OD6 Metals (ASX: OD6) leans on historic Union Carbide data to sharpen Quinn processing pathway confidence ahead of modern validation

OD6 Metals Managing Director and Chief Executive Officer Brett Hazelden said historical Union Carbide metallurgical testwork completed in 1957 at the Quinn Fluorspar Project in Nevada demonstrates that conventional grinding and flotation produced high grade fluorspar concentrates, including acid grade material in certain cases.

He said the dataset provides a historical reference point for how the mineralisation responds to standard processing methods, while remaining unvalidated under modern metallurgical standards and subject to confirmation through current testwork.

The company said the information is being used to guide the design and sequencing of its next phase of metallurgical programs. These programs will focus on validation, optimisation and assessment of variability across representative samples, with the aim of building a modern dataset that can confirm or refine the historical outcomes.

What has genuinely changed in confidence in the Quinn processing route from historical Union Carbide testwork given it is still unvalidated by modern metallurgy?

What has changed is the existence of a documented historical benchmark indicating that the Quinn mineralisation can be upgraded using conventional grinding and flotation to produce high grade fluorspar products, including acid grade material in certain cases.

This shifts the technical focus from establishing whether the material is broadly amenable to processing towards understanding how consistently and efficiently that outcome can be reproduced under modern conditions.

We are clear that this does not replace modern metallurgical validation. However, it does provide a structured reference point that informs how we design and prioritise our current work program.

In practical terms, it allows us to concentrate effort on optimisation and variability testing rather than early stage flowsheet definition. That is a more disciplined and efficient way to progress the technical understanding of the project.

How should investors think about sequencing and spend across ore sorting, flotation optimisation and bulk sampling before you have a decision grade view on economics?

We are approaching the work as a staged validation program, where each component is designed to answer a specific technical question before moving to the next level of certainty.

Ore sorting is being assessed first to determine whether we can upgrade feed material prior to processing. That has direct implications for downstream mass pull, plant scale and overall efficiency, but at this stage it remains a technical test rather than an economic assumption.

Flotation optimisation follows as the core processing step. This work is focused on confirming concentrate quality, recovery and reagent response under modern conditions, using the historical dataset as a reference point rather than a substitute for testing.

Bulk sampling sits across both streams. Its role is to ensure the material being tested is representative at scale and supports continuity between laboratory results and larger scale validation work.

The sequencing is designed to ensure capital is deployed in a controlled manner that progressively reduces uncertainty rather than attempting to resolve all variables at once or assume economics prematurely.

What are the key remaining uncertainties in mineralisation variability and continuity that could undermine the reproducibility of the reported recoveries at scale?

The main uncertainty relates to how representative the historical test samples are of the broader mineralised system across Mammoth and Horseshoe. The historical records provide limited spatial detail, and in some cases the exact sample locations within the deposits were not recorded.

While the results demonstrate performance from both low grade and higher grade material types, they do not yet define how these domains connect or transition across the broader system. That limits confidence in understanding variability and continuity at scale.

In addition, the historical testwork was preliminary in nature and did not include systematic variability testing across different lithologies or modern controlled sampling protocols. As a result, there is still uncertainty around how consistent the reported recoveries would be when tested across a wider and more representative dataset.

The key issue is therefore not whether upgrading is possible, but whether that performance can be reliably reproduced across the full geological and material range of the project under modern testing conditions.

Does the historical dataset materially reduce technical work and cost, or mainly reframe risk ahead of an unchanged validation and development pathway?

The historical dataset primarily improves the efficiency and focus of the technical program rather than materially changing the underlying validation pathway.

It provides evidence that conventional grinding and flotation were able to produce high grade and acid grade concentrates from different feed types. That allows us to proceed with a defined processing hypothesis rather than starting from first principles, which improves targeting in the early stages of work.

However, the core technical requirements remain unchanged. We still need modern ore sorting, flotation optimisation and bulk sampling to confirm reproducibility, variability and scalability under current standards.

The benefit of the dataset is therefore in reducing iteration and improving sequencing discipline, rather than removing substantive technical steps or materially changing the scale of validation required before any development decisions can be considered.

From historical signal to modern certainty

The historical Union Carbide dataset provides a useful technical reference point for understanding the Quinn processing pathway, but it does not replace the need for modern validation.

OD6 Metals focus now moves to a structured program of ore sorting, flotation optimisation and bulk sampling designed to progressively test variability, confirm recoveries and define scalability under current standards.

This staged approach is intended to build a decision grade dataset in a disciplined way, ensuring capital is allocated efficiently while technical uncertainty is systematically reduced.

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