Los Alamos National Laboratory

Los Alamos National Laboratory

Delivering science and technology to protect our nation and promote world stability

Remote Sensing Applications

We design and field new types of remote sensing instruments whose aim is to detect optical signatures of importance in proliferation detection and other defense missions such as battlefield threats and intelligence gathering

Contact Us  

  • Group Leader
  • Kirk Rector
  • Deputy Group Leader
  • Harshini Mukundan
  • Team Leader
  • Kevin Mitchell
  • Group Office
  • (505) 667-7121

This team develops remote sensing technologies and algorithms

Theoretical research is aimed at the development of detection algorithms (for gas chemicals, solid materials, and others) to extract signals from high levels of background clutter.

Uncovering the secrets of perovskite solar cells

Our recent investigations have discovered the physical origin of why solution-processed, large-grain hybrid (organic/inorganic) perovskites give rise to solar cells with exceptional (~18%) power conversion efficiencies.

In this key finding, C-PCS researchers have found that charge carriers created by light are not free electrons and holes, but manifest themselves as “large” polarons, a much heavier entity resulting from long-range interactions between photo-excited charges and polar molecules of the hybrid perovskite lattice. The large polaron has interesting physical properties that result an effective “shielding” from optical phonons and electronic impurities, which are otherwise detrimental for efficient transport in semiconducting materials.

As a result, current-killing charge recombination is much slower than charge transfer, making charge collection in the device much more efficient. This result is consistent with the superior transport properties of hybrid-perovskites materials as compared to other solution-processed materials, and explains the far superior solar cell performance.

schematic of the electronic bands

A schematic of the electronic bands in the hybrid perovskite material with relevant relaxation processes. Most important, due to polaron formation, the rate of charge recombination in the material is much less than the rate of charge extraction in a device.

Monica CookMonica Cook has received LDRD Early Career Award as Principal Investigator for a project titled, "Deep Learning for Multispectral and Hyperspectral Target Detection in Remote Sensing Data.“ Monica's work will develop methods for analyzing data collected from remote hyperspectral imaging. Researchers in every field that incorporates data analytics are aware of the rapidly changing landscape of data and computing. Datasets are becoming larger and processing power more advanced, both of which must be utilized to maintain state of the art performance. Deep learning is just one example of a field born out of these advancements. These techniques, only realizable with accelerated processing tools, learn a representation of big data using multiple layers. Neural networks, originally developed in neuroscience research to model brain function, can discern complex patterns within a dataset and have obtained unprecedented performance in signal processing tasks such as image classification and speech recognition.

Hyperspectral imagery generates an image cube

Hyperspectral imagery generates an “image cube” in which each pixel is associated with spectra from various portions of the electromagnetic spectrum. These spectra can result from a single material, or from a mixture of materials, and must be analyzed to extract information from the scene.

C-PCS’s Kevin Mitchell was among the Laboratory leads of the Remote Conversion Venture, specifically the Kevin MitchellPatronus Campaign, that received special recognition from NNSA’s Defense Nuclear Nonproliferation for “professionalism and exemplary service to the American public, Department of Energy, and NNSA for the development of advanced remote sensing nuclear detection capabilities that have advanced the nonproliferation goals of the United States.”

Recognition for members of the Remote Sensing Applications team

Quantum Smoke, an experimental campaign designed to test, for the first time ever, the minimum detectable quantity of specific materials in the environment, received a 2013 Large Team Distinguished Performance Award. The “Bulldog Team” an international group assembled for the purpose of using remote sensing for nuclear threat reduction, received a DOE Achievement Award for their work with the UK Ministry of Defense in the Bulldog Environmental Studies at the Springfield Fuels Ltd., site near Preston, UK. The degree of cooperation embodied in this team has placed the UK and US in a world-leading position in the application of remote sensing of real-world industrial processes and practices relevant to nuclear threat reduction.

Recognition for members of the Remote Sensing Applications team