Search NASA⌕ Search

SEARCH · Search NASA

Results for “high throughput screening”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Screening and evaluation of biomass upgrading strategies for sustainable transportation fuel production with biomass-derived volatile fatty acids

Biomass conversion to fuels and chemicals is crucial to decarbonization, but choosing an advantageous upgrading pathway out of many options is challenging. Rigorously evaluating all candidate pathways (process simulation, product property testing) requires a prohibitive amount of research effort; even simple upgrading schemes have hundreds of possible permutations. We present a method enabling high-throughput screening by approximating upgrading unit operations and drop-in compatibility of products (e.g., fuel properties) and apply it to volatile fatty acid (VFA) conversion to liquid transportation fuels via a MATLAB script, VFA Upgrading to Liquid Transportation fUels Refinery Estimation (VULTURE). VULTURE selects upgrading configurations that maximize fuel blend bio-derived content. We validate VULTURE's approximations through surrogate fuel property testing and process simulation. Techno-economic and life cycle analyses suggest that VFA upgrading processes down-selected by VULTURE are profitable and have low carbon intensities, demonstrating the potential for the strategy to accelerate process development timelines at decreased costs.

09 BIOMASS FUELS↗
DOE OSTI2020

Graph-Directed Approach for Downselecting Toxins for Experimental Structure Determination

Conotoxins are short, cysteine-rich peptides of great interest as novel therapeutic leads and of great concern as lethal biological agents due to their high affinity and specificity for various receptors involved in neuromuscular transmission. Currently, of the approximately 6000 known conotoxin sequences, only about 3% have associated structural characterization, which leads to a bottleneck in rapid high-throughput screening (HTS) for identification of potential leads or threats. In this work, we combine a graph-based approach with homology modeling to expand the library of conotoxin structures and to identify those conotoxin sequences that are of the greatest value for experimental structural characterization. The latter would allow for the rapid expansion of the known structural space for generating high quality template-based models. Our approach generalizes to other evolutionarily-related, short, cysteine-rich venoms of interest. Overall, we present and validate an approach for venom structure modeling and experimental guidance and employ it to produce a 290%-larger library of approximate conotoxin structures for HTS. We also provide a set of ranked conotoxin sequences for experimental structure determination to further expand this library.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Creation of Polymer Datasets with Targeted Backbones for Screening of High-Performance Membranes for Gas Separation

A simple approach was developed to computationally construct a polymer dataset by combining simplified molecular-input line-entry system (SMILES) strings of a targeted polymer backbone and a variety of molecular fragments. This method was used to create 14 polymer datasets by combining seven polymer backbones and molecules from two large molecular datasets (MOSES and QM9). Polymer backbones that were studied include four polydimethylsiloxane (PDMS) based backbones, poly(ethylene oxide) (PEO), poly(allyl glycidyl ether) (PAGE), and polyphosphazene (PPZ). The generated polymer datasets can be used for various cheminformatics tasks, including high-throughput screening for gas permeability and selectivity. This study utilized machine learning (ML) models to screen the polymers for CO2/CH4 and CO2/N2 gas separation using membranes. Several polymers of interest were identified. Here the results highlight that employing an ML model fitted to polymer selectivities leads to higher accuracy in predicting polymer selectivity compared to using the ratio of predicted permeabilities.

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

Accurate Prediction of Algal Biomass Lipid, Protein, and Carbohydrate Composition with Machine Learning Regression Modelling of Near-IR Spectra

During large scale algal biomass cultivation, it is difficult to reliably control relative composition to target levels. Rapid determination of chemical composition is feasible by using near infrared (NIR) spectral data. We sought to build and improve on reliable high-throughput screening prediction method based on partial least squares regression (PLSR) by the application of artificial neural networks (ANN) and associated optimization strategies. The algal biomass sample set was designed and created in an iterative process of culturing in physiologically diverse conditions at the GAI field site, followed by compositional analyses at NREL. The workflow allowed us to identify gaps in compositional space for informing the subsequent cultivation and sampling efforts and generated a high quality set of 210 unique samples with chemical analysis results, spectral scanning data, and cultivation metadata. We observed a significant improvement in the performance of carbohydrate content predictions using an optimized ANN model compared to PLSR, with > 16% reduction in mean absolute percent error (MAPE) when tested on the same set of reserved data. The optimized ANN models for FAME and protein prediction performed exceptionally well with 5.99% and 5.09% MAPE, respectively. Application of these methods to detection and quantification of minor biomass constituents that are relevant to certain product streams has shown positive preliminary results, opening the possibility for extensions to the outputs of this powerful data type. All models are accompanied by prediction uncertainties and unsupervised spectral outlier detection to alert an operator to unreliable spectral data. These tools can be deployed for rapid determination of algal culture status, and cultivation and biomass quality improvement.

algal biofuels↗
DOE OSTI2022

Biosensor for branched-chain amino acid metabolism in yeast and applications in isobutanol and isopentanol production

Abstract Branched-chain amino acid (BCAA) metabolism fulfills numerous physiological roles and can be harnessed to produce valuable chemicals. However, the lack of eukaryotic biosensors specific for BCAA-derived products has limited the ability to develop high-throughput screens for strain engineering and metabolic studies. Here, we harness the transcriptional regulator Leu3p from Saccharomyces cerevisiae to develop a genetically encoded biosensor for BCAA metabolism. In one configuration, we use the biosensor to monitor yeast production of isobutanol, an alcohol derived from valine degradation. Small modifications allow us to redeploy Leu3p in another biosensor configuration that monitors production of the leucine-derived alcohol, isopentanol. These biosensor configurations are effective at isolating high-producing strains and identifying enzymes with enhanced activity from screens for branched-chain higher alcohol (BCHA) biosynthesis in mitochondria as well as cytosol. Furthermore, this biosensor has the potential to assist in metabolic studies involving BCAA pathways, and offers a blueprint to develop biosensors for other products derived from BCAA metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Human in vitro metabolism of an environmental mixture of polycyclic aromatic hydrocarbons (PAH) found at the Portland Harbor Superfund Site

Polycyclic aromatic hydrocarbons (PAHs) are widespread environmental contaminants that pose health risks to humans. Toxicity testing approaches of PAHs have evolved from traditional rodent models to New Approach Methodologies (NAMs), such as high-throughput screening in zebrafish, enabling rapid evaluation of chemical hazards. However, translating toxicity findings from laboratory systems to humans remains difficult due to complexity and species-specific differences. Chemical dosimetry modeling offers a quantitative framework to bridge this gap, but its accuracy depends on robust knowledge of PAH metabolism. The objective of this study was to measure human metabolism rates of Supermix-10, the ten most abundant PAHs found at the Portland Harbor Superfund Site, to support development of human pharmacokinetic models. We incubated individual PAHs from Supermix-10 in pooled human liver microsomes and quantified parent PAH disappearance using high-performance liquid chromatography (HPLC) with UV and florescent detection. To assess the potential of mixture interactions, we also measured metabolism of all 10 compounds in an equimolar mixture and compared rates of parent disappearance to those observed for individual PAHs. All Supermix-10 PAHs demonstrated rapid parent compound disappearance in human hepatic microsomes. PAHs grouped into three metabolism patterns: high metabolism rates and capacity (2-methylnaphthalene, acenaphthylene, fluorene, naphthalene), high affinity metabolism that rapidly achieves low-level saturation (benzo[a]anthracene, chrysene), and moderate metabolism rates and capacity (fluoranthene, pyrene, retene, phenanthrene). Smaller PAHs exhibited faster metabolism, and higher metabolism rates correlated inversely with molecular weight. When incubated in an equimolar mixture, Supermix-10 demonstrated significantly slower metabolism (47–89 %) compared to metabolism of individual PAHs at the same concentration. These findings enhance our understanding of PAH metabolism in humans and demonstrate significant mixture interactions under the conditions tested. Furthermore, our findings offer insights into the metabolic behavior of Supermix-10 and provide critical metabolism rate data to support the development of physiological based pharmacokinetic (PBPK) models. Dosimetry models can translate PAH chemical dosimetry from high-throughput testing platforms, like zebrafish and cellular system assays, to human exposures enhancing the accuracy and reliability of PAH risk assessments.

2-methylnaphthalene↗
DOE OSTI2023

A rapid assay for assessing bacterial effects on Arabidopsis thermotolerance

Abstract Background The role of beneficial microbes in mitigating plant abiotic stress has received considerable attention. However, the lack of a reproducible and relatively high-throughput screen for microbial contributions to plant thermotolerance has greatly limited progress in this area, this slows the discovery of novel beneficial isolates and the processes by which they operate. Results We designed a rapid phenotyping method to assess the effects of bacteria on plant host thermotolerance. After testing multiple growth conditions, a hydroponic system was selected and used to optimize an Arabidopsis heat shock regime and phenotypic evaluation. Arabidopsis seedlings germinated on a PTFE mesh disc were floated onto a 6-well plate containing liquid MS media, then subjected to heat shock at 45 °C for various duration. To characterize phenotype, plants were harvested after four days of recovery to measure chlorophyll content. The method was extended to include bacterial isolates and to quantify bacterial contributions to host plant thermotolerance. As an exemplar, the method was used to screen 25 strains of the plant growth promoting Variovorax spp. for enhanced plant thermotolerance. A follow-up study demonstrated the reproducibility of this assay and led to the discovery of a novel beneficial interaction. Conclusions This method enables rapid screening of individual bacterial strains for beneficial effects on host plant thermotolerance. The throughput and reproducibility of the system is ideal for testing many genetic variants of Arabidopsis and bacterial strains.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors

Here, density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, ρ (r). Approaches that directly predict ρ (r) of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, ρ (r) is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict ρ (r) for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline Ga 2 O 3 , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Diversifying hierarchical ionic assembly by docking cations to anions as salt bridges

Here, we expand the diversity of building blocks available for ionic assembly by introducing tertiary (3°) ammonium cations into anion complexes. We use proton transfer between 3° amines and organo-phosphoric acids to generate H-bonding cations (R 3 NH + ) and anions (RHPO 4 – ) that co-assemble with cyanostar macrocycles into assemblies with 2:2:2 stoichiometry. At the heart is a supramolecular dimer where phosphate anions form salt bridges by H-bonding with cations. Unlike conventional 4° ammonium cations, 3,000 commercial amines provide diversity for high-throughput screening of 72 combinations (9 nitrogen bases and 8 acids), producing 13 privileged partners for quantitative assembly. Yields depend on the solvent and sterics of salt bridge formation. Ten more nitrogen bases connect to fluorophores (pyrene), photocatalysts (quinoline), drugs (Cipralex, Zytiga), and ionic liquids (imidazole). The synthesis and examination of 82 new salts exemplify how acid-base chemistry can open a pipeline to a diversity of building blocks for exploring hierarchical ionic assembly.

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

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO 2 . Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. In conclusion, a strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

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

Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

The design of selective and active C–H activation catalysts for direct methane-to-methanol conversion is challenging. Bioinspired complexes that form high-valent metal–oxo intermediates capable of hydrogen abstraction and rebound hydroxylation are promising candidates. This promise has made them a target for computational high-throughput screening, typically simplified through the use of linear free energy relationships (LFERs). However, their mid-row transition-metal centers have numerous accessible spin and oxidation states that increase the combinatorial scale of design efforts. Here, we carry out a computational design screen of over 2500 mid-row 3d transition-metal complexes with four metals in numerous spin and oxidation states. We demonstrate the importance of spin/oxidation state in dictating design principles, limiting the generalization of strategies derived for widely studied high-spin Fe(II) catalysts to other metals or spin/oxidation states. Combined assessment of the effect of ligand-field tuning on reaction step energetics and on the identity of the ground state allows us to propose refined design strategies for spin-allowed methane-to-methanol catalysis. We observe weak coupling of energetics and design principles between reaction steps (e.g., oxo formation vs methanol release), meaning that LFERs do not generalize across our larger catalyst set. To rationalize relative reactivity in known catalysts, we instead compute independent reaction energies and propose strategies for further improvements in catalyst design.

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

Unraveling H2 chemisorption and physisorption on metal decorated graphene using quantum Monte Carlo

Molecular hydrogen has the potential to significantly reduce the use of carbon dioxide emitting energy processes. However, hydrogen gas storage is a major bottleneck for its large-scale use as current storage methods are energy intensive. Among different storage methods, physisorbing molecular hydrogen at ambient pressure and temperatures is a promising alternative—particularly in light of the advancements in tunable lightweight nanomaterials and high throughput screening methods. Nonetheless, understanding hydrogen adsorption in well-defined nanomaterials remains experimentally challenging and reference information is scarce despite the proliferation of works predicting hydrogen adsorption. We focus on Li, Na, Ca, and K, decorated graphene sheets as substrates for molecular hydrogen adsorption, and compute the most accurate adsorption energies available to date using quantum diffusion Monte Carlo (DMC). Building on our previous insights at the density functional theory (DFT) level, we find that a weak covalent chemisorption of molecular hydrogen, known as Kubas interaction, is feasible on Ca decorated graphene according to DMC, in agreement with DFT. This finding is in contrast to previous DMC predictions of the 4H2/Ca+ gas cluster (without graphene) where chemisorption is not favored. However, we find that the adsorption energy of hydrogen on metal decorated graphene according to a widely used DFT method is not fully consistent with DMC. The reference adsorption energies reported herein can be used to find better work-horse methods for application in large-scale modeling of hydrogen adsorption. Furthermore, the implications of this work affect strategies for finding suitable hydrogen storage materials and high-throughput methods.

Chemistry↗
DOE OSTI2020

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.

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

Prediction of ambient pressure superconductivity in cubic ternary hydrides with MH 6 octahedra

Exploring high-temperature superconducting (high-T c ) material at ambient pressure holds immense significance for physics, chemistry, and materials science. In this study, we perform a high-throughput screening of strong electron-phonon interactions in X 2 MH 6 compounds (X = Li, Na, Mg, Al, K, Ca, Ga, Rb, Sr, and In; M are 3d, 4d, and 5d transition metals). These compounds have a cubic structure featuring an MH 6 octahedron motif. Our screening calculations suggest that 26 compounds exhibit dynamic stability and strong electron-phonon coupling. Among them, Mg 2 RhH 6 , Mg 2 IrH 6 , Al 2 MnH 6 , and Li 2 CuH 6 show promising energetic stability and T c of more than 50 K at ambient pressure. This study underscores promising high-T c compounds at ambient pressure with distinctive MH 6 motifs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Design of Twisted Two-Dimensional Heterostructures and Performance Regulation Descriptor for Electrocatalytic Ammonia Production from Nitric Oxide

The electrocatalytic reduction of nitric oxide (NO) to ammonia (NH 3 ) represents an attractive alternative for valorizing waste NO streams (NORR). However, discovering efficient catalysts for NO-to-NH 3 conversion remains challenging. We have designed metal-intercalated twisted graphene-BN heterostructures, in which metal atoms act as electron-transfer bridges. The twisted configuration facilitates cross-interface charge transfer, redistributing electrons from the graphene–metal interface to the metal–BN interface and BN surface. This electronic modulation enables boron atom adjacent to the metal center in BN to serve as active sites, promoting strong chemisorption and enhanced activation of NO. After high-throughput screening of the stability and NO capture ability of various transition metal-intercalated twisted heterostructures, we have investigated systematically the NORR pathways across 30 candidates. The results show that the rBN-Ti-Gθ and rBN-V-Gθ heterostructures exhibit exceptional NO-to-NH 3 catalytic performance under optimized twisting conditions. Additionally, using sure independence screening and sparsifying operator (SISSO) for model training, we propose a descriptor and establish a relationship between the twist angle and catalytic activity. This study bridges the gap in applying twisted heterostructures to NORR electrocatalysis and provides new insights and strategies for designing high-performance NORR catalysts.

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

Gene co-expression network analysis in zebrafish reveals chemical class specific modules

Zebrafish is a popular animal model used for high-throughput screening of chemical hazards, however, investigations of transcriptomic mechanisms of toxicity are still needed. Here, our goal was to identify genes and biological pathways that Aryl Hydrocarbon Receptor 2 (AHR2) Activators and flame retardant chemicals (FRCs) alter in developing zebrafish. Taking advantage of a compendium of phenotypically-anchored RNA sequencing data collected from 48-h post fertilization (hpf) zebrafish, we inferred a co-expression network that grouped genes based on their transcriptional response. Genes responding to the FRCs and AHR2 Activators localized to distinct regions of the network, with FRCs inducing a broader response related to neurobehavior. AHR2 Activators centered in one region related to chemical stress responses. We also discovered several highly co-expressed genes in this module, including cyp1a, and we subsequently show that these genes are definitively within the AHR2 signaling pathway. Systematic removal of the two chemical types from the data, and analysis of network changes identified neurogenesis associated with FRCs, and regulation of vascular development associated with both chemical classes. We also identified highly connected genes responding specifically to each class that are potential biomarkers of exposure. Overall, we created the first zebrafish chemical-specific gene co-expression network illuminating how chemicals alter the transcriptome relative to each other. In addition to our conclusions regarding FRCs and AHR2 Activators, our network can be leveraged by other studies investigating chemical mechanisms of toxicity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Development of Masitinib Derivatives with Enhanced Mpro Ligand Efficiency and Reduced Cytotoxicity

Recently, a high-throughput screen of 1900 clinically used drugs identified masitinib, an orally bioavailable tyrosine kinase inhibitor, as a potential treatment for COVID-19. Masitinib acts as a broad-spectrum inhibitor for human coronaviruses, including SARS-CoV-2 and several of its variants. In this work, we rely on atomistic molecular dynamics simulations with advanced sampling methods to develop a deeper understanding of masitinib’s mechanism of M pro inhibition. To improve the inhibitory efficiency and to increase the ligand selectivity for the viral target, we determined the minimal portion of the molecule (fragment) that is responsible for most of the interactions that arise within the masitinib-Mpro complex. We found that masitinib forms highly stable and specific H-bond interactions with Mpro through its pyridine and aminothiazole rings. Importantly, the interaction with His 163 is a key anchoring point of the inhibitor, and its perturbation leads to ligand unbinding within nanoseconds. Based on these observations, a small library of rationally designed masitinib derivatives (M1–M5) was proposed. Our results show increased inhibitory efficiency and highly reduced cytotoxicity for the M3 and M4 derivatives compared to masitinib.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Machine learning for design principles for single atom catalysts towards electrochemical reactions

Machine learning (ML) integrated density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of heterogeneous catalysts such as single atom catalysts (SACs) through the establishment of deep structure–activity relationships. Here, this review provides recent progress in the ML-aided rational design of heterogeneous catalysts with the focus on SACs in terms of structure–activity relationships, feature importance analysis, high-throughput screening, stability, and metal–support interactions for electrochemistry. Support vector machine (SVM), random forest regression (RFR), and deep neural networks (DNN) along with atomic properties are mainly used for the design of SACs. The ML results have shown that the number of electrons in the d orbital, oxide formation enthalpy, ionization energy, Bader charge, d-band center, and enthalpy of vaporization are mainly the most important parameters for the defining of the structure–activity relationships for electrochemistry. However, the black-box nature of ML techniques occasionally makes a physical interpretation of descriptors, such as the Bader charge, d-band center, and enthalpy of vaporization, non-trivial. At the current stage, ML application is limited by the lack of a large and high-quality database. Future prospects for the development of a large database and a generalized ML algorithm for SAC design are discussed to give insights for further studies in this field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗