Search NASA⌕ Search

SEARCH · Search NASA

Results for “Mining”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Thermodynamic properties of the Nd-Bi system via emf measurements, $\mathrm{DFT}$ calculations, machine learning, and $\mathrm{CALPHAD}$ modeling

Thermodynamic properties of the Nd-Bi system were investigated using a combination of experimental measurements, first-principles calculations based on density functional theory (DFT), data mining and machine learning (DM + ML) predictions, and calculation of phase diagrams (CALPHAD) modeling. The electromotive force (emf) of Nd-Bi alloys in molten LiCl-KCl-NdCl 3 at 773–973 K was measured via coulometric titration of Nd into Bi for the determination of thermochemical properties such as activity coefficients and solubilities of Nd in Bi. A new peritectic reaction of [liquid + NdBi 2 = Nd 3 Bi 7 ] at 774 K was confirmed using differential scanning calorimetry, structural (X-ray diffraction), and microstructural (scanning electron microscopy) analyses. The unknown crystal structure of NdBi2 was suggested to be a mixture of the anti-La 2 Sb configuration and the La 2 Te-type configuration based on ML predictions for over 26,000 data-mined AB 2 -type configurations together with DFT-based verifications. Using the newly acquired experimental data and DFT-based calculations, the thermodynamic description of the Nd-Bi system was remodeled, and a more complete Nd-Bi phase diagram was calculated, including the Nd 3 Bi 7 compound, invariant transition reactions, and liquidus temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Landscape composition and configuration have scale-dependent effects on agricultural pest suppression

Increasing landscape heterogeneity (composition and configuration) can enhance natural enemy populations and support pest suppression in agricultural landscapes. Using a network-based data mining approach, we examined independent gradients of landscape composition and configuration at six spatial scales that were associated with pest suppression services measured at 32 sites in Michigan and Wisconsin, USA. We compared the relative effects of landscape composition and configuration across scales with those of local crop type (corn or grassland). We found that multiple gradients of configurational heterogeneity were independent of composition and strongly associated with pest suppression, with different configuration metrics being predictive of pest suppression depending on the spatial scales and regions considered. Landscapes that were more configurationally heterogeneous at smaller spatial scales consistently supported higher pest suppression. In Michigan, pest suppression increased in landscapes with high edge contrast between annual crops and surrounding habitats and high edge density of grassland within 250-500 m radii. In Wisconsin, pest suppression increased with large core area of grassland and high field density within a 250 m radius. The main compositional effect we found was a positive relationship between grassland cover and pest suppression occurring at larger spatial scales (1000-1500 m) and occurring in Wisconsin but not in Michigan. Our findings demonstrate that effects of landscape composition and configuration on pest suppression differ across spatial scales and vary regionally. The network-based data mining techniques used here could be useful for disentangling intercorrelated landscape metrics in a variety of other contexts in landscape ecology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Distribution of rare earth and other critical elements in lignites from the Eocene Jackson Group, Texas

Coal is increasingly evaluated as a source of rare earth elements (REEs) in the United States to address the overreliance on imported REEs. The objective of this study was to assess the distribution of REEs in lignites from selected mining areas in the Texas Gulf Coastal Plain region. Thirty-one archived lignite and rock samples previously collected by the U.S. Geological Survey were analyzed for their rare earth element and critical mineral content. These include samples from one core (5400 and 5500 lignite horizons) and two opencast lignite mines (Gibbons Creek 3500 and 4500 horizons, and San Miguel horizons A to D) in the Eocene Jackson Group of the Texas Gulf of Mexico Coastal Plain. Some lithologies in the Gibbons Creek 3500 and 4500 lignite-bearing sections have high total rare earth, yttrium (Y), and scandium (Sc) (REYSc) values, up to 7800 ppm (ash basis) REYSc. The lignite lithologies show an enrichment in rare earths, [samarium (Sm) through gadolinium (Gd)]. The basal Gibbons Creek 3500 lignite bench shows a heavy rare earth element enrichment pattern resembling that often seen in peats through high volatile A bituminous coals. The 5500 lignite sequence, overlying the latter lignite sections, shows a light rare earth enrichment. The San Miguel lignite benches have heavy rare earth enrichments with a negative europium (Eu) anomaly.

58 GEOSCIENCES↗
DOE OSTI2026

Evaluation of coal-associated sediments, wastes, and AMD sludge in the Southern Appalachian Basin as feedstock materials for REE and Li recovery

Critical minerals (CM) such as rare earth elements (REE+) and Lithium (Li) are essential to technological innovation, energy transitions, global economic and defense security, necessitating the search for unconventional resources and efficient recovery methods to avert supply chain disruptions. Here, this study evaluates coal-associated sediments (underclay and roof rock) and wastes from the Pennsylvanian Pottsville Formation of the Southern Appalachian Basin (SAB) as potential feedstocks for CM recovery. A total of 34 samples (15 underclays, 12 roof rocks, 5 Acid Mine Drainage (AMD) sludges, and 2 coal mining wastes) were characterized using XRD, XRF, ICP-MS, and μ-XRF analytical methods. The REE+ and Li concentrations of these materials ranged from 46.8 to 334.4 ppm and from 11.1 to 519 ppm, respectively, with one underclay sample (Hendrix 3456) yielding the highest values for both. Bulk mineralogy for all samples was dominated by aluminosilicate clay phases, particularly illite and kaolinite. All samples exhibited REY def, rel% values >26% and C outl indices that ranged from 0.69 to 0.94, classifying their REE ore potential as Category II (Promising) as defined by Seredin and Dai (2012). Extractability tests (EPA method 3051 A) yielded low REE+ and Li recoveries, with maximum values of 3.3% and 3.6%, respectively, suggesting associations with resistant minerals like clay and phosphates. Elemental mapping indicates that REE+ is associated with phosphate, whereas statistical analysis suggests that REE+ are associated with aluminosilicates, suggesting heterogeneous associations or minimal phosphate contribution. Li also correlated positively with Al 2 O 3 , indicating an aluminosilicate host. This study highlights the potential of coal-associated sediments in the SAB.

Clay minerals↗
DOE OSTI2022

Removal of As(V) from wastewaters using magnetic iron oxides formed by zero-valent iron electrocoagulation

Electrocoagulation of zero-valent iron has been widely applied to the removal of dissolved arsenic, but the solid-liquid separation of arsenic-containing precipitates remains technically challenging. In this work, zero-valent iron was electrochemically oxidized to magnetic iron oxides for the removal of As(V) from simulated and actual mining wastewaters. The results indicated that lepidocrocite was formed when zero-valent iron was oxidized by dissolved oxygen, but ferrihydrite and green rust were first formed and then transformed to magnetic iron oxides (mainly magnetite and maghemite) in the electrochemical oxidation from 0 to 0.9 V (vs. SCE), which facilitates the adsorption of As(V) and subsequent solid-liquid separation under a magnetic field. In simulated As(V)-containing solution with initial pH 7.0, zero-valent iron was electrochemically oxidized to magnetite and maghemite at 0.6 V (vs. SCE) for 2 h. The As(V) concentration first decreased from 5127.5 to 26.8 μg L -1 with a removal ratio of 99.5%. In actual mining wastewaters, zero-valent iron was electrochemically oxidized to maghemite at 0.6 V (vs. SCE) for 24 h, and the As(V) concentration decreased from 5486.4 to 3.6 μg L -1 with a removal ratio of 99.9%. The removal ratio of As(V) increased slightly with increasing potential, and increased first and then decreased with increasing initial pH. Compared with that of SO 4 2- and NO 3 - , the presence of Cl - significantly enhanced the removal of As(V). This work provides a highly efficient, facile and low-cost technique for the treatment of arsenic-containing wastewaters.

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

Microbiological and geochemical characterization of As-bearing tailings and underlying sediments

Over the past 100 years, extensive oxidation of As-bearing sulfide-rich tailings from the abandoned Long Lake Gold Mine (Canada) has resulted in the formation of acid mine drainage (pH 2.0-3.9) containing high concentrations of dissolved As (similar to 400 mg L -1 ), SO 4 2- , Fe and other metals. Dissolved As is predominantly present as As(III), with increased As(V) near the tailings surface. Pore-gas O 2 is depleted to < 1 vol% in the upper 30-80 cm of the tailings profile. The primary sulfides, pyrite and arsenopyrite, are highly oxidized in the upper portions of the tailings. Elevated proportions of sulfide-oxidizing prokaryotes are present in this zone (mean 32.3% of total reads). The tailings are underlain by sediments rich in organic C. Enrichment in δ 34 S-SO 4 in pore-water samples in the organic C-rich zone is consistent with dissimilatory sulfate reduction. Synchrotron-based spectroscopy indicates an abundance of ferric arsenate phases near the impoundment surface and the presence of secondary arsenic sulfides in the organic-C beneath the tailings. The persistence of elevated As concentrations beneath the tailings indicates precipitation of secondary As sulfides is not sufficient to completely remove dissolved As. The oxidation of sulfides and release of As is expected to continue for decades. The findings will inform future remediation efforts and provide a foundation for the long-term monitoring of the effectiveness of the remediation program.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

SNAD transient miner: Finding missed transient events in ZTF DR4 using k-D trees

Here we report the automatic detection of 11 transients (7 possible supernovae and 4 active galactic nuclei candidates) within the Zwicky Transient Facility fourth data release (ZTF DR4), all of them observed in 2018 and absent from public catalogs. Among these, three were not part of the ZTF alert stream. Our transient mining strategy employs 41 physically motivated features extracted from both real light curves and four simulated light curve models (SN Ia, SN II, TDE, SLSN-I). These features are input to a k-D tree algorithm, from which we calculate the 15 nearest neighbors. After pre-processing and selection cuts, our dataset contained approximately a million objects among which we visually inspected the 105 closest neighbors from seven of our brightest, most well-sampled simulations, comprising 89 unique ZTF DR4 sources. Our result illustrates the potential of coherently incorporating domain knowledge and automatic learning algorithms, which is one of the guiding principles directing the SNAD team. It also demonstrates that the ZTF DR is a suitable testing ground for data mining algorithms aiming to prepare for the next generation of astronomical data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Evaluation of SNOLAB background mitigation procedures through the use of an ICP-MS based dust monitoring methodology

Dust particulate fallout on materials in use for rare-event searches is a concerning source of radioactive backgrounds due to the presence of the naturally occurring radionuclides 40 K, 232 Th, 238 U, and their progeny in dust. Much effort is dedicated to inform radioactive backgrounds from dust and evaluate the efficacy of mitigation procedures. A great portion of such effort relies on fallout models and assumed dust composition. Here, in this work, an ICP-MS based methodology was employed for a direct determination of fallout rates of radionuclides and stable isotopes of interest from dust particulate at the SNOLAB facility. Hosted in an active mine, the SNOLAB underground laboratory strives to maintain experimental areas at class 2000 cleanroom level. This work validates the mitigation procedures in place at SNOLAB and informs dust backgrounds during laboratory activities. Fallout rates of major constituents of the local rock were measured two to three orders of magnitude lower in the clean experimental areas compared to non-clean transition areas from the mine to the laboratory. An increase of approximately two orders of magnitude in the fallout rate of stable Pb isotopes was determined in an experimental area during activities involving handling of Pb bricks. Increased 40 K, 232 Th, and 238 U fallout rates were measured in clean experimental areas during activities generating particulate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Agent-based modeling of supply disruptions in the global rare earths market

Several independent assessments have identified rare earth elements (REEs) as critical materials, notably neodymium (Nd), praseodymium (Pr), and dysprosium (Dy) used in permanent magnets. Factors affecting their criticality include expected growth in demand arising from their unique performance-enhancing properties in consumer, energy, and military applications and the supply risk associated with China's dominance in their production. We demonstrate the use of Argonne's Global Critical Materials (GCMat) agent-based model to explore the possible consequences to REE market dynamics of different types of regional supply disruptions including a temporary loss of production, shutdown of capacity, and diversion of supply. Results suggest that supply disruptions may foster earlier and more REE mine starts outside of China, although some of these mines may not be able to sustain operations post disruption. Further, price and associated market responses such as production, capacity, and demand tended to extend beyond the disruption period. Such market impacts in the magnet supply chain could affect the costs and availability of a number of emerging clean energy technology applications such as electric vehicles and wind turbines. In the future, GCMat could be used to evaluate the effectiveness of mitigation strategies - including recycling, conservation, product substitution, and diversification of supplies - on reducing the severity of disruptions in REE markets.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Samarium isotope compositions of uranium ore concentrates: A novel nuclear forensic signature

Uranium ore is mined and milled to produce uranium ore concentrate (UOC), a regulated product of the nuclear fuel cycle. Additionally, diversion of UOC from the fuel cycle into possible weapons production is a key concern in global nonproliferation efforts. As such, the ability to trace the origin of seized nuclear materials is imperative to law enforcement efforts. Although isotopic signatures of UOCs have proven fruitful to pinpoint sample provenance, new isotopic signatures are needed because most existing isotopic signatures are not indicative of the original ore body from which the U is derived. In this work, we developed a new method to separate samarium (Sm) from a U-rich sample matrix and report the first Sm isotope compositions of 32 UOCs derived from a variety of worldwide uranium mines. Relative to terrestrial standards, approximately half the UOCs have resolved and anticorrelated 149 Sm- 150 Sm isotope compositions, consistent with the capture of thermal neutrons by 149 Sm in the ore body. The UOCs with anomalous Sm isotope compositions tend to derive from older (~>1.5Ga) and higher-grade ore bodies, although other factors, such as the presence of neutron moderators like water, also play a role. Nonetheless, the Sm isotope compositions of UOCs directly reflects the neutron fluence over the history of the original ore body and can be used to discern different geologic conditions associated with that ore body. Overall, this work demonstrates the potential use of Sm isotopes as a novel nuclear forensics signature for origin assessment of UOCs.

58 GEOSCIENCES↗
DOE OSTI2021

Trace explosive residue detection of HMX and RDX in post-detonation dust from an open-air environment

Explosives are often used in industry, geology, mining, and other applications, but it is not always clear what remains after a detonation or the fate and transport of any residual material. The goal of this study was to determine to what extent intact molecules of high explosive (HE) compounds are detectable and quantifiable from post-detonation dust and particulates in a field experiment with varied topography. We focused on HMX (1,3,5,7-Tetranitro-1,3,5,7-tetrazocane), which is less studied in field detonation literature, as the primary explosive material and RDX (1,3,5-Trinitroperhydro-1,3,5-triazine) as the secondary material. The experiment was conducted at Site 300, Lawrence Livermore National Laboratory’s Experimental Test Site, in California, USA. Two 20.4 kg and one 40.8 kg above ground explosions (primarily comprised of LX-14, an HMX-based polymer-bonded high explosive) were detonated on an open-air firing area on separate days. The complex terrain of the firing area (e.g., buildings, berm, low-height obstacles) was advantageous to study HE deposition in relation to plume dynamics. Three types of samples were collected up to 100 m away from each shot: surface swipes of aluminum plates, surface swipes of fixed objects, and filters from air samples. We used atmospheric flow tube-mass spectrometry (AFT-MS) to quantify picogram levels of molecular residue of HE material in the post-detonation dust. An aliquot of sample extract in methanol (e.g., 1 µL of 0.5 mL) was placed onto a resistive material and then thermally desorbed into the AFT-MS. We successfully detected and quantified both HMX and RDX in many of the samples. Based on mass (pg) detected and solution dilution, we back-calculated the mass collected on the swipe or filter (ng per sample). The aerial distribution of molecular residue was consistent with the path of the plume, which was strongly determined by wind speed and direction at the time of each shot. The quantity of material detected appeared to correlate more with distance from the shot and the wind conditions than with shot size. This study demonstrates that the picogram detection levels of AFT-MS are well-suited for quantification of analytes (e.g., HMX and RDX) in environmental samples.

atmospheric flow tube-mass spectrometry (AFT-MS), ↗
DOE OSTI2021

Development of platforms for functional characterization and production of phenazines using a multi-chassis approach via CRAGE

Phenazines (Phzs), a family of chemicals with a phenazine backbone, are secondary metabolites with diverse properties such as antibacterial, anti-fungal, or anticancer activity. The core derivatives of phenazine, phenazine-1-carboxylic acid (PCA) and phenazine-1,6-dicarboxylic acid (PDC), are themselves precursors for various other derivatives. Recent advances in genome mining tools have enabled researchers to identify many biosynthetic gene clusters (BGCs) that might produce novel Phzs. Here, to characterize the function of these BGCs efficiently, we performed modular construct assembly and subsequent multi-chassis heterologous expression using chassis-independent recombinase-assisted genome engineering (CRAGE). CRAGE allowed rapid integration of a PCA BGC into 23 diverse γ-proteobacteria species and allowed us to identify top PCA producers. We then used the top five chassis hosts to express four partially refactored PDC BGCs. A few of these platforms produced high levels of PDC. Specifically, Xenorhabdus doucetiae and Pseudomonas simiae produced PDC at a titer of 293 mg/L and 373 mg/L, respectively, in minimal media. These titers are significantly higher than those previously reported. Furthermore, selectivity toward PDC production over PCA production was improved by up to 9-fold. The results show that these strains are promising chassis for production of PCA, PDC, and their derivatives, as well as for function characterization of Phz BGCs identified via bioinformatics mining.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Perspectives on Cobalt Supply through 2030 in the Face of Changing Demand

Lithium-ion battery demand, particularly for electric vehicles, is projected to increase by over 300% throughout the next decade. With these expected increases in demand, cobalt (Co)-dependent technologies face the risk of significant impact from supply concentration and mining limitations in the short term. In this study, increased extraction and secondary recovery form the basis of modeling scenarios that examine implications on Co supply to 2030. Demand for Co is estimated to range from 235 to 430 ktonnes in 2030. This upper bound on Co demand in 2030 corresponds to 280% of world refinery capacity in 2016. Supply from scheduled and unscheduled production as well as secondary production is estimated to range from 320 to 460 ktonnes. Our analysis suggests the following: (1) Co price will remain relatively stable in the short term, given that this range suggests even a supply surplus, (2) future Co supply will become more diversified geographically and mined more as a byproduct of nickel (Ni) over this period, and (3) for this demand to be met, attention should be paid to sustained investments in refined supply of Co and secondary recovery.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Uranium recovery from seawater using amidoxime-based braided polymers synthesized from acrylic fibers

Global demand for nuclear energy is expected to rise in the coming decades. To meet these growing needs, new uranium resources must be explored. One of the potential alternatives to traditional uranium mining is oceanic uranium. The capture and recovery of uranium from the ocean has been under investigation for some time, with many recent studies focused on amidoxime based adsorbents. These adsorbents while able to achieve high uranium recovery capacities are, nevertheless, expensive to produce and adsorb a significant amount of hard to remove vanadium. The purpose of this study is to evaluate the adsorption performance of amidoxime based polymer braids synthesized from acrylic fibers which are designed to significantly cut polymer synthesis and conditioning costs. Adsorption experiments were performed in a recirculating raceway flume system at environmental conditions, approximately 10.8 oC, with 40-micron prefiltered seawater over 28 days for small and large sized LCW polymer braids. For both braid sizes, the adsorption of vanadium was far lower on the LCW materials than on Oak Ridge National Laboratory’s amidoxime based AF1 and AI8 small braids, with the LCW small braids outperforming both materials with respect to uranium adsorption under similar conditions. Adsorption modeling was used to simulate the performance at higher temperatures based on 20oC experiments previously performed with similar materials. Simulation results indicated that the small LCW braids would have a less significant advantage with respect to uranium adsorption over both AF1 and AI8 at 20oC and adsorb slightly less uranium than AF1 at 31oC. Vanadium adsorption by LCW small braids was less than one third of vanadium adsorption by either AF1 or AI8 for all temperatures. This behavior indicates that the LCW materials are able to achieve comparable uranium adsorption to other amidoxime adsorbents previously developed, while being cheaper to produce and adsorbing significantly less vanadium.

Wiechert, Alexander I.↗
DOE OSTI2023

HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search

Abstract Recent analyses of public microbial genomes have found over a million biosynthetic gene clusters, the natural products of the majority of which remain unknown. Additionally, GNPS harbors billions of mass spectra of natural products without known structures and biosynthetic genes. We bridge the gap between large-scale genome mining and mass spectral datasets for natural product discovery by developing HypoRiPPAtlas, an Atlas of hypothetical natural product structures, which is ready-to-use for in silico database search of tandem mass spectra. HypoRiPPAtlas is constructed by mining genomes using seq2ripp, a machine-learning tool for the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs). In HypoRiPPAtlas, we identify RiPPs in microbes and plants. HypoRiPPAtlas could be extended to other natural product classes in the future by implementing corresponding biosynthetic logic. This study paves the way for large-scale explorations of biosynthetic pathways and chemical structures of microbial and plant RiPP classes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

Chemistry↗
DOE OSTI2022

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

Abstract We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Automated Construction of a Photocatalysis Dataset for Water-Splitting Applications

We present an automatically generated dataset of 15,755 records that were extracted from 47,357 papers. These records contain water-splitting activity in the presence of certain photocatalysts, along with additional information about the chemical reaction conditions under which this activity was recorded. These conditions include any co-catalysts and additives that were present during water splitting, the length of time for which the photocatalytic experiment was conducted, and the type of light source used, including its wavelength. Despite the text extraction of such a wide range of chemical reaction attributes, the dataset afforded good precision (71.2%) and recall (36.3%). These figures-of-merit were calculated based on a random sample of open-access papers from the corpus. Mining such a complex set of attributes required the development of novel techniques in knowledge extraction and interdependency resolution, leveraging inter- and intra-sentence relations, which are also described in this paper. We present a new version (version 2.2) of the chemistry-aware text-mining toolkit ChemDataExtractor, in which these new techniques are included.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗