Los Alamos National Labs with logo 2021

2013 R&D 100 Award Entries

Discoveries, developments, advancements, and inventions pouring from Los Alamos make America—and the world—a better and safer place and bolster national security.
KiloPower: Making Deep-Space Exploration Feasible Again winner

KiloPower: Making Deep-space Exploration Feasible AgainWe have developed a small-space reactor known as KiloPower that can provide long-term power—approximately 15 to 30 years—to a deep-space probe or satellite. To produce electricity, KiloPower uses a nuclear fission system as a heat source that transfers heat via a heat pipe to a small Stirling engine-based power converter to produce electricity.

KiloPower uses plentiful uranium instead of scarce plutonium, generates 500 to 1500 watts of electricity, and minimizes hazards and guarantees performance as a result of its safe and simple design.

With KiloPower, it is possible for NASA and other government and industrial organizations to continue developing probes and spacecraft for the exploration of deep space. Other applications include providing power on the surfaces of planets, mobile power for forward-operating bases (of interest to the Department of Defense), and power in remote locations (of interest to intelligence agencies).

Mantevo Suite 1.0 winner

Mantevo Suite 1.0Mantevo Suite 1.0 is the first integrated collection of full-featured mini apps developed to explore complex design spaces. Mini apps are exceptional performance applications, allowing earlier, informed design decisions for future computing applications. Major companies, universities, and laboratories use mini apps for these purposes.

Sandia National Laboratories led the work, which included Los Alamos, Lawrence Livermore National Laboratory (LLNL), the UK Atomic Weapons Establishment, NVIDIA Corporation, University of Bristol, and University of Warwick.

Los Alamos and LLNL co-developed the CoMD mini app, a simple proxy for the computations in a typical molecular dynamics application. The implementation mimics that of LANL’s SPaSM (Scalable Parallel Short-range Molecular dynamics) code. The OpenCL implementation enables testing on multicore and graphics processing unit architectures, array-of structures, and structure of-arrays data layouts.

MiniMAX: Miniature, Mobile, Agile, X-Ray System winner

MiniMAX: Miniature, Mobile, Agile, X-ray SystemMiniMAX is a compact, completely self-contained, battery-operated, portable x-ray imaging system. At just under 5 pounds, MiniMAX outperforms x-ray systems that weigh between 30 and 500 pounds and cost three to six times as much.

Applications for MiniMAX include

  • homeland security (postal inspection of suspicious packages and explosive ordnance disposal),
  • nondestructive testing,
  • weld inspection,
  • disaster relief (triage broken bones and identify corpses with dental x-rays), and
  • field and veterinary medicine
Multi-Mode Passive Detection System winner

Multi-Mode Passive Detection SystemThe Multi-Mode Passive Detection System (MMPDS) is a scanning device that uses naturally occurring muon particles from cosmic rays for rapid detection of unshielded to heavily shielded nuclear and radiological threats, explosives, and other contraband. MMPDS detects, identifies, and locates (in 3-D) nuclear and radiological threats. Additional modality enables explosives, precious metals, and narcotics detection.

MMPDS can scan vehicles, rail cars, and cargo containers. The automated, single-scan operation facilitates the flow of commerce for transportation hubs and border crossings. It produces no ionizing radiation and is completely safe for people, animals, plants, and food.

The Earth’s upper atmosphere is under constant bombardment by cosmic radiation that produces showers of secondary particles, which rapidly decay into a constant flux of highly penetrating muons (about 200 per square meter per second). Because the muon angular trajectory changes as a function of the density and atomic weight of the material traversed, a unique “signature” for the substance can be developed. The ability to identify distinct material density enables the MMPDS to detect unshielded to heavily shielded nuclear threats with near-zero false alarms. The system also includes a highly sensitive gamma detection capability, which quickly identifies the intensity and location of gamma-producing material.

The development history of MMPDS and its technology demonstrates the application of research to practical solutions for serious issues. Cosmic ray muons have been studied since their discovery in the 1930s. Christopher Morris (Subatomic Physics, P-25) led a LANL team that originally demonstrated the possibility of using charged particles to generate images of objects. Funded initially through the Laboratory Directed Research and Development program, the team built a prototype and demonstrated the initial feasibility of the technology. The Lab, under a cooperative research and development agreement (CRADA) with Decision Sciences International Corporation, advanced the science of charged-particle imaging through the commercialization of the technology.

AMCASS: Automated Multi-Column Actinide Separation System

AMCASS: Automated Multi-Column Actinide Separation System Currently, scientists perform actinide separations manually. Such processes are tedious, time-consuming, hazardous, and prone to operator error. To eliminate such problems, we have developed AMCASS (automated multi-column actinide separation system), a breakthrough technology that replaces all manual processes with fully automated functions.

A robotic chromatograph controlled by software, AMCASS separates actinide matrices from trace impurities in plutonium and uranium metals and oxides so that scientists can determine trace elemental constituents by using chemical analytical instruments without matrix interferences. AMCASS automates functions such as fluidics transportation, chemical separation, and auto-sampling. Fabricated by J2 Scientific, AMCASS uses software to control a robotic arm and pump systems to upload samples, aspirate solutions, collect fractions, and clean/regenerate columns without operator intervention.

AMCASS enables quick, precise, and automated sample analysis while reducing the use of raw materials and chemicals, thereby producing much less waste and minimizing radiation exposure to operators.

FuSS: Fuels Synthesized from Sugars

FuSS: Fuels Synthesized from Sugars We have developed a chemical process that transforms biomass-derived molecules into fuels and platform chemicals. Known as FuSS (Fuels Synthesized from Sugars), this process is performed under relatively mild and energy-efficient conditions.

A breakthrough technology, FuSS enables a “direct-ring opening” of furan rings, which comprise four carbons and one oxygen atom. Furan rings are ubiquitous in biomass-derived molecules. Converting these rings into linear chains is a necessary step in the production of energy-dense fuels because such linear chains can then be reduced and hydrodeoxygenated into alkanes used as gasoline and diesel fuel. The ring-opening reaction requires relatively mild conditions using common acids as catalysts.

FuSS has the potential to

  • reduce America’s dependence on oil
  • decrease the production of harmful greenhouse gases
  • ensure long-term availability of renewable materials used to manufacture consumer products
PathScan: A Leap Forward in Network Defense Technology

PathScan The software tool PathScan quickly analyzes data from a large computer network to identify—in real time—attacks on the network.

Hackers display traversal behavior when attacking a network. By traversal, we mean the movement of a hacker over a sequence of computers. Hackers have many reasons for this traversal, including searching for valuable data and establishing themselves throughout the network to avoid easy removal.

PathScan targets such traversal behavior by (1) building behavioral models that reflect normal activity, followed by (2) passively monitoring network traffic and comparing it with behavioral models. Our approach consists of the following steps:

  1. Build statistical models that reflect the historical network traffic between each pair of communicating computers on large computer networks
  2. Enumerate millions—even billions—of small paths within a network
  3. Analyze each path, testing whether observed data are similar to the historical behavior according to the models built in step 1, or alternatively, that the data appear to be caused by a hacker moving along such a path

Using this approach, PathScan detects hacker infiltration of a network before the hacker can access its secure assets or cause network disruption.