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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics

Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3x) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. In conclusion, new methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.

13 HYDRO ENERGY↗
DOE OSTI2025

Advanced Method Optimization with Categorical and Constrained Continuous Parameters

Traditional approaches to analytical method optimization (e.g., univariate and “guess-and-check”) can be time-consuming, costly, and often fail to identify true optima within the parameter space. Previous work defined and implemented a generalized technique for method optimization for continuous method parameters, but a knowledge gap remains for the incorporation of categorical variables into these advanced method optimization schemes. This work presents and validates a generalized optimization approach that incorporates both continuous and categorical variables while also utilizing a multivariate, multiobjective optimization scheme with Karush–Kuhn–Tucker conditions to bound the optimization space to solutions within the physical limitations of the parameter space. Method optimization from a case study using GC–MS for the analysis of 11 analytical standards with objectives to minimize peak width and maximize peak height resulted in a 3 orders of magnitude improvement in the average peak height and a 2 orders of magnitude improvement in the average peak width compared to the least optimal (but reasonable) instrumental parameters utilized in this study. This approach to optimization allows for a customizable method optimization in which users can include both continuous and categorical variables to achieve objectives specific to their analytical goals. This approach significantly reduces the labor and cost associated with traditional method development approaches and can be applied in a variety of scientific fields across a range of laboratory techniques (e.g., instrument method development, sample preparation, and extraction techniques).

Amorphous materials↗
DOE OSTI2025

Enhanced Spatial Proteomics and Metabolomics from a Single Tissue Section Using MALDI-MSI and LCM-microPOTS Platforms

Spatially resolved mass spectrometry (MS)-based multi-omics workflows are becoming more utilized for revealing the complex biology that occurs within tissues. However, these approaches commonly require multiple independent tissue sections to analyze the metabolite and protein compositions of these samples. This poses a significant challenge in preserving cell- or region-specific molecular fidelity, as variations between tissue sections can compromise the accurate correlation of molecular data. Here, in this study, we developed workflows for comprehensive multi-omics profiling from a single tissue section (STS) using different MS modalities. We enhanced the functionality of an electrically insulated substrate by employing metal-assisted approaches that enabled both MS-based untargeted spatial metabolomics and proteomics from STS. This allowed metabolite imaging using matrix-assisted laser desorption/ionization-MS imaging (MALDI-MSI), without compromising it for subsequent proteome profiling with laser capture microdissection (LCM)-based technology. Specifically, implementing copper tape as a backing for polyethylene naphthalate (PEN) slides enabled the detection of >140 metabolites across a poplar root tissue section using MALDI-trapped ion mobility spectrometry time of flight (timsTOF)-MS. Afterwards, we detected 6,571 unique proteins from two distinct root regions by leveraging LCM technology coupled to our microdroplet based sample preparation approach. We also developed an alternative workflow utilizing gold-coated PEN substrates for imaging with MALDI-Fourier-transform ion cyclotron resonance (FTICR)-MS, which permitted the profiling of >170 metabolites and the identification of 6,542 unique proteins across a single poplar root tissue section. These results were comparable to using each assay independently without modifications. These approaches offer new opportunities for high-resolution molecular profiling of multiple omics-levels across biological tissues.

Veličković, Marija [Pacific Northwest National Lab↗
DOE OSTI2026

Calibration-free analysis of Li isotope ratios using laser ablation and laser absorption spectroscopy

We introduce a rapid, calibration-free, all-optical method for high-precision lithium isotope ratio measurements in solid materials using laser ablation combined with tunable laser absorption spectroscopy. A new asynchronous acquisition method is used to acquire time-resolved, high-resolution spectra of the 6Li and 7Li D1 and D2 transitions near 671 nm. Isotope ratios and atomic column densities are extracted from measured spectra via a physics-based fitting model including hyperfine structure. Under 1 Torr air, spectra recorded = 0.75 ms after plasma onset exhibit narrow linewidths corresponding to Doppler temperatures = 400 K, enabling resolution of the isotope peaks with high signal-to-noise ratios. Analysis of LiAlO2 samples with varying 6Li:7Li ratios demonstrates isotopic precisions of 0.6–1.7% for spectra acquired in 30 s. Isotope ratios determined from the spectral fits show accuracy within –0.3% to –1.7% of reference ICP-MS measurements without requiring calibration to external standards. By eliminating sample preparation and enabling spatially resolved isotopic mapping, this method offers a rapid analysis approach to lithium isotope determination in solid materials relevant to nuclear energy, safeguards, and geochemistry.

Phillips, Mark C.↗
DOE OSTI2026

Development of a Flow-through Cell for Ultrasonic Extraction (UE)─Single Particle (SP)─ICP-MS─an Approach for Nano/Microparticle Elemental and Isotopic Analysis

Nanotechnology is a salient part of the scientific landscape, and analytical approaches are rapidly evolving to enable small-scale characterization of nano- and microparticle compositions and impurities. Here, a flow-through sonicating cell was developed for direct particle extraction from a silicon wafer and integrated with an inductively coupled plasma–mass spectrometer (ICP-MS) for subsequent elemental and isotopic characterization of the released particles. Ultrasonic extraction (UE)─single particle (SP)─ICP-MS offers controlled particle mobilization from solid substrates and allows for increased sample throughput by eliminating the need for pre-extraction of particles and decreasing sample preparation steps. Coupling this device to an ICP-MS with a time-of-flight (TOF) mass analyzer, it is possible to distinguish unique isotopic compositions of the particles. Both tungsten and nickel isotopically tagged particles, which were deposited on Si wafers, are presented here with analysis via UE─SP─ICP-MS. This approach could support efforts in the fields of particle synthesis, nuclear safeguards and forensics, environmental monitoring, and semiconductor industries in which the detection of particles from substrates and wafers is critical.

Paul, Molly [ORNL] (ORCID:0009000009672055)↗
DOE OSTI2020

Improved Single-Cell Proteome Coverage Using Narrow-Bore Packed NanoLC Columns and Ultrasensitive Mass Spectrometry

Single-cell proteomics can provide unique insights into biological processes by resolving heterogeneity that is obscured by bulk measurements. Gains in the overall sensitivity and proteome coverage through improvements in sample processing and analysis increase the information content obtained from each cell, particularly for less abundant proteins. Here we report on improved single-cell proteome coverage through the combination of the previously developed Nanodroplet Processing in One Pot for Trace Samples (nanoPOTS) platform with further miniaturization of liquid chromatography (LC) separations and implementation of an ultrasensitive latest-generation mass spectrometry (MS) instrument. Following nanoPOTS sample preparation, protein digest from single cells were separated using a 20-µm-i.d. in-house-packed nanoLC column. Separated peptides were ionized using an etched fused silica emitter capable of stable ionization at the ~20 nL/min flow rate provided by the LC separation. Ultrasensitive LC-MS analysis was achieved using the Orbitrap Eclipse Tribrid mass spectrometer. An average of 362 protein groups were identified by MS/MS from single HeLa cells, and 874 protein groups were identified using the Match Between Runs (MBR) feature of MaxQuant. This represents a >70% increase in label-free proteome coverage for single cells relative to previous efforts using larger-bore (30-µm-i.d.) LC columns coupled to a previous-generation MS (Orbitrap Fusion Lumos).

Cong, Yongzheng↗
DOE OSTI2021

Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. In this work, larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nanoscale Compositional Mapping of Commercial LiNi 0.8 Co 0.15 Al 0.05 O 2 Cathodes Using Atom Probe Tomography

Nickel-rich cathodes provide improved specific capacity that lead to higher gravimetric energy density, critical for electric vehicles. However, poor long-term capacity retention at elevated temperatures/high C-rates (the rate of charge and discharge on a battery) stem from material issues: surface phase changes, corrosive side reactions with the electrolyte, ion dissolution and propagation of cracks. Introducing dopants, developing nanoscale surface coatings and graded core-shell structures have all improved the electrochemical performance of nickel-rich cathodes. However, a material level understanding on the effect of Li composition and distribution in Ni-rich cathodes is limited, due to a lack of characterization methods available that can directly image Li at the nanoscale. Hence, it is critical to establish methods such as atom probe tomography (APT), having both nanometer scale spatial resolution and high compositional sensitivity to quantitatively analyze battery cathodes. To fully realize its potential as a method for quantitative compositional analysis of commercial Li-ion batteries, we provide a comprehensive description of the challenges in sample preparation and analyze the dependency of the analysis parameters, specifically laser pulse energy on the measured stoichiometry of elements in a high Ni content cathode material; LiNi 0.8 Co 0.15 Al 0.05 O 2 (NCA). In this work, our findings show the stoichiometry variations cannot be explained by charge state ratios or Ga implantation damage alone during FIB preparation, indicating that additional factors such as crystallographic orientation may need to be considered to achieve quantitative nanoscale compositional analysis of such battery cathodes using APT.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Atomic-Scale STEM Analysis Shows Structural Changes of Au–Pd Nanoparticles in Various Gaseous Environments

We report dynamical restructuring effects in free-standing Au 0.75 Pd 0.25 nanoparticles occurring in gaseous environments at elevated temperatures. The freshly prepared sample was found to have a core–shell structure with a Pd-rich phase on the surface. The evolution of sample composition and morphology under exposure to 1 bar of pure gases, namely O 2 , H 2 , air, CO, and CO 2 , at different temperatures was studied using in situ scanning transmission electron microscopy (STEM) and energy-dispersive X-ray spectroscopy (EDS). We observed sharper facets on the surface of the particles under O 2 or air at 400 °C. Small islands of Pd were present on the surface, although some Pd was redistributed inside the bulk when the temperature was increased under O 2 . Subtle changes in surface roughness were noted when O 2 was substituted with H 2 at 400 °C, an observation correlated to density functional theory (DFT) calculations. The particles lost clean surface facets when CO was introduced at room temperature and at 200 °C. No substantial changes could be observed after exposure to CO 2 at 250 °C. The adsorption of CO molecules on the surface modifies the surface of the particles and decreases the facet prevalence. Furthermore, these in situ observations show how gases can induce subtle modification of the surface of nanocatalysts, potentially impacting their chemical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Calibration-Free Quantitative Analysis of Lithium-Ion Battery (LiB) Electrode Materials Using Laser-Induced Breakdown Spectroscopy (LIBS)

Recent years have seen a surge in the demand for high-performance battery electrode materials for automotive and various electronic device applications. Improving battery performance requires precise knowledge of the structure–composition properties of active electrode materials. To this effect, quantitative and precise estimation of the composition of advanced electrode materials, containing trace amounts of dopants, provide immense value toward developing next-generation high-capacity battery materials. Herein, we demonstrate the application of calibration-free laser-induced breakdown spectroscopy (LIBS) as a powerful analytical tool for rapid and reliable quantitative spectrochemical characterizations of layered Li metal oxide cathodes containing Mo and Cr dopants (<5 atom %). Specifically, we employ the LIBS using an internal calibration methodology to establish the quantitative elemental ratios of major (Ni, Mn, Co) and trace dopant (Cr, Mo) transition metals to bulk Li contents in diverse cathode material samples such as LiNi 0.5 Mn 0.5 O 2 (NM-50/50), LiNi 0.33 Mn 0.33 Co 0.33 O 2 (NMC), LiNi 0.317 Mn 0.317 Co 0.317 Cr 0.05 O 2 (Cr-doped NMC), and LiNi 0.5– x /2 Mn 0.5– x /2 Mo x O 2 ( x = 0.03, 0.04, 0.05) (Mo-doped NM-50/50) that were synthesized via sol–gel routes. The LIBS-estimated elemental compositions are in good agreement with the nominal stoichiometric values. Furthermore, ex situ LIBS characterizations presented here pave the path for future high-efficacy application of calibration-free quantitative LIBS for rapid in situ analyses of elemental composition changes in battery electrode materials under operation that need not resort to off-site analytical techniques requiring cumbersome sample preparations and/or external standards.

25 ENERGY STORAGE↗
DOE OSTI2021

Controlled Experiments and Optimized Theory of Absorption Spectra of Li Metal and Salts

Investigation of Li metal and ionic compounds through experimental and theoretical spectroscopy has been of tremendous interest due to their prospective applications in Li-metal and Li-ion batteries. Li K-edge soft X-ray absorption spectroscopy (sXAS) provides the most direct spectroscopic characterization; unfortunately, due to the low core-level energy and the highly reactive surface, Li-K sXAS of Li metal has been extremely challenging, as evidenced by many controversial reports. Here, through controlled and ultra-high energy resolution experiments of two kinds of in situ prepared samples, we report the intrinsic Li-K sXAS of Li-metal that displays a prominent leading peak that has not been revealed before. Furthermore, theoretical simulations show that, due to the low number of valence electrons in Li, the Li-K sXAS is strongly affected by the response of the valence electrons to the core hole. We successfully reproduce the Li-K sXAS by state-of-the-art calculations with considerations of a number of relevant parameters such as temperature, energy resolution, and, especially, contributions from transitions which are forbidden in the single-particle treatment. Such a comparative experimental and theoretical investigation is further extended to a series of Li ionic compounds, which highlight the importance of considering the total and single-particle energies for obtaining an accurate alignment of the spectra. Furthermore, our work provides the first reliable Li-K sXAS of the Li metal surface with advanced theoretical calculations. The experimental and theoretical results provide a critical benchmark for studying Li chemistry in both metallic and ionic states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Nanoscale Elemental Mapping of Intact Solid–Liquid Interfaces and Reactive Materials in Energy Devices Enabled by Cryo-FIB/SEM

Many modern energy devices rely on solid–liquid interfaces, highly reactive materials, or both, for their operation and performance. The difficulty of characterizing such materials means these devices often lack high-resolution characterization in an unaltered state. Here, we demonstrate how cryogenic sample preparation and transfer can extend the capabilities of FIB/SEM techniques to solid–liquid interfaces and reactive materials common to energy devices by preserving their integrity through all stages of preparation and characterization. We additionally show how cryo-FIB/SEM paired with energy dispersive X-ray spectroscopy enables nanoscale elemental mapping of cross-sections produced in these materials and discuss strategies to achieve optimal results. Finally, we consider current limitations of the technique and propose future developments that could enhance its capabilities. Our results illustrate that cryo-FIB/SEM will be a useful technique for fields where solid–liquid interfaces or reactive materials play an important role and could, thus far, not be characterized at high resolution.

25 ENERGY STORAGE↗
DOE OSTI2025

An In Situ , Automated High-Explosives Aging Method Utilizing Two-Dimensional Gas Chromatography–Mass Spectrometry

Understanding chemical changes that occur in high explosives as they age is of great importance to the safe employment and storage of these compounds. Traditional methods of aging high explosives even under accelerated aging conditions are time intensive with durations on the order of months to years. The nature of traditional aging analyses reduces each sample to a snapshot data point often separated widely in time, requiring many assumptions as to how the degradation products develop. Further complicating matters, several analytical techniques are typically employed for each sample analysis in order to ascertain an entire picture of the decomposition pathways. To address these shortcomings with existing methods, a new method of accelerated aging of high explosives utilizing comprehensive two-dimensional gas chromatography coupled to high-resolution mass spectrometry (GC × GC-HRMS) was developed using 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20) as a model compound for method development. This in situ automated method reduces the time scale of aging to a matter of hours using the inlet of the GC × GC as the aging vessel. GC × GC in combination with HRMS allowed for the collection of both evolved gases and other decomposition products produced during the entire aging process in real time with HRMS providing far greater certainty in identification of explosives aging products. Additionally, this method allowed for a higher throughput of samples with greatly simplified sample preparation. Chemometric analysis of the GC × GC-HRMS data set via the alteration analysis (ALA) enabled discovery of statistically significant chemical changes providing insight into the variation of decomposition pathways with varying aging temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Exploring Approaches for Accelerated Mass Spectrometric Detection of Fluorochemicals from Aqueous Solutions Using Indirect, Adsorbent-Facilitated Desorption Ionization

There is a growing need for rapid, low-cost analytical tools to screen water supplies for persistent contaminants. Due to their ubiquitous presence in the environment and their detrimental effects on human health, there is a public interest in widespread testing for perfluoroalkyl substances (PFAS). However, the most common methods for PFAS analysis can be time-intensive, requiring a preconcentration step followed by liquid chromatography–mass spectrometry (LC–MS), and are cost-prohibitive for most members of the public. To address these challenges, this study explores a novel method for rapid analysis of aqueous analytes by concentrating analytes on solid adsorbents and detecting them using mass spectrometric analysis via desorption ionization directly from the adsorbent surface. We demonstrate this general approach of indirect solution analysis (ISA) with desorption electrospray ionization (ISA-DESI) and laser desorption ionization (ISA-LDI). Various adsorbent materials were screened, including commercial mesoporous carbons, alumina, and metal–organic frameworks (MOFs). Using ISA-DESI, we consistently detect concentrations as low as 5–100 parts per trillion (ppt) for perfluorooctanoic acid (PFOA) and 10–100 ppt for perfluoroundecanoic acid (PFUnDA). This approach enables rapid detection in under 5 min, making it suitable for applications requiring trace detection with fast response times. We further demonstrate the use of ISA-DESI on a range of environmentally relevant contaminants. Advantages of this indirect solution analysis approach include simplified sample preparation, accelerated analysis, and low cost with readily available materials, making it a promising tool to accelerate mass spectrometric PFAS detection for identifying contaminated sites, performing laboratory studies, and monitoring industrial waste streams.

Bays, Nathan Robert [Sandia National Laboratories ↗
DOE OSTI2026

Synthesis of NiRu-Layered Double Hydroxide for Enhanced Oxygen Evolution Reaction

Nickel−ruthenium layered double hydroxide (NiRu-LDH) was synthesized by coprecipitation using formamide as both solvent and intercalant. The synthesis was performed at various temperatures to study the impact of temperature on electrochemical performance. Temperature plays a vital role in optimizing LDH synthesis to enhance oxygen evolution reaction (OER) efficiency. The optimal temperature yielded a well-defined morphology with balanced crystallinity, leading to more active sites and improved charge transfer. Electrochemical tests indicated that the NiRu-LDH sample prepared at 50 °C (NiRu-LDH-50) required only 280 mV overpotential versus the reversible hydrogen electrode to achieve 10 mA/cm 2 and exhibited a low Tafel slope of 52 mV/dec, demonstrating excellent catalytic kinetics. This study presents the synthesis of NiRu-LDH via coprecipitation and emphasizes how reaction temperature influences LDH structural properties and OER performance.

Catalysts↗
DOE OSTI2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Fungi↗
DOE OSTI2023

Highly Stable Fe/Co-TPY-MIL-88(NH 2 ) Metal–Organic Framework (MOF) in Enzymatic Cascade Reactions for Chemiluminescence-Based Detection of Extracellular Vesicles

Metal-Organic Frameworks (MOFs) can deliver many advantages when acting as enzyme mimics to assist with signal amplification in molecular detection: they have abundant active catalytic sites per unit volume of the material; their structures and elemental compositions are highly tunable, and their high specific surface area and porous property can assist with target separation and enrichment. In the present work, we have demonstrated that, by adding the pore partition agent, 2,4,6-tris(4-pyridyl)pyridine (TPY) during synthesis of the bimetallic Fe/Co-MIL-88(NH 2 ) MOF to block the open metal sites, a highly porous MOF of Fe/Co-TPY-MIL-88(NH 2 ) can be produced. This material also exhibits high stability in basic solutions and biofluids and possesses high peroxidase-mimicking activity, which can be utilized to produce long-lasting chemiluminescence (CL) from luminol and H 2 O 2 . Moreover, acting as the peroxidase-mimic, the Fe/Co-TPY-MIL-88(NH 2 ) MOF can form the enzymatic cascade with glucose oxidase (GOx) for biomarker detection. Further, when applied to detect extracellular vesicles (EVs), the MOF material and GOx are brought to the proximity on the EVs through two surface proteins, which triggers the enzyme cascade to produce high CL from glucose and luminol. EVs within the concentration range of 5 × 10 5 - 4 ×10 7 particles/mL can be detected, and the method can be used to analyze EV contents in human serum without sample preparation and EV purification. Overall, our work demonstrates that the high versatility and tunability of the MOF structures could bring in significant benefits to biosensing and enable ultrasensitive detection of biomarkers with judicious material designs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring

An automated platform has been developed to assist researchers in the rapid development of optical spectroscopy sensors to quantify species from spectral data. This platform performs calibration and validation measurements simultaneously. Real-time, in situ monitoring of complex systems through optical spectroscopy has been shown to be a useful tool; however, building calibration models requires development time, which can be a limiting factor in the case of radiological or otherwise hazardous systems. While calibration time can be reduced through optimized design of experiments, this study approached the challenge differently through automation. The ATLAS (Automated Transient Learning for Applied Sensors) platform used pneumatic control of stock solutions to cycle flow profiles through desired calibration concentrations for multivariate model construction. Additionally, the transients between desired concentrations based on flow calculations were used as validation measurements to understand model predictive capabilities. This automated approach yielded an incredible 76% reduction in model development time and a 60% reduction in sample volume versus estimated manual sample preparation and static measurements. The ATLAS system was demonstrated on two systems: a three-lanthanide system with Pr/Nd/Ho representing a use case with significant overlap or interference between analyte signatures and an alternate system containing Pr/Nd/Ni to demonstrate a use case in which broad-band corrosion species signatures interfered with more distinct lanthanide absorbance profiles. Both systems resulted in strong model prediction performance (RMSEP < 9%). Lastly, ATLAS was demonstrated as a tool to simulate process monitoring scenarios (e.g., column separation) in which models can be further optimized to account for day-to-day changes as necessary (e.g., baseline correction). Ultimately, ATLAS offers a vital tool to rapidly screen monitoring methods, investigate sensor fusion, and explore more complex systems (i.e., larger numbers of species).

47 OTHER INSTRUMENTATION↗