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Correction for ‘Neptunium transition probabilities estimated through laser induced breakdown spectroscopy (LIBS) measurements’ by H. B. Andrews et al. , J. Anal. At. Spectrom. , 2022, DOI: 10.1039/d1ja00423a.
Supervised machine learning methods have demonstrated increased utility for the quantification of lanthanide and actinide elements in atomic spectroscopy applications. This study implements laser-induced breakdown spectroscopy (LIBS) for the identification of plutonium surrogate material (CeO 2 ) in soil matrices by training supervised machine learning methods on the recorded spectral data. A bagged ensemble using Random Forest yields the highest sensitivity predictions with a detection limit of 0.015 wt.% CeO 2 . However, high precision in Ce content prediction required the use of a stacked ensemble regression, which provided the superlative Ce quantification model with an error of 0.107% and a detection limit of 0.022 wt.%. Furthermore, the high performance of the stacked ensemble demonstrates its potential to enhance the accuracy and sensitivity of nuclear contaminant detection using field-deployable spectroscopic analyzers in real-world scenarios.
COnovate, LLC has developed a patented material called graphene monoxide and demonstrated that its unique chemical and crystal structure enables lithium-ion batteries (LIB) with greater specific capacity, safer and faster charging, and better low temperature performance than the industry standard. The current capability gap with COnovate’s technology is to scale up this new active anode material, developing processing technology to commercially relevant levels. This project will develop the means for progressive scale-up to 100g, 1kg, and 10kg levels, incorporating industrial synthesis and processing technologies, and deliver an industrial procedure/batch record draft for use by potential full-scale manufacturers to develop metric-ton production levels. The project goals of 10kg production and industrial procedure for full-scale production are necessary for COnovate’s customers to adequately evaluate and adopt their technology, and to pursue purchase orders for tons of material. MERF will confirm material analytics and performance in half cell tests during scaling and deliver materials to COnovate for QA/QC testing during the iterative scale up efforts. The final product batches will be delivered to COnovate for battery assembly and lifetime testing.
The spectroscopic constants for the triplet and singlet states of LiB below about 30 000/ cm are determined using an internally contracted multireference configuration interaction approach in conjunction with [6s 5p 3d 2f] atomic natural orbital basis sets. The ground state is (sup 3)Pi as found in previous work. No excited triplet states are found to be ideal for characterizing the ground state; the (1)(sup 3)Sigma(sup -) state has a transition energy that is too small for many experimental approaches and the (2)(sup 3)Pi and (3)(sup 3)Pi states have bond lengths that are significantly longer than the ground state, resulting in transition intensities that are spread out over many vibrational levels of the ground state.
In this paper, we explore the use of remote LIBS combined with pulsed-laser Raman spectroscopy for mineral analysis at a distance of 10 meters. Samples analyzed include: carbonates (both biogenic and abiogenic), silicates, and sulfates. Additional information is contained in the original extended abstract.
Identification of non-silicate samples on Mars, such as carbonates, sulfates, nitrates, or evaporites in general, is important because of their association with aqueous processes and their potential as exobiology sites. Infrared (IR) and thermal emission (TE) spectroscopy have been considered the primary tools for remote identification of these minerals. This includes current and future orbital assets such as TES on MGS, THEMIS on Mars Odyssey, OMEGA on Mars Express, CRISM on MRO, and now the Mini-TES on the MER rovers. While reflectance and emission spectroscopy have clearly been the method of choice for these missions, the technique is not always successful in mineral identifications due to dust, surface weathering chemistry, coatings, or surface texture. Here we describe and show IR spectra of several such samples, and then report on the relative success of LIBS analyses in determining the rock type.
These first results demonstrate that LIBS spectra can be an interesting tool to estimate the ablated volume. When the ablated volume is bigger than 9.10(exp 6) cubic micrometers, this method has less than 10% of uncertainties. Far enough to be directly implemented in the KArLE experiment protocol. Nevertheless, depending on the samples and their mean grain size, the difficulty to have homogeneous spectra will increase with the ablated volume. Several K-Ar dating studies based on this approach will be implemented. After that, the results will be shown and discussed.
The Mars Science Laboratory (MSL) Curiosity rover landed on Mars in August 2012, and has been exploring the planet ever since. Dr. Horton E. Newsom will discuss the MSL's design and main goal, which is to characterize past environments that may have been conducive to the evolution and sustainability of life. He will also discuss Curiosity's science payload, and remote sensing, analytical capabilities, and direct discoveries of the Chemistry & Camera (ChemCam) instrument, which is the first Laser Induced Breakdown Spectrometer (LIBS) to operate on another planetary surface and determine the chemistry of the rocks and soils.
An actively Q-switched laser induced breakdown spectroscopy (LIBS) probe, utilizing an optical fiber, a pump beam transmitted through the optical fiber, a coupler, and a lens for collimating the pump beam. The actively Q-switched laser, coupled to a sensor which provides information to a computer that controls a high voltage pulser providing a pulse to a Pockels cell located within the laser which can selectively cause the laser to pulse, resulting in high energy pulses and a second lens for focusing the output pulse such that it creates a plasma or spark. The light from the spark is captured and directed back through an optical system to remote equipment for elemental and/or molecular analysis.
Here, this work implements a mid-level data fusion methodology on spectral data from handheld X-ray fluorescence and laser-induced breakdown spectroscopy analyzers to quantify plutonium surrogate (CeO 2 ) contamination in soil samples for the first time. Spectral data from each analyzer were used independently to train supervised machine learning regressions to predict Ce concentration. Fused features from both data sets were then used to train the same models, comparing prediction performance by evaluating model precision and sensitivity. Fusing principal component scores from the two sensors yielded an order of magnitude improvement in precision and sensitivity of predictions made with an artificial neural network, compared to predictions made by models trained on independent sensor data. As a result, a boosted ensemble trained on the fused spectral features yielded an ideal predictor with root-mean-squared error on the order of 10 –6 and calculated limit of detection order 10 –5 wt %.
LiBC4N4 is Tetraauricupride structured and crystallizes in the cubic P-43m space group. The structure is zero-dimensional and consists of one boron, metallic molecule and one Li(CN)4 cluster. In the Li(CN)4 cluster, Li1+ is bonded in a tetrahedral geometry to four equivalent N3- atoms. All Li–N bond lengths are 2.06 Å. C2+ is bonded in a single-bond geometry to one N3- atom. The C–N bond length is 1.16 Å. N3- is bonded in a linear geometry to one Li1+ and one C2+ atom.
Li(NH2)4BH4 crystallizes in the orthorhombic Pca2_1 space group. The structure is zero-dimensional and consists of four BH4 clusters and four Li(NH2)4 clusters. In each BH4 cluster, B3+ is bonded in a tetrahedral geometry to four H+0.67+ atoms. There is two shorter (1.23 Å) and two longer (1.24 Å) B–H bond length. There are four inequivalent H+0.67+ sites. In the first H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one B3+ atom. In the second H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one B3+ atom. In the third H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one B3+ atom. In the fourth H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one B3+ atom. In each Li(NH2)4 cluster, Li1+ is bonded in a distorted tetrahedral geometry to four N3- atoms. There are a spread of Li–N bond distances ranging from 2.06–2.14 Å. There are four inequivalent N3- sites. In the first N3- site, N3- is bonded in a distorted water-like geometry to one Li1+ and two H+0.67+ atoms. There is one shorter (1.02 Å) and one longer (1.03 Å) N–H bond length. In the second N3- site, N3- is bonded in a distorted water-like geometry to one Li1+ and two H+0.67+ atoms. Both N–H bond lengths are 1.03 Å. In the third N3- site, N3- is bonded in a distorted water-like geometry to one Li1+ and two H+0.67+ atoms. Both N–H bond lengths are 1.03 Å. In the fourth N3- site, N3- is bonded in a distorted water-like geometry to one Li1+ and two H+0.67+ atoms. Both N–H bond lengths are 1.03 Å. There are eight inequivalent H+0.67+ sites. In the first H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one N3- atom. In the second H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one N3- atom. In the third H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one N3- atom. In the fourth H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one N3- atom. In the fifth H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one N3- atom. In the sixth H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one N3- atom. In the seventh H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one N3- atom. In the eighth H+0.67+ site, H+0.67+ is bonded in a single-bond geometry to one N3- atom.
No abstract available
The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA), partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. Additionally, a boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting.
Automated NGFC system design tool set to develop optimized, next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system designs with >70% fuel efficiency and <30 g/kWh CO2 emissions
We will employ a machine learning approach with intelligent data mining and database construction to analyze enormous data repositories for identifying and extracting geographic-dependent cell design specifications from publicly accessible grid-scale energy storage usage databases in an automated way at scale.
This dataset contains covariance libraries created for SCALE. These will be distributed by providing documentation and metadata in GitLab repos hosted by ORNL (code.ornl.gov/scale/data/cov-libs), and the main datasets are hosted in S3-based ORNL servers. The data are also contained in Constellation. More documentation can be found in PUB ID 263037.
Abstract not provided.