Chemical Species Research Articles

Review: Perovskite Nanostructures Materials Versatile Platform for Advance Biosensor Applications

The distinct presence of a central atom surrounded by eight ligands leads to higher light absorption and charge carrier mobility in perovskite materials. The peculiar nature of the structure inspires all the scientists and researchers to work more in sustainable applications, such as solar cells, light emitting diodes, transistor and biosensors. The capability of perovskite material in detecting smaller molecules such as O2, NO2 and CO2 is higher. Therefore, several biosensors are demonstrated based on the perovskite nanomaterial to various chemical and biological species with both solid and solution states. The immense sources of research articles thrived the author, to review the perovskite nanomaterials in the dimension of biosensor application extensively. This review covers major three areas of perovskite nanomaterial, such as components and characteristics of biosensors, properties and preparation of perovskite materials and application and research trends of perovskite nanostructure biosensor.

Comparisons of Different Representative Species Selection Schemes for Reduced-Order Modeling and Chemistry Acceleration of Complex Hydrocarbon Fuels

The simulation of engine combustion processes, such as autoignition, an important process in the co-optimization of fuel-engine design, can be computationally expensive due to the large number of thermo-chemical scalars needed to describe the full chemical system. Yet, the inherent correlations between the different chemical species during oxidation can significantly reduce the complexity of representing this system. One strategy is to select a subset of representative species that accurately captures the combustion process at a fraction of the computational cost of the full system. In this study, we compare the performance of four different techniques to select these species. They include the two-step principal component analysis (PCA) approach, directed relation graphs (DRGs), the global pathway selection (GPS) approach, and the manifold-informed species selection method. A parametric study of the representative species selection is carried out on data from the simulation of homogeneous and perfectly stirred reactors by investigating seven cumulative variances and 47 different cut-off percentages for the two-step PCA, and 65 and 51 thresholds for the DRGs and GPS, respectively. Results show that these selection methods capture key important species that can accurately describe the chemical system and track each stage of oxidation. The two-step PCA is sensitive to the cumulative variance, and DRGs and GPS are sensitive to the choice of target variables. By selecting key representative species and reducing the number of thermo-chemical scalars, these three methods can be used to develop computationally efficient hybrid chemistry schemes.

An extended Monte Carlo simulation code for modeling gas chromatography experiments with superheavy elements and their homologs

AbstractMonte Carlo simulations are commonly used to model the behavior of chemical species of the heaviest elements and their homologs in gas chromatography experiments. In this paper, we present an extension of the fundamental Monte Carlo simulation proposed by Zvara in 1985. While preserving the core functionality, our code features two enhancements: first, it allows simulating experiments in which a primary radioisotope decays into a daughter isotope belonging to a different element, hence exhibiting different chemical properties. Second, it allows modeling scenarios where conversion of an initial chemical species to a different one can occur at temperatures high enough to overcome an activation barrier, facilitating simulations of related physisorption and chemisorption processes. This Monte Carlo code is applicable to open tubular and rectangular chromatography columns.

Comparison of dissolved iodine measurements in seawater between inductively coupled plasma mass spectrometry and voltammetry.

Two analytical methods, inductively coupled plasma mass spectrometry (ICP-MS) combined with high-performance liquid chromatography (HPLC) and voltammetry (VM), for three chemical species of dissolved iodine (iodide, iodate, and total dissolved iodine: TDI) were compared for dozens of coastal seawater samples owing to the compatibility of data between both methods. The median differences in the measured concentrations of TDI, total inorganic dissolved iodine (TII, the sum of iodide and iodate), and iodate between ICP-MS and VM were equivalent to 9.2, 13, and 14%, respectively. These differences were within the ranges that could be explained by the repeated-measurement precision of each measurement method for TDI, TII, and iodate. The difference for iodide was 19%, which was larger than the value based on the repeated-measurement precision for both methods. This is considered to be caused by the chemical instability and lower concentrations of iodide compared to other iodine species in seawater, in addition to the heterogeneity of natural samples. Finally, both methods provided reasonable measurement values for the iodine concentration in natural seawater samples.

Enhancing the capacitance and robustness of graphene supercapacitors by adding a coating of activated-carbon/Bi2Al4O9 on their electrodes

Recently, using activated carbon (AC) for supercapacitors (SCs) has attracted attention because such carbon can be easily derived from bio-waste. However, the capacitance of the AC based SCs is in general low. For this reason, we present in this research the synthesis of AC from aquatic water-lily plant, which is considered a pest. First, SCs were fabricated only with graphene electrodes and produced a capacitance (Cs)/Energy-density (E) of 150.3 F g-1/30.1 Wh kg-1. Later, a coating of AC was deposited on the graphene electrodes and now the devices had higher Cs and E of 446.7 F g-1 and 89.4 Wh kg-1, respectively. The Cs/E of the devices was enhanced even more to 641.9 F g-1/128.3 Wh kg-1 after depositing a composite powder of AC/Bi2Al4O9 (BiAl) on the SC electrodes. Thus, the capacitance increased by 197% and 327% after introducing AC and AC/BiAl to the SCs. Other benefits of introducing the AC/BiAl composite to the electrodes were: i) the output voltage was enhanced 40% with respect to the device made with only AC; ii) the capacitance retention for 1000 bending cycles was maintained over 95%, this means that adding the AC/BiAl to the SC electrodes increased the mechanical resistance/stability of the devices; iii) the SCs produced a constant operating voltage of 0.33 V when they immersed in motor oil, which is not commonly reported in the literature; and iv) the SCs maintained a voltage of 0.37 when they are subjected to bending in real time. If no BiAl is added to the SC electrodes, the output voltage is lower (0.15 V). On the other hand, Raman and XPS spectroscopies demonstrated that the devices made with AC/BiAl had on their electrodes oxygen vacancies and chemical species such as: Bi5+/Bi3+/Bi0 and Al3+/Al0, which worked as redox centers for the charge storage. In general, this work demonstrated that it is possible the fabrication of AC-based SCs with capacitances that surpass the values already reported in literature for devices made with AC (50–150 F g-1). Also, we confirmed that adding BiAl to the system increased the robustness (mechanical/electrochemical stability) of the devices, which is of interest for the flexible electronics.

One-Dimensional Magnetic Conduction Channels across Zigzag Graphene Nanoribbon/Hexagonal Boron Nitride Heterojunctions.

We examine the electronic structure of recently fabricated in-plane heterojunctions of zigzag graphene nanoribbons embedded in hexagonal boron nitride. We focus on hitherto unexplored interface configurations in which both edges of the nanoribbon are bonded to the same chemical species, either boron or nitrogen atoms. Using ab initio and mean-field Hubbard model calculations, we reveal the emergence of one-dimensional magnetic conducting channels at these interfaces. These channels originate from the energy shift of the magnetic interface states that is induced by charge transfer between the nanoribbon and hexagonal boron nitride. We further address the response of these heterojunctions to external electric and magnetic fields, demonstrating the tunability of energy and spin splittings in the electronic structure. Our findings establish that zigzag graphene nanoribbon/hexagonal boron nitride heterojunctions are a suitable platform for exploring and engineering spin transport in the atomically thin limit, with potential applications in integrated spintronic devices.

Association of ischemic placental disease in a Southern California birth cohort and PM2.5 chemical species and oxidative potential markers

Abstract Road traffic is a significant source of particulate matter pollution, whose exposure is a significant risk factor in pregnancy-related health outcomes. The exact mechanisms behind the relationship between traffic-related air pollution (TRAP) exposure and adverse pregnancy outcomes remain unclear. We aim to assess the relationship between exposure to brake and tire wear-associated metals and oxidative potential and ischemic placental disease (IPD). Data were assembled from a population of women who sought specialized prenatal care at UCLA between 2016 and 2019 in Los Angeles, CA. Modeled first trimester exposures to chemical constituents and oxidative stress potential of PM2.5, black carbon, and PM2.5 mass concentration. Speciated measurements included tracers of brake wear (barium), tire wear (zinc), and oxidative potential markers based on metal concentrations (KM-SUB-ELF ROS) or laboratory assays (DTT loss, OH radical formation). Exposures were modeled by integrating data from filter samples, a low-cost PM2.5 sensor network, and land-use data. We used logistic regression to estimate the associations between air pollution exposures and IPD, adjusting for covariates assessed through medical records and interviews. Scaled to the interquartile range, odds ratios (95\% CI) were as follows: barium OR: 1.7 (1.1, 2.7), zinc OR: 1.4 (.86, 2.4), and oxidative potential markers, both modeled as well as measured through DTT loss and OH formation assays (ORs ranging from 1.1-2.0). Effect sizes for PM2.5 and black carbon were lower than most measurements (ORs: 1.3-1.4). We observe higher effect sizes with Ba and oxidative potential markers compared to traditional measurements such as PM2.5 mass and black carbon. Our findings suggest two key points: (i) metals associated with brake and tire wear, currently unregulated, may play a role in the relationship between TRAP and adverse pregnancy outcomes, and (ii) reducing tailpipe emissions may not be sufficient to protect pregnant women from TRAP.

Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran

Abstract. Tropospheric ozone time series consist of the effects of various scales of motion, from meso-scales to large timescales, which are often challenging for global models to capture. This study uses two global datasets, namely the reanalysis and the daily forecast of the Copernicus Atmosphere Monitoring Service (CAMS), to assess the capability of these products in presenting ozone's features on regional scales. We obtained 16 relevant meteorological and several pollutant species, such as O3, CO, NOx, etc., from CAMS. Furthermore, we employed a comprehensive set of in situ measurements of ozone at 27 urban stations in Iran for the year 2020. We decomposed the time series into three spectral components, i.e., short (S), medium (M), and long (L) terms. To cope with the scaling issue between the measured data and the CAMS' products, we developed a downscaling approach based on a long short-term memory (LSTM) neural network method which, apart from modeled ozone, also assimilated meteorological quantities as well as lagged O3 observations. Results show the benefit of applying the LSTM method instead of using the original CAMS products for providing O3 over Iran. It is found that lagged O3 observation has a larger contribution than other predictors in improving the LSTM. Compared with the S, the M component shows more associations with observations, e.g., correlation coefficients larger than 0.7 for the S and about 0.95 for the M in both models. The performance of the models varies across cities; for example, the highest error is for areas with high emissions of O3 precursors. The robustness of the results is confirmed by performing an additional downscaling method. This study demonstrates that coarse-scale global model data, such as CAMS, need to be downscaled for regulatory purposes or policy applications at local scales. Our method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.

Chemical evolution of the Galactic bulge with different stellar populations

The metallicity distribution function (MDF) of the Galactic bulge is characterized by a multi-peak shape, with a metal-poor peak centered at $ Fe/H sim-0.3 $ dex and a metal-rich peak centered at $ Fe/H sim+0.3 $ dex. The bimodality of the MDF is also reflected in the alpha /Fe versus Fe/H abundance ratios, suggesting the presence of different stellar populations in the bulge. In this work we aim to reproduce the observed MDF of the Galactic bulge by testing a scenario in which the metal-poor component of the bulge is formed by stars formed in situ, during a strong burst of star formation, while the metal-rich population is formed by stars created in situ during a second burst of star formation and/or stars accreted from the innermost part of the Galactic disk as an effect of a growing bar. We adopted a chemical evolution model that is able to follow the evolution of several chemical species with detailed nucleosynthesis prescriptions. In particular, because of the importance of the production of Fe in constraining the MDF, close attention is paid to the production of this element in both Type Ia supernovae and massive stars. In particular, we included yields from rotating massive stars with different rotational velocity prescriptions. Our model also takes the infall and outflow of gas into account, as well as the effect of stellar migration. Results are compared to sim 13000 stars from the SDSS/APOGEE survey that belong to the region located at a Galactocentric distance $ R_ GC kpc We successfully reproduce the observed double-peak shape of the bulge MDF as well as the abundance trends of the alpha elements relative to Fe by assuming both (i) a multi-burst star formation history with a quenching of the first burst of $ 10^2\ Myr $ and (ii) migration of stars from the innermost part of the Milky Way disk, as an effect of a growing bar. According to our results, the fraction of the stellar mass of the bulge-bar that belongs to the inner disk is $ sim40<!PCT!> $. In terms of the nucleosynthesis, we conclude that models that assume either no rotation for massive stars or a distribution of rotational velocities that favors slow rotation at high metallicities best reproduce the observed MDF as well as the alpha /Fe and the Ce/Fe versus Fe/H abundance patterns.

Thermal Runaway Mechanism in Ni-Rich Cathode full-cells of Lithium-ion Batteries: The role of Multidirectional Crosstalk.

Crosstalk, the exchange of chemical species between battery electrodes, significantly accelerates thermal runaway (TR) of lithium-ion batteries (LIBs). To date, the understanding of their main mechanisms has centered on single-directional crosstalk of oxygen (O2) gas from the cathode to the anode, underestimating the exothermic reactions during TR. However, the role of multidirectional crosstalk in steering additional exothermic reactions has yet to be elucidated due to the difficulties of correlative in situ analyses of full cells. Herein, we elucidate the way in which such crosstalk triggers self-amplifying feedback that dramatically exacerbates TR within enclosed full cells, by employing synchrotron-based high-temperature X-ray diffraction, mass spectrometry, and calorimetry. Our findings reveal that ethylene (C2H4) gas generated at the anode promotes O2 evolution at the cathode. This O2 then returns to the anode, further promoting additional C2H4 formation and creating a self-amplifying loop, thereby intensifying TR. Furthermore, CO2, traditionally viewed as an extinguishing gas, engages in the crosstalk by interacting with lithium at the anode to form Li2CO3, thereby accelerating TR beyond prior expectations. These insights have led us to develop an anode coating that impedes the formation of C2H4 and O2, to effectively mitigate TR. This article is protected by copyright. All rights reserved.

New Year Fireworks Influence on Air Quality in Case of Stagnant Foggy Conditions

Urban science plays a pivotal role in understanding the complex interactions between fireworks, air quality, and urban environments. Dense firework smoke worsens air quality and poses a health hazard to the public. In this study, we show a situation where extremely foggy meteorological conditions coincided with intense anthropogenic emissions, including fireworks, in an urban area. For the first time, the chemical composition and sources of non-refractory submicron aerosol (NR-PM1) in outdoor and indoor air were characterized in Vilnius (Lithuania) using an aerosol chemical speciation monitor (ACSM) and Positive Matrix Factorization for the period before the fireworks, on New Year’s Eve, and after the fireworks in 2020/2021; thus, typical changes were assessed. Due to stagnant weather conditions and increased traffic, the highest concentrations of black carbon (BC) (13.8 μg/m3) were observed before the fireworks display. The contribution of organic (Org) fraction to the total NR-PM1 mass concentration, in the comparison of the values of a typical night and New Year’s Eve (from 9 p.m. to 6 a.m.), increased from 43% to 70% and from 47% to 60% in outdoor and indoor air, respectively. Biomass-burning organic aerosol (BBOA, 48% (44%)) and hydrocarbon-like organic aerosol (HOA, 35% (21%)) dominated the organic fraction indoors and outdoors, respectively. HOA was likely linked to increased traffic during the event, while BBOA may have been related to domestic heating and fireworks.

PHREEQ C Modelling Tool Application to Determine the Effect of Anions on Speciation of Selected Metals in Water Systems within Kajiado North Constituency in Kenya

Heavy metals and anions in water pose a significant risk to living organisms, including humans, and must therefore be carefully monitored. There are numerous approaches and methods of analysis to choose from. Using the PHREEQ C chemical modeling technique, this study looked at the effect of certain anions on the speciation of selected metals in the water environment of Kajiado North. Twenty-five sampling points were used in the study, which covered five Kajiado North election wards. Samples were taken twice during the dry and rainy seasons, in November 2019 and March 2020. Using standard methods, physicochemical characteristics were analyzed, concentrations of selected heavy metals using the (ICP- OES) 7000 series, and selected anions using a multi parameter photometer, their concentration values compared to the specified maximum contaminant level and speciation analysis done using the Computer-based technique PHREEQ C. The range observed for pH, temperature, electrical conductivity, sulphate, chloride, fluoride, nitrate and carbonates was 7.43- 9.30, 23.0 -28.0oC, 335-1265μS/cm, 4.54-361.41mgL-1, 5.19-184.6 mgL-1, 0.01-2.90 mgL-1, 0.1-8.6 mgL-1, and 3.18-55.19 mgL-1 respectively. The range observed for heavy metals was 0.06-1.08 mgL-1 , 0.00 -0.05082 mgL-1, 0.01-0.02597 mgL-1, 0.01-0.01610 mgL-1, 0.00 -0.0327 mgL-1, 0.00-42.7842 mgL-1, and below detection limit, for Fe, Mn, Cu, Zn, Pb, Mg, and Cd respectively. The findings revealed that Cd, Zn, Cu, Cl-, NO2-, and eElectrical cconductivity concentrations were below the WHO levels. At certain sampling points, however, SO42-, F-, Pb, Fe, and Mn were above WHO limits. In some water systems, traces of SO42-, F-, Cu, Fe, Cd, and Mn were found to pose significant health risks, according to PHREEQ C speciation analysis. Anion concentrations, pH, temperature, and metal concentrations are all important variables in modeling. Metals formed anion complexes and the total abundance of metal species was classified at the highest levels as Fe(OH)3, PbCO3, Zn(CO3)2, Cu(CO3)2, Mn+2, MgF-, and Mg+2. At a water system temperature of 25oC without anions, it was found to have the highest chemical species. CuCO3, CuNO3, and CuOH+ complexes are also formed at low pH. Based on the data for different metal species at different pH values, the bioavailability of metal species definitely depends on pH.

Periodic Stratification of Colloids in a Liquid Phase Produced by a Precipitation Reaction and Gel Swelling.

Pattern formation is a frequent phenomenon occurring in animate and inanimate systems. The interplay between the mass transport of the chemical species and the underlying chemical reaction networks generates most patterns in chemical systems. Periodic precipitation is an emblematic example of reaction-diffusion patterns, in which the process generates a spatial periodic structure in porous media. Here, we use the dormant reagent method to produce colloidal particles of Prussian blue (PB) and PB analogues at the liquid-gel interface. The generated particles produced a stable periodic stratification pattern in time in the liquid phase placed on top of the solid hydrogel. The phenomenon is governed by periodic swelling of the gel driven by the osmotic stress and stability of the formed particles. To illustrate the phenomenon, we developed an extended reaction-diffusion model, which incorporated the gel swelling and sedimentation effect of the formed colloids and could qualitatively reproduce the pattern formation in the liquid phase.

Toward Accurate Quantum Mechanical Thermochemistry: (1) Extensible Implementation and Comparison of Bond Additivity Corrections and Isodesmic Reactions.

Obtaining accurate enthalpies of formation of chemical species, ΔHf, often requires empirical corrections that connect the results of quantum mechanical (QM) calculations with the experimental enthalpies of elements in their standard state. One approach is to use atomization energy corrections followed by bond additivity corrections (BACs), such as those defined by Petersson et al. or Anantharaman and Melius. Another approach is to utilize isodesmic reactions (IDRs) as shown by Buerger et al. We implement both approaches in Arkane, an open-source software that can calculate species thermochemistry using results from various QM software packages. In this work, we collect 421 reference species from the literature to derive ΔHf corrections and fit atomization energy corrections and BACs for 15 commonly used model chemistries. We find that both types of BACs yield similar accuracy, although Anantharaman- and Melius-type BACs appear to generalize better. Furthermore, BACs tend to achieve better accuracy than IDRs for commonly used model chemistries, and IDRs can be less robust because of the sensitivity to the chosen reference species and reactions. Overall, Anantharaman- and Melius-type BACs are our recommended approach for achieving accurate QM corrections for enthalpies.

Chemical inventory of the envelope of the Class I protostar L1551 IRS 5

Episodic accretion in protostars leads to luminosity outbursts that end up heating their surroundings. This rise in temperature pushes the snow lines back, enabling the desorption of chemical species from dust grain surfaces, which may significantly alter the chemical history of the accreting envelope. However, a limited number of extensive chemical surveys of eruptive young stars have been performed thus far. In the present study, we carry out a large spectral survey of the binary Class I protostar L1551 IRS 5, known to be a FUor-like object, in the 3mm and 2mm bands with the IRAM-30m telescope. As a result, we detected more than 400 molecular lines. The source displays a great chemical richness with the detection of 75 species, including isotopologues. Among these species, there are 13 hydrocarbons, 25 N-bearing species, 30 O-bearing species, 15 S-bearing species, 12 deuterated molecules, and a total of 10 complex organic molecules (l-C$_4$H$_2$, CH$_3$CCH, CH$_2$DCCH, CH$_3$CHO, CH$_3$CN, CH$_3$OCH$_3$, CH$_3$OCHO, CH$_3$OH, CH$_2$DOH, and HC$_5$N). With the help of local thermodynamic equilibrium (LTE) and non-LTE models, we determined the column densities of most molecules as well as excitation and kinetic temperatures. While most of those molecules trace the cold envelope ($ 20$ K), the OCS and CH$_3$OH emission arise from the warm ($> 100$ K) innermost ($< 2$ regions. We compared the chemical inventory of L1551 IRS 5 and its column density ratios, including isotopic ratios, with other protostellar sources. A broad chemical diversity is seen among Class I objects. More observations with both single-dish telescopes and interferometers are needed to characterize the diversity in a larger sample of protostars, while more astrochemical models would help explain this diversity, in addition to the impact of luminosity outbursts on the chemistry of protostellar envelopes.

Thermodynamic and stoichiometric laws ruling the fates of growing systems

We delve into growing open chemical reaction systems (CRSs) characterized by autocatalytic reactions within a variable volume, which changes in response to these reactions. Understanding the thermodynamics of such systems is crucial for comprehending biological cells and constructing protocells because it sheds light on the physical conditions necessary for their self-replication. Building on our recent work, where we developed a thermodynamic theory for growing CRSs featuring basic autocatalytic motifs with regular stoichiometric matrices, we now expand this theory to include scenarios where the stoichiometric matrix has a nontrivial left kernel space. This extension introduces conservation laws, which limit the variations in chemical species due to reactions, thereby confining the system's possible states to those compatible with its initial conditions. By considering both thermodynamic and stoichiometric constraints, we clarify the environmental and initial conditions that dictate the CRSs' fate—whether they grow, shrink, or reach equilibrium. We also find that the conserved quantities significantly influence the equilibrium state achieved by a growing CRS. These results are derived independently of specific thermodynamic potentials or reaction kinetics, therefore underscoring the fundamental impact of conservation laws on the growth of the system. Published by the American Physical Society 2024

Unveiling Crucial Chemical Processing Parameters Influencing the Performance of Solution‐Processed Inorganic Thermoelectric Materials

AbstractProduction of thermoelectric materials from solution‐processed particles involves the synthesis of particles, their purification and densification into pelletized material. Chemical changes that occur during each one of these steps render them performance determining. Particularly the purification steps, bypassed in conventional solid‐state synthesis, are the cause for large discrepancies among similar solution‐processed materials. In present work, the investigation focuses on a water‐based surfactant free solution synthesis of SnSe, a highly relevant thermoelectric material. We show and rationalize that the number of leaching steps, purification solvent, annealing, and annealing atmosphere have significant influence on the Sn : Se ratio and impurity content in the powder. Such compositional changes that are undetectable by conventional characterization techniques lead to distinct consolidated materials with different types and concentration of defects. Additionally, the profound effect on their transport properties is demonstrated. We emphasize that understanding the chemistry and identifying key chemical species and their role throughout the process is paramount for optimizing material performance. Furthermore, we aim to demonstrate the necessity of comprehensive reporting of these steps as a standard practice to ensure material reproducibility.

Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

East Asian countries have been conducting source apportionment of fine particulate matter (PM2.5) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance and calibration. Conventional preprocessing of missing values, such as exclusion or median replacement, causes biases in the estimated source contributions by changing the PMF input. Machine learning (ML) can estimate the missing values by training on constituent data, meteorological data, and gaseous pollutants. Complete data from the Seoul Supersite in 2018 was taken, and a random 20% was set as missing. PMF was performed by replacing missing values with estimates. Percent errors of the source contributions were calculated compared to those estimated from complete data. Missing values were estimated using a random forest analysis. Estimation accuracy (r2) was as high as 0.874 for missing carbon species and low at 0.631 when ionic species and trace elements were missing. For the seven highest contributing sources, replacing the missing values of carbon species with estimates minimized the percent errors to 2.0% on average. However, replacing the missing values of the other chemical species with estimates increased the percent errors to more than 9.7% on average. Percent errors were maximal at 37% on average when missing values of ionic species and trace elements were replaced with estimates. Missing values, except for carbon species, need to be excluded. This approach reduced the percent errors to 7.4% on average, which was lower than those due to median replacement. Our results show that reducing the biases in source apportionment is possible by replacing the missing values of carbon species with estimates. To improve the biases due to missing values of the other chemical species, the estimation accuracy of the ML needs to be improved.

FUME 2.0 – Flexible Universal processor for Modeling Emissions

Abstract. This paper introduces FUME 2.0, an open-source emission processor for air quality modeling, and documents the software structure, capabilities, and sample usage. FUME provides a customizable framework for emission preparation tailored to user needs. It is designed to work with heterogeneous emission inventory data, unify them into a common structure, and generate model-ready emissions for various chemical transport models (CTMs). Key features include flexibility in input data formats, support for spatial and temporal disaggregation, chemical speciation, and integration of external models like MEGAN. FUME employs a modular Python interface and PostgreSQL/PostGIS backend for efficient data handling. The workflow comprises data import, geographical transformation, chemical and temporal disaggregation, and output generation steps. Outputs for mesoscale CTMs CMAQ, CAMx, and WRF-Chem and the large-eddy-simulation model PALM are implemented along with a generic NetCDF format. Benchmark runs are discussed on a typical configuration with cascading domains, with import and preprocessing times scaling near-linearly with grid size. FUME facilitates air quality modeling from continental to regional and urban scales by enabling effective processing of diverse inventory datasets.

Application of volume‐translated rescaled perturbed‐chain statistical associating fluid theory equation of state to pure compounds using an expansive experimental database

AbstractThe performance of the perturbed‐chain statistical associating fluid theory‐type equations of state (PC‐SAFT‐type EOSs) is compromised in predicting properties of pure compounds in the critical region. In our previous research, we introduced an improved volume‐translated rescaled PC‐SAFT EOS (VTR‐PC‐SAFT EOS) by incorporating a dimensionless distance‐function. Such VTR‐PC‐SAFT EOS is built based on a critical point‐based PC‐SAFT EOS that can reproduce the critical temperature, critical pressure, and critical molar volume of pure compounds. VTR‐PC‐SAFT EOS is found to significantly improve the accuracy of phase behavior predictions in both critical and noncritical regions for pure compounds. In this study, we assess the performance of VTR‐PC‐SAFT EOS in reproducing the critical and noncritical properties of 251 pure compounds, encompassing 20 distinct chemical species. The testing results indicate that, compared to the other three PC‐SAFT‐type EOSs, the VTR‐PC‐SAFT EOS can consistently provide more accurate representations of critical and noncritical properties of 251 pure compounds.