Cooperative Agreement for affiliated Partner with Northern Great Plains Cooperative Ecosystem Studies Unit | G27AS00052

Frequently Asked Questions (FAQs)

1) What is the funding opportunity title?

The opportunity title is "Cooperative Agreement for affiliated Partner with Northern Great Plains Cooperative Ecosystem Studies Unit."

2) What is the Funding Opportunity Number?

The Funding Opportunity Number is G27AS00052.

3) Which federal agency is offering this opportunity?

This opportunity is offered by the US Geological Survey (USGS).

4) What type of award is this?

This is a discretionary award using a cooperative agreement instrument.

5) What program is this issued through?

The cooperative agreement is issued through the Cooperative Ecosystem Studies Units (CESU) Program.

6) Who is eligible to apply?

Eligibility is restricted to organizations that are already participating partners of the Northern Great Plains CESU. Applicants must already be part of that CESU network to be considered.

7) How many awards does the opportunity anticipate making?

The opportunity anticipates a single award.

8) What is the maximum award amount (award ceiling)?

The award ceiling is $450,000.

9) What activity category does this opportunity fall under?

The activity category is Science and Technology and other Research and Development.

10) What is the CFDA number listed for this opportunity?

The CFDA number provided is 15.808.

11) What are the posting and closing dates?

The posting date is 2026-10-14, and the stated closing date is 2026-11-14.

12) What problem is the project trying to solve?

The project focuses on improving how molecularly imprinted polymer electrochemical sensors (MIP-ES) perform on real-world environmental water samples, where naturally occurring constituents can distort or mask the electrochemical signal. In complex waters, background chemistry (such as dissolved organic matter and variable ionic composition) can create interferences that resemble, overwhelm, or otherwise confound the signal from the target contaminant.

13) What contaminants are highlighted as a key example?

Per- and polyfluoroalkyl substances (PFAS) are highlighted as the key example of contaminants that are difficult to measure accurately with electrochemical sensors in complex matrices.

14) Why are complex environmental waters challenging for electrochemical sensing?

The solicitation explains that naturally occurring constituents and background solution chemistry can create interference signals. Examples given include humic acids, fulvic acids, ions, and non-fluorinated chemical analogs. These can look similar to, or overwhelm, the sensor signal caused by the contaminant itself, leading to potential false positives or false negatives.

15) What is the main purpose or intended deliverable of the work?

The main purpose is to create a quantitative correction method that makes MIP-ES readings more reliable and defensible when applied to complex waters relevant to drinking water protection. The deliverable is framed as more than a sensor demonstration: it is a data-driven correction strategy and associated tools intended to improve trustworthiness of field measurements.

16) What technical approach is proposed to address matrix interference?

The described approach focuses on building and validating a ratiometric comparison method. Rather than relying on a single absolute sensor response, the method compares normalized current-difference ratios between a matrix-matched control (analyte intentionally excluded) and an unknown sample (same matrix, analyte may or may not be present).

17) What is a "matrix-matched control" in the context of this project?

In this solicitation, a matrix-matched control is a solution that contains the same background constituents as the environmental sample but intentionally excludes the target analyte. It is used as a closely related comparator to help isolate analyte-driven signal from matrix-driven interference.

18) How does the ratiometric method interpret results?

The interpretation described is: a ratio close to 1 suggests the analyte is absent because the unknown behaves like the analyte-free matrix match. A ratio that is statistically different from 1 indicates the analyte is likely present and producing an additional binding-related signal beyond matrix effects.

19) What measurements feed into the ratiometric framework?

The solicitation highlights using differential pulse voltammetry (DPV) to collect current differences after two incubation periods in the sample matrices: at 1 minute and at 15 minutes. Those time-separated current values are intended to feed directly into the ratiometric framework to account for time-dependent binding and background drift.

20) Which electrochemical methods and equipment are specifically mentioned?

The work plan notes that MIP-ES fabrication and sensing will require multichannel potentiostats capable of running differential pulse voltammetry (DPV) and cyclic voltammetry (CV). DPV is specifically highlighted as the measurement mode used to collect current differences for the ratiometric method.

21) What is the staged work plan described in the solicitation?

The work is staged to begin with bench-scale proof-of-concept testing and then move toward field-deployed trials once the method is shown to be stable and repeatable under controlled conditions.

22) What is the public value or broader impact described for this project?

The project is framed as supporting government priorities around protecting human health and strengthening drinking water safety. If successful, the correction method would make field measurements more trustworthy by reducing false positives or false negatives caused by changing water chemistry, which is described as a key barrier to broader deployment of electrochemical sensing platforms in environmental monitoring.

23) What does the project aim to improve: the sensor itself or how results are interpreted?

Based on the description provided, the focus is on developing a quantitative correction strategy (a ratiometric comparison method) that improves how MIP-ES readings are interpreted and defended in complex waters, specifically by separating analyte-binding signal from non-target interferences.

24) What kinds of non-target interferences are explicitly mentioned?

The opportunity explicitly mentions humic acids, fulvic acids, ions, and non-fluorinated chemical analogs as examples of constituents that can contribute to non-target interference signals in complex water matrices.