PNNL

Alex Hagen is a data scientist who works broadly across detection and material interdiction spaces to improve analysis using modern machine learning methods. After a half decade designing neutron detectors and active interrogation techniques and subsequently analyzing data from such experiments, he knows how to combine field implementation with advanced analytical techniques.

His research has been published across many nuclear engineering and physics journals, including in Journal of Physics, Nuclear Instruments and Methods, and the Transactions of Nuclear Science. His conference presentations have also taken him across the world, including to the International Conference of Nuclear Engineering in Prague in the Czech Republic and the Advanced Computing and Analysis Techniques conference in Saas-Fe, Switzerland. He has contributed to several high-energy physics collaborations, including Belle2, and PICO.

Hagen has received several commendations. Most recently, he was awarded the 2023 Overall Core Values Award in Pacific Northwest National Laboratory’s AI and Data Analytics Division. In 2019, he was nominated as one of PNNL National Security Directorate's author of the year; in 2017 he was awarded the Department of Energy's Innovations in Nuclear Technology Research and Development Award.

• Machine Learning
• Deep Learning
• Signature Science
• Uncertainty Quantification
• Contraband Detection and Interdiction
• Nuclear Non-Proliferation
• Nuclear Material Detection and Analysis
• Deployment of Machine Learning Applications

PhD in Nuclear Engineering, Purdue University

MS in Nuclear Engineering, Purdue University

BS in Nuclear Engineering, Purdue University

• Member and Vice President, Board of Directors, Golden Broncho Non-Profit Organization, 2022 – present
• Leader, Department of Homeland Security Science and Technology Directorate AI for Customs Metrics Subcommittee, August 2018 – May 2020
• Graduate, PNNL’s Scientist and Engineer Development Program, 2018 – 2020 
• President, Purdue Nuclear Engineering Graduate Organization, 2012 – 2017 
• Chair, Purdue Graduate Student Government Advancement Committee, 2014 – 2016 
• Clerk, Purdue Graduate Student Government, 2015 – 2016  

• Outstanding Performance Award for the “Emergency Management of Tomorrow Research,” 2024
• Outstanding Contribution Award, PNNL, 2023
• Overall Award, Artificial Intelligence and Data Analytics Division, PNNL, 2022
• Outstanding Performance Award, PNNL, 2022
• Outstanding Contribution Award, PNNL, 2020
• Nominee, Computing and Analytics Collaboration Core Value Award, PNNL, 2020
• Nominee, Author of the Year Award, National Security Directorate, PNNL, 2019
• Outstanding Performance Award for “Radiation Portal Monitor Replacement Program,” 2019
• Outstanding Performance Award for “Modeling Hydrogen Radiolysis from Fukushima...,” 2019
• Innovation in Nuclear Technology R&D Award, Department of Energy, 2017
• Best Paper Award, American Nuclear Society Young Member’s Group, 2016
• IEEE Sensors Conference Demonstration 1st Prize, 2016
• Outstanding Service Award, Purdue Nuclear Engineering, 2016

J.L Barr. et al. Emergency Management of Tomorrow Research – Artificial Intelligence Landscape Assessment. Tech. rep. PNNL-36083. Pacific Northwest National Laboratory, 2024. 

N.J Betzsold et al. EOC of the Future: TTX Technology Primer. Table Top Exercise. 2024. 

R.A Clark. et al. “Plutonium Processing Signature Development at Pacific Northwest National Laboratory.” In: Proceedings of the NuFor (Nuclear Forensics) Conference. Manchester, United Kingdom, 2024. 

A.R Hagen., B.C. Archambault, and T.F. Grimes. “The historical and Applied development of tensioned metastable fluid detectors at Purdue University.” In: Presented at Radiobioassay and Radiochemical Measurements Conference. PNNL-SA-205283. 2024. 

Artificial Intelligence’s Opportunities in Emergency Management November (2024). 

A.R Hagen., N.H. Ly, and C.A. Nizinski. “Quantitative Analysis with Late Supervision.” In: Presented at National Nuclear Security Administration Defense Nuclear Nonproliferation Research and Development Artificial Intelligence Workshop. PNNL-SA-193935. 2024. 

A.R. Hagen and authors not released. Report title not released. Tech. rep. PNNL-SA-198322. Limited Distribution, 2024. 

A.R. Hagen and authors not released. Software title not released. Deployed Software. 2024. Alex Hagen · November 22, 2024 

A.R Hagen. et al. “A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Mate- rial Properties from Existing Models.” In: Proceedings of the 65th Annual Meeting of the Institute of Nuclear Materials Management. PNNL-SA-200177. 2024.

A.R Hagen. et al. “Pacific Northwest National Laboratory Progress for the Morphological Data Exploitation Workshop.” In: Proceedings of the Morphological Data Exploitation (MODE) Workshop. PNNL-SA-203271. Robust Morphological Inter-Laboratory Project. 2024. 

A.M Lesperance. et al. Emergency Management of Tomorrow Research Final Summary Report. Tech. rep. PNNL- 36203. Pacific Northwest National Laboratory, 2024. 

N.H Ly et al. “Assessing Unsupervised Learning Models for Process Attribution Analyses.” In: Proceedings of the 65th Annual Meeting of the Institute of Nuclear Materials Management. 2024. 

N.H Ly. et al. “Improving Microstructures Segmentation via Pretraining with Synthetic Data.” In: Under Review for Computational Materials Science (2024). 

C.A Nizinski. and A.R. Hagen. “Nuclear Forensic Science for Morphology Signatures.” In: Proceedings of the Stan- ford Synchrotron Radiation Lightsource (SSRL) Workshop: Environmental and nuclear radiological systems. PNNL-SA- 203845. 2024. 

C.A Nizinski. et al. “Morphology comparison of uranyl oxalate precipitations across scales.” In: Proceedings of the 65th Annual Meeting of the Institute of Nuclear Materials Management. PNNL-SA-199863. 2024. 

M.T Oostrom. et al. “Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials.” In: Proceedings of The Mineral, Metals and Materials Society Specialty Congress. Cleveland Hilton, Cleveland, Ohio, 2024. 

M.T Oostrom. et al. “Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstructural Evolution During Irradiation of Materials.” In: Under Review for PLoS One (2024). 

M.T Oostrom. et al. Dataset for Bayesian SegNet for Semantic Segmentation with Improved Interpretation of Microstruc- tural Evolution During Irradiation of Materials. Electronic Dataset. 2024. 

M.T Oostrom. et al. Defect Detection. Open Sourced Software. 2024. 

E Brayfindley. et al. Defense Threat Reduction Agency Chemical/Biological and Nuclear Technologies Artificial Intelligence and Machine Learning Workshop. 2023. 

Alex Hagen and Shane Jackson. “Synthesis parameter effect detection using quantitative representations and high dimensional distribution distances.” In: arXiv preprint arXiv:2304.01120 (2023). 

A.R. Hagen. “Building statistically rigorous analytical methods for high consequence decisions in national security.” In: Presented at Montana State National Security Seminar Series. PNNL-SA-184238. 2023. 

A.R. Hagen. “Presentation title not released.” In: Venue not released. PNNL-SA-187198. 2023. 

A.R. Hagen. “Presentation title not released.” In: Venue not released. PNNL-SA-193424. 2023. 

A.R Hagen. and J.A. Evans. “Operationalizing Machine Learning.” In: Venue not released. PNNL-SA-183935. 2023. 

A.R. Hagen and authors not released. “Presentation title not released.” In: Venue not released. PNNL-SA-184077. 2023. 

A.R. Hagen and authors not released. Report title not released. Tech. rep. PNNL-SA-184896. Limited Distribution, 2023. 

A.R Hagen. et al. “Further experimental evidence of the photon insensitivity and robustness of tensioned metastable fluid detectors in high intensity or high energy photon fields.” Accepted by Journal of Nuclear Engineering and Radiation Science. 2023. 

A.R Hagen. et al. “Pacific Northwest National Laboratory Progress for the Morphological Data Exploitation Workshop.” In: Proceedings of the Morphological Data Exploitation (MODE) Workshop. PNNL-SA-188988. Robust Morphological Inter-Laboratory Project. 2023. 

A.R Hagen. et al. Plutonium Provenance Testing with Unsupervised and Semi-supervised methods Report. Tech. rep. PNNL-33947. Pacific Northwest National Laboratory, 2023. 

A.R Hagen. et al. “Quantitative Analysis with Late Supervision (QALS).” In: Venue not released. PNNL-SA-182045. 2023. 

C.M Hainje. et al. “Investigation of Process History and Underlying Phenomena Associated with the Synthesis of Plutonium Oxides using Vector Quantizing Variational Autoencoder.” In: Chemometrics and Intelligent Laboratory Systems 240.PNNL-SA-179134. doi:10.1016/j.chemolab.2023.104909 (2023). 

N.H Ly. et al. Improving Model Robustness Against Variations in Micrograph Quality with Unsupervised Domain Adap- tation. Accompanying data and software. 2023. 

N.H Ly. et al. “Improving Robustness for Model Discerning Synthesis Process of Uranium Oxide with Unsupervised Domain Adaptation.” In: Frontiers in Nuclear Engineering 2.PNNL-SA-176828. doi:10.3389/fnuen.2023.1230052 (2023). 

N.H Ly. et al. “Improving uranium oxides processing history discernment with synthetic data.” In: Presented at American Chemical Society. PNNL-SA-188818. San Francisco, California, 2023. 

L.W McDonald. et al. “Review of Multi-Faceted Morphologic Signatures of Actinide Process Materials for Nu- clear Forensic Science.” In: Journal of Nuclear Materials 588. PNNL-SA-190209.doi:10.1016/j.jnucmat.2023.154779 (2023). 

C.A Nizinski. et al. “Identifying morphological trends in plutonium (III) oxalate.” In: Presented at NuFor (Nuclear Forensics). PNNL-SA-190760. London, United Kingdom, 2023. 

Naoto Aizawa et al. “Reviewer’s Recognition.” In: Journal of Nuclear Engineering and Radiation Science 8 (2022), pp. 010202–1. L.M. Burke et al. Data Science for Safeguards Practitioners Course. PNNL-SA-174335. 2022. 

R.A Clark. et al. “Plutonium Processing Signature Development.” In: Presented at University of Utah Seminar Series and Brigham Young University Seminar Series. PNNL-SA-178782. Salt Lake City And Provo, Utah, 2022. 

C.N Cramer. et al. Chemical Biological Defense Application Development Internship Competition. 2022. 

A.R. Hagen. Report title not released. Tech. rep. PNNL-32975. Venue not released, 2022. 

A.R. Hagen and authors not released. “Presentation title not released.” In: Venue not released. PNNL-SA-174263. 2022. 

A.R. Hagen and authors not released. Report title not released. Tech. rep. PNNL-SA-172855. Limited Distribution, 2022. 

A.R Hagen., R. Taleyarkhan, and B.C. Archambault. “Tensioned Metastable Fluid Detectors for Dosimetry and Contamination Monitoring.” In: Presented at Nuclear and Facility Safety Workshop. PNNL-SA-170534. 2022. 

A.R Hagen et al. “DBCal: Density Based Calibration of classifier predictions for uncertainty quantification.” In: A preprint arXiv:2204.00150, PNNL-SA-171360 (2022). 

A.R Hagen. et al. “Pacific Northwest National Laboratory Progress for the Morphological Data Exploitation Workshop.” In: Proceedings of the Morphological Data Exploitation (MODE) Workshop. PNNL-SA-170833. Robust Morphological Inter-Laboratory Project. 2022. 

A.R Hagen. et al. “Reduction of detection limit and quantification uncertainty due to interferent by neural clas- sification with abstention.” In: Nuclear Instruments and Methods in Physics Research. Section A: Accelerators Spectrometers Detectors and Associated Equipment 1040.PNNL-SA-171868. doi:10.1016/j.nima.2022.167174 (2022). 

A.R Hagen. et al. “SEM Pulse Classification Classifier Based Counting Experiments and Abstention.” In: Presented at Nuclear Science and Applications Research and Development (NSARD). PNNL-SA-171865. 2022. 

E.R Jurrus. and A.R. Hagen. “Discovery and Exploitation of Forensic Signatures using Deep Learning.” In: Pre- sented at Artificial Intelligence and Machine Learning for International Atomic Energy Agency Safeguards. PNNL-SA- 170866. 2022. 

E King. et al. hysics-informed machine-learning model of temperature evolution under solid phase processes. Tech. rep. PNNL-SA-172184. Pacific Northwest National Laboratory, 2022. 

D.G MacDonald. et al. “Open Architecture for Scanning and Imaging Systems (OASIS): An Airport of the Future Testbed.” In: Venue not released. PNNL-SA-169525. 2022. 

D.E Meier. et al. “Recent Developments on the Production and Morphological Analysis of Plutonium Oxalate and Oxide compounds.” In: Presented at Plutonium Futures - The Science. PNNL-SA-177885. Avignon, France, 2022. 

C.A Nizinski. et al. “Characterization of Uncertainties and Model Generalizability for Convolutional Neural Net- work Predictions of Uranium Ore Concentrate Morphology.” In: Chemometrics and Intelligent Laboratory Systems 225.PNNL-SA-168845. doi:10.1016/j.chemolab.2022.104556 (2022). 

Cody A Nizinski et al. “Characterization of uncertainties and model generalizability for convolutional neural network predictions of uranium ore concentrate morphology.” In: Chemometrics and Intelligent Laboratory Systems 225 (2022), p. 104556. 

S Ozerov. et al. “Clinical 6 MV X-Ray Facility Photo-Neutron/Fission Interrogations with TMFD Sensors.” In: Nuclear Instruments and Methods in Physics Research. Section A: Accelerators Spectrometers Detectors and Associated Equipment 1029.PNNL-SA-168849. doi:10.1016/j.nima.2022.166395 (2022). 

K Pazdernik. et al. “Well-calibrated and domain-informed deep learning methods for quantifying the change in microstructural features post-radiation on LiAlO2 pellets.” In: Venue not released. PNNL-SA-179402. 2022. 

B.D Pierson. et al. “Alpha/Beta gated Gamma-Gamma Spectroscopy of Mixed Fission Products for Trace Analy- sis.” In: Presented at Methods and Applications of Radio-Chemistry (MARC). PNNL-SA-171807. Kailua, Kona, Hawaii, 2022. 

B.D Pierson. et al. “Alpha/Beta-gated Gamma-Gamma Spectroscopy of Mixed Fission Products for Trace Analy- sis.” In: Journal of Radioanalytical and Nuclear Chemistry 331.PNNL-SA-172636. doi:10.1007/s10967-022-08606-5 (2022). 

M Bertolli. et al. “Artificial Intelligence and Machine Learning – Emerging Technologies and Applications in Nuclear Security.” In: Presented at WINS Workshop: Introduction to the Role of Artificial Intelligence in Strengthening the Security of Nuclear Facilities. PNNL-SA-164261. Vienna, Austria, 2021. 

M.K Girard. et al. “Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Anal- ysis.” In: Journal of Nuclear Materials 552.PNNL-SA-156254. doi:10.1016/j.jnucmat.2021.152983 (2021). 

A.R. Hagen and authors not released. “Presentation title not released.” In: Venue not released. PNNL-SA-163006. 2021. 

A.R. Hagen and authors not released. “Presentation title not released.” In: Venue not released. PNNL-SA-165593. 2021. 

A.R. Hagen and authors not released. “Presentation title not released.” In: Venue not released. PNNL-SA-162065. 2021. 

A.R. Hagen and authors not released. Report title not released. Tech. rep. PNNL-30980. Venue not released, 2021. 

A.R. Hagen and authors not released. Report title not released. Tech. rep. PNNL-SA-161093. Limited Distribution, 2021. 

A.R Hagen. et al. “Accelerated Computation of a High Dimensional Kolmogorov-Smirnov Distance.” https: //arxiv.org/abs/2106.13706. 2021. 

A.R Hagen. et al. “Decision Trees for Optimizing the Minimum Detectable Concentration of Radioxenon Detectors.” In: Journal of Environmental Radioactivity 229.PNNL-SA-154711. doi:10.1016/j.jenvrad.2021.106542 (2021). 

K.D Jarman. et al. “Statistical Analysis of Interference Correction by SEM Pulse Classification and Simulation of First Dynode Interaction Causes for Pulse Shape Differences.” In: Presented at Independent Review of EIon Project. PNNL-SA-162847. 2021. 

A.B Luttman et al. “Artificial Intelligence and Machine Learning – Emerging Technologies and Applications in Nuclear Security.” In: Proceedings of the INMM/ESARDA Annual Meeting. PNNL-SA-162157. https://www. wins . org / wp - content / uploads / 2024 / 02 / 5. - Artificial - Intelligence - and - Machine - Learning-Evangelina-Brayfindley.pdf, 2021. 

RO Abdel Rahman et al. “Reviewer’s Recognition.” In: Journal of Nuclear Engineering and Radiation Science 7 (2021), pp. 010202–1. 

A.R. Hagen and authors not released. “Presentation title not released.” In: Venue not released. PNNL-SA-152495. 2020. 

A.R. Hagen and authors not released. “Presentation title not released.” In: Venue not released. PNNL-SA-152543. 2020. 

A.R. Hagen and authors not released. “Presentation title not released.” In: Venue not released. PNNL-SA-157870. 2020. 

A.R. Hagen and authors not released. Report title not released. Tech. rep. PNNL-30035. Venue not released, 2020. 

A.R Hagen. et al. “A Proposed High Dimensional Kolmogorov-Smirnov Distance.” In: Presented at Machine Learn- ing and the Physical Sciences: Workshop at the 34th Conference on Neural Information Processing Systems. PNNL-SA- 158210. 2020. 

A.R. Hagen et al. DDKS: d-Dimensional Kolmogorov-Smirnov distance. Open Sourced Software. 2020. 

A.R Hagen. et al. “Detection of photoneutrons or photofission in targets using 6MV photointerrogation and TMFD sensors.” In: Presented at IEEE Nuclear Science Symposium. PNNL-SA-157474. 2020. 

A.R Hagen. et al. “Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs.” In: Journal of Physics: Conference Series. Vol. 1525. PNNL-SA-143856. doi:10.1088/1742-6596/1525/1/012104. 2020. 

N Hume. et al. “MAC-TMFD: A Novel Multi-Armed Centrifugally Tensioned Metastable Fluid Detector (Gamma- Blind) – Neutron-Alpha Recoil-Fission Spectrometer.” In: Nuclear Instruments and Methods in Physics Research. Section A Accelerators Spectrometers Detectors and Associated Equipment 949.doi:10.1016/j.nima.2019.162869 (2020). 

Pazdernik K. and A.R. Hagen. ETI Summer School - Data Science for Safeguards Introduction. PNNL-SA-155339. Consortium for Enabling Technologies and Innovation. 2020. 

E.J Kautz. et al. “A Machine Learning Approach to Thermal Conductivity Modelling of Irradiated Nuclear Fuels.” In: Presented at The Minerals, Materials Society Conference. PNNL-SA-151391. San Diego, California, 2020. 

R Taleyarkhan. et al. “Neutron Spectroscopy and H*10 Dosimetry with Tensioned Metastable Fluid Detectors.” In: Nuclear Instruments and Methods in Physics Research. Section A Accelerators Spectrometers Detectors and Associated Equipment 959.PNNL-SA-146957. doi:10.1016/j.nima.2019.163278 (2020). 

Rusi P Taleyarkhan et al. Systems and methods for interrogating containers for special nuclear materials. US Patent 10,718,874. 2020. 

C Amole et al. “Data-driven modeling of electron recoil nucleation in PICO C 3 F 8 bubble chambers.” In: Physical Review D 100.8 (2019), p. 082006. 

T.F Grimes. and A.R. Hagen. “Tension Metastable Fluid Detectors (TMFDs) and Applications.” In: Presented at PICO collaboration meeting. PNNL-SA-143279. SNOLAB, Canada, 2019. 

T.F Grimes., A.R. Hagen, and C.M. Jackson. “Deep Learning for Particle Discrimination.” In: Presented at PICO collaboration meeting. PNNL-SA-146738. Queen’s University, Kingston, Canada, 2019. 

A.R. Hagen and authors not released. “Presentation title not released.” In: Venue not released. PNNL-SA-146468. 2019. 

A.R. Hagen and authors not released. “Presentation title not released.” In: Venue not released. PNNL-SA-141716. 2019. 

A.R. Hagen and authors not released. Report title not released. Tech. rep. PNNL-29123. Venue not released, 2019. 

A.R Hagen. et al. “Scaling the training of particle classification on Liquid Argon Time Projection Chamber events to multiple GPUs.” In: Presented at 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research. PNNL-SA-141721. Saas-Fee, Switzerland, 2019. 

E.J Kautz. et al. “A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels.” In: Computational Materials Science 161.PNNL-SA-138923. doi:10.1016/j.commatsci.2019. (2019). 

K Pazdernik. et al. Data Science for Safeguards Practitioners Course. 2019. 

A. Buttman. et al. Artificial Intelligence Threats and Protections for Global Material Security (GMS). Tech. rep. PNNL-SA-149624. Pacific Northwest National Laboratory, 2019. 

Brian Archambault et al. “Large-Array Special Nuclear Material Sensing With Tensioned Metastable Fluid Detectors.” In: IEEE Sensors Journal 18.19 (2018), pp. 7868–7874. 

E.C Buck. et al. “Radiolysis Modeling at PNNL.” In: Venue not released. PNNL-ACT-SA-10377. 2018. 

R Goychayev et al. Flyer and Syllabus for Data Science for Safeguards Practitioners Course. Data Science for Safeguards Practitioners Course. 2018. 

 TF Grimes et al. “Enhancing the performance of a tensioned metastable fluid detector based active interro- gation system for the detection of SNM in< 1 m3 containers using a D–D neutron interrogation source in moderated/reflected geometries.” In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 884 (2018), pp. 31–39. 

Alexander R Hagen. “Detection and Interdiction of Shielded and Unshielded Special Nuclear Material Using Tensioned Metastable Fluid Detectors.” In: (2018). 

A.R. Hagen and authors not released. “Presentation title not released.” In: Venue not released. PNNL-ACT-SA- 10383. 2018. 

A.R. Hagen and authors not released. Report title not released. Tech. rep. PNNL-27780. Venue not released, 2018. 

J.M Johns. et al. “A machine learning approach to thermal conductivity modeling: A case study in irradiated uranium-molybdenum nuclear fuel.” In: Presented at NUMAT. PNNL-SA-138990. Seattle, Washington, 2018. 

BC Archambault et al. “Development of a Centrifugal Tensioned Metastable Fluid Detector Array to Detect SNM using Active Neutron Interrogation.” In: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE. 2017, pp. 1–4. 

N Boyle et al. “Detection of Radon-Progeny and Other Alpha-Emitting Radionuclides in Air Using Tensioned Metastable Fluid Detectors.” In: International Conference on Nuclear Engineering. Vol. 57878. American Society of Mechanical Engineers. 2017, V009T15A019. 

TF Grimes et al. “Interrogation of 1m 3 Suspicious Objects via IEC DD Interrogating Neutrons and Tension Metastable Fluid Detectors.” In: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE. 2017, pp. 1–3. 

Brian Archambault et al. “Threshold rejection mode active interrogation of SNMs using continuous beam DD neutrons with centrifugal and acoustic tensioned metastable fluid detectors.” In: IEEE Transactions on Nuclear Science 64.7 (2016), pp. 1781–1788. 

Alexander Bakken et al. “Thermal and ionizing radiation induced degradation and resulting formulation and per- formance of tailored poly (lactic acid) based hot melt adhesives.” In: International Journal of Adhesion and Adhesives 71 (2016), pp. 66–73. 

Alex Hagen, Brian Archambault, and Rusi Taleyarkhan. “Detection of Special Nuclear Material in Cargo using Continuous Neutron Interrogation and Tension Metastable Fluid Detectors.” In: Transactions of the American Nuclear Society 115 (2016). 

Rusi Taleyarkhan et al. “Live demonstration: Femto-to-macro scale interdisciplinary sensing with tensioned metastable fluid detectors.” In: 2016 IEEE SENSORS. IEEE. 2016, pp. 1–1. 

Brian C Archambault et al. “Advancements in the development of a directional-position sensing fast neutron detector using acoustically tensioned metastable fluids.” In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 784 (2015), pp. 176–183. 

Alexander R Hagen et al. “Characterization and Optimization of a Tensioned Metastable Fluid Nuclear Parti- cle Sensor Using Laser-Based Profilometry.” In: Journal of Nuclear Engineering and Radiation Science 1.4 (2015), p. 041004. 

Jeffrey A Webster et al. “High-efficiency gamma-beta blind alpha spectrometry for nuclear energy applications.” In: Journal of Nuclear Engineering and Radiation Science 1.3 (2015), p. 031006. 

Alexander R Hagen. “Multiphysics modeling in optimization of acoustically tensioned metastable fluid neutron detectors.” PhD thesis. Purdue University School of Nuclear Engineering, 2014. 

N Hume et al. “The MAC-TMFD: Novel multi-armed centrifugally tensioned metastable fluid detector (Gamma- Blind)—Neutron-alpha recoil spectrometer.” In: 2013 IEEE International Conference on Technologies for Homeland Security (HST). IEEE. 2013, pp. 435–440. 

RP Taleyarkhan et al. “Real-time monitoring of actinides in chemical nuclear fuel reprocessing plants.” In: Chem- ical Engineering Research and Design 91.4 (2013), pp. 688–702. 

Rusi Taleyarkhan et al. Polylactic acid adhesive compositions and methods for their preparation and use. US Patent 20150322310A1. 2013. 

B Archambault et al. “Development of a 4π directional fast neutron detector using tensioned metastable fluids.” In: 2012 IEEE Conference on Technologies for Homeland Security (HST). IEEE. 2012, pp. 423–428.