SEARCH · Engineering Papers

Results for “descriptors”

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.

SourceResearch connectionFrom yearThrough year

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

At least 487 records · Page 27

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.

Adsorption↗

DOE OSTI2025

Facet-Dependent Hydrogen Evolution Reaction on M 2 P (M = Ni, Co, Fe) Single Crystals

Transition-metal phosphides (MPs) are promising earth-abundant catalysts for hydrogen evolution reactions (HERs) due to their remarkable activity and stability. To further improve their properties, facet control is a key strategy. The growth of shape-selected nanoparticles may substantially enhance electrocatalytic activity, but this approach requires fundamental studies of facet-specific catalytic properties. There are only a few reports on the facet effects of MPs, which leads to a limited understanding of the activity of each facet and hampers catalyst design. Here, in this study, we grew large hexagonal-prism-shaped single crystals of three representative M 2 P (M = Ni, Co, and Fe) catalysts using metal flux routes. Two facets of M 2 P single crystals were tested to study facet-dependent HER activities, and it was consistently demonstrated that for all M 2 P crystals, a tip facet [(0001) for Ni 2 P/Fe 2 P and (010) for Co 2 P] had a higher activity than the side facet [(101̅0) for Ni 2 P/Fe 2 P and (100) for Co 2 P]. HER activity between the same facet elucidated the activity ordered between different transition metals as Fe 2 P > Co 2 P > Ni 2 P under low-potential regions. At high applied potentials, this trend is reversed due to the differences in Tafel slopes, with Ni 2 P becoming the most active catalyst, such that the activity of the (0001) facet of Ni 2 P approaches that of Pt. The calculated surface density of states (DOS) of each facet and its local curvature were found to be a useful descriptor for the activity trends among different transition metals of the same facets.

Co2P↗

DOE OSTI2026

Group-Additivity–Embedded Multiscale Modeling for Electric Field-Enhanced Nanocatalysis

Elucidating structure-performance relationships remains a central challenge in field-enhanced catalysis, where nanoparticles exhibit nonuniform surface sites with site-dependent responses to electric fields. Low-coordination sites (edges, corners, and tips) are particularly electric field-sensitive (EF), leading to nonuniform charge distribution, adsorption energies, and catalytic activity. Here, using ammonia decomposition on a ruthenium cluster as a model system, we develop a transferable multiscale framework integrating density functional theory, group additivity (GA), Brønsted-Evans-Polanyi scaling, and microkinetic modeling to predict EF-dependent activity across nonuniform cluster sites. Across sites and fields, the nitrogen adsorption energy (E N ) emerges as the governing descriptor, yielding robust volcano relationships whose optimum shifts systematically with field: negative fields strengthen N binding via electron accumulation, while positive fields weaken N binding via charge depletion, moving the optimal E N toward weaker binding. Microkinetic analysis shows that N≡N bond formation remains the key kinetic bottleneck over most conditions; positive fields lower the effective barrier and, critically, increase the fraction of near-optimal active sites, leading to a net enhancement in overall activity relative to zero-field and negative-field cases. By capturing EF- and site-dependent energetics with high accuracy and low computational cost, this GA-embedded multi-scale simulation workflow provides a physically interpretable route to predict and design field-enhanced nanocatalysis.

ammonia decomposition↗

DOE OSTI2026

Quantifying the Impact of Metal Population Distribution in MFI-Supported Mo Catalysts for Methane Dehydroaromatization

Precise evaluation of intrinsic kinetic behavior in Mo/MFI catalysts for methane dehydroaromatization (MDA) is confounded by variations in Mo dispersion and speciation, which are influenced by metal loading and zeolite acidity. This work advances the utility of H 2 -temperature programmed reduction (H 2 -TPR) for characterizing Mo/MFI catalysts by enabling quantitative comparison of MoO x populations distinguished by reduction behavior and linked to initial catalytic performance. A comprehensive H 2 reduction pathway is established through systematic H 2 - TPR studies varying catalyst composition (1–10 wt % Mo, Si/Al = 15, 40, ∞), supplemented by UV-Raman spectroscopy, X-ray powder diffraction, N 2 physisorption, NH 3 -TPD, and advanced spectroscopic analysis (in situ XAS with principal component analysis/multivariate curve resolution—alternating least squares). Two low-temperature H 2 -TPR regions capture distinct Mo populations undergoing initial Mo(VI)→Mo(IV) reduction: a lower-temperature population (Mo-RI) associated primarily with highly dispersed, anchored MoO x species expected to predominantly reside within MFI channels, and a higher-temperature population (Mo-RII) corresponding to a broader set of MoO x species that becomes increasingly bulk-like/extrazeolitic at higher Mo loading. Quantification of these populations provides practical, kinetically relevant descriptors for comparing initial MDA rates across catalysts with varying Mo loading, Si/Al ratio, and MoO x heterogeneity. Application of lower-temperature Mo-RI estimates to kinetic measurements reveals a minimum threshold of ∼0.12 × 10 –3 mol Mo-RI/g cat , above which initial forward benzene rates normalized to this population converge despite differences in metal loading and zeolite Brønsted acidity. This threshold coincides with a transition toward a common C 2 -mediated benzene-forming regime, as indicated by approach-to-equilibrium analysis of methane-to-ethane, ethane-to-ethylene, and ethylene-to-benzene reaction steps. Above this threshold, normalized initial benzene rates are nearly invariant with increasing Mo-RII/Mo-RI population ratio, indicating that excess Mo-RII populations, including bulk-like/extrazeolitic MoO x domains present at higher loading, do not measurably suppress benzene formation associated with anchored and mostly channel-confined Mo population under the initial-rate conditions examined.

Mo/MFI↗

DOE OSTI2026

Ultra-Low Amounts of Transition Metals in Carbon-Based Catalysts Improve Their Alkaline OER Performance: A Systematic Study

The pursuit of green hydrogen production highlights a persistent gap in electrocatalyst research: while academic efforts prioritize cost-efficiency via activity enhancement, industrial viability demands greater emphasis on electrochemical stability. Carbon-based electrocatalysts, particularly those incorporating transition metals, have shown promise in alkaline oxygen evolution (OER) due to their high activity, cost-efficiency, and resource-efficiency. However, these catalysts suffer from insufficient stability under oxidizing conditions compared to pure transition metal catalysts due to erosion of the carbon support resulting from carbon corrosion, among other degradation mechanisms. In this systematic study, the influence of ultra-low amounts (<1 wt %) of iron, cobalt, nickel, and their most common combinations on the stability of a hydrothermally derived, N-doped carbon support and the overall catalyst performance during alkaline OER is systematically explored. By identifying critical stability descriptors and correlating them with synthesis conditions and catalyst properties, primary and secondary corrosion pathways are unraveled. Subsequently, through careful adjustment of carbonization temperature and composition of incorporated transition metals, overall catalyst corrosion can be suppressed immensely. Especially, the inclusion of Ni and Fe is paramount for the formation of stable catalyst materials under laboratory conditions (10 mA/cm 2 in 0.1 M KOH), which is surprisingly unconstrained by the degree of graphitization of the carbon support. Mixing this electrochemically stable material with a graphitic carbon powder results in an excellent stability of over 400 h at 100 mA/cm 2 in 1 M KOH, implying great potential for the future improvement of carbon-based electrodes under oxidizing conditions toward industrial application.

N-doped carbon↗

DOE OSTI2026

Surface Charge Predicts the Presence of Cation Effects in Electrocatalysis

The identity of electrolyte cations has an important influence on the rates of many electrocatalytic reactions, but these effects are not always observed. Recently, we found that the surface charge of the catalyst, quantified by the potential of zero total charge (PZTC), is a useful heuristic for predicting when cation effects will be observed for the oxygen reduction reaction (ORR). Here, we demonstrate that this descriptor allows us to rationalize the observation or absence of cation effects across a range of conditions, reactions (the hydrogen evolution reaction, the ORR, methanol oxidation, ethylene glycol oxidation, glycerol oxidation, and glucose reduction) and metal surfaces (Pt, Pd, Ag, and Au). These results suggest that when the reaction’s operating potential is negative of the metal’s PZTC, electrolyte cations accumulate at the catalyst surface and influence reaction rates. As a result, when reactions occur positive of the PZTC, cation effects are not observed.

Cations↗

DOE OSTI2026

Enabling Stable and Salt-Free Formic Acid Production via Reactive Interface Optimization in Forward-Bias Bipolar-Membrane CO2 Electrolyzers

Electrochemical conversion of CO2 to liquid products is limited by parasitic reactions that consume products and destabilize operation. Here, we show that salt-free formic acid synthesis in a forward-bias bipolar-membrane CO2 electrolyzer is governed by a coupled reaction-transport instability driven by parasitic anode formic acid oxidation. Operando mass spectrometry reveals that this process proceeds through CO-mediated poisoning, identifying catalyst tolerance to CO as a key descriptor of anode selectivity. Among the catalysts examined, PtRu/C preferentially promotes hydrogen oxidation over formic acid oxidation. Coupling this catalyst with transport-interface engineering improves product removal, enhances Faradaic and energy efficiency, and enables stable operation at 200 mA/cm2 for 190 h with a voltage decay of 0.64 mV/h. Techno-economic analysis indicates an 18% reduction in minimum selling price and highlights product concentration as the dominant cost lever. These results establish a general framework for suppressing product oxidation in liquid-product electrolyzers.

Hu, Leiming↗

DOE OSTI2026

Quantitative Analysis and Prediction of Thermal Runaway Metrics of High-Nickel Oxide Cathodes by Machine Learning Models

The pursuit of higher energy density in lithium-ion batteries has made high-nickel (Ni) layered oxides leading cathode candidates for next-generation electric vehicles. However, their poor thermal stability, particularly at Ni contents ≥ 90%, increases the risk of cathode-initiated thermal runaway. Furthermore, we present a data-driven framework combining linear and nonlinear machine learning models to predict key thermal runaway descriptors from a high-throughput differential scanning calorimetry database. With cathode composition and state of charge (SOC) as input features, the ensemble model accurately predicts peak temperature, heat release, and peak heat flow. SHAP analysis identifies Ni content and SOC as the dominant factors controlling thermal runaway temperature, while SOC primarily governs heat release and peak heat flow. Al, Mg, and Mn improve thermal stability by strengthening metal–oxygen bonding and delaying structural transformation, whereas B mainly reduces heat release through surface passivation. Validation with a new cathode composition confirms accurate prediction of SOC-dependent thermal runaway behavior and critical SOC.

25 ENERGY STORAGE↗

DOE OSTI2021

Toward the Development of a High-Voltage Mg Cathode Using a Chromium Sulfide Host

Development of Mg-ion batteries as advanced electrochemical energy storage systems relies on the design and discovery of high-voltage positive electrode (cathode) materials. To date, a variety of sulfide cathodes have been reported (e.g., Mg x Mo 6 S 8 , Mg x Ti 2 S4, etc.), but the voltages of these materials are too low to prepare a high energy density Mg cell. Theoretical computations predicted that Mg x Cr 2 S 4 operating with the high-voltage Cr 3+/4+ redox couple would serve as a suitable cathode candidate, but experimental attempts to extract Mg 2+ from the lattice have been largely unsuccessful. Here, we show that reversible electrochemical activity within a thiospinel framework (AB 2 S 4 ) relies on a redox-active transition metal present in the B site; otherwise, anionic redox activity triggers decomposition of the spinel structure. Since Cr and S states are highly coupled in MgCr 2 S 4 , the Cr 3+/4+ redox couple is inaccessible so that reversible (de)intercalation of Mg 2+ cannot occur and charging leads to dissolution of the active material. These findings point to an insufficiency in the screening criteria previously used to identify MgCr 2 S 4 as a promising Mg cathode. Thus, a computable descriptor based on the electronic structure of the discharged material is proposed to predict the prevalence of cation vs anion redox and improve future surveys of potential candidates. It is unlikely that the high-voltage Cr redox couple will be accessible to oxidation in the presence of sulfur within the restrictions of a spinel framework; however, it is possible that a suitable layered Mg x CrS 2 structure could serve as a reversible high-voltage Mg cathode.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Spectroscopic Probe Molecule Selection Using Quantum Theory, First-Principles Calculations, and Machine Learning

Probe molecule vibrational spectra have a long history of being used to characterize materials including metals, oxides, metal-organic frameworks, and even human proteins. Furthermore, recent advances in machine learning have enabled computationally generated spectra to aid in detailed characterization of complex surfaces with probe molecules. Despite widespread use of probe molecules, the science of probe molecule selection is underdeveloped. Here, we develop physical concepts, including orbital interaction energy and the energy overlap integral, to explain and predict the ability of probe molecules to discriminate structural descriptors. We resolve the crystal orbital overlap population (COOP) to specific molecular orbitals and quantify their bonding character, which directly influences vibrational frequencies. Using only a single adsorbate calculation from density function theory (DFT), we compute the interaction energy of individual adsorbate molecular orbitals with adsorption site atomic orbitals across many different sites. Combining the molecular orbital resolved COOP and changes in orbital interaction energy enables probe molecule selection for improved discrimination of various sites. We demonstrate these concepts by comparing the predicted effectiveness of carbon monoxide (CO), nitric oxide (NO), and ethylene (C 2 H 4 ) to probe Pt adsorption sites. Finally, using a previously developed machine learning framework, we show that models trained on hundreds of thousand C 2 H 4 spectra, computed from DFT, which regress surface binding-type and generalized coordination number (GCN), outperform those trained using CO and NO spectra. Lastly, a python package, pDOS_overlap, for implementing the electron density based analysis on any combination of adsorbates and materials, is also made available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. Overall, this analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Automated Experiment in 4D-STEM: Exploring Emergent Physics and Structural Behaviors

Automated experiments in 4D scanning transmission electron microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and 4D-STEM-based descriptors. With this, efficient and “intelligent” probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. Therefore, we verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS 3 . This approach establishes a pathway for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam-sensitive materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Biomimetic Control over Bimetallic Nanoparticle Structure and Activity via Peptide Capping Ligand Sequence

Here, the controlled design of bimetallic nanoparticles (BNPs) is a key goal in tailoring their catalytic properties. Recently, biomimetic pathways demonstrated potent control over the distribution of different metals within BNPs, but a direct understanding of the peptide effect on the compositional distribution at the interparticle and intraparticle levels remains lacking. We synthesized two sets of PtAu systems with two peptides and correlated their structure, composition, and distributions with the catalytic activity. Structural and compositional analyses were performed by a combined machine learning-assisted refinement of X-ray absorption spectra and Z-contrast measurements by scanning transmission electron microscopy. The difference in the catalytic activities between nanoparticles synthesized with different peptides was attributed to the details of interparticle distribution of Pt and Au across these markedly heterogeneous systems, comprising Pt-rich, Au-rich, and Au core/Pt shell nanoparticles. The total amount of Pt in the shells of the BNPs was proposed to be the key catalytic activity descriptor. This approach can be extended to other systems of metals and peptides to facilitate the targeted design of catalysts with the desired activity.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

High-Yield Room-Temperature Aryl 211 At Radiolabeling via Redox-Activated Column-Shipped 211 At

Efficient late-stage formation of aryl– 211 At bonds is relevant to expanding the scope of targeted α-therapy radiopharmaceuticals. In this work, a catalyst-enabled astatodeboronation approach is evaluated using wet chemically regenerated 211 At recovered from shipped 3-octanone-impregnated columns. A series of aryl boronic esters bearing electron-donating and electron-withdrawing substituents were radiolabeled under mild conditions, with reaction parameters examined through time-dependent studies. Radiochemical conversions ranging from 68–97% were observed across the substrate set. Density functional theory calculations were employed as a qualitative framework to contextualize relative B–C and At–C bonding descriptors. Overall, this study demonstrates that wet chemically regenerated 211 At can be applied to the late-stage labeling of aryl boronic esters under mild conditions and provides guidance for future exploration of 211 At radiolabeling strategies.

211At-aryl labeling↗

DOE OSTI2021

Accurate Thermochemistry of Complex Lignin Structures via Density Functional Theory, Group Additivity, and Machine Learning

A molecular-level understanding of lignin structures and bond dissociation energies could facilitate depolymerization technologies. Still, this information is currently limited due to the lack of databases and the simplification of surrogate models. Here, substitution effects on seven common linkages in lignin polymers are systematically investigated. An automated reaction network generator is employed to create a database of structures. A new group additivity (GA) model based on principal component analysis (PCA) descriptors is introduced and trained on gas-phase density functional theory data of 4100 species at the M06-2X/6-311++G(d,p) level. Hydrogen bonds, local steric, and nonaromatic ring contributions are also incorporated. Lastly, we improve the accuracy of the group additivity model to reach the G4 theory by computing a data set of 770 species at this level and using a data fusion approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Ionic liquids (ILs) have unique solvent properties and have thus garnered significant interest. However, exhaustive experimental determination of the physicochemical properties of ILs is unrealistic due to the large structural diversity of anions and cations, their high cost, the requirements of elevated temperature and pressure, and the time required. To circumvent these experimental costs, computational approaches to accurately calculate these properties have emerged. Here in the present study, we present a demonstration of two machine learning (ML) models for the prediction of two critical IL physical properties, the surface tension and the speed of sound, across a wide range of temperatures and pressures. The models make use of molecular descriptors derived from the COSMO-RS, a quantum chemical-based model. The ML models show excellent agreement with experimental observations, with an R2 value of 0.96–0.99 and RMSE of 1.71 mN/m and 16.12 m/s for the surface tension and speed of sound, respectively. This work paves the way for the development of COSMO-RS-informed ML models for the prediction of IL properties which can help to further optimize and accelerate technology development for ILs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Coherent Acoustic Interferometry during the Photodriven Oxygen Evolution Reaction Associates Strain Fields with the Reactive Oxygen Intermediate (Ti–OH*)

The oxygen evolution reaction (OER) from water requires the formation of metastable, reactive oxygen intermediates to enable oxygen–oxygen bond formation. Conversely, such reactive intermediates could also structurally modify the catalyst. Here, a descriptor for the overall catalytic activity, the first electron and proton transfer OER intermediate from water, (M–OH*), has been associated with significant distortions of the metal–oxygen bonds upon charge-trapping. Time-resolved spectroscopy of in situ, photodriven OER on transition metal oxide surfaces has characterized M–OH* for the charge trapping and the symmetry of the lattice distortions by optical and vibrational transitions, respectively, but had yet to detect an interfacial strain field arising from a surface coverage M–OH*. Here, we utilize picosecond, coherent acoustic interferometry to detect the uniaxial strain normal to the SrTiO 3 /aqueous interface directly caused by Ti–OH*. The spectral analysis applies a fairly general methodology for detecting a combination of the spatial extent, magnitude, and generation time of the interfacial strain through the coherent oscillations’ phase. For lightly n-doped SrTiO 3 , we identify the strain generation time (1.31 ps), which occurs simultaneously with Ti–OH* formation, and a tensile strain of 0.06% (upper limit 0.6%). In addition to fully characterizing this intermediate across visible, mid-infrared, and now GHz-THz probes on SrTiO 3 , we show that strain fields occur with the creation of some M–OH*, which modifies design strategies for tuning catalytic activity and provides insight into photo-induced degradation so prevalent for OER. To that end, the work put forth here provides a unique methodology to characterize intermediate-induced interfacial strain across OER catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗