Sandia is developing a suite of complementary technologies to help the emerging algae industry detect and quickly recover from algal pond crashes, an obstacle to large-scale algae cultivation for future biofuels.
The research, which focuses on monitoring and diagnosing algal pond health, draws on Sandia’s longstanding expertise in microfluidics technology, its strong bioscience research program, and significant internal investments.
Because of the way algae is grown and produced in most algal ponds, they are prone to attack by fungi, rotifers, viruses, or other predators. Consequently, algal pond collapse is a critical issue that companies must solve to produce algal biofuels cost-effectively. The issue was identified as a key component in DOE’s National Algal Biofuels Technology Roadmap.
A three-pronged technical approach
Sandia is addressing the algal pond crash issue in three complementary ways:
- Developing a real-time monitoring tool for algal ponds that can detect indications of a problem days in advance of a crash.
- Successfully applying pathogen detection and characterization technologies honed through the Labs’ Rapid Threat Organism Recognition (RapTOR) work.
- Employing its innovative SpinDx diagnostic device to dig deeper into problems after they’ve occurred to identify specific biological agents responsible for crashes.
Sandia’s Tom Reichardt (8128), a researcher who works in Sandia’s remote sensing unit, led development of an online algal reflectance monitor through an internally funded project. The instruments are typically set up alongside the algal pond, continuously monitoring, analyzing the algae’s concentration levels, examining its photosynthesis activity, and performing other diagnostics.
“In real time, it will tell you if things are going well with the growth of your algae or whether it’s beginning to show signs of trouble,” says Tom. However, he cautions, while this real-time monitoring will warn pond operators when the ponds have been attacked, it may not be able to identify the attacker.
Quick ID is key
To help pinpoint the problems, a Sandia team led by researcher Todd Lane (8623) recently developed a process to quickly and accurately identify pond crash agents through ultra-high-throughput sequencing using RapTOR.
RapTOR, originally developed for homeland security purposes, was developed to solve the “unknown unknowns” problem — lethal agents that could be weaponized from ordinary viruses or disguised to look harmless. It was designed to serve as a tool to rapidly characterize a biological organism with no pre-existing knowledge.
Todd’s team also created a method for creating a field-ready assay for those agents, something that works quickly and is relatively inexpensive. They are applying SpinDx, a device developed by other Sandia/California researchers that can, among other capabilities, analyze important protein markers and process up to 64 assays from a single sample, all in a matter of minutes.
Finally, a Sandia team led by researcher Jeri Timlin (8622), in collaboration with the University of Nebraska’s Van Etten Lab, enhanced the RapTOR diagnostics by studying interactions of a certain virus with algal cells. Using hyperspectral imaging, they identified spectroscopic signatures of viral infections arising from changes in algal pigmentation. These signatures potentially could be exploited for early detection and subsequent mitigation of viral infections in algal ponds.
An ‘arsenal’ for pond operators
“It’s important for the growth of an algal industry to develop a method where algal pond operators can learn immediately when there’s a problem with their ponds from a biological agent standpoint,” says Todd. “It’s equally important that they learn — within a very short period of time, like 24 hours — what specific agent is eating away at their algae, and have a technology available that could develop an assay to combat the agent. Our tools come very close to accomplishing all of those things.
“We couldn’t really do an exhaustive characterization of all of the kinds of agents that could be at the root of pond crashes,” Todd says. “But we confirmed some that had been identified before, and we found some others that weren’t familiar to the research community. The important achievement was to develop the methodology, which hadn’t existed before.”
In practical terms, the process developed by Sandia involves a central facility where pond operators would send samples of agents that have appeared in their ponds, and assays that could be deployed back to the pond site. That’s where SpinDx comes in.
Pond site operators, Todd says, know their environments best and, especially with instruments like those developed by Tom, understand the signs that could indicate “sick” ponds. He envisions pond operators using a SpinDx-like device as part of their regular arsenal of equipment so they could run early detection tests whenever they sensed instability in their ponds. They could then provide samples to an off-site facility, which in turn would send back assays to allow the operator to investigate the problem more thoroughly and ward off pond crashes before they occur.
“That’s the beauty of SpinDx,” says Todd. “The disks are inexpensive, require little technical expertise, and can be manipulated by non-scientists.”
Next step: More robust demonstrations
Now that the core principles of pathogen detection and characterization technologies for pond crash forensics have been successfully proved, the next step will be to conduct more robust demonstrations. Serendipitously, Todd’s and Tom’s groups will be continuing their work as part of the Algae Testbed Public-Private Partnership (ATP3) led by Arizona State University (ASU), the first national algae testbed. The Sandia team will apply the technologies, collect more data, and seek additional collaborations.
“Our results over these past couple of years have been compelling, but now we need to deploy the technology into real-world ponds,” Todd explains. The original work, he says, has moved from the laboratory environment into the operational realm, with only modest research and development now necessary.
Sandia will make use of an algal test bed facility at ASU known as the Arizona Center for Algae Technology and Innovation (AzCATI). The facility features algal ponds and closed photobioreactor algae cultivation systems of various sizes and serves as a hub for research, testing, and commercialization of algae-based products.
To view brief interviews of Sandia remote sensing researcher Tom Reichardt, Sandia biochemist Aaron Collins (8622) and AcCATI program manager John McGowen, visit Sandia’s YouTube channel.