Handling Eight-Analyte Chemical Mixtures Using Multimodal Spectroscopy

The American Chemical Society (ACS) Fall 2026 Meeting will be held this year in the McCormick Place Convention Center, in Chicago, Illinois, from August 23^rd to the 27^th, 2026.^1,2 As part of the conference program, there are dedicated talks and poster sessions scheduled that will cover the latest trends and advancements in analytical chemistry and related fields, including spectroscopy.²

During one of the poster sessions, Joseph Conner of Oak Ridge National Laboratory will present his team’s work on a new automated sensor-calibration platform to handle chemical mixtures with eight distinct analytes. This step is aimed at making real-time monitoring in nuclear and radioisotope facilities faster and safer. Conner’s poster, titled “Expanding the Automated Transient Learning for Applied Sensors (ATLAS) Platform with Multimodal Spectroscopy for Robust Calibration Development,” will be presented on Monday, August 24, from 1:00 to 3:00 pm EDT.¹ The presentation will detail the multimodal spectroscopy integration and the regression modeling approach used to manage the increased dimensionality of the eight-analyte system.

What is the ATLAS Platform?

Conner’s poster will present findings from a study that builds on the Automated Transient Learning for Applied Sensors (ATLAS) platform. This is a system originally developed to calibrate optical sensors for in situ chemical monitoring without the manual sample preparation that typically slows down work in hazardous environments.³ In its earlier form, ATLAS used automated pneumatic controls to cycle stock solutions through target concentrations, cutting model development time by 76% and sample volume by 60% compared with estimated manual methods.^1,3 That version was tested on a simpler three-analyte lanthanide system.

What will Conner’s poster cover?

During the poster presentation, Conner will detail the findings from his team’s latest study. He will show how the ATLAS platform has been scaled up to handle eight analytes simultaneously. This scale-up intended to better approximate the chemical complexity of actual radioisotope processing streams, where multiple species must be tracked at once.¹ That added complexity brings a corresponding rise in spectral overlap and data dimensionality, which are problems that can degrade the accuracy of single-technique measurements.¹

What were the changes that the researchers made to the ATLAS platform that facilitated this scaling up?

The main change the researchers made was adding to new measurement modes to ATLAS's existing absorbance spectroscopy. These measurement modes include Raman spectroscopy, which reads vibrational signatures unique to each analyte, and fluorescence, which is used for species that lack strong absorbance or Raman signals.¹

What did the combination of these three techniques accomplish?

By combining the three abovementioned techniques, the research team was able to produce overlapping but complementary data sets. What this result accomplishes is that it improved the predictive performance of the models used. How it worked was that the data sets were uploaded into partial least squares regression (PLSR) to build out the multivariate predictive models.¹ The ATLAS platform also pulls spectral data during the transient flow periods between steady-state concentrations, rather than relying solely on static readings, which the researchers report improves the resulting models' predictive performance.¹

What are the key takeaways from this presentation?

This presentation could have important implications for facilities that monitor nuclear materials. For example, the new capabilities of this ATLAS platform can help improve calibration speed and reduce radiation exposure for personnel.¹ Manual calibration of sensors in these settings requires extensive safety precautions and consumes time that automated methods can eliminate.¹ By demonstrating that ATLAS can maintain accuracy at a higher level of chemical complexity, Conner and his team at Oak Ridge National Laboratory proved that the ATLAS platform has great potential for use in a wider range of remote and hazardous monitoring applications beyond the initial lanthanide test cases.¹

References

1. Conner, J. Expanding the Automated Transient Learning for Applied Sensors (ATLAS) Platform with Multimodal Spectroscopy for Robust Calibration Development. Presented at the American Chemical Society Fall 2026 Meeting, Chicago, Illinois, August 24, 2026. Available at: https://acs.digitellinc.com/live/37/session/588156
2. Wetzel, W. Why Spectroscopists Should Attend the ACS Fall 2026 Conference. Spectroscopy Online, 2026. https://www.spectroscopyonline.com/view/why-spectroscopists-should-attend-the-acs-fall-2026-conference (accessed July 27, 2026).
3. Andrews, H. B.; Sadergaski, L. R. Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring. ACS Sens. 2024, 9 (11), 6257–6264. DOI: 10.1021/acssensors.4c02211