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Battery Cell & Electrolyte Supply

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Federal

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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The Battery Cell and Electrolyte Supply subcontract, issued by the Battelle Energy Alliance for the Department of Energy, seeks the procurement of non-lithium electrochemical cells and electrolytes. The required technologies, such as sodium-ion or iron-air systems, must demonstrate a discharge capacity of at least 10 hours and meet rigorous safety and thermal stability testing standards. Performance for this contract will take place in Idaho Falls, Idaho. Interested parties must submit their responses by the deadline of September 25, 2026. The opportunity is categorized under NAICS code 325199.

General Info

Procurement of non-lithium cells and electrolytes in Idaho Falls by September 25, 2026.

Agency

Department Of Energy → Battelle Energy Alliance–doe CntrView Agency

NAICS

325199 - All Other Basic Organic Chemical ManufacturingView NAICS

Place of Performance

Idaho Falls, ID, 83415, USA

Set-Aside

NONE

Documents

This scope was carved out of INL-26-050.

The full solicitation package (1 document), including the RFP, is on the prime solicitation, not on this scope.

View the prime solicitation

Long-Duration Energy Storage (LDES) Battery System

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Organization & Contact Information

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AgencyDepartment Of Energy → Battelle Energy Alliance–doe Cntr
ContactsNo contacts available
OfficeN/A
Organization / Agency
Department Of Energy → Battelle Energy Alliance–doe Cntr
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Office AddressN/A
ContactsNo contact information available

Full Description

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Supply of non-lithium electrochemical cells (e.g., sodium-ion, iron-air) and electrolytes meeting ≥10-hour discharge, thermal stability, and safety testing requirements.

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Available for Licensing: Machine Learning-Enhanced Spectroscopy Technology for High-Resolution Radiation Detection Using Low-Cost Detectors
Solicitation # BA-1346
This technology enables high-resolution gamma- and x-ray spectroscopy using low-cost, room-temperature detectors like sodium iodide scintillators by applying a compact convolutional neural network to reconstruct spectra traditionally only achievable with expensive, cryogenically cooled high-purity germanium detectors. The machine learning model, consisting of four convolution-max pooling layers and dense layers with approximately 1.6 million parameters and a total size of 6.2 MB, processes low-resolution input data to produce spectral outputs with resolution comparable to HPGe systems, while eliminating the need for liquid nitrogen cooling, reducing system size, and lowering overall cost by at least tenfold. The approach is detector-agnostic and compatible with a range of scintillators including CsI and plastic, making it adaptable for gamma rays, x-rays, neutrons, and charged particles without compromising count-rate performance or peak integrity. The solution significantly enhances deployability in mobile, field, space-based, and confined environments where traditional high-resolution systems are impractical due to weight, power, or thermal constraints. It supports higher throughput with minimal dead time and peak deformation, making it ideal for applications requiring real-time isotope identification without complex infrastructure. Target markets include nuclear safeguards, homeland security for special nuclear material detection, space radiation monitoring, industrial quality control, medical diagnostics, and environmental monitoring. The technology is available for licensing through the Department of Energy’s Idaho National Laboratory, with a response deadline of August 1, 2026, and no implied obligation for government procurement. Primary contact for inquiries is Javier Martinez at INL, located in Idaho Falls, Idaho.
Analytical Laboratory Instrument Manufacturing

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