Chemical Background Research Articles

A review of the key ingredients in industrial formulations of baby wet wipes.

The skin of newborns is classified as sensitive, with a higher risk of skin barrier disruption and irritation of a diapered area. Despite dermatologist recommendations to use only water and a cloth for cleaning, most of the population still relies on the comforts of modern parenting, which includes intensive daily usage of baby wet wipes. Novel baby formulations are designed following the concept of infant skin health, containing a gentle cleanser, suitable emollient, and buffer system enabling a slightly acidic pH value and they are free of ethyl alcohol. Thus, it is important to understand the chemical background of such a complex liquid formulation, with emphasis on its safety. In line with this, the present paper discusses the scientific background of various chemical compounds found in baby wipe formulations to improve the understanding of wet wipe designs and direct them toward more skin-friendly solutions.

A review on chemical mechanisms of kraft pulping

Abstract Kraft pulping of wood is based on efficient depolymerization and solubilization of lignin, while cellulose is relatively undamaged. Non-cellulose cell wall polysaccharides are however in some cases heavily degraded, especially pectin and to a lesser degree also glucomannan while, xylan is relatively stable. In this mini-review, the most important reactions in lignin and polysaccharide degradation in kraft pulping are described, both the technically favorable and the problematic reactions, and the chemical background to discuss the advantages and drawbacks of the process. An attempt to put the different reactions in the perspective of the goals of the pulping process is made and a special focus is on the development of color in the pulp fiber during the kraft pulping.

Matrix- and surface-assisted laser desorption/ionization-mass spectrometry analysis of fingermark components for forensic studies: current trends and future prospects.

The chemical analysis of fingermarks (FMs) has attracted considerable attention in the realm of forensic investigations. Techniques based on direct ionization of a sample by laser irradiation, specifically matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS), have provided excellent figures of merit for analyzing high molecular-weight compounds. However, it can be challenging to analyze low molecular-weight compounds using MALDI-MS owing to potential interference produced by the organic matrices in the low molecular-weight region, which can impede the detection of small molecules (m/z < 700Da). Alternately, surface-assisted laser desorption/ionization-mass spectrometry (SALDI-MS) has shown great promise for small molecules analysis owing to the unique properties of the nanostructures used, particularly, minimal chemical background in low m/z region improved theproduction of ions involved in this method. The advancement of MALDI-MS and SALDI-MS has propelled their application in the analysis of FM components, focused on gaining deep insights into individual traits. This review aims to outline the current role of MALDI-MS and SALDI-MS in the chemical analysis of FMs. It also describes the latest achievements in forensic intelligence derived from fingermark analysis using these powerful methods. The accomplishments include the understanding of certain characteristics and lifestyles of donors. The review offers a comprehensive overview of the challenges and demands in this field. It suggests potential enhancements in this rapidly expanding domain to bridge the gap between research and practical police casework.

Fast and Accurate Disulfide Bridge Detection

Recombinant expression of proteins, propelled by therapeutic antibodies, has evolved into a multibillion dollar industry. Essential here is the quality control assessment of critical attributes, such as sequence fidelity, proper folding, and posttranslational modifications. Errors can lead to diminished bioactivity and, in the context of therapeutic proteins, an elevated risk for immunogenicity. Over the years, many techniques were developed and applied to validate proteins in a standardized and high-throughput fashion. One parameter has, however, so far been challenging to assess. Disulfide bridges, covalent bonds linking two cysteine residues, assist in the correct folding and stability of proteins and thus have a major influence on their efficacy. Mass spectrometry promises to be an optimal technique to uncover them in a fast and accurate fashion. In this work, we present a unique combination of sample preparation, data acquisition, and analysis facilitating the rapid and accurate assessment of disulfide bridges in purified proteins. Through microwave-assisted acid hydrolysis, the proteins are digested rapidly and artifact-free into peptides, with a substantial degree of overlap over the sequence. The nonspecific nature of this procedure, however, introduces chemical background, which is efficiently removed by integrating ion mobility preceding the mass spectrometric measurement. The nonspecific nature of the digestion step additionally necessitates new developments in data analysis, for which we extended the XlinkX node in Proteome Discoverer to efficiently process the data and ensure correctness through effective false discovery rate correction. The entire workflow can be completed within 1 h, allowing for high-throughput, high-accuracy disulfide mapping.

Comparative transcriptomics illuminates the cellular responses of an aeroterrestrial zygnematophyte to UV radiation.

The zygnematophytes are the closest relatives of land plants and comprise several lineages that adapted to a life on land. Species of the genus Serritaenia form colorful, mucilaginous capsules, which surround the cells and block harmful solar radiation, one of the major terrestrial stressors. In eukaryotic algae, this "sunscreen mucilage" represents a unique photoprotective strategy, whose induction and chemical background are unknown. We generated a de novo transcriptome of Serritaenia testaceovaginata and studied its gene regulation under moderate ultraviolet radiation (UVR) that triggers sunscreen mucilage under experimental conditions. UVR induced the repair of DNA and the photosynthetic apparatus as well as the synthesis of aromatic specialized metabolites. Specifically, we observed pronounced expressional changes in the production of aromatic amino acids, phenylpropanoid biosynthesis genes, potential cross-membrane transporters of phenolics, and extracellular, oxidative enzymes. Interestingly, the most upregulated enzyme was a secreted class III peroxidase, whose embryophyte homologs are involved in apoplastic lignin formation. Overall, our findings reveal a conserved, plant-like UVR perception system (UVR8 and downstream factors) in zygnematophyte algae and point to a polyphenolic origin of Serritaenia's sunscreen pigment, whose synthesis might be extracellular and oxidative, resembling that of plant lignins.

Wash-in leptogenesis after the evaporation of primordial black holes

Wash-in leptogenesis is a powerful mechanism to generate the baryon asymmetry of the Universe that treats right-handed-neutrino interactions on the same footing as electroweak sphaleron processes: as mere spectator processes acting on the background of chemical potentials in the Standard Model plasma. Successful wash-in leptogenesis requires this chemical background to be CP-violating, which can be achieved by violating any of the more than ten global charges that are conserved in the Standard Model at very high temperatures. In this paper, we demonstrate that the primordial charge asymmetries required for wash-in leptogenesis can be readily produced by evaporating primordial black holes (PBHs). Our argument is based on the fact that the Hawking radiation emitted by PBHs contains more or less any state in the particle spectrum. Therefore, if heavy states with CP-violating decays are present in the ultraviolet, PBH evaporation will unavoidably lead to the production of these states. We illustrate this scenario by means of a simple toy model where PBH evaporation leads to the production of heavy particles that we call asymmetrons and whose decay results in a primordial charge asymmetry for right-handed electrons, which in turn sets the initial conditions for wash-in leptogenesis. We focus on the parameter region where the decay of the initial thermal asymmetron abundance occurs long before PBH evaporation and only results in a negligible primordial charge asymmetry. PBH evaporation at later times then serves as a mechanism to resurrect the asymmetron abundance and ensure the successful generation of the baryon asymmetry after all. We conclude that PBHs can act as asymmetry-producing machines that grant access to whatever CP-violating physics may be present in the ultraviolet, rekindling it at lower energies where it can be reprocessed into a baryon asymmetry by right-handed neutrinos.

Investigation and improvement of electrochemically activated peroxydisulfate systems for treating incineration leachate

The aim of this study was to develop processes to efficiently treat incineration leachate with complex pollutant compositions. The performance of the electrocoagulation-activated peroxydisulfate system with an iron anode (Fe-EC/PDS) and the electrooxidation-activated peroxydisulfate system with Fe2+ addition (EO/Fe2+/PDS) for treating actual low-concentration incineration leachate (LCIL) was compared. Under the optimal operating conditions determined through experiments, the Fe-EC/PDS system ([PDS]0 = 33.6 mM, current density = 13.07 mA/cm2, natural pH0 of LCIL (≈ 7), and reaction time = 60 min) could achieve 50.26% CODcr removal, which was higher than the value of 48.73% achieved in the EO/Fe2+/PDS system ([PDS]0 = 50.4 mM, [Fe2+]0 = 33.6 mM, current intensity = 26.14 mA/cm2, natural pH0 of LCIL, and reaction time = 120 min), and the reaction rate of the former was more than twice that of the latter. Furthermore, detailed analysis revealed that these two systems demonstrated different treatment capabilities for different types of organic contaminants, although both produced the reactive species SO4∙− and HO·. To achieve high efficiency in treating incineration leachate, hybrid processes that combined the medium-pressure ultraviolet activated peroxydisulfate system (MPUV/PDS) with Fe-EC/PDS and EO/Fe2+/PDS, respectively, were constructed for the first time. The hybrid processes exhibited good pollutant treatment performance, reducing CODcr in the high-concentration incineration leachate (HCIL) from 1401.18 mg/L to 88.58 mg/L within 200 min. Moreover, it was found that the use of MPUV/PDS for pretreatment could result in higher CODcr removal in the Fe-EC/PDS and EO/Fe2+/PDS systems (18.33% and 37.19% increase, respectively) with lower specific energy consumption (SEC) by mitigating the interference from leachate chemical background.

A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx

Abstract. Satellites monitoring air pollutants (e.g., nitrogen oxides; NOx = NO + NO2) or greenhouse gases (GHGs) are widely utilized to understand the spatiotemporal variability in and evolution of emission characteristics, chemical transformations, and atmospheric transport over anthropogenic hotspots. Recently, the joint use of space-based long-lived GHGs (e.g., carbon dioxide; CO2) and short-lived pollutants has made it possible to improve our understanding of emission characteristics. Some previous studies, however, lack consideration of the non-linear NOx chemistry or complex atmospheric transport. Considering the increase in satellite data volume and the demand for emission monitoring at higher spatiotemporal scales, it is crucial to construct a local-scale emission optimization system that can handle both long-lived GHGs and short-lived pollutants in a coupled and effective manner. This need motivates us to develop a Lagrangian chemical transport model that accounts for NOx chemistry and fine-scale atmospheric transport (STILT–NOx) and to investigate how physical and chemical processes, anthropogenic emissions, and background may affect the interpretation of tropospheric NO2 columns (tNO2). Interpreting emission signals from tNO2 commonly involves either an efficient statistical model or a sophisticated chemical transport model. To balance computational expenses and chemical complexity, we describe a simplified representation of the NOx chemistry that bypasses an explicit solution of individual chemical reactions while preserving the essential non-linearity that links NOx emissions to its concentrations. This NOx chemical parameterization is then incorporated into an existing Lagrangian modeling framework that is widely applied in the GHG community. We further quantify uncertainties associated with the wind field and chemical parameterization and evaluate modeled columns against retrieved columns from the TROPOspheric Monitoring Instrument (TROPOMI v2.1). Specifically, simulations with alternative model configurations of emissions, meteorology, chemistry, and inter-parcel mixing are carried out over three United States (US) power plants and two urban areas across seasons. Using the U.S. Environmental Protection Agency (EPA)-reported emissions for power plants with non-linear NOx chemistry improves the model–data alignment in tNO2 (a high bias of ≤ 10 % on an annual basis), compared to simulations using either the Emissions Database for Global Atmospheric Research (EDGAR) model or without chemistry (bias approaching 100 %). The largest model–data mismatches are associated with substantial biases in wind directions or conditions of slower atmospheric mixing and photochemistry. More importantly, our model development illustrates (1) how NOx chemistry affects the relationship between NOx and CO2 in terms of the spatial and seasonal variability and (2) how assimilating tNO2 can quantify systematic biases in modeled wind directions and emission distribution in prior inventories of NOx and CO2, which laid a foundation for a local-scale multi-tracer emission optimization system.

An artificial intelligence course for chemical engineers

Artificial intelligence and machine learning are revolutionising fields of science and engineering. In recent years, process engineering has widely benefited from this novel modelling and optimisation approach. The open literature can offer several examples of their applications to chemical engineering problems. Increasing investments are devoted to these techniques from different industrial areas, but insufficient information on a structured course covering these topics in a chemical engineering curriculum could be found. The course in this paper intends to reduce this gap. We introduce one of the first courses on artificial intelligence applications in a chemical engineering curriculum. The course targets Master's students with a chemical engineering background and insufficient knowledge of statistical approaches. It covers the main aspects by utilising frontal lectures and hands-on exercises with active learning methods. This paper shows the methodology we adapted to introduce students to machine learning techniques and how they responded to each class. The student performances for each test are shown, as well as the survey results based on student feedback and suggestions. This work contains essential guidelines for educators who will provide an artificial intelligence course in a chemical engineering curriculum.

An improved multi-modal representation-learning model based on fusion networks for property prediction in drug discovery

Accurate characterization of molecular representations plays an important role in the property prediction based on deep learning (DL) for drug discovery. However, most previous researches considered only one type of molecular representations, resulting in that it difficult to capture the full molecular feature information. In this study, a novel DL framework called multi-modal molecular representation learning fusion network (MMRLFN) is developed, which could simultaneously learn and integrate drug molecular features from molecular graphs and SMILES sequences. The developed MMRLFN method is composed of three complementary deep neural networks to learn various features from different molecular representations, such as molecular topology, local chemical background information, and substructures at varying scales. Eight public datasets involving various molecular properties used in drug discovery were employed to train and evaluate the developed MMRLFN. The obtained models showed better performances than the existing models based on mono-modal molecular representations. Additionally, a thorough analysis of the noise resistance and interpretability of the MMRLFN has been carried out. The generalization ability and effectiveness of the MMRLFN has been verified by case studies as well. Overall, the MMRLFN can accurately predict molecular properties and provide potentially valuable information from large datasets, thereby maximizing the possibility of successful drug discovery.

Modern AAC additive—A ready tool helping with decarbonization of AAC production by reduction of water, cement, lime and energy consumption

AbstractEuropean Association of AAC producers (EAACA) has set the goal for the AAC industry to become carbon‐neutral by 2050, so it is now more important than ever to explore ways to achieve it. One of the readily available solutions is a dedicated additive for AAC production, production, the effects of which positively affect the reduction of CO2e emissions. The additive is a tool helping the chemical reaction and offers improvements in two key emission sources for AAC. The first one is cement and lime usage, responsible for 74% of CO2e emissions in AAC production. A few percent cutbacks of binders consumption generate direct results in this area. The second one is the production process itself, being accountable for 13% of CO2e emissions. Lowering water content in the mix directly results in a lower requirement for steam during autoclaving, therefore impacting energy usage. Reductions in these two areas can be calculated in terms of CO2e emissions, as well as economical savings. The financial advantages generated by the additive are always higher than the cost of it. The chemical and process background presented in this paper is supported with examples from AAC plants worldwide, and complemented by GHG methodology calculation where 3,6% of CO2e emissions reductions can be achieved solely by using modern additive for production.

Robust Design of a Dimethyl Ether Production Process Using Process Simulation and Robust Bayesian Optimization.

As greenhouse gases such as CO2 continue to promote global warming, the reduction of CO2 emissions is attracting increasing attention. In this study, we design a process for producing dimethyl ether (DME), which is a promising means of using CO2 as a resource. Design variables such as temperature and pressure need to be optimized to reduce CO2 emissions while maintaining high product purity and DME production. Conventional process designs determine these design variables from the chemical background and through trial-and-error simulations, which are very time-consuming. The proposed method optimizes the design variables efficiently by repeating the process simulations and selecting promising candidates for the design variables using machine learning. For an adaptive design of experiments, Bayesian optimization is used to achieve the objectives of the DME process while efficiently optimizing the design variables. In addition, we also optimize the design variables considering variations in the temperature and pressure data, meaning robust Bayesian optimization. The proposed method successfully identifies design variables that satisfy all experimental targets in an average of 54 simulations while achieving 100% of the targets with product purity 0.95-1.00, amount of DME in the product 350-845 kmol/h, and CO2 emissions 0-835 kmol/h, confirming the effectiveness of the proposed robust Bayesian optimization method.

First-Order Individual Gas Sensors as Next Generation Reliable Analytical Instruments.

It is conventionally expected that the performance of existing gas sensors may degrade in the field compared to laboratory conditions because (i) a sensor may lose its accuracy in the presence of chemical interferences and (ii) variations of ambient conditions over time may induce sensor-response fluctuations (i.e., drift). Breaking this status quo in poor sensor performance requires understanding the origins of design principles of existing sensors and bringing new principles to sensor designs. Existing gas sensors are single-output (e.g., resistance, electrical current, light intensity, etc.) sensors, also known as zero-order sensors (Karl Booksh and Bruce R. Kowalski, Analytical Chemistry, DOI: 10.1021/ac00087a718). Any zero-order sensor is undesirably affected by variable chemical background and sensor drift that cannot be distinguished from the response to an analyte. To address these limitations, we are developing multivariable gas sensors with independent responses, which are first-order analytical instruments. Here, we demonstrate self-correction against drift in two types of first-order gas sensors that operate in different portions of the electromagnetic spectrum. Our radiofrequency sensors utilize dielectric excitation of semiconducting metal oxide materials on the shoulder of their dielectric relaxation peak and achieve self-correction of the baseline drift by operation at several frequencies. Our photonic sensors utilize nanostructured sensing materials inspired by Morpho butterflies and achieve self-correction of the baseline drift by operation at several wavelengths. These principles of self-correction for drift effects in first-order sensors open opportunities for diverse emerging monitoring applications that cannot afford frequent periodic maintenance that is typical of traditional analytical instruments.

Detailed Comparison of Xenon APPI (9.6/8.4 eV), Krypton APPI (10.6/10.0 eV), APCI, and APLI (266 nm) for Gas Chromatography High Resolution Mass Spectrometry of Standards and Complex Mixtures.

Photoionization schemes for mass spectrometry, either by laser or discharge lamps, have been widely examined and deployed. In this work, the ionization characteristics of a xenon discharge lamp (Xe-APPI, 9.6/8.4 eV) have been studied and compared to established ionization schemes, such as atmospheric pressure chemical ionization, atmospheric pressure photoionization with a krypton discharge lamp (Kr-APPI, 10.6/10 eV) and atmospheric pressure laser ionization (266 nm). Addressing the gas-phase ionization behavior has been realized by gas chromatography coupled to high-resolution mass spectrometry without the usage of a dopant. For standard substances, it has been found that Xe-APPI is able to ionize a broad range of polycyclic aromatic hydrocarbons as well as their heteroatom-containing and alkylated derivatives. However, thiol and ester compounds could not be detected. Moreover, Xe-APPI revealed a high tendency to generate oxygenated artifacts, most likely due to a VUV absorption band of oxygen at 148 nm. Beneficially, almost no chemical background, commonly caused by APCI or Kr-APPI due to column blood, plasticizers or impurities, is observed. This advantage is noteworthy for evolved gas analysis without preseparation or for chromatographic coelution. For the complex mixtures, Xe-APPI revealed the predominant generation of radical cations via direct photoionization with a high selectivity toward aromatic core structures with low alkylation. Interestingly, both Xe-APPI and Kr-APPI could sensitively detect sterane cycloalkanes, validated by gas chromatographic retention. The narrowly ionized chemical space could let Xe-APPI find niche applications, e.g., for strongly contaminated samples to reduce the background.

Quantification of pharmaceutical compounds in tissue and plasma samples using selective ion accumulation with multiple mass isolation windows.

Quantification of pharmaceutical compounds using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is an alternative to traditional liquid chromatography (LC)-MS techniques. Benefits of MALDI-based approaches include rapid analysis times for liquid samples and imaging mass spectrometry capabilities for tissue samples. As in most quantification experiments, the use of internal standards can compensate for spot-to-spot and shot-to-shot variability associated with MALDI sampling. However, the lack of chromatographic separation in traditional MALDI analyses results in diminished peak capacity due to the chemical noise background, which can be detrimental to the dynamic range and limit of detection of these approaches. These issues can be mitigated by using a hybrid mass spectrometer equipped with a quadrupole mass filter (QMF) that can be used to fractionate ions based on their mass-to-charge ratios. When the masses of the analytes and internal standards are sufficiently disparate in mass, it can be beneficial to effect multiple narrow mass isolation windows using the QMF, as opposed to a single wide mass isolation window, to minimize chemical noise while allowing for internal standard normalization. Herein, we demonstrate a MALDI MS quantification workflow incorporating multiple sequential mass isolation windows enabled on a QMF, which divides the total number of MALDI laser shots into multiple segments (i.e., one segment for each mass isolation window). This approach is illustrated through the quantitative analysis of the pharmaceutical compound enalapril in human plasma samples as well as the simultaneous quantification of three pharmaceutical compounds (enalapril, ramipril, and verapamil). Results show a decrease in the limit of detection, relative standard deviations below 10%, and accuracy above 85% for drug quantification using multiple mass isolation windows. This approach has also been applied to the quantification of enalapril in brain tissue from a rat dosed in vitro. The average concentration of enalapril determined by imaging mass spectrometry is in agreement with the concentration determined by LC-MS, giving an accuracy of 104%.

Simplified Liquid Chromatography–Mass Spectrometry Methods for Gestagen Analysis in Animal Fat and Liver

Gestagens, a class of veterinary drugs also called progestogens, are synthetic hormones used to increase feed efficiency and rate of gain in heifers. The Canadian Food Inspection Agency analyzes progestogens melengestrol acetate (MGA), megestrol acetate, and chlormadinone acetate using liquid chromatography–mass spectrometry (LC–MS). Our conventional gestagen method for kidney fat has many time-consuming steps, including solid-phase extraction. A sample preparation procedure having fewer clean-up steps was developed for routine diagnostic analysis of kidney fat and provided similar results faster, and at lower cost. A confirmatory liver method for gestagens, developed using salt-assisted extraction, employed minimal clean-up steps that resulted in high chemical background at the desired lower limit of quantification (LLOQ). Differential ion mobility spectrometry, specifically high-field asymmetric waveform ion mobility spectrometry (FAIMS), was used to filter chemical background in the gas phase. The effect of the ionization probe position on FAIMS parameters, including sensitivity, is described. With LC-FAIMS-MS, chemical background for each gestagen was virtually eliminated, resulting in a quantitative liver method having the desired 0.6 ng/g LLOQ and estimated limits of detection (LODs) up to 140 times lower than LC-MS. Incurred MGA samples, analyzed using kidney fat and liver methods from the same animal, show levels within the quantitative ranges of both methods.

Lipase-catalysed changes in essential oils revealed by comprehensive two-dimensional gas chromatography

Candida antarctica lipase A (CALA) was applied for the chemo-selective enzymatic transesterification of terpene and phenyl alcohols in 35 different essential oil samples. Comprehensive two-dimensional gas chromatography with mass spectrometry (GC×GC‒MS) analysis enabled the separation and tentative identification of a cohort of 125 compounds, allowing the instant visualisation of the reaction process changes, amid the complex chemical background of the samples. The results indicate that 42 out of 79 alcohols so-identified were fully or partially esterified within 48 h of reaction, with primary alcohols being the substrates of preference of the enzyme (90–100% conversion), followed by secondary alcohols (mostly ~ 80–100% conversion). No significant conversion of tertiary alcohols and phenols was observed using the tested conditions. Overall, the enzyme’s performance was consistent for primary alcohol substrates identified in multiple samples of different compositions. The observed selectivity, efficiency, robustness, scalability (enzyme/substrate working concentration ratio > 1:160), potential reusability, mild reaction conditions, and other factors make this process a greener and more sustainable alternative for industry applications, particularly for the manufacture of novel flavours and fragrances.Graphical

“Design a Sensor”: Implementation of Entrepreneurial-Minded Learning in Undergraduate General Chemistry

This article describes the design and implementation of a sensors-based entrepreneurial-minded learning (EML) module in a large (>150 students) first-year General Chemistry II course. Students worked in groups create a novel sensor that solves an existing scientific problem or improves upon an existing sensor. Students proposed and explained the chemical background of their sensors as well as presented their final products to the class in the style of a brief elevator pitch. This module necessitates that students think innovatively, integrate knowledge from varied sources, and collaborate with their peers. The aim of implementing this EML module is to spur creative thinking, innovation, and entrepreneurial learning among first-year undergraduate chemistry students. We conducted course surveys to gauge students’ reception of this module and found that the majority of students enjoyed this activity and learned valuable skills from their experience. Moving forward, we advocate that these and similar learning modules are implemented in large, introductory course settings due to the many benefits that they offer to students.

Intensity of sample processing methods impacts wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes

Wastewater SARS-CoV-2 surveillance has been deployed since the beginning of the COVID-19 pandemic to monitor the dynamics in virus burden in local communities. Genomic surveillance of SARS-CoV-2 in wastewater, particularly efforts aimed at whole genome sequencing for variant tracking and identification, are still challenging due to low target concentration, complex microbial and chemical background, and lack of robust nucleic acid recovery experimental procedures. The intrinsic sample limitations are inherent to wastewater and are thus unavoidable. Here, we use a statistical approach that couples correlation analyses to a random forest-based machine learning algorithm to evaluate potentially important factors associated with wastewater SARS-CoV-2 whole genome amplicon sequencing outcomes, with a specific focus on the breadth of genome coverage. We collected 182 composite and grab wastewater samples from the Chicago area between November 2020 to October 2021. Samples were processed using a mixture of processing methods reflecting different homogenization intensities (HA + Zymo beads, HA + glass beads, and Nanotrap), and were sequenced using one of the two library preparation kits (the Illumina COVIDseq kit and the QIAseq DIRECT kit). Technical factors evaluated using statistical and machine learning approaches include sample types, certain sample intrinsic features, and processing and sequencing methods. The results suggested that sample processing methods could be a predominant factor affecting sequencing outcomes, and library preparation kits was considered a minor factor. A synthetic SARS-CoV-2 RNA spike-in experiment was performed to validate the impact from processing methods and suggested that the intensity of the processing methods could lead to different RNA fragmentation patterns, which could also explain the observed inconsistency between qPCR quantification and sequencing outcomes. Overall, extra attention should be paid to wastewater sample processing (i.e., concentration and homogenization) for sufficient and good quality SARS-CoV-2 RNA for downstream sequencing.

Experimental Determination of a Chiral Ternary Solubility Diagram and Its Interpretation in Gas Antisolvent Fractionation

Although crystallization has been widely applied for the enantiomeric enrichment of non-racemates both in research and in industrial applications, the physical-chemical background of chiral crystallizations is not as frequently discussed. A guide for the experimental determination of such phase equilibrium information is lacking. In the current paper, the experimental investigation of chiral melting phase equilibria, chiral solubility phase diagrams and their application in atmospheric and supercritical carbon dioxide-assisted enantiomeric enrichment is described and compared. Benzylammonium mandelate is a racemic compound; it shows eutectic behavior when molten. A similar eutonic composition was observed in its methanol phase diagram at 1 °C. The influence of the ternary solubility plot could be unequivocally discovered in atmospheric recrystallization experiments, which proved that the crystalline solid phase and the liquid phase were in an equilibrium. The interpretation of the results obtained at 20 MPa and 40 °C, using the methanol-carbon dioxide mixture as a pseudo-component, was more challenging. Although the eutonic composition was found to be the limiting enantiomeric excess value in this purification process as well, the high-pressure gas antisolvent fractionation results were only clearly thermodynamically controlled in certain concentration ranges.