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Galaxolide (HHCB) is the most common synthetic musk compound detected in numerous daily products. Despite its persistence in the aquatic environment, the photodegradation of HHCB remains poorly understood. In this study, we investigated the direct and indirect photolysis kinetics of HHCB under simulated sunlight and UVC light. Our aim was to determine the role of reactive oxygen species (ROS) responsible for HHCB degradation in the aquatic environment and to identify its transformation products. The influence of environmental factors on indirect photolysis was investigated by testing both synthetic waters (containing humic acid, carbonate (CO 3 2- ), and nitrate (NO 3 - )) and real waters (riverine and effluent). Hydrogen peroxide (H 2 O 2 /UVC) was tested to simulate the wastewater treatment process. Quencher experiments were conducted to identify the role of ROS in HHCB photodegradation, including hydroxyl radicals (˙OH), carbonate radicals (CO 3 ˙ - ), triplet states of dissolved organic matter ( 3 DOM*), and singlet oxygen ( 1 O 2 ). The results clearly indicated that HHCB was efficiently degraded by direct photolysis under both light conditions. The presence of H 2 O 2 led to the most efficient HHCB degradation due to the high production of ˙OH induced under UVC. Indirect photolysis contribution was observed, induced by ˙OH, CO 3 ˙ - , 3 DOM*, and 1 O 2 to different extents depending on the light and matrix composition. The experiments led to the detection of transformation products: HHCB lactone, a well-known transformation product, and two other substances with proposed structures. This study provides a comprehensive identification of the processes involved in the direct and indirect photodegradation of HHCB, which could serve as the basis for evaluating and modeling the fate of HHCB in aquatic environments.

Reductions in sulfur (S) atmospheric deposition in recent decades have been attributed to S deficiencies in crops. Similarly, global soil selenium (Se) concentrations were predicted to drop, particularly in Europe, due to increases in leaching attributed to increases in aridity. Given its international importance in agriculture, reductions of essential elements, including S and Se, in European soils could have important impacts on nutrition and human health. Our objectives were to model current soil S and Se levels in Europe and predict concentration changes for the 21st century. We interrogated four machine-learning (ML) techniques, but after critical evaluation, only outputs for linear support vector regression (Lin-SVR) models for S and Se and the multilayer perceptron model (MLP) for Se were consistent with known mechanisms reported in literature. Other models exhibited overfitting even when differences in training and testing performance were low or non-existent. Furthermore, our results highlight that similarly performing models based on RMSE or R 2 can lead to drastically different predictions and conclusions, thus highlighting the need to interrogate machine learning models and to ensure they are consistent with known mechanisms reported in the literature. Both elements exhibited similar spatial patterns with predicted gains in Scandinavia versus losses in the central and Mediterranean regions of Europe, respectively, by the end of the 21st century for an extreme climate scenario. The median change was -5.5% for S (Lin-SVR) and -3.5% (MLP) and -4.0% (Lin-SVR) for Se. For both elements, modeled losses were driven by decreases in soil organic carbon, S and Se atmospheric deposition, and gains were driven by increases in evapotranspiration.

Many phenolic compounds (PhCs) in biomass burning and fossil fuel combustion emissions can partition into atmospheric aqueous phases ( e.g. , cloud/fog water and aqueous aerosols) and undergo reactions to form secondary organic aerosols (SOAs) and brown carbon (BrC). Redox-active transition metals, particularly Fe and Cu, are ubiquitous species in atmospheric aqueous phases known to participate in Fenton/Fenton-like chemistry as a source of aqueous ˙OH. However, even though the concentrations of water-soluble Cu are close to those of water-soluble Fe in atmospheric aqueous phases in some areas, unlike Fe, the effects that Cu have on SOA and BrC formation in atmospheric aqueous phases have scarcely been studied and remain poorly understood. We investigated the effects of Cu(II) on PhC reaction rates and BrC formation during the aqueous oxidation of four PhCs (guaiacol, catechol, syringol, and vanillin) by ˙OH generated from Fenton-like chemistry under different pH conditions. While the PhCs reacted when both H 2 O 2 and Cu(II) were present in the absence ( i.e. , dark oxidation) and presence ( i.e. , photooxidation) of light, the reaction rates were at least one order of magnitude higher during photooxidation. Higher PhC reaction rates were measured at higher pH during both dark oxidation and photooxidation as a result of higher ˙OH concentrations produced by Fenton-like chemistry. Only water-soluble BrC was formed during dark oxidation and photooxidation when Cu(II) was present. Mass absorption coefficients (10 3 to 10 4 cm 2 g -1 ) comparable to those of biomass burning BrC were measured during dark oxidation and photooxidation when Cu(II) was present. Light absorption was enhanced at higher pH during dark oxidation and photooxidation, which indicated that higher quantities and/or more absorbing BrC chromophores were formed at higher pH. The effects that Cu(II) had on the PhC reaction rates and the composition of SOAs and BrC formed depended on the PhC base structure ( i.e. , benzenediol vs. methoxyphenol). Overall, these results show how aqueous reactions involving Cu(II), H 2 O 2 , and PhCs can be an efficient source of daytime and nighttime water-soluble BrC and SOAs, which can have significant implications for how the atmospheric fates of PhCs are modeled for areas with substantial concentrations of water-soluble Cu in highly to moderately acidic cloud/fog water and aqueous aerosols.

To identify toxicity drivers within poorly characterized high-molar-weight disinfection by-products (DBPs), relatively stable high-yield initial transformation products generated from aromatic amino acids and peptides and humic substances have drawn much attention. In this study, initial transformation products in chlorination of the indole moiety in tryptophan (Trp) are proposed and their formation mechanisms were investigated using a quantum chemical computational method. The results indicate that 3-Cl-Trp+ is initially formed after the Cl + of HOCl attacks the indole moiety, and nucleophilic addition with nucleophilic agents (H 2 O and OCl - ) is thermodynamically preferred over deprotonation to generate 2-X-3-Cl-indoline moiety (X = OH and OCl), which is in contrast to indole. Over 25 types of initial transformation products are proposed from the 2-X-3-Cl-indoline moiety and two ring opening pathways were found at N1-C2 and C2-C3 bonds. Significantly, most structures of initial transformation products proposed based on experimental detection m/z values were confirmed using quantum chemical calculations and some new products are proposed in this work. The results are helpful to expand our understanding of the intrinsic reactivity of aromatic ring towards chlorination by hypochlorous acid.

Extreme climatic conditions, like heat waves or cold spells, associated to high concentrations of air pollutants are responsible for a broad range of effects on human health. Consequently, in the recent years, the question on how urban and peri-urban forests may improve both air quality and surface climate conditions at city-scale is receiving growing attention by scientists and policymakers, with previous studies demonstrating how nature-based solutions (NBS) may contribute to reduce the risk of population to be exposed to high pollutant levels and heat stress, preventing, thus, premature mortality. In this study we present a new modeling framework designed to simulate air quality and meteorological conditions from regional to urban scale, allowing thus to assess the impacts of both air pollution and heat stress on human health at urban level. To assess the model reliability, we evaluated the model's performances in reproducing several relevant meteorological, chemical, and biological variables. Results show how our modeling system can reliably reproduce the main meteorological, chemical, and biological variables over our study areas, thus this tool can be used to estimate the impact of air pollution and heat stress on human health. As an example of application, we show how common heat stress and air pollutant indices used for human health protection change when computed from regional to urban scale for the cities of Florence (Italy) and Aix en Provence (France)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

In this work, we propose a new protocol for producing model microplastics from an industrial polymer and compare it to a conventional method, cryomilling. Polypropylene industrial pellets were chosen due to their widespread production and frequent presence in the environment, making them a notable source of microplastics. Both protocols start with aging under Ultra-Violet light of the pellets but differ in the subsequent mechanical stress applied-strong vs. soft-to break down the photodegraded pellets into microplastics. All generated particles were fully characterized in terms of size, shape, oxidation rate, and stability in aqueous media. Microplastics produced via cryomilling exhibited significant size and oxidation heterogeneity and tended to aggregate in water. Although the new protocol involving soft mechanical stress required a longer preparation time, it simulated more accurately the environmental degradation of raw plastic. This method successfully produced oxidized microplastics with a controlled size distribution centered around 50 µm which remained stable in water without stabilizers., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Glais Margaux reports financial support was provided by IPC. FALHER Thierry reports a relationship with IPC that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Complex dynamical systems are often governed by equations containing many unknown parameters whose precise values may or may not be important for the system's dynamics. In particular, for chemical and biochemical systems, there may be some reactions or subsystems that are inessential to understanding the bifurcation structure and consequent behavior of a model, such as oscillations, multistationarity and patterning. Due to the size, complexity and parametric uncertainties of many (bio)chemical models, a dynamics-preserving reduction scheme that is able to isolate the necessary contributors to particular dynamical behaviors would be useful. In this contribution, we describe model reduction methods for mass-action (bio)chemical models based on the preservation of instability-generating subnetworks known as critical fragments. These methods focus on structural conditions for instabilities and so are parameter-independent. We apply these results to an existing model for the control of the synthesis of the NO-detoxifying enzyme Hmp in Escherichia coli that displays bistability., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Partial least squares (PLS) regression is a valuable chemometric tool for property prediction when coupled with gas chromatography (GC). Since the separation run time and stationary phase selection are crucial for effective PLS modeling, we study these GC parameters on the prediction of viscosity, density and hydrogen content for 50 aerospace fuels. Due to the diversity of compounds in the fuels (primarily alkanes, cycloalkanes, and aromatics), we explore both polar and non-polar stationary phase columns. The robustness for the PLS models was evaluated by their normalized root mean square error of cross-validation (NRMSECV). PLS models built for viscosity across 1-min, 3-min, 7-min, and 10-min time window (TW) high-speed GC separations produced nearly the same NRMSECV with the polar column data with an average (standard deviation) of 4.41 % (0.34 %) versus the non-polar column data of 4.69 % (0.15 %). In contrast, while the NRMSECV of density modeling with the polar column data varied more than the viscosity models, averaging 7.54 % (0.67 %), the non-polar column data produced a significantly higher average NRMSECV of 10.06 % (0.35 %). Similarly, for hydrogen content, the NRMSECV with the polar column data averaged 9.50 % (0.87 %), which was significantly lower than the NRMSECV with the non-polar column data averaging 12.10 % (0.88 %). We also investigated the impact of smoothing the GC data on the corresponding PLS models. By applying varying degrees of smoothing, we can effectively obtain similar chromatographic peak patterns in a shorter TW. For example, a 10-min smoothed chromatogram appears like the 1-min separation with no smoothing but resulted in nearly the same NRMSECV. Overall, the fast separation with a 1-min TW produced robust PLS models for viscosity with either stationary phase column, whereas for density and hydrogen content the polar stationary phase column produced superior PLS models, thus with proper stationary phase selection, a fast separation run time could be readily applied with optimal PLS property modeling results., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Generative deep learning models enable data-driven de novo design of molecules with tailored features. Chemical language models (CLM) trained on string representations of molecules such as SMILES have been successfully employed to design new chemical entities with experimentally confirmed activity on intended targets. Here, we probe the application of CLM to generate multi-target ligands for designed polypharmacology. We capitalize on the ability of CLM to learn from small fine-tuning sets of molecules and successfully bias the model towards designing drug-like molecules with similarity to known ligands of target pairs of interest. Designs obtained from CLM after pooled fine-tuning are predicted active on both proteins of interest and comprise pharmacophore elements of ligands for both targets in one molecule. Synthesis and testing of twelve computationally favored CLM designs for six target pairs reveals modulation of at least one intended protein by all selected designs with up to double-digit nanomolar potency and confirms seven compounds as designed dual ligands. These results corroborate CLM for multi-target de novo design as source of innovation in drug discovery., (© 2024. The Author(s).)

Toxicity identification plays a key role in maintaining human health, as it can alert humans to the potential hazards caused by long-term exposure to a wide variety of chemical compounds. Experimental methods for determining toxicity are time-consuming, and costly, while computational methods offer an alternative for the early identification of toxicity. For example, some classical ML and DL methods, which demonstrate excellent performance in toxicity prediction. However, these methods also have some defects, such as over-reliance on artificial features and easy overfitting, etc. Proposing novel models with superior prediction performance is still an urgent task. In this study, we propose a motifs-level graph-based multi-view pretraining language model, called 3MTox, for toxicity identification. The 3MTox model uses Bidirectional Encoder Representations from Transformers (BERT) as the backbone framework, and a motif graph as input. The results of extensive experiments showed that our 3MTox model achieved state-of-the-art performance on toxicity benchmark datasets and outperformed the baseline models considered. In addition, the interpretability of the model ensures that the it can quickly and accurately identify toxicity sites in a given molecule, thereby contributing to the determination of the status of toxicity and associated analyses. We think that the 3MTox model is among the most promising tools that are currently available for toxicity identification., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Isosaccharinic acid (HISA, or ISA in its deprotonated form) is the main degradation product of cellulose under alkaline conditions. It can form strong complexes with radionuclides and other toxic metal ions, eventually enhancing their mobility in the context of nuclear waste repositories and other environmental systems. 99 Tc is a redox-sensitive, long-lived fission product produced in high yield in nuclear reactors. The solubility of 99 Tc(IV) was investigated in 0.5 M NaCl‒NaISA‒NaOH solutions with 6 ≤ pH m  ≤ 12.5 and 10 -6  M ≤ [ISA] ≤ 0.2 M. Complete chemical and thermodynamic models were derived on the basis of solubility data, (pe + pH m ) measurements, redox speciation, and solid phase characterization. These models include the previously unreported aqueous complexes TcO(OH)(ISA) 2 ‒ and TcO(OH) 2 (ISA) 2 2- . In spite of the small size and high polarizability of the Tc 4+ metal ion, the Tc(IV)-ISA complexes described in this work are significantly weaker than other ISA complexes formed with larger M 4+ metal ions, i.e., Zr, Pu and U. This unexpected behavior can be possibly explained by the strong hydrolysis of Tc(IV) and corresponding stabilization of the TcO 2+ moiety, which does not occur for other M(IV) systems. Thermodynamic data derived in this work can be implemented in geochemical calculations of relevance in the context of nuclear waste disposal and other environmental applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Selecting an adequate model to represent the mass transfer mechanisms occurring in a chromatographic process is generally complicated, which is one of the reasons why monolithic chromatography is scarcely simulated. In this study, the chromatographic separation of model proteins bovine serum albumin (BSA), β-lactoglobulin-A, and β-lactoglobulin-B on an anion exchange monolith was simulated based on experimental parameter determination, simultaneous model testing, and validation under three statistical criteria: retention time, dispersion accuracies, and Pearson correlation coefficient. Experimental characterization of morphologic, physicochemical, and kinetic parameters was performed through volume balances, pressure drop analysis, breakthrough curve analysis, and batch adsorptions. Free Gibbs energy indicated a spontaneous adsorption process for proteins and counterions. Dimensionless numbers were estimated based on height equivalent to a theoretical plate analysis, finding that pore diffusion controlled β-lactoglobulin separation, whereas adsorption/desorption kinetics was the dominant mechanism for BSA. The elution profiles were modeled using the transport dispersive model and the reactive dispersive model coupled with steric mass action (SMA) isotherms because these models allowed to consider most of the mass transport mechanisms that have been described. RDM-SMA presented the most accurate simulations at pH 6.0 and at low (250 mM) and high (400 mM) NaCl concentrations. This simulation will be used as reference to forecast the purification of these proteins from bovine whey waste and to extrapolate this methodology to other monolith-based separations using these three statistical criteria that have not been used previously for this purpose., (© 2024 Wiley‐VCH GmbH.)

Selective catalytic NH 3 -to-N 2 oxidation (NH 3 -SCO) is highly promising for abating NH 3 emissions slipped from stationary flue gas after-treatment devices. Its practical application, however, is limited by the non-availability of low-cost catalysts with high activity and N 2 selectivity. Here, using defect-rich nitrogen-doped carbon nanotubes (NCNT-AW) as the support, we developed a highly active and durable copper-based NH 3 -SCO catalyst with a high abundance of cuprous (Cu + ) sites. The obtained Cu/NCNT-AW catalyst demonstrated outstanding activity with a T 50 (i.e. the temperature to reach 50% NH 3 conversion) of 174°C in the NH 3 -SCO reaction, which outperformed not only the Cu catalyst supported on N-free O-functionalized CNTs (OCNTs) or NCNT with less surface defects, but also those most active Cu catalysts in open literature. Reaction kinetics measurements and temperature-programmed surface reactions using NH 3 as a probe molecule revealed that the NH 3 -SCO reaction on Cu/NCNT-AW follows an internal selective catalytic reaction (i-SCR) route involving nitric oxide (NO) as a key intermediate. According to mechanistic investigations by X-ray photoelectron spectroscopy, Raman spectroscopy, and X-ray absorption spectroscopy, the superior NH 3 -SCO performance of Cu/NCNT-AW originated from a synergy of surface defects and N-dopants. Specifically, surface defects promoted the anchoring of CuO nanoparticles on N-containing sites and, thereby, enabled efficient electron transfer from N to CuO, increasing significantly the fraction of SCR-active Cu + sites in the catalyst. This study puts forward a new idea for manipulating and utilizing the interplay of defects and N-dopants on carbon surfaces to fabricate Cu + -rich Cu catalysts for efficient abatement of slip NH 3 emissions via selective oxidation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

A polemic is given regarding several of the calculated curve-fit parameters that Zhou and coworkers reported in their published paper. The calculated curve-fit parameters for the Combined Nearly Ideal Binary Solvent/Redlich-Kister, Jouyban-Acree-van't Hoff, Sun and modified Apelblat models were found to give mole fraction solubilities that exceeded unity. Our analysis also found that the mean relative absolute percent deviations provided by the authors were significantly underestimated., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to declare., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The adsorption of trihalomethanes (THMs) from drinking water was investigated in the current study through comparison studies of kaolinite and ZnO@kaolinite nanocomposites. The clay structural network's successful immobilization on the zincite hexagonal structure of ZnO nanoparticles' lattice layers was verified by the SEM/EDX analysis. Under the optimum conditions, the maximum removal of THMs was achieved by kaolinite and ZnO@kaolinite nanocomposites after 60 min. The adsorption performance of the ZnO@kaolinite nanocomposites was greater than that of kaolinite because the former had a larger surface area than the latter. The Freundlich isotherm model best matched the adsorption experimental data, which also reveals the existence of multilayer adsorption on a diverse surface with the greatest correlation ( R 2 = 0.956 and 0.954, respectively) for both nanoadsorbents using the pseudo-first-order (PFO), pseudo-second-order (PSO), mixed 1, 2-order (MFSO), and intraparticle diffusion (IPD) models. The mechanism by which THMs in drinking water adsorb onto nanoadsorbents was examined. This revealed that both intraparticle and film diffusion were involved in the adsorption process. Kaolinite and ZnO@kaolinite nanocomposites can be used in water treatment to remove THMs due to their great recyclable and reusable properties, even after six cycles., Competing Interests: The authors declare there is no conflict., (© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

The presence of carbonates or humic substances (HS) will significantly affect the species and chemical behavior of U(VI) in solution, but lacking systematic exploration of the coupling effect of carbonates and HS under near real environmental conditions at present. Herein, the sorption behavior of U(VI) on illite was systematically studied in the co-existence of carbonates and HS including both humic acid (HA) and fulvic acid (FA) by batch technique. The distribution coefficients (K d ) increased as function of time and temperature but decreased with increasing concentrations of initial U(VI), Ca 2+ , and Mg 2+ , as well as ion strength. At pH 2.0-10.5, the K d values first increased rapidly and then decreased visibly, with its maximum value appearing at pH 5.0, owning to the changes in the interaction between illite and the dominant species of U(VI) from electrostatic attraction to electrostatic repulsion. The sorption was a heterogeneous, spontaneous, and endothermic chemical process, which could be well described by pseudo-second-order kinetic and Flory-Huggins isotherm models. When carbonates and HA/FA coexisted, the K d values always increased first and then decreased as a function of pH, with the only difference for HA and FA being the key pH (pH key ) at which the promoting and inhibiting effects on the sorption of U(VI) onto illite undergo a transition. The carbonates and HS have a synergistic inhibitory effect on the U(VI) sorption onto illite at pH 7.8. FTIR and XPS spectra demonstrated that the hydroxyl groups on the illite surface and in the HS were involved in U(VI) sorption on illite in the presence of carbonates. These results provide valuable data for a deeper understanding of U(VI) migration in geological media., Competing Interests: Declaration of competing interest The authors declare that no known competing financial interests or personal relationships could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Crystalline rock is used as the host rock for the disposal of high-level radioactive waste. Two cationic elements (Cs(I) and Ni(II)) and three anionic elements (Se(IV/VI), Mo(VI), and U(VI)) were selected to comprehensively evaluate the sorption behaviors of these radionuclides on crystalline granite and biotite gneiss. The anionic elements showed weak sorption (log K d (L·kg -1 ) < 1) and little competition effect, while the cationic elements (log K d (L·kg -1 ) = 2-3) showed clear competition (18-98% in K d values) even at low concentrations. Analysis by pseudo-second-order kinetics showed that Cs(I) sorbed at similar rates on both rocks (20% faster on biotite gneiss), but Ni(II) sorbed 190% faster on biotite gneiss than on granite. That is why the retardation factors for Cs(I) and Ni(II) were reversed in the biotite gneiss column compared to their distribution coefficients. Therefore, the sorption kinetics cannot be neglected in groundwater systems with high flow rates. In the desorption column test, the retardation followed the order of the distribution coefficient. The desorption column test revealed that the distribution coefficient determines the strength of sorption on crystalline rocks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Models predicting lipid oxidation in oil-in-water (O/W) emulsions are a requirement for developing effective antioxidant solutions. Existing models do, however, not include explicit equations that account for composition and structural features of O/W emulsions. To bridge this gap, a mechanistic kinetic model for lipid oxidation in emulsions is presented, describing the emulsion as a one-dimensional three phase (headspace, water, and oil) system. Variation in oil droplet sizes, overall surface area of oil/water interface, oxidation of emulsifiers, and the presence of catalytic transition metals were accounted for. For adequate predictions, the overall surface area of oil/water interface needs to be determined from the droplet size distribution obtained by dynamic and static light scattering (DLS, SLS). The kinetic model predicted well the formation of oxidation products in both mono- and polydisperse emulsions, with and without presence of catalytic transition metals., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Arend Dubbelboer and John P.M. Van Duynhoven are employed by companies that manufacture and market oil based food products., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This article is the second of a series of two articles. In the first article of the series, a new K v distribution model and an experimental methodology to measure the K v distribution were introduced. In this second part, the K v distribution is integrated into a lyo-simulation tool, to more accurately predict the variability of the product temperature, primary drying time, total sublimation mass flow and Pirani signal. The K v distribution is also integrated into the graphical design space. The impact of incorporating the R p distribution is briefly discussed. The comparison of the simulation tool with actual product temperature monitoring, Pirani signal or overall sublimation flow shows very good agreement in the case studies presented. Overall, the lyo-simulation incorporating the K v distribution is a very useful tool to support industrial development, i.e. process optimization, scale assessment, technology transfer, and troubleshooting of the lyophilization process., Competing Interests: Declaration of competing interests The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: All the authors worked for Sanofi at the time where the research work was done, except one working for Astek - IT&M STATS (contracting company for Sanofi). All the work was Sanofi financed., (Copyright © 2024 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.)

The adsorption of heavy metals on iron oxides generally increases with pH and is almost complete at neutral to slightly alkaline pH. However, almost complete adsorption on a linear scale does not imply sufficient removal of the heavy metals in terms of their toxicity. Here, we elucidated the chemical reactions that determine the solid-liquid partitioning of Pb(II) and Cd(II) on goethite at high pH. While the removal of both heavy metals was almost complete on a linear scale above pH 7 for Pb(II) and pH 9 for Cd(II), the dissolved metal concentrations decreased on a logarithmic scale with pH, reaching minima at around pH 10 for Pb(II) and pH 10-11 for Cd(II), and then they increased with pH thereafter. The XAFS spectra of Pb(II)- or Cd(II)-adsorbed goethite prepared at pH > 11 were almost the same as those at neutral pH, suggesting that removal of the heavy metals from solution was achieved by a single adsorption reaction over the entire pH range. Based on the observed macroscopic and microscopic adsorption behaviors at high pH, a robust surface complexation model was developed to predict the solid-liquid partitioning of divalent heavy metals over the entire pH range., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Silver (9 wt.%) was loaded on Co 3 O 4 -nanofiber using reduction and impregnation methods, respectively. Due to the stronger electronegativity of silver, the ratios of surface Co 3+ /Co 2+ on Ag/Co 3 O 4 were higher than on Co 3 O 4 , which further led to more adsorbed oxygen species as a result of the charge compensation. Moreover, the introducing of silver also obviously improved the reducibility of Co 3 O 4 . Hence the Ag/Co 3 O 4 showed better catalytic performance than Co 3 O 4 in benzene oxidation. Compared with the Ag/Co 3 O 4 synthesized via impregnation method, the one prepared using reduction method (named as AgCo-R) exhibited higher contents of surface Co 3+ and adsorbed oxygen species, stronger reducibility, as well as more active surface lattice oxygen species. Consequently, AgCo-R showed lowest T 90 value of 183°C, admirable catalytic stability, largest normalized reaction rate of 1.36 × 10 -4 mol/(h·m 2 ) (150°C), and lowest apparent activation energy (E a ) of 63.2 kJ/mol. The analyzing of in-situ DRIFTS indicated benzene molecules were successively oxidized to phenol, o-benzoquinone, small molecular intermediates, and finally to CO 2 and water on the surface of AgCo-R. At last, potential reaction pathways including five detailed steps were proposed., Competing Interests: Declaration of Competing Interest The authors declare they do not have any known competing commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2023. Published by Elsevier B.V.)

Oil sands process-affected water (OSPW) is a source of atmospheric emission for polycyclic aromatic compounds (PACs), compounds known to have toxic effects on humans. Estimating emissions and assessing the chemical fate of PACs requires measured or predicted physical-chemical properties such as Henry's law constants (H), that can be used to predict chemical transfer into the atmosphere. OSPW is a complex water-based mixture that is highly variable in composition and nature and contains both organic and inorganic ions. This study uses COSMO-RS solvation theory to estimate and compare Henry's law constants for a set of PACs in both water and theoretically modelled OSPW, to assess the expected deviation that occurs from pure water H values due to the ionic content within OSPW. Experimental measurements of Henry's law constants for PACs in pure water and OSPW using EVA-coated passive dosing and sampler beads were also made in support of our theoretical predictions. For the theory work, OSPW composition data for the Athabasca oil sands in Alberta were used to model a simulated OSPW environment with realistic sodium, chloride, fluoride, sulfate, potassium, bicarbonate, and naphthenic acid concentrations. Theory results indicate that the combined presence of these ions at OSPW concentrations has a negligible effect on H values, causing on average a 3% or 0.014 log unit deviation. By comparison, temperature has a much larger influence on H values, with estimations showing an average 0.20 log unit increase for a 5 °C increase in temperature. The experimental results demonstrate that Henry's law constants can be accurately and precisely measured with this technique in pure water but with less precision in OSPW. Nevertheless, the experimental results support the conclusion that Henry's law constants for OSPW can be accurately estimated assuming a pure water phase., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Microplastics in urban runoff undergo rapid fragmentation and accumulate in the soil, potentially endangering shallow groundwater. To improve the understanding of microplastic transport in groundwater, column experiments were performed to compare the transport behavior of fragmented microplastics (FMPs ∼1-µm diameter) and spherical microplastics (SMPs ∼1-, 10-, and 20-µm diameter) in natural gravel (medium and fine) and quartz sand (coarse and medium). Polystyrene microspheres were physically abraded with glass beads to mimic the rapid fragmentation process. The experiments were conducted at a constant flow rate of 1.50 m day -1 by injecting two pore volumes of SMPs and FMPs. Key findings indicate that SMPs showed higher breakthrough, compared to FMPs in natural gravel, possibly due to size exclusion of the larger SMPs. Interestingly, FMPs exhibited higher breakthrough in quartz sand, likely due to tumbling and their tendency to align with flow paths, while both sizes (larger and smaller relative to FMPs) of SMPs exhibited higher removal in quartz sand. Therefore, an effect due to shape and size was observed., (© 2024 The Author(s). Journal of Environmental Quality published by Wiley Periodicals LLC on behalf of American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.)

A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed. The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) that simulates the computation process of two functions: a feature function defined by a two-layered model on chemical graphs and a prediction function constructed by a machine learning method. To improve the learning performance of prediction functions in the framework, we design a method that splits a given data set C into two subsets C (i) ,i=1,2 by a hyperplane in a chemical space so that most compounds in the first (resp., second) subset have observed values lower (resp., higher) than a threshold θ. We construct a prediction function ψ to the data set C by combining prediction functions ψ i ,i=1,2 each of which is constructed on C (i) independently. The results of our computational experiments suggest that the proposed method improved the learning performance for several chemical properties to which a good prediction function has been difficult to construct.

In order to study the degradation process of dioxins in industrial flue gas, the decomposition of o-dichlorobenzene (o-DCB) in a DBD plasma catalytic reactor was investigated. The results showed that an NTP-catalyzed system, especially using the CuMnTiO x catalyst, had better o-DCB degradation performance compared to plasma alone. The combination of the CuMnTiO x catalyst with NTP can achieve a degradation efficiency of up to 97.2% for o-DCB; the selectivity of CO and CO 2 and the carbon balance were 40%, 45%, and 85%, respectively. The dielectric constant and electrical property results indicated that the surface discharge capacity of the catalysts played a major role in the degradation of o-DCB, and a higher dielectric constant could suppress the plasma expansion and enhance the duration of the plasma discharge per discharge cycle. According to the O1s XPS and O 2 -TPD results, the conversion of CO to CO 2 follows the M-v-K mechanism; thus, the active species on the catalyst surface play an important role. Moreover, the CuMnTiO x and NTP mixed system exhibited excellent stability, which is probably because Cu doping improved the lifetime of the catalyst. This work can provide an experimental and theoretical basis for research in the degradation of o-DCB by plasma catalyst systems., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

A chemistry module has been implemented in Geant4-DNA since Geant4 version 10.1 to simulate the radiolysis of water after irradiation. It has been used in a number of applications, including the calculation of G-values and early DNA damage, allowing the comparison with experimental data. Since the first version, numerous modifications have been made to the module to improve the computational efficiency and extend the simulation to homogeneous kinetics in bulk solution. With these new developments, new applications have been proposed and released as Geant4 examples, showing how to use chemical processes and models. This work reviews the models implemented and application developments for modeling water radiolysis in Geant4-DNA as reported in the ESA BioRad III Project., (© 2024 American Association of Physicists in Medicine.)

Pyrolysis and combustion experiments were carried out on a single wood pellet in a drop tube furnace, under a heating rate of 5 °C/min. Kinetic modeling was done using the Extended Independent Parallel Reaction model. Three reactions were considered in the pyrolysis case and only one in the combustion case. In the pyrolysis case, a thermal model was proposed which is coupled with the kinetic model to correct the temperature inside the pellet. In the combustion case, the mass rate curve was compared to that of the main gaseous emissions. The mass and mass rate curves between the combustions of a pellet in the drop tube furnace and of pellet sawdust in a thermobalance under the same heating rate were compared., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Comprehensive Air Quality Model with extensions (CAMx)-Decoupled Direct Method (DDM) simulations of first-order ozone (O 3 ) sensitivity to nitrogen oxides (NO x ) and volatile organic compounds (VOCs) emissions were performed and combined with modelled [Formula: see text] ratios to obtain a range of thresholds for determining O 3 -sensitivity regimes for different areas of China. Utilising the new threshold ranges for photochemical indicators, the method for determining O 3 formation in the Ozone Source Apportionment Technology (OSAT) module within CAMx was improved by a dynamically varied threshold of [Formula: see text] ratio. The O 3 concentration contributions in the newly added transition regime were apportioned to NO x and VOCs emissions in proportion to the relationship between the [Formula: see text] ratio and first-order O 3 sensitivity. The source contributions of O 3 concentrations from different emission sectors from June to September 2019 were compared using the original and improved CAMx-OSAT. The results showed that the O 3 concentration contributions changed significantly in the NO x -limited regime, with a maximum decrease of 21.89%, while the contributions increased by up to 7.57% in the VOC-limited regime, and were within 15 µg/m 3 in the transition regime. The modified OSAT module enabled a more sophisticated attribution of O 3 to precursor emissions and may have far-reaching implications for informing O 3 pollution control policy., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2023. Published by Elsevier B.V.)

After the Fukushima Daiichi nuclear power plant accident, the terrestrial environment became severely contaminated with radiocesium. Consequently, the river and lake water in the Fukushima area exhibited high radiocesium levels, which declined subsequently. The partition coefficient of 137 Cs between the suspended sediment (SS) and dissolved phases, K d , was introduced to better understand the dynamic behavior of 137 Cs in different systems. However, the K d values in river water, ranging from 2 × 10 4 to 7 × 10 6  L kg -1 , showed large spatiotemporal variability. Therefore, the factors controlling the 137 Cs partition coefficient in natural water systems should be identified. Herein, we introduce a chemical model to explain the variability in 137 Cs K d in natural water systems. The chemical model includes the complexation of Cs + with mineral and organic binding sites in SS, metal exchange reactions, and the presence of colloidal species. The application of the chemical model to natural water systems revealed that Cs + is strongly associated with binding sites in SS, and a major chemical interaction between 137 Cs and the binding sites in SS is the isotope exchange reaction between stable Cs and 137 Cs, rather than metal exchange reactions with other metal ions such as potassium ions. To explain the effect of the SS concentration on K d , the presence of colloidal 137 Cs passing through a filter is significant as the dominant dissolved species of 137 Cs in river water. These results suggest that a better understanding of stable Cs dissolved in natural water is important for discerning the geochemical and ecological behaviors of 137 Cs in natural water., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In this study, we have utilized theoretical calculations to predict the reaction active sites of naproxen when reacting with radicals and to further study the thermodynamics and kinetics of the reactions with ·OH and SO 4 -· . The evidence, derived from the average local ionization energy and electrostatic potential, points to the naphthalene ring as the preferred site of attack, especially for the C2, C6, C9, and C10 sites. The changes in Gibbs free energy and enthalpy of the reactions initiated by ·OH and SO 4 -· ranged between -19.6 kcal/mol - 26.3 kcal/mol and -22.3 kcal/mol -18.5 kcal/mol, respectively. More in-depth investigation revealed that RA2 pathway for ·OH exhibited the lowest free energy of activation, suggesting this reaction is more inclined to proceed. The second-order rate constant results indicate the ·OH attacking reaction is faster than reactions initiated by SO 4 ·- , yet controlled by diffusion. The consistency between theoretical findings and experimental data underscores the validity of this computational method for our study., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The separation and purification of plant-based Active Pharmaceutical Ingredients (API) from extracts is a crucial part in pharmaceutical process development. For the purification of the antimalarial drug component artemisinin (ARTE) from an Artemisia anna L. toluene extract, antisolvent crystallization is considered. Solubilities of ARTE in binary solvent mixtures of toluene and two potential antisolvents, n-heptane and ethanol, were determined at temperatures from 278.15 K to 313.15 K. The experimental work was supported by the application of various models, utilizing varying amounts of experimental input data. The goal was the identification of models that are able to predict solubilities in binary solvent mixtures sufficiently accurate and, thus, can help to reduce the experimental effort for future solvent screenings. In this study, we applied the PC-SAFT model both with and without fitting the binary interaction parameter k ij between ARTE and the respective solvent, as well as the empirical Jouyban-Acree model. From the experiments, n-heptane demonstrated to be a promising antisolvent, while ethanol acted more as a cosolvent. All models tested were capable of distinguishing between effective and ineffective antisolvents. The purely predictive PC-SAFT model applied with k ij = 0 exhibited the largest deviation from the experimental data. This was followed by the PC-SAFT model including fitted k ij values, based on at least four experimental data points. The Jouyban-Acree model fitted the data most accurately. Its parametrization required a minimum of ten experimental data points., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Reversed-phase (RP) liquid chromatography is an important tool for the characterization of materials and products in the pharmaceutical industry. Method development is still challenging in this application space, particularly when dealing with closely-related compounds. Models of chromatographic selectivity are useful for predicting which columns out of the hundreds that are available are likely to have very similar, or different, selectivity for the application at hand. The hydrophobic subtraction model (HSM1) has been widely employed for this purpose; the column database for this model currently stands at 750 columns. In previous work we explored a refinement of the original HSM1 (HSM2) and found that increasing the size of the dataset used to train the model dramatically reduced the number of gross errors in predictions of selectivity made using the model. In this paper we describe further work in this direction (HSM3), this time based on a much larger solute set (1014 solute/stationary phase combinations) containing selectivities for compounds covering a broader range of physicochemical properties compared to HSM1. The molecular weight range was doubled, and the range of the logarithm of the octanol/water partition coefficients was increased slightly. The number of active pharmaceutical ingredients and related synthetic intermediates and impurities was increased from four to 28, and ten pairs of closely related structures (e.g., geometric and cis-/trans- isomers) were included. The HSM3 model is based on retention measurements for 75 compounds using 13 RP stationary phases and a mobile phase of 40/60 acetonitrile/25 mM ammonium formate buffer at pH 3.2. This data-driven model produced predictions of ln α (chromatographic selectivity using ethylbenzene as the reference compound) with average absolute errors of approximately 0.033, which corresponds to errors in α of about 3 %. In some cases, the prediction of the trans-/cis- selectivities for positional and geometric isomers was relatively accurate, and the driving forces for the observed selectivity could be inferred by examination of the relative magnitudes of the terms in the HSM3 model. For some geometric isomer pairs the interactions mainly responsible for the observed selectivities could not be rationalized due to large uncertainties for particular terms in the model. This suggests that more work is needed in the future to explore other HSM-type models and continue expanding the training dataset in order to continue improving the predictive accuracy of these models. Additionally, we release with this paper a much larger data set (43,329 total retention measurements) at multiple mobile phase compositions, to enable other researchers to pursue their own lines of inquiry related to RP selectivity., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dwight R. Stoll reports equipment, drugs, or supplies were provided by Agilent Technologies Inc. Dwight R. Stoll reports financial support was provided by National Science Foundation. Other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Real-time state estimation in chromatography is a useful tool to improve monitoring of biopharmaceutical downstream processes, combining mechanistic model predictions with real-time data acquisition to obtain an estimation that surpasses that of either approach individually. One common technique for real-time state estimation is Kalman filtering. However, non-linear adsorption isotherms pose a significant challenge to Kalman filters, which are dependent on fast algorithm execution to function. In this work, we apply Kalman filtering of non-constant elution conditions using a non-linear adsorption isotherm using a novel approach where dual Kalman filters are used to estimate the states of the adsorption modifier, salt, and the components to be separated. We performed offline tuning of the Kalman filters on real chromatogram data from a linear gradient, ion-exchange separation of two proteins. The tuning was then validated by running the Kalman filters in parallel with a chromatographic separation in real time. The resulting, tuned, dual Kalman filters improved the L2 norm by 53 % over the open-loop model prediction, when compared to the true elution profiles. The Kalman filters were also applicable in real-time with a signal sampling frequency of 5 s, enabling accurate and robust estimation and paving the way for future applications beyond monitoring, such as real-time optimal pooling control., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The single-component Mollerup model, with over 40 direct applications and 442 citations, is the most widely used activity model for chromatographic mechanistic modeling. Many researchers have extended this formula to multi-component systems by directly adding subscripts, a modification deemed thermodynamically inconsistent (referred to as the reference model). In this work, we rederived the asymmetric activity model for multi-component systems, using the van der Waals equation of state, and termed it the multi-component Mollerup model. In contrast to the reference model, our proposed model accounts for the contributions of all components to the activity. Three numerical experiments were performed to investigate the impact of the three different activity models on the chromatographic modeling. The results indicate that our proposed model represents a thermodynamically consistent generalization of the single-component Mollerup model to multi-component systems. This communication advocates adopting of the multi-component Mollerup model for activity modeling in multi-component chromatographic separation to enhance thermodynamic consistency., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The common presence of glycidyl esters (GEs) in refined vegetable oils has been a concern for food safety. The present study aimed to investigate the inhibitory effects of three carotenoids derived from Haematococcus pluvialis microalga on GE formation in both rice oil and a chemical model during heating. The addition of astaxanthin (AS), lutein (LU), and β-carotene (CA) at 0.6 mg/g in rice oil can reduce GE formation by 65.0%, 57.1%, and 57.5%, respectively, which are significantly higher than those achieved by common antioxidants such as l-ascorbyl palmitate (39.0%), α-tocopherol (18.5%), tert -butyl hydroquinone (42.7%), and quercetin (26.2%). UPLC-Q-TOF-MS/MS analysis showed that two new compounds, that is, propylene glycol monoester and diester of palmitic acid, were formed in the CA-added chemical model, which provided direct experimental evidence for the inhibition of antioxidants including AS, LU, and CA against GE formation not only by indirect antioxidative action but also by direct radical reactions to competitively prevent the formation of cyclic acyloxonium intermediates. Furthermore, it was interestingly found that only AS could react with the GEs. The adduct of AS with GEs, astaxanthin-3-O-propanetriol esters, was preliminarily identified using Q-TOF-MS/MS in the heated AS-GE model, suggesting that reacting with GEs might represent another distinct mechanism of AS to eliminate GEs.

The capture of CO 2 by biochar has recently become one of the cornerstones of circular economy models for a sustainable society. In this work, we synthesized an activated biocarbon using Trametes gibbosa (BioACTG) in a one-step synthesis. We investigated CO 2 adsorption mechanisms under five different temperatures using a statistical physics approach. The data was better represented by the multilayer model with two distinguished energies, providing more accurate values for the estimated parameters. According to the number of carbon dioxide molecules per site (n) and the densities of the receptor sites (D zif ), the tendency to form a second layer increased as the temperature increased. The adsorption of CO 2 on BioACTG was exothermic (the values of Q asat  = 15.5 mmol/g at 273 K decrease to 10.5 mmol/g at 353 K), and the temperature influenced CO 2 as well as the morphological features of the process. A computational approach was used to investigate the electronic properties of the adsorbate, showing that its lowest unoccupied orbital (LUMO) heavily contributed to the high efficiency of the process which was ruled by pore diffusion mechanisms driven by energetic fluctuations. Other molecules present in CO 2 -rich mixtures were also investigated, showing that their concentration limited their competitiveness with CO 2 ., Competing Interests: Declaration of competing interest The authors declare that they have no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This study investigated the influence of pH on the retention of solutes using a mixed-mode column with carboxyl (-COOH) groups acting as weak cation exchanger bonded to the terminal of C18 ligands (C18-WCX column) and a traditional reversed-phase C18 column. First, a model based on electrostatic theory was derived and successfully used to predict the retention of charged solutes (charged, and ionizable) as a function of mobile phase pH on a C18-WCX column. While the Horváth model predicts the pH-dependent retention of ionizable solutes in reversed-phase liquid chromatography (RPLC) solely based on solute ionization, the developed model incorporates the concept of surface potential generated on the surface of the stationary phase and its variation with pH. To comprehensively understand the adsorption process, adsorption isotherms for these solutes were individually acquired on the C18-WCX and reversed-phase C18 columns. The adsorption isotherms followed the Langmuir model for the uncharged solute and the electrostatically modified Langmuir model for charged solutes. The elution profiles for the single components were calculated from these isotherms using the equilibrium dispersion column model and were found to be in close agreement with the experimental elution profiles. To enable modelling of two-component cases involving charged solute(s), a competitive adsorption isotherm model based on electrostatic theory was derived. This model was later successfully used to calculate the elution profiles of two components for scenarios involving (a) a C18 Column: two charged solutes, (b) a C18 Column: one charged and one uncharged solute, and (c) a C18-WCX Column: two charged solutes. The strong alignment between the experimental and calculated elution profiles in all three scenarios validated the developed competitive adsorption model., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

In downstream processing, the intricate nature of the interactions between biomolecules and adsorbent materials presents a significant challenge in the prediction of their binding and elution behaviors. This complexity is further heightened in multi-modal chromatography (MMC), which employs two distinct binding mechanisms. To gain a deeper understanding of the involved interactions, simulating the adsorption of biomolecules on resin surfaces is a focal point of ongoing research. However, previous studies often simplified the adsorbent surface, modeling it as a flat or slightly curved plane without including a realistic backbone structure. Here, we introduce and validate two novel workflows aimed at predicting peptide binding behaviors in MMC, specifically targeting methacrylate-based resins. Our first achievement was the development of an all-atom model of a commercial MMC resin surface, incorporating its polymethacrylic backbone. Furthermore, we established and tested a workflow for rapid calculations of binding free energies (ΔG) with 10 linear peptides as target molecules. These ΔG calculations were effectively used to predict Langmuir constants, achieving a high coefficient of determination (R²) of 0.96. In subsequent benchmarking tests, our model outperformed established, simpler resin surface models in terms of predictive capabilities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Simulations were conducted to evaluate the potential of several hundred reversed-phase columns to separate small molecules. By calculating the retention factor of compounds in randomly generated virtual mixtures via the HSM (hydrophobic subtraction model) and applying basic chromatography theory, the simulation can estimate the retention time and peak width of every virtual compound and calculate the resolution between every adjacent pair of compounds. A preferred column set based on the number of successful separations of randomly generated virtual mixtures was developed. The tandem-column liquid chromatography (TC-LC) approach can separate 53.2 % of the 16-compound samples using 20 tandem-column pairs, while a single-column approach can only separate 42.6 % of the 16-compound samples with 20 single columns. The preferred set of columns obtained from the simulation was almost the same as the empirical set of columns previously obtained. In screening applications, TC-LC can achieve a comparably successful separation factor (selectivity) with a smaller column inventory (nine 50-mm columns) compared to the larger inventory needed by single-column LC (twenty-one 100-mm columns)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Herein, an improved subtraction model was proposed to characterise the polar stationary phases in supercritical fluid chromatography (SFC). Fifteen stationary phases were selected, including two types of aromatic columns, Waters Torus and Viridis series columns, as well as silica and amino columns. Ethylbenzene and Torus 1-AA were defined as the reference solute and column, respectively. Identifying the interaction with the maximum contribution to retention in SFC separation and using it as the initial term is a key step in modelling. The dipole, or induced dipole interaction (θ ' P), replaced the hydrophobic interaction (η ' H) as the starting term. The improved model was expressed as logα=η ' H+β ' A+α ' B+κ ' C+θ ' P+ε ' E+σ ' S, where the term ε ' E indicated that anion exchange interaction was intentionally supplemented. A 7-step modelling process, including bidirectional fitting and residual analysis, was proposed. The obtained column parameters had reasonable physical significance, with the adjusted determination coefficient (R 2 adj ) greater than 0.999 and the standard error (SE) less than 0.029. Methodological validation was further performed using the other four columns and 12 solutes that were not involved in the modelling. The result revealed good predictions of solutes' retention, as demonstrated by R 2 adj from 0.9923 to 0.9979 and SE from 0.0636 to 0.1088. This study indicated the feasibility of using the improved subtraction model to characterise polar stationary phases in SFC, with the most crucial being the determination of an initial term, followed by the addition of a new descriptor and the selection of an appropriate reference column. The study expanded the application scope of the subtraction model in SFC, which will help gain an in-depth understanding of the SFC separation mechanism., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2024. Published by Elsevier B.V.)

Quantitative Structure-Retention Relationship models were developed to identify phenolic compounds using a typical LC- system, with both UV and MS detection. A new chromatographic method was developed for the separation of fifty-two standard phenolic compounds. Over 5000 descriptors for each standard were calculated using AlvaDesc software and then selected through Genetic Algorithm. The selected descriptors were used as variables for models construction and to obtain a better understanding of the retention behaviour of phenols during reverse-phase separation. Three distinct molecule sets, including fifty-two phenolic compounds (Set 1), 32 flavonoids (Set 2) and 15 mono-substituted flavonoids were divided into training and validation sets to build Partial Least Square, Multiple Linear Regression and Partial Least Square-Artificial Neural Network models. To assess the predictivity of the models, these were tested on a bergamot juice sample. Partial Least Square and Partial Least Square-Artificial Neural Network exhibit the lowest prediction error, and the latter showed the best predictive power in real sample recognition. The building and implementation of such predictive models showed to be a powerful tool to identify phenolic compounds based on retention data and avoiding the use of expensive and sophisticated detectors such as tandem MS., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Chromatographic separation processes are most often modeled in the form of partial differential equations (PDEs) to describe the complex adsorption equilibria and kinetics. However, identifying parameters in such a model requires substantial computational effort. In this work, a novel parameter estimation approach using a Physics-informed Neural Network (PINN) model is developed and tested for a binary component system. Numerical accuracy of our PINN model is confirmed by validating its simulations against those of the finite element method (FEM). Furthermore, model parameters in the kinetic model are estimated by the PINN model with sufficient accuracy from the observed data at the column outlet, where parameter fitting error can be reduced by up to 35.0 % from the conventional method. In a comparison with the conventional numerical method, our approach can reduce the computational time by up to 95 %. The robustness of the PINN model has also been demonstrated by estimating model parameters from noisy artificial experimental data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Mechanistic models are powerful tools for chromatographic process development and optimization. However, hydrophobic interaction chromatography (HIC) mechanistic models lack an effective and logical parameter estimation method, especially for multi-component system. In this study, a parameter-by-parameter method for multi-component system (called as mPbP-HIC) was derived based on the retention mechanism to estimate the six parameters of the Mollerup isotherm for HIC. The linear parameters (k s,i and k eq,i ) and nonlinear parameters (n i and q max,i ) of the isotherm can be estimated by the linear regression (LR) and the linear approximation (LA) steps, respectively. The remaining two parameters (k p,i and k kin,i ) are obtained by the inverse method (IM). The proposed method was verified with a two-component model system. The results showed that the model could accurately predict the protein elution at a loading of 10 g/L. However, the elution curve fitting was unsatisfactory for high loadings (12 g/L and 14 g/L), which is mainly attributed to the demanding experimental conditions of the LA step and the potential large estimation error of the parameter q max . Therefore, the inverse method was introduced to further calibrate the parameter q max , thereby reducing the estimation error and improving the curve fitting. Moreover, the simplified linear approximation (SLA) was proposed by reasonable assumption, which provides the initial guess of q max without solving any complex matrix and avoids the problem of matrix unsolvable. In the improved mPbP-HIC method, q max would be initialized by the SLA and finally determined by the inverse method, and this strategy was named as SLA+IM. The experimental validation showed that the improved mPbP-HIC method has a better curve fitting, and the use of SLA+IM reduces the error accumulation effect. In process optimization, the parameters estimated by the improved mPbP-HIC method provided the model with excellent predictive ability and reasonable extrapolation. In conclusion, the SLA+IM strategy makes the improved mPbP-HIC method more rational and can be easily applied to the practical separation of protein mixture, which would accelerate the process development for HIC in downstream of biopharmaceuticals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The use of a ternary mobile-phase system comprising ammonium sulphate, sodium chloride, and phosphate buffer was explored to tune retention and enhance selectivity in hydrophobic interaction chromatography. The accuracy of the linear solvent-strength model to predict protein retention with the ternary mobile-phase system based on isocratic scouting runs is limited, as the extrapolated retention factor at aqueous buffer conditions (k 0 ) cannot be reliably established. The Jandera retention model utilizing a salt concentration averaged retention factor (k¯ 0 ) in aqueous buffer for ternary systems overcomes this bottleneck. Gradient retention factors were derived based on isocratic scouting runs after numerical integration of the isocratic Jandera model, leading to retention-time prediction errors below 11 % for linear gradients. Furthermore, an analytical expression was formulated to predict HIC retention for both linear and segmented linear gradients, considering the linear solvent-strength (LSS) model within ternary salt systems, relying on a fixed k 0 . The approach involved conducting two gradient scouting runs for each of the two binary salt systems to determine model parameters. Retention-time prediction errors for linear gradients were below 12 % for lysozyme and 3 % for trypsinogen and α-chymotrypsinogen A. Finally, the analytical expression for a ternary mobile-phase system was used in combination with a genetic algorithm to tune the HIC selectivity. With an optimized segmented ternary gradient, a critical-pair separation for a mixture of 7 proteins was achieved within 15 min with retention-time prediction errors ranging between 0.7 and 15.7 %., Competing Interests: Declaration of competing interest The authors declare that they have no conflict in interest., (Copyright © 2024. Published by Elsevier B.V.)

Applying the recently introduced patchwork model for porous media, we present a new step forward in the modelling of eddy dispersion in chromatographic columns. The logarithmic law describing the velocity dependency emerging from this patchwork model is supplemented with a retention factor dependency via first principles modelling of the variations in flow resistance and retention capacity caused by the packing disorder. Furthermore, it is shown the derived expression is also able to fit the eddy dispersion originating from the wall effect on the packing. When applied to literature data of eddy dispersion, the newly introduced logarithmic law has a goodness of fit that is at least equal to that of Knox' empirical power law (R 2 >0.98). The main difference is that, whereas Knox' power law requires a separate fit for each component due to the retention factor dependency, the present model simultaneously fits all plate height curves measured on one chromatographic column, using only two parameters with a clear physical meaning., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The solvation parameter model uses six descriptors identified as excess molar refraction, E, dipolarity/polarizability, S, overall hydrogen-bond acidity, A, overall hydrogen-bond basicity, B, McGowan's characteristic volume, V, and the gas-liquid partition constant on hexadecane at 25 °C, L to model the distribution of neutral compounds in biphasic systems. Abraham's version of this model uses all six descriptors with two separate linear free energy relationship models for the transfer of compounds from a gas phase to a condensed phase and between condensed phases. Goss proposed a modification to this model that uses a single calibration model regardless of the physical state for each phase and five of the descriptors employed in Abraham's model (E descriptor is eliminated). The capability of Abraham's model and the Goss-modified model to characterize the contribution of intermolecular interaction to retention for gas and reversed-phase liquid chromatographic systems and distribution in liquid-liquid partition systems is evaluated using the WSU compound descriptor database. These more accurate values for the Abraham descriptors have not been utilized previously for the evaluation of the Goss-modified model and should be more capable of discerning subtle differences in model performance. It is shown that model quality defined by statistical parameters favors Abraham's model over the Goss-modified model with differences in model quality greater for systems in which Abraham's model indicates a significant contribution from electron lone pair interactions and for systems in which one phase is a solvent containing perfluoroalkyl substituents. There is a small systematic difference for the terms describing the combined contributions of cavity formation and dispersion interactions and for interactions of a dipole-type. The contribution of hydrogen-bonding interactions is virtually identical for the two models. The model intercepts are generally different and potentially assigned to a larger contribution from lack-of-fit for the Goss-modified model. Although the Abraham model descriptors have been routinely employed for applications using the Goss-modified model the possibility that Goss-model specific descriptors should be employed was evaluated. Using the Solver method and Goss-model specific calibration models for chromatographic and liquid-liquid partition systems a new set of Goss-specific descriptors was calculated for 28 varied compounds. These descriptors show good statistical agreement with the Abraham descriptor values with an average deviation of 0.009, -0.003, -0.004, and -0.023, respectively, for the S, A, B, and L descriptors, corresponding to a relative absolute deviation in percent of 2.2 %, 3.9 %, 4.3 %, and 1.2 %, respectively., Competing Interests: Declaration of competing interest The author declares that he has no conflicts of interest in connection with this study., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The solvation parameter model uses six compound descriptors to model equilibrium properties in biphasic systems formally defined as excess molar refraction, E, dipolarity/polarizability, S, overall hydrogen-bond acidity, A, overall hydrogen-bond basicity, B, McGowan's characteristic volume, V, and the gas-liquid partition constant on hexadecane at 25 °C, L. The V descriptor can be assigned from structure and the E descriptor for compounds liquid at 20 °C can be calculated from its refractive index and characteristic volume. The E descriptor for compounds solid at 20 °C and the S, A, B, and L descriptors are assigned from experimental properties traditionally obtained by chromatographic, liquid-liquid partition, and solubility measurements. Here I report an efficient experimental design using the Solver method for the accurate assignment of descriptors for neutral compounds that simultaneously minimizes laboratory resources. This multi-technique approach requires 3 retention factor measurements in a 60 °C temperature range per compound on four columns by gas chromatography, 3 retention factor measurements in a 30 % (v/v) acetonitrile composition range per compound on two columns by reversed-phase liquid chromatography, and eight partition constant measurements by liquid-liquid partition in totally organic and aqueous biphasic systems for a total of 26 experimental measurements. The accuracy of the descriptor assignments was validated by comparison with the values in the Wayne State University (WSU) descriptor database taken as the best estimate of the true descriptor values. The E, S, A, B and L descriptors were assigned simultaneously by the Solver method using the above approach without significant bias and with an average absolute deviation (AAD) of 0.054, 0.018, 0.015, 0.013, and 0.040, respectively, compared with the WSU database values, corresponding to a relative absolute average deviation in percent (RAAD) of 7.2, 1.9, 3.6, 5.1, and 0.84 %, respectively, for 32 varied compounds. This streamlined approach represents a significant improvement on earlier single-technique approaches used as the starting point for the development of the multi-technique approach. For compounds of variable hydrogen-bond basicity modifications to the multi-technique approach were implemented while maintaining the same number of experimental measurements. Acceptable descriptor assignments for B/B° were obtained for compounds liquid at 20 °C for which the E descriptor was available by calculation. For solid compounds at 20 °C the E and B/B° descriptors are restricted to qualitative application where approximate values may be acceptable., Competing Interests: Declaration of competing interest The author declares that he has no conflict of interest in connection with this study., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In Australia, trifluralin is one of the commonly used herbicides to manage annual grasses and some broadleaf weeds. However, it may have some ecosystem impacts such as high toxicity to terrestrial and aquatic life, so it is vital to monitor the degradation of trifluralin for a considerable period for environmental safety. For risk assessment purposes, it is necessary to estimate the half-life of trifluralin, which is often evaluated using derived mathematical dissipation models. In the literature, bi-exponential (BEXP) and gamma models were suggested for modelling the dissipation of trifluralin in soil. Both models provide the half-life estimate without discussing the uncertainty of the estimate, which is a shortcoming in the literature. In this paper, we used simulation to illustrate the importance of estimate's uncertainty (standard error) and demonstrated a method to compute the standard error for the half-life estimate mathematically for kinetic dissipation models. Later, we evaluated the performance of the two suggested models using statistical indices. The computation of the half-life and the standard error of the half-life estimate were discussed. This allows us to describe the inference of the half-life parameter and determine whether the half-life estimates are significantly different against the co-variate (moisture) levels. We demonstrated the method to calculate the standard error of the half-life of trifluralin, which allows us to determine the statistical difference between the estimates. In this study, we found that the half-life of trifluralin in soil tends to increase with increasing moisture levels, and the half-life of trifluralin in soil with 100% moisture level is significantly greater than 40% and 70% moisture levels. Our findings suggest that soil moisture levels should be carefully considered before trifluralin application to minimize the non-target environmental damage., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Tetracycline (TC) and Cu(II) coexist commonly in various waters, which may infiltrate into the subterranean environment through runoff and leaching, resulting in substantial ecological risks. However, the underlying mechanisms why Cu(II) affects the transport of TC in porous media remain to be further explored and supported by more evidence, especially the role of complexation. In this study, the transport of TC with coexisting Cu(II) was comprehensively explored with column experiments and density functional theory (DFT) calculation. At natural environmental concentrations, Cu(II) significantly inhibited the transport of TC in the quartz sand column. Cu(II) augmented the retention of TC in the column mainly via electrostatic force and complexation. The interaction between TC and TC-Cu complexes on the surface of SiO 2 was investigated with first-principles calculations for the first time. There were strong van der Waals forces and coordination bonds on the surface of complexes and SiO 2 , leading to higher adsorption energy than that of TC and inhibiting its penetration. This study offers novel insights and theoretical framework for the transport of antibiotics in the presence of metal ions to better understand the fate of antibiotics in nature.