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1 Purpose The purpose of this chemical physical property listing is to provide data required to apply the DOE SCAPA Protective Action Criteria (PAC) values to calculation of the LLNL Quantity (Q) Value thresholds for facility chemical hazard classification.
Apollo 11 and 12 crystalline rocks petrographical analysis and textural-mineralogical-chemical group classification, considering local stratigraphy reconstruction
Until recently, infrared observations of exoplanetary atmospheres have typically been interpreted using models that assumed solar elemental abundances. With the chemical composition fixed, attempts have been made to classify hot Jupiter atmospheres on the basis of stellar irradiation. However, recent observations have revealed deviations from predictions based on such classification schemes, and chemical compositions retrieved from some data sets have also indicated non-solar abundances. The data require a two-dimensional (2D) characterization scheme with dependence on both irradiation and chemistry. In this work, we suggest the carbon-to-oxygen (C/O) ratio as an important second dimension for characterizing exoplanetary atmospheres. In hot-hydrogen-dominated atmospheres, the C/O ratio critically influences the relative concentrations of several spectroscopically dominant species. Between a C/O of 0.5 (solar value) and 2, the H{sub 2}O and CH{sub 4} abundances can vary by several orders of magnitude in the observable atmosphere, and new hydrocarbon species such as HCN and C{sub 2}H{sub 2} become prominent for C/O {>=} 1, while the CO abundance remains almost unchanged. Furthermore, a C/O {>=} 1 can preclude a strong thermal inversion due to TiO and VO in a hot Jupiter atmosphere, since TiO and VO are naturally underabundant for C/O {>=} 1. We, therefore, suggest a new 2D classification scheme for hydrogen-dominated exoplanetary atmospheres with irradiation (or temperature) and C/O ratio as the two dimensions. We define four classes in this 2D space (O1, O2, C1, and C2) with distinct chemical, thermal, and spectral properties. Based on the most recent observations, we characterize the thermal structure and C/O ratios of six hot Jupiters (XO-1b, CoRoT-2b, WASP-14b, WASP-19b, WASP-33b, and WASP-12b) in the framework of our proposed 2D classification scheme. While the data for several systems in our sample are consistent with C-rich atmospheres, new observations are required to conclusively constrain their C/O ratios in the day side as well as the terminator regions of their atmospheres. We discuss how observations using existing and forthcoming facilities can constrain C/O ratios in exoplanetary atmospheres.
Life cycle assessments (LCAs) are essential for understanding the environmental impacts of material production. However, gaps in life cycle inventory (LCI) data for material and chemical inputs present a key challenge for LCA practitioners, especially in the early design stages. Strategies for filling in these gaps require additional time and expertise, which can hinder the LCA’s completion. This study combined automatic material classification and probabilistic under-specification to create a time-efficient method to fill material LCI data gaps. To illustrate the proposed method, proxy environmental impact distributions were generated using publicly available material LCI data classified into the ChemOnt chemical taxonomy using the open-source chemical classification software ClassyFire. Input materials with data gaps were then classified into the same taxonomy, where proxy environmental impact values could be selected from the available distributions to quickly fill in any data gaps. Although these methods were applied to classify material production processes available in the Federal LCA Commons and Ecoinvent databases, they can be applied to any LCA database. This study shows that classifying materials by their chemical structure produces taxonomies with increased granularity relative to industrial classification, improving the ability of under-specified proxy data to be used for differentiating the environmental impacts of competing designs.
Studsviks' Processing Facility Erwin (SPFE) has been treating Low-Level Radioactive Waste using its patented THOR process for over 13 years. Studsvik has been mixing and processing wastes of the same waste classification but different chemical and isotopic characteristics for the full extent of this period as a general matter of operations. Studsvik utilizes the accountability method to track the movement of radionuclides from acceptance of waste, through processing, and finally in the classification of waste for disposal. Recently the NRC has proposed to revise the 1995 Branch Technical Position on Concentration Averaging and Encapsulation (1995 BTP on CA) with additional clarification (draft BTP on CA). The draft BTP on CA has paved the way for large scale blending of higher activity and lower activity waste to produce a single waste for the purpose of classification. With the onset of blending in the waste treatment industry, there is concern from the public and state regulators as to the robustness of the accountability method and the ability of processors to prevent the inclusion of hot spots in waste. To address these concerns and verify the accountability method as applied by the SPFE, as well as the SPFE's ability to control waste package classification, testing of actual waste packages was performed. Testing consisted of a comprehensive dose rate survey of a container of processed waste. Separately, the waste package was modeled chemically and radiologically. Comparing the observed and theoretical data demonstrated that actual dose rates were lower than, but consistent with, modeled dose rates. Moreover, the distribution of radioactivity confirms that the SPFE can produce a radiologically homogeneous waste form. The results of the study demonstrate: 1) the accountability method as applied by the SPFE is valid and produces expected results; 2) the SPFE can produce a radiologically homogeneous waste; and 3) the SPFE can effectively control the waste package classification. (authors)
ABSTRACT Studies of the kinematics and chemical compositions of Galactic globular clusters (GCs) enable the reconstruction of the history of star formation, chemical evolution, and mass assembly of the Galaxy. Using the latest data release (DR16) of the SDSS/APOGEE survey, we identify 3090 stars associated with 46 GCs. Using a previously defined kinematic association, we break the sample down into eight separate groups and examine how the kinematics-based classification maps into chemical composition space, considering only α (mostly Si and Mg) elements and Fe. Our results show that (i) the loci of both in situ and accreted subgroups in chemical space match those of their field counterparts; (ii) GCs from different individual accreted subgroups occupy the same locus in chemical space. This could either mean that they share a similar origin or that they are associated with distinct satellites which underwent similar chemical enrichment histories; (iii) the chemical compositions of the GCs associated with the low orbital energy subgroup defined by Massari and collaborators is broadly consistent with an in situ origin. However, at the low-metallicity end, the distinction between accreted and in situ populations is blurred; (iv) regarding the status of GCs whose origin is ambiguous, we conclude the following: the position in Si–Fe plane suggests an in situ origin for Liller 1 and a likely accreted origin for NGC 5904 and NGC 6388. The case of NGC 288 is unclear, as its orbital properties suggest an accretion origin, its chemical composition suggests it may have formed in situ.
The bioconcentration factor (BCF) is an important bioaccumulation hazard assessment metric in many regulatory contexts. Its assessment is required by the REACH regulation (Registration, Evaluation, Authorization and Restriction of Chemicals) and by CLP (Classification, Labeling and Packaging). We challenged nine well-known and widely used BCF QSAR models against 851 compounds stored in an ad-hoc created database. The goodness of the regression analysis was assessed by considering the determination coefficient (R{sup 2}) and the Root Mean Square Error (RMSE); Cooper's statistics and Matthew's Correlation Coefficient (MCC) were calculated for all the thresholds relevant for regulatory purposes (i.e. 100 L/kg for Chemical Safety Assessment; 500 L/kg for Classification and Labeling; 2000 and 5000 L/kg for Persistent, Bioaccumulative and Toxic (PBT) and very Persistent, very Bioaccumulative (vPvB) assessment) to assess the classification, with particular attention to the models' ability to control the occurrence of false negatives. As a first step, statistical analysis was performed for the predictions of the entire dataset; R{sup 2}>0.70 was obtained using CORAL, T.E.S.T. and EPISuite Arnot–Gobas models. As classifiers, ACD and log P-based equations were the best in terms of sensitivity, ranging from 0.75 to 0.94. External compound predictions were carried out for the models that had their own training sets. CORAL model returned the best performance (R{sup 2}{sub ext}=0.59), followed by the EPISuite Meylan model (R{sup 2}{sub ext}=0.58). The latter gave also the highest sensitivity on external compounds with values from 0.55 to 0.85, depending on the thresholds. Statistics were also compiled for compounds falling into the models Applicability Domain (AD), giving better performances. In this respect, VEGA CAESAR was the best model in terms of regression (R{sup 2}=0.94) and classification (average sensitivity>0.80). This model also showed the best regression (R{sup 2}=0.85) and sensitivity (average>0.70) for new compounds in the AD but not present in the training set. However, no single optimal model exists and, thus, it would be wise a case-by-case assessment. Yet, integrating the wealth of information from multiple models remains the winner approach. - Highlights: • REACH encourages the use of in silico methods in the assessment of chemicals safety. • The performances of nine BCF models were evaluated on a benchmark database of 851 chemicals. • We compared the models on the basis of both regression and classification performance. • Statistics on chemicals out of the training set and/or within the applicability domain were compiled. • The results show that QSAR models are useful as weight-of-evidence in support to other methods.
Large-scale and continuous conformational changes in the RNA self-folding process present significant challenges for structural studies, often requiring trade-offs between resolution and observational scope. Here, we utilize individual-particle cryo-electron tomography (IPET) to examine the post-transcriptional self-folding process of designed RNA origami 6-helix bundle with a clasp helix (6HBC). By avoiding selection, classification, averaging, or chemical fixation and optimizing cryo-ET data acquisition parameters, we reconstruct 120 three-dimensional (3D) density maps from 120 individual particles at an electron dose of no more than 168 e - Å -2 , achieving averaged resolutions ranging from 23 to 35 Å, as estimated by Fourier shell correlation (FSC) at 0.5. Each map allows us to identify distinct RNA helices and determine a unique tertiary structure. Statistical analysis of these 120 structures confirms two reported conformations and reveals a range of kinetically trapped, intermediate, and highly compacted states, demonstrating a maturation folding landscape likely driven by helix-helix compaction interactions.
Abstract The chemical heterointerfaces in hybrid electrode materials play an important role in overcoming the intrinsic drawbacks of individual materials and thus expedite the in‐depth development of electrochemical energy storage. Benefiting from the three enhancement effects of accelerating charge transport, increasing the number of storage sites, and reinforcing structural stability, the chemical heterointerfaces have attracted extensive interest and the electrochemical performances of hybrid electrode materials have been significantly optimized. In this review, recent advances regarding chemical heterointerface engineering in hybrid electrode materials are systematically summarized. Especially, the intrinsic behaviors of chemical heterointerfaces on hybrid electrode materials are refined based on built‐in electric field, van der Waals interaction, lattice mismatch and connection, electron cloud bias and chemical bond, and their combination. The strategies for introducing chemical heterointerfaces are classified into in situ local transformation, in situ growth, cosynthesis, and other strategy. The recent progress about the chemical heterointerfaces engineering specially focusing on metal‐ion batteries, supercapacitors, and Li–S batteries are introduced in detail. Furthermore, the classification and characterization of chemical heterointerfaces are briefly described. Finally, the emerging challenges and perspectives about future directions of chemical heterointerface engineering are proposed.
The responses of aerosol particles to heating are important for measurements of their chemical, physical, and optical properties, classification, and determination of origin. However, the thermal behavior of organic aerosol particles is largely unknown. We provide a method to analyze such thermal behavior through heating from room temperature to >600°C by using a heating holder within a transmission electron microscope (TEM). Here we describe in-situ shape and size changes and variations in the compositions of individual particles before and after heating. We use ambient samples from wildland and agricultural biomass fires in North America collected during the 2013 Biomass Burn Observation Project (BBOP). The results indicate that individual tar balls (TB; spherical organic material) from biomass burning retained, on average, up to 30% of their volume when heated to 600°C. Chemical analysis reveals that K and Na remain in the residues, whereas S and O were lost. In contrast to bulk sample measurements of carbonaceous particles using thermal/optical carbon analyzers, our single-particle results imply that many individual organic particles consist of multiple types of organic matter having different thermal stabilities. Beyond TBs, our results suggest that because of their thermal stability some organic particles may not be detectable by using aerosol mass spectrometry or thermal/optical carbon analyzers. This result can lead to an underestimate of the abundance of TBs and other organic particles, and therefore biomass burning may have more influence than currently recognized in regional and global climate models.
Regression and pattern recognition techniques were applied to 16 chemical species in 34 Apollo 17 basalts. The classification scheme of Pratt et al., (1977) was used. Data were absent for 8 MnO, 3 Hf, 3 Tb, and 8 Cr2O3 analyses. Linear regression studies were utilized to predict these and values obtained were added to the original data base. Pattern recognition techniques were then applied to predict classifications for 30 different Apollo 17 rake basalts analyzed by Murali et al., (1977).
Highlights: Black-Right-Pointing-Pointer Enzymes are vital tools. Black-Right-Pointing-Pointer Bifunctional enzymes. Black-Right-Pointing-Pointer Peculiar biocatalysts. -- Abstract: The chemical industry has come under increasing pressure to make chemical production more eco-friendly and independent to fossil resources. The development of industrial processes based on micro-organisms can especially help to eliminate the use or the generation of hazardous substances and can support the transition from dependence on fossil resources towards real sustainable and eco-safety industrial processes. The biocatalysts are the best solution given by nature that can be used to improve some biotechnological applications. In this research review, we report some peculiar properties of biocatalysts, implicated in a range of metabolic pathways and biotechnological tools.
The purpose of this study was to explore the possibility of combining the epidermal equivalent (EE) potency assay with the assay which assesses release of interleukin-18 (IL-18) to provide a single test for identification and classification of skin sensitizing chemicals, including chemicals of low water solubility or stability. A protocol was developed using different 3D-epidermal models including in house VUMC model, epiCS® (previously EST1000™), MatTek EpiDerm™ and SkinEthic™ RHE and also the impact of different vehicles (acetone:olive oil 4:1, 1% DMSO, ethanol, water) was investigated. Following topical exposure for 24 h to 17 contact allergens and 13 non-sensitizers a robust increase in IL-18 release was observed only after exposure to contact allergens. A putative prediction model is proposed from data obtained from two laboratories yielding 95% accuracy. Correlating the in vitro EE sensitizer potency data, which assesses the chemical concentration which results in 50% cytotoxicity (EE-EC{sub 50}) with human and animal data showed a superior correlation with human DSA{sub 05} (μg/cm{sup 2}) data (Spearman r = 0.8500; P value (two-tailed) = 0.0061) compared to LLNA data (Spearman r = 0.5968; P value (two-tailed) = 0.0542). DSA{sub 05} = induction dose per skin area that produces a positive response in 5% of the tested population Also a good correlation was observed for release of IL-18 (SI-2) into culture supernatants with human DSA{sub 05} data (Spearman r = 0.8333; P value (two-tailed) = 0.0154). This easily transferable human in vitro assay appears to be very promising, but additional testing of a larger chemical set with the different EE models is required to fully evaluate the utility of this assay and to establish a definitive prediction model. - Highlights: • A potential epidermal equivalent assay to label and classify sensitizers • Il-18 release distinguishes sensitizers from non sensitizers • IL-18 release can rank sensitizer potency • EC50 (chemical concentration causing 50% decrease in cell viability) ranks potency • In vitro: human DSA{sub 05} correlation is better than in vitro: LLNA correlation.
We present a detailed abundance analysis of 23 elements for a newly discovered carbon-enhanced metal-poor (CEMP) star, HE 0414−0343, from the Chemical Abundances of Stars in the Halo Project. Its spectroscopic stellar parameters are T{sub eff} = 4863 K, logg=1.25, ξ = 2.20 km s{sup −1}, and [Fe/H] = −2.24. Radial velocity measurements covering seven years indicate HE 0414−0343 to be a binary. HE 0414−0343 has [C/Fe]=1.44 and is strongly enhanced in neutron-capture elements but its abundances cannot be reproduced by a solar-type s-process pattern alone. Traditionally, it could be classified as a “CEMP-r/s” star. Based on abundance comparisons with asymptotic giant branch (AGB) star nucleosynthesis models, we suggest a new physically motivated origin and classification scheme for CEMP-s stars and the still poorly understood CEMP-r/s. The new scheme describes a continuous transition between these two so-far distinctly treated subgroups: CEMP-sA, CEMP-sB, and CEMP-sC. Possible causes for a continuous transition include the number of thermal pulses the AGB companion underwent, the effect of different AGB star masses on their nucleosynthetic yields, and physics that is not well approximated in 1D stellar models such as proton ingestion episodes and rotation. Based on a set of detailed AGB models, we suggest the abundance signature of HE 0414−0343 to have arisen from a >1.3 M{sub ⊙} mass AGB star and a late-time mass transfer that transformed HE 0414−0343 into a CEMP-sC star. We also find that the [Y/Ba] ratio well parametrizes the classification and can thus be used to easily classify any future such stars.
The Conquista chondrite is described and classified as an H4. The mineral composition is reported. H-group classification is based on described microscopic, electron microprobe, and bulk chemical studies. The evidence for petrologic type 4 classification includes the pronounced well-developed chondritic texture; the slight compositional variations in constituent phases; the high Ca contents of pyroxene and the presence of pigeonite; glassy to microcrystalline interstitial material rich in alkalis and SiO2; and twinned low-Ca clinopyroxene.
High resolution transmission electron microscopy (HRTEM) observations were made of a mixture of mica and montmorillonite from fine-grained calcium, aluminum inclusions (CAI) in the Allende C3(V) meteorite. A petrographic thin section having a diameter of 4 mm contained CAI fragments ranging from less than 1 to 50 microns. The observed textural and chemical characteristics placed the inclusion in the fine-grained alkali-rich spinel aggregate category of Warks' (1979) classifications of CAIs and as type 3 in Kornacki's classifications of fine grains in Allende. Chemical analyses were performed on the phyllosilicate grains observed in the TEM scan by means of an X ray observed, and the proximity to the matrix boundary suggests a metamorphism which included aqueous alteration at a relatively low temperature.
Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.
Real-time classification of non-recycled municipal solid waste (NMSW) is essential for efficient valorization. This study introduces ClassNMSW, a comprehensive framework for classifying 22 NMSW subclasses under industrial constraints by using hyperspectral imaging (HSI). A primary innovation of this work is the development of a variance-controlled spectral extraction algorithm. Unlike traditional methods that rely on simple averaging, this approach systematically investigates the extent of pixel extraction to minimize the loss of critical chemical information while maximizing data reduction thus ensuring high spectral fidelity with low computational cost. The approach developed in this work integrates automated, computer-vision-based background removal, eliminating the need for the manual thresholding common in current literature. To resolve ambiguities among chemically similar subclasses, a tiered classification and multi-camera fusion strategy (NIR17 and NIR22) is implemented. Results demonstrate that ClassNMSW achieves an object-wise weighted accuracy of 98.70% for single-sensor configurations and 100% under sensor fusion. A novel rolling-window strategy satisfies desired end-to-end latency of <2 s, satisfying the strict deterministic requirements of high-speed industrial sorting environments. The ClassNMSW framework provides a scalable foundation for advancing circularity and resource recovery in large-scale waste valorization operations.