Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Benjamin Place (Fed)

Research Chemist

Ben is currently a Research Chemist within the Organic Chemical Metrology Group at the National Institute of Standards and Technology (NIST) in Gaithersburg, MD. He first started at NIST as a National Research Council (NRC) Postdoctoral Fellow, working with adviser Dr. Catherine Rimmer, to develop novel techniques using multidimensional liquid chromatography with high resolution mass spectrometry. Ben has worked on the quantitative measurement of fatty acids in food and dietary supplement reference materials and per- and polyfluoroalkyl substances (PFAS) in food and environmental reference materials.

Currently, Ben’s research has been on the development and validation of non-targeted analysis methods using liquid chromatography with high resolution mass spectrometry. This work includes the coordination Method Assessment for Non-Targeted Analysis (MANTA) Program interlaboratory study and participating as a member of the Benchmarks and Publications for Non-Targeted Analysis (BP4NTA) Working Group. Recently, he has focused on the development of tools and reference data for the identification of novel PFAS in environmental samples with the Strategic Environmental Research & Development Program (SERDP).

Professional Memberships

  • Member, Society of Environmental Toxicology & Chemistry (SETAC)
  • Member, American Chemical Society (ACS)


  • NIST/MML Accolade for MML Strategic Partnership, 2019
  • NIST/MML Accolade for Service to MML, 2015
  • MML Angel Investor Award for High-throughput, Surface-based Ionization Technique, 2014
  • Laboratory Teaching Assistant Award (Oregon State University), 2009
  • ACS Outstanding Student in Chemistry Award (Alma College), 2008


Certification of Standard Reference Material® 2386 Avocado Powder

Melissa M. Phillips, Laura Wood, Joseph Browning, George Caceres, Grace Hahm, Mahboubeh Hanaee, Abigail Lee, Karen Murphy, Rabia Oflaz, Rick L. Paul, Benjamin Place, Jeanice "Brown Thomas ", James H. Yen
The National Institute of Standards and Technology (NIST) recently released SRM 2386 Avocado Powder which has value assignment for over 70 analytes. This

Use of non-targeted and suspect screening analysis to detect sources of human exposure to environmental contaminants

Katherine Manz, Anna Feerick, Joseph Braun, Yong-Lai Feng, Amber Hall, Jeremy Koelmel, Carlos A. Manzano, Seth Newton, Kurt Pennell, Benjamin Place, Krystal Godri Pollitt, Carsten Prasse, Joshua Young
Non-targeted analysis (NTA) and suspect screening analysis (SSA) are powerful techniques that rely on high-resolution mass spectrometry (HRMS) and computational

Per- and Polyfluoroalkyl Substances in New Firefighter Turnout Gear Textiles

Andrew Maizel, Andre Thompson, Meghanne Tighe, Samuel Escobar Veras, Alix Rodowa, Ryan Falkenstein-Smith, Bruce A. Benner Jr., Kathleen Hoffman, Michelle K. Donnelly, Olivia Hernandez, Nadine Wetzler, Trung Ngu, Jessica Reiner, Benjamin Place, John Kucklick, Kate Rimmer, Rick D. Davis
Turnout gear is increasingly recognized as a potential source of per- and polyfluoroalkyl substance (PFAS) exposure to firefighters. To determine the type

Characterization of Reference Materials 8690 to 8693

Jessica Reiner, Benjamin Place, N. Alan Heckert, Katherine Peter, Alix Rodowa
The National Institute of Standards and Technology (NIST) Reference Materials (RMs) 8690 Per- and Polyfluoroalkyl Substances (PFAS) in Aqueous Film-Forming

Interlaboratory Attribute Analytics Metrics from the MAM Consortium Round Robin Study

Trina Mouchahoir, John E. Schiel, Rich Rogers, N. Alan Heckert, Benjamin Place, Aaron Ammerman, Xiaoxiao Li, Tom Robinson, Brian Schmidt, Chris M. Chumsae, Xinbi Li, Anton V. Manuilov, Bo Yan, Gregory O. Staples, Da Ren, Alexander J. Veach, Dongdong Wang, Wael Yared, Zoran Sosic, Yan Wang, Li Zang, Anthony M. Leone, Peiran Liu, Richard Ludwig, Li Tao, Wei Wu, Ahmet Cansizoglu, Andrew Hanneman, Greg W. Adams, Irina Perdivara, Hunter Walker, Margo Wilson, Arnd Brandenburg, Nick DeGraan-Weber, Stefano Gotta, Joe Shambaugh, Melissa Alvarez, X. Christopher Yu, Li Cao, Chun Shao, Andrew Mahan, Hirsh Nanda, Kristen Nields, Nancy Nightlinger, Ben Niu, Jihong Wang, Wei Xu, Gabriella Leo, Nunzio Sepe, Yan-Hui Liu, Bhumit A. Patel, Douglas Richardson, Yi Wang, Daniela Tizabi, Oleg V. Borisov, Yali Lu, Ernest L. Maynard, Albrecht Gruhler, Kim F. Haselmann, Thomas N. Krogh, Carsten P. Sönksen, Simon Letarte, Sean Shen, Kristin Boggio, Keith Johnson, Wenqin Ni, Himakshi Patel, David Ripley, Jason C. Rouse, Ying Zhang, Carly Daniels, Andrew Dawdy, Olga Friese, Thomas W. Powers, Justin B. Sperry, Josh Woods, Eric Carlson, K. Ilker Sen, St John Skilton, Michelle Busch, Anders Lund, Martha Stapels, Xu Guo, Sibylle Heidelberger, Harini Kaluarachchi, Sean McCarthy, John Kim, Jing Zhen, Ying Zhou, Sarah Rogstad, Xiaoshi Wang, Jing Fang, Weibin Chen, Ying Qing Yu, John G. Hoogerheide, Rebecca Scott, Hua Yuan
The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process
Created May 31, 2018, Updated December 8, 2022