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Institution

Pacific Northwest National Laboratory

FacilityRichland, Washington, United States
About: Pacific Northwest National Laboratory is a facility organization based out in Richland, Washington, United States. It is known for research contribution in the topics: Catalysis & Aerosol. The organization has 11581 authors who have published 27934 publications receiving 1120489 citations. The organization is also known as: PNL & PNNL.
Topics: Catalysis, Aerosol, Mass spectrometry, Ion, Adsorption


Papers
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Journal ArticleDOI
TL;DR: FC-Cu-EDA-SAMMS has great potential to be used as orally administered drug for limiting the absorption of radioactive Cs and toxic Tl in gastrointestinal tract and was less affected by the solution pH, competing cations, and matrices.

343 citations

Journal ArticleDOI
TL;DR: The objective of this work is to review those aspects of the field that are pertinent to targeted α-particle emitter therapy and to provide guidance and recommendations for human α- Particle Emitter dosimetry.
Abstract: The potential of α-particle emitters to treat cancer has been recognized since the early 1900s. Advances in the targeted delivery of radionuclides and radionuclide conjugation chemistry, and the increased availability of α-emitters appropriate for clinical use, have recently led to patient trials of radiopharmaceuticals labeled with α-particle emitters. Although α-emitters have been studied for many decades, their current use in humans for targeted therapy is an important milestone. The objective of this work is to review those aspects of the field that are pertinent to targeted α-particle emitter therapy and to provide guidance and recommendations for human α-particle emitter dosimetry.

343 citations

Journal ArticleDOI
01 Apr 2019-Nature
TL;DR: The vibrational normal modes in a single molecule are imaged using tip-enhanced Raman spectromicroscopy performed in the atomistic near-field, and ångström-scale resolution is attained at subatomic separation between the tip atom and a molecule in the quantum tunnelling regime of plasmons.
Abstract: The internal vibrations of molecules drive the structural transformations that underpin chemistry and cellular function. While vibrational frequencies are measured by spectroscopy, the normal modes of motion are inferred through theory because their visualization would require microscopy with angstrom-scale spatial resolution—nearly three orders of magnitude smaller than the diffraction limit in optics1. Using a metallic tip to focus light and taking advantage of the surface-enhanced Raman effect2 to amplify the signal from individual molecules, tip-enhanced Raman spectromicroscopy (TER-SM)3,4 reaches the requisite sub-molecular spatial resolution5, confirming that light can be confined in picocavities6–10 and anticipating the direct visualization of molecular vibrations11–13. Here, by using TER-SM at the precisely controllable junction of a cryogenic ultrahigh-vacuum scanning tunnelling microscope14–16, we show that angstrom-scale resolution is attained at subatomic separation between the tip atom and a molecule in the quantum tunnelling regime of plasmons6,8,9,17. We record vibrational spectra within a single molecule, obtain images of normal modes and atomically parse the intramolecular charges and currents driven by vibrations. Our analysis provides a paradigm for optics in the atomistic near-field. The vibrational normal modes in a single molecule are imaged using tip-enhanced Raman spectromicroscopy performed in the atomistic near-field.

342 citations

Journal ArticleDOI
TL;DR: This Perspective discussed the best practices for reporting lab-scale performance metrics in battery papers, and explained metrics such as anode energy density, voltage hysteresis, mass of non-active cell components and anode/cathode mass ratio.
Abstract: Batteries have shaped much of our modern world. This success is the result of intense collaboration between academia and industry over the past several decades, culminating with the advent of and improvements in rechargeable lithium-ion batteries. As applications become more demanding, there is the risk that stunted growth in the performance of commercial batteries will slow the adoption of important technologies such as electric vehicles. Yet the scientific literature includes many reports describing material designs with allegedly superior performance. A considerable gap needs to be filled if we wish these laboratory-based achievements to reach commercialization. In this Perspective, we discuss some of the most relevant testing parameters that are often overlooked in academic literature but are critical for practical applicability outside the laboratory. We explain metrics such as anode energy density, voltage hysteresis, mass of non-active cell components and anode/cathode mass ratio, and we make recommendations for future reporting. We hope that this Perspective, together with other similar guiding principles that have recently started to emerge, will aid the transition from lab-scale research to next-generation practical batteries. This Perspective discussed the best practices for reporting lab-scale performance metrics in battery papers.

342 citations

Journal ArticleDOI
Edoardo Aprà1, Eric J. Bylaska1, W. A. de Jong2, Niranjan Govind1, Karol Kowalski1, T. P. Straatsma3, Marat Valiev1, H. J. J. van Dam4, Yuri Alexeev5, J. Anchell6, V. Anisimov5, Fredy W. Aquino, Raymond Atta-Fynn7, Jochen Autschbach8, Nicholas P. Bauman1, Jeffrey C. Becca9, David E. Bernholdt10, K. Bhaskaran-Nair11, Stuart Bogatko12, Piotr Borowski13, Jeffery S. Boschen14, Jiří Brabec15, Adam Bruner16, Emilie Cauet17, Y. Chen18, Gennady N. Chuev19, Christopher J. Cramer20, Jeff Daily1, M. J. O. Deegan, Thom H. Dunning21, Michel Dupuis8, Kenneth G. Dyall, George I. Fann10, Sean A. Fischer22, Alexandr Fonari23, Herbert A. Früchtl24, Laura Gagliardi20, Jorge Garza25, Nitin A. Gawande1, Soumen Ghosh20, Kurt R. Glaesemann1, Andreas W. Götz26, Jeff R. Hammond6, Volkhard Helms27, Eric D. Hermes28, Kimihiko Hirao, So Hirata29, Mathias Jacquelin2, Lasse Jensen9, Benny G. Johnson, Hannes Jónsson30, Ricky A. Kendall10, Michael Klemm6, Rika Kobayashi31, V. Konkov32, Sriram Krishnamoorthy1, M. Krishnan18, Zijing Lin33, Roberto D. Lins34, Rik J. Littlefield, Andrew J. Logsdail35, Kenneth Lopata36, Wan Yong Ma37, Aleksandr V. Marenich20, J. Martin del Campo38, Daniel Mejía-Rodríguez39, Justin E. Moore6, Jonathan M. Mullin, Takahito Nakajima, Daniel R. Nascimento1, Jeffrey A. Nichols10, P. J. Nichols40, J. Nieplocha1, Alberto Otero-de-la-Roza41, Bruce J. Palmer1, Ajay Panyala1, T. Pirojsirikul42, Bo Peng1, Roberto Peverati32, Jiri Pittner15, L. Pollack, Ryan M. Richard43, P. Sadayappan44, George C. Schatz45, William A. Shelton36, Daniel W. Silverstein46, D. M. A. Smith6, Thereza A. Soares47, Duo Song1, Marcel Swart, H. L. Taylor48, G. S. Thomas1, Vinod Tipparaju49, Donald G. Truhlar20, Kiril Tsemekhman, T. Van Voorhis50, Álvaro Vázquez-Mayagoitia5, Prakash Verma, Oreste Villa51, Abhinav Vishnu1, Konstantinos D. Vogiatzis52, Dunyou Wang53, John H. Weare26, Mark J. Williamson54, Theresa L. Windus14, Krzysztof Wolinski13, A. T. Wong, Qin Wu4, Chan-Shan Yang2, Q. Yu55, Martin Zacharias56, Zhiyong Zhang57, Yan Zhao58, Robert W. Harrison59 
Pacific Northwest National Laboratory1, Lawrence Berkeley National Laboratory2, National Center for Computational Sciences3, Brookhaven National Laboratory4, Argonne National Laboratory5, Intel6, University of Texas at Arlington7, State University of New York System8, Pennsylvania State University9, Oak Ridge National Laboratory10, Washington University in St. Louis11, Wellesley College12, Maria Curie-Skłodowska University13, Iowa State University14, Academy of Sciences of the Czech Republic15, University of Tennessee at Martin16, Université libre de Bruxelles17, Facebook18, Russian Academy of Sciences19, University of Minnesota20, University of Washington21, United States Naval Research Laboratory22, Georgia Institute of Technology23, University of St Andrews24, Universidad Autónoma Metropolitana25, University of California, San Diego26, Saarland University27, Sandia National Laboratories28, University of Illinois at Urbana–Champaign29, University of Iceland30, Australian National University31, Florida Institute of Technology32, University of Science and Technology of China33, Oswaldo Cruz Foundation34, Cardiff University35, Louisiana State University36, Chinese Academy of Sciences37, National Autonomous University of Mexico38, University of Florida39, Los Alamos National Laboratory40, University of Oviedo41, Prince of Songkla University42, Ames Laboratory43, University of Utah44, Northwestern University45, Universal Display Corporation46, Federal University of Pernambuco47, CD-adapco48, Cray49, Massachusetts Institute of Technology50, Nvidia51, University of Tennessee52, Shandong Normal University53, University of Cambridge54, Advanced Micro Devices55, Technische Universität München56, Stanford University57, Wuhan University of Technology58, Stony Brook University59
TL;DR: The NWChem computational chemistry suite is reviewed, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.
Abstract: Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.

342 citations


Authors

Showing all 11848 results

NameH-indexPapersCitations
Yi Cui2201015199725
Derek R. Lovley16858295315
Xiaoyuan Chen14999489870
Richard D. Smith140118079758
Taeghwan Hyeon13956375814
Jun Liu13861677099
Federico Capasso134118976957
Jillian F. Banfield12756260687
Mary M. Horowitz12755756539
Frederick R. Appelbaum12767766632
Matthew Jones125116196909
Rainer Storb12390558780
Zhifeng Ren12269571212
Wei Chen122194689460
Thomas E. Mallouk12254952593
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023130
2022459
20211,794
20201,795
20191,598
20181,619