Data Base similarity (DBsimilarity) of natural products to aid compound identification on MS and NMR pipelines, similarity networking, and more.

INTRODUCTION: We developed Data Base similarity (DBsimilarity), a user-friendly tool designed to organize structure databases into similarity networks, with the goal of facilitating the visualization of information primarily for natural product chemists who may not have coding experience. METHOD: DBsimilarity, written in Jupyter Notebooks, converts Structure Data File (SDF) files into Comma-Separated Values (CSV) files, adds chemoinformatics data, constructs an MZMine custom database file and an NMRfilter candidate list of compounds for rapid dereplication of MS and 2D NMR data, calculates similarities between compounds, and constructs CSV files formatted into similarity networks for Cytoscape. RESULTS: The Lotus database was used as a source for Ginkgo biloba compounds, and DBsimilarity was used to create similarity networks including NPClassifier classification to indicate biosynthesis pathways. Subsequently, a database of validated antibiotics from natural products was combined with the G. biloba compounds to identify promising compounds. The presence of 11 compounds in both datasets points to possible antibiotic properties of G. biloba, and 122 compounds similar to these known antibiotics were highlighted. Next, DBsimilarity was used to filter the NPAtlas database (selecting only those with MIBiG reference) to identify potential antibacterial compounds using the ChEMBL database as a reference. It was possible to promptly identify five compounds found in both databases and 167 others worthy of further investigation. CONCLUSION: Chemical and biological properties are determined by molecular structures. DBsimilarity enables the creation of interactive similarity networks using Cytoscape. It is also in line with a recent review that highlights poor biological plausibility and unrealistic chromatographic behaviors as significant sources of errors in compound identification.

NMR as a tool for compound identification in mixtures.

INTRODUCTION: Natural products and metabolomics are intrinsically linked through efforts to analyze complex mixtures for compound annotation. Although most studies that aim for compound identification in mixtures use MS as the main analysis technique, NMR has complementary advances that are worth exploring for enhanced structural confidence. OBJECTIVE: This review aimed to showcase a portfolio of the main tools available for compound identification using NMR. MATERIALS AND METHODS: COLMAR, SMART-NMR, MADByTE, and NMRfilter are presented using examples collected from real samples from the perspective of a natural product chemist. Data are also made available through Zenodo so that readers can test each case presented here. CONCLUSION: The acquisition of 1 H NMR, HSQC, TOCSY, HSQC-TOCSY, and HMBC data for all samples and fractions from a natural products study is strongly suggested. The same is valid for MS analysis to create a bridged analysis between both techniques in a complementary manner. The use of NOAH supersequences has also been suggested and demonstrated to save NMR time.

Dimensionally reduced machine learning model for predicting single component octanol-water partition coefficients.

MF-LOGP, a new method for determining a single component octanol-water partition coefficients ([Formula: see text]) is presented which uses molecular formula as the only input. Octanol-water partition coefficients are useful in many applications, ranging from environmental fate and drug delivery. Currently, partition coefficients are either experimentally measured or predicted as a function of structural fragments, topological descriptors, or thermodynamic properties known or calculated from precise molecular structures. The MF-LOGP method presented here differs from classical methods as it does not require any structural information and uses molecular formula as the sole model input. MF-LOGP is therefore useful for situations in which the structure is unknown or where the use of a low dimensional, easily automatable, and computationally inexpensive calculations is required. MF-LOGP is a random forest algorithm that is trained and tested on 15,377 data points, using 10 features derived from the molecular formula to make [Formula: see text] predictions. Using an independent validation set of 2713 data points, MF-LOGP was found to have an average [Formula: see text] = 0.77 ± 0.007, [Formula: see text] = 0.52 ± 0.003, and [Formula: see text] = 0.83 ± 0.003. This performance fell within the spectrum of performances reported in the published literature for conventional higher dimensional models ([Formula: see text] = 0.42-1.54, [Formula: see text] = 0.09-1.07, and [Formula: see text] = 0.32-0.95). Compared with existing models, MF-LOGP requires a maximum of ten features and no structural information, thereby providing a practical and yet predictive tool. The development of MF-LOGP provides the groundwork for development of more physical prediction models leveraging big data analytical methods or complex multicomponent mixtures.

Data Fusion-based Discovery (DAFdiscovery) pipeline to aid compound annotation and bioactive compound discovery across diverse spectral data.

INTRODUCTION: Data Fusion-based Discovery (DAFdiscovery) is a pipeline designed to help users combine mass spectrometry (MS), nuclear magnetic resonance (NMR), and bioactivity data in a notebook-based application to accelerate annotation and discovery of bioactive compounds. It applies Statistical Total Correlation Spectroscopy (STOCSY) and Statistical HeteroSpectroscopy (SHY) calculation in their data using an easy-to-follow Jupyter Notebook. METHOD: Different case studies are presented for benchmarking, and the resultant outputs are shown to aid natural products identification and discovery. The goal is to encourage users to acquire MS and NMR data from their samples (in replicated samples and fractions when available) and to explore their variance to highlight MS features, NMR peaks, and bioactivity that might be correlated to accelerated bioactive compound discovery or for annotation-identification studies. RESULTS: Different applications were demonstrated using data from different research groups, and it was shown that DAFdiscovery reproduced their findings using a more straightforward method. CONCLUSION: DAFdiscovery has proven to be a simple-to-use method for different situations where data from different sources are required to be analyzed together.

Hydroxyapatite catalyzed hydrothermal liquefaction transforms food waste from an environmental liability to renewable fuel.

Food waste is an abundant and inexpensive resource for the production of renewable fuels. Biocrude yields obtained from hydrothermal liquefaction (HTL) of food waste can be boosted using hydroxyapatite (HAP) as an inexpensive and abundant catalyst. Combining HAP with an inexpensive homogeneous base increased biocrude yield from 14 ± 1 to 37 ± 3%, resulting in the recovery of 49 ± 2% of the energy contained in the food waste feed. Detailed product analysis revealed the importance of fatty-acid oligomerization during biocrude formation, highlighting the role of acid-base catalysts in promoting condensation reactions. Economic and environmental analysis found that the new technology has the potential to reduce US greenhouse gas emissions by 2.6% while producing renewable diesel with a minimum fuel selling price of $1.06/GGE. HAP can play a role in transforming food waste from a liability to a renewable fuel.

Chemical Composition of Leaves, Stem, and Roots of Peperomia pellucida (L.) Kunth.

Peperomia pellucida is a species known in the Amazon as "erva-de-jabuti" that has been used in several therapeutic applications based on folk medicine. Herein, we describe the classes, subclasses, and the main compounds of the leaves, stems, and roots from P. pellucida by ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry associated with molecular networks, mirror plot on the GNPS library, and machine learning. These data show compounds that were annotated for the first time in the Peperomia genus, such as 2',4',5'-trihydroxybutyrophenonevelutin, dehydroretrofractamide C, and retrofractamide B.

Enabling low power acoustics for capillary sonoreactors.

Capillary reactors demonstrate outstanding potential for on-demand flow chemistry applications. However, non-uniform distribution of multiphase flows, poor solid handling, and the risk of clogging limit their usability for continuous manufacturing. While ultrasonic irradiation has been traditionally applied to address some of these limitations, their acoustic efficiency, uniformity and scalability to larger reactor systems are often disregarded. In this work, high-speed microscopic imaging reveals how cavitation-free ultrasound can unclog and prevent the blockage of capillary reactors. Modeling techniques are then adapted from traditional acoustic designs and applied to simulate and prototype sonoreactors with wider and more uniform sonication areas. Blade-, block- and cylindrical shape sonotrodes are optimized to accommodate longer capillary lengths in sonoreactors resonating at 28 kHz. Finally, a novel helicoidal capillary sonoreactor is proposed to potentially deal with a high concentration of solid particles in miniaturized flow chemistry. The acoustic designs and first principle rationalization presented here offer a transformative step forward in the scale-up of efficient capillary sonoreactors.

Flow Toolkit for Measuring Gas Diffusivity in Liquids.

Precise knowledge of gas diffusivity in liquids is critical for describing complex multiphase reaction systems. Here we present a high-throughput flow concept to measure gas diffusivity in liquids. This strategy takes advantage of the tube-in-tube reactor design whereby semipermeable Teflon AF-2400 tubes facilitate fast mass transfer between gas and liquid without directly contacting the two fluids. Coupled pseudosteady-state flux balances over the gas and liquid describe the gas dissolution rate and corresponding diffusivity with the aid of a single gas flow meter and a continuously ramped liquid flow rate. This in situ method demonstrates excellent accuracy in diffusion coefficient measurements, with less than 5% deviation from established techniques.

Automated in Situ Measurement of Gas Solubility in Liquids with a Simple Tube-in-Tube Reactor.

Data on the solubilities of gases in liquids are foundational for assessing a variety of multiphase separations and gas-liquid reactions. Taking advantage of the tube-in-tube reactor design built with semipermeable Teflon AF-2400 tubes, liquids can be rapidly saturated without direct contacting of gas and liquid. The gas solubility can be determined by performing steady-state flux balances of both the gas and liquid flowing into the reactor system. Using this type of reactor, a fully automated strategy has been developed for the rapid in situ measurement of gas solubilities in liquids. The developed strategy enables precise gas solubility measurements within 2-5 min compared with 4-5 h using conventional methods. This technique can be extended to the discrete multipoint steady-state and continuous ramped-multipoint data acquisition methods. The accuracy of this method has been validated against several gas-liquid systems, showing less than 2% deviation from known values. Finally, this strategy has been extended to measure the temperature dependence of gas solubilities in situ and to estimate the local enthalpy of dissolution across a defined temperature range.

Design and Scaling Up of Microchemical Systems: A Review.

The past two decades have witnessed a rapid development of microreactors. A substantial number of reactions have been tested in microchemical systems, revealing the advantages of controlled residence time, enhanced transport efficiency, high product yield, and inherent safety. This review defines the microchemical system and describes its components and applications as well as the basic structures of micromixers. We focus on mixing, flow dynamics, and mass and heat transfer in microreactors along with three strategies for scaling up microreactors: parallel numbering-up, consecutive numbering-up, and scale-out. We also propose a possible methodology to design microchemical systems. Finally, we provide a summary and future prospects.

Spontaneous Aerosol Ejection: Origin of Inorganic Particles in Biomass Pyrolysis.

At high thermal flux and temperatures of approximately 500 °C, lignocellulosic biomass transforms to a reactive liquid intermediate before evaporating to condensable bio-oil for downstream upgrading to renewable fuels and chemicals. However, the existence of a fraction of nonvolatile compounds in condensed bio-oil diminishes the product quality and, in the case of inorganic materials, catalyzes undesirable aging reactions within bio-oil. In this study, ablative pyrolysis of crystalline cellulose was evaluated, with and without doped calcium, for the generation of inorganic-transporting aerosols by reactive boiling ejection from liquid intermediate cellulose. Aerosols were characterized by laser diffraction light scattering, inductively coupled plasma spectroscopy, and high-speed photography. Pyrolysis product fractionation revealed that approximately 3 % of the initial feed (both organic and inorganic) was transported to the gas phase as aerosols. Large bubble-to-aerosol size ratios and visualization of significant late-time ejections in the pyrolyzing cellulose suggest the formation of film bubbles in addition to the previously discovered jet formation mechanism.

Reactive Liftoff of Crystalline Cellulose Particles.

The condition of heat transfer to lignocellulosic biomass particles during thermal processing at high temperature (>400 °C) dramatically alters the yield and quality of renewable energy and fuels. In this work, crystalline cellulose particles were discovered to lift off heated surfaces by high speed photography similar to the Leidenfrost effect in hot, volatile liquids. Order of magnitude variation in heat transfer rates and cellulose particle lifetimes was observed as intermediate liquid cellulose droplets transitioned from low temperature wetting (500-600 °C) to fully de-wetted, skittering droplets on polished surfaces (>700 °C). Introduction of macroporosity to the heated surface was shown to completely inhibit the cellulose Leidenfrost effect, providing a tunable design parameter to control particle heat transfer rates in industrial biomass reactors.

Quantitative carbon detector (QCD) for calibration-free, high-resolution characterization of complex mixtures.

Current research of complex chemical systems, including biomass pyrolysis, petroleum refining, and wastewater remediation requires analysis of large analyte mixtures (>100 compounds). Quantification of each carbon-containing analyte by existing methods (flame ionization detection) requires extensive identification and calibration. In this work, we describe an integrated microreactor system called the Quantitative Carbon Detector (QCD) for use with current gas chromatography techniques for calibration-free quantitation of analyte mixtures. Combined heating, catalytic combustion, methanation and gas co-reactant mixing within a single modular reactor fully converts all analytes to methane (>99.9%) within a thermodynamic operable regime. Residence time distribution of the QCD reveals negligible loss in chromatographic resolution consistent with fine separation of complex mixtures including cellulose pyrolysis products.

Fast Pyrolysis of Wood for Biofuels: Spatiotemporally Resolved Diffuse Reflectance In†situ Spectroscopy of Particles.

Fast pyrolysis of woody biomass is a promising process capable of producing renewable transportation fuels to replace gasoline, diesel, and chemicals currently derived from nonrenewable sources. However, biomass pyrolysis is not yet economically viable and requires significant optimization before it can contribute to the existing oil-based transportation system. One method of optimization uses detailed kinetic models for predicting the products of biomass fast pyrolysis, which serve as the basis for the design of pyrolysis reactors capable of producing the highest value products. The goal of this work is to improve upon current pyrolysis models, usually derived from experiments with low heating rates and temperatures, by developing models that account for both transport and pyrolysis decomposition kinetics at high heating rates and high temperatures (>400 °C). A new experimental technique is proposed herein: spatiotemporally resolved diffuse reflectance in situ spectroscopy of particles (STR-DRiSP), which is capable of measuring biomass composition during fast pyrolysis with high spatial (10 µm) and temporal (1 ms) resolution. Compositional data were compared with a comprehensive 2D single-particle model, which incorporated a multistep, semiglobal reaction mechanism, prescribed particle shrinkage, and thermophysical properties that varied with temperature, composition, and orientation. The STR-DRiSP technique can be used to determine the transport-limited kinetic parameters of biomass decomposition for a wide variety of biomass feedstocks.

Enhanced molecular transport in hierarchical silicalite-1.

Fundamental understanding of the mass transport of petrochemical and biomass derived molecules in microporous and mesoporous solid catalysts is important for developing the next generation of heterogeneous catalysts for traditional hydrocarbon processing including biomass pyrolysis and upgrading. Hierarchical zeolites with both micropores and mesopores exhibit enhanced mass transport and unique catalytic performance in reactions involving large molecules. However, quantitative description of mass transport in such materials remains elusive, owing to the complicated structure of hierarchical pores and difficulty in the synthesis of the materials with controllable structures. In this work, zero length column chromatography (ZLC) was used to study temperature-dependent diffusion of cyclohexane in silicalite-1, self-pillared pentasil (SPP) zeolite, and three-dimensionally ordered mesoporous imprinted (3DOm-i) silicalite-1. The samples were synthesized with controllable characteristic diffusion lengths from micrometer scale (ca. 20 µm) to nanometer scale (ca. 2 nm), allowing systematic study of the effect of mesoporosity on the mass transport behavior of hierarchical zeolites. The results show that the introduction of mesoporosity can indeed significantly facilitate the mass transport of cyclohexane in hierarchical silicalite-1 by reducing diffusional time constants, indicating rapid overall adsorption and desorption. However, when the length scale of the material approaches several nanometers, the contribution from the surface resistance, or "surface barrier", to overall mass transfer becomes dominant.