (AAL) Initiated by O(3P) at 298, 550, and 700 K.

In recent years, biofuels have been receiving significant attention because of their potential for decreasing carbon emissions and providing a long-term renewable solution to unsustainable fossil fuels. Currently, lactones are some of the alternatives being produced. Many lactones occur in a range of natural substances and have many advantages over bioethanol. In this study, the oxidation of alpha-angelica lactone initiated by ground-state atomic oxygen, O(3P), was studied at 298, 550, and 700 K using synchrotron radiation coupled with multiplexed photoionization mass spectrometry at the Lawrence Berkeley National Lab (LBNL). Photoionization spectra and kinetic time traces were measured to identify the primary products. Ketene, acetaldehyde, methyl vinyl ketone, methylglyoxal, dimethyl glyoxal, and 5-methyl-2,4-furandione were characterized as major reaction products, with ketene being the most abundant at all three temperatures. Possible reaction pathways for the formation of the observed primary products were computed using the CBS–QB3 composite method.

Quantifying the challenge of reaching a 100% renewable energy power system for the United States

Quantifying the challenge of reaching a 100% renewable energy power system for the United States

Technological Advances in Charged-Particle Therapy.

Charted-particle therapy (CPT) benefits cancer patients by localizing doses in the tumor volume while minimizing the doses delivered to normal tissue through its unique physical and biological characteristics. The world’s first CPT applied on humans was proton beam therapy (PBT), which was performed in the mid-1950s. Among heavy ions, carbon ions showed the most favorable biological characteristics for the treatment of cancer patients. Carbon ions show coincidence between the Bragg peak and maximum value of relative biological effectiveness. In addition, they show low oxygen enhancement ratios. Therefore, carbon-ion radiotherapy (CIRT) has become mainstream in the treatment of cancer patients using heavy ions. CIRT was first performed in 1977 at the Lawrence Berkeley Laboratory. The CPT technology has advanced in the intervening decades, enabling the use of rotating gantry, beam delivery with fast pencil-beam scanning, image-guided particle therapy, and intensity-modulated particle therapy. As a result, as of 2019, a total of 222,425 and 34,138 patients with cancer had been treated globally with PBT and CIRT, respectively. For more effective and efficient CPT, many groups are currently conducting further studies worldwide. This review summarizes recent technological advances that facilitate clinical use of CPT.

The Colorado East River Community Observatory Data Collection

AbstractThe U.S. Department of Energy's (DOE) Colorado East River Community Observatory (ER) in the Upper Colorado River Basin was established in 2015 as a representative mountainous, snow‐dominated watershed to study hydrobiogeochemical responses to hydrological perturbations in headwater systems. The ER is characterized by steep elevation, geologic, hydrologic and vegetation gradients along floodplain, montane, subalpine, and alpine life zones, which makes it an ideal location for researchers to understand how different mountain subsystems contribute to overall watershed behaviour. The ER has both long‐term and spatially‐extensive observations and experimental campaigns carried out by the Watershed Function Scientific Focus Area (SFA), led by Lawrence Berkeley National Laboratory, and researchers from over 30 organizations who conduct cross‐disciplinary process‐based investigations and modelling of watershed behaviour. The heterogeneous data generated at the ER include hydrological, genomic, biogeochemical, climate, vegetation, geological, and remote sensing data, which combined with model inputs and outputs comprise a collection of datasets and value‐added products within a mountainous watershed that span multiple spatiotemporal scales, compartments, and life zones. Within 5 years of collection, these datasets have revealed insights into numerous aspects of watershed function such as factors influencing snow accumulation and melt timing, water balance partitioning, and impacts of floodplain biogeochemistry and hillslope ecohydrology on riverine geochemical exports. Data generated by the SFA are managed and curated through its Data Management Framework. The SFA has an open data policy, and over 70 ER datasets are publicly available through relevant data repositories. A public interactive map of data collection sites run by the SFA is available to inform the broader community about SFA field activities. Here, we describe the ER and the SFA measurement network, present the public data collection generated by the SFA and partner institutions, and highlight the value of collecting multidisciplinary multiscale measurements in representative catchment observatories.

Opus 12's CO2 transformation and the importance of collaboration

Opus 12's CO2 transformation and the importance of collaboration