Search

Solving the challenges faced during the measurement of the cross-interface transfer of perfluoroalkyl acids (PFAAs) in lakes is crucial for clarifying environmental behaviours of these chemicals and their efficient governance. This study developed a multimedia fugacity model based on the quantitative water-air-sediment interaction (QWASI) covering abiotic/biotic matrices to investigate the cross-interface transfer and fate of PFAAs in Luoma Lake, a typical PFAA-contaminated shallow lake in eastern China. The accuracy and reliability of the established model were confirmed using Percent bias and Monte Carlo simulation, respectively. Using the QWASI model, the multimedia transfer of the PFAAs and their accumulation and persistence in different sub-compartments were described and measured, and the differences among individual PFAAs were explored. The simulation results showed that the sedimentation and resuspension of PFAAs were the most intense cross-interfacial transfers, and the sediments served as a chemical sink in the long term. A significant negative correlation of N C-F (the number of CF bonds) with the relative outflow flux (T W · out-ct ) but a positive correlation with the relative net transfer across the interface between water and aquatic plants (T p-ct ) was detected, indicating that the PFAA migration capacity decreased but the bioaccumulation potential increased with the CF bond number. The persistence in water (P w ) of individual PFAAs ranged from 19.65d (PFOA) to 32.22d (PFOS), with an average of 26.15d; their persistence in sediment (P s ) ranged from 432d (PFBA) to 3216d (PFOS), with an average of 1524d, increasing linearly with an increase in N C-F . The water advection flows into and out of the lake (Q W · in and Q W · out ), the PFAA concentration of water inflow (C W · in ), and bioconcentration factor of aquatic plants (BCF p ) were the primary parameters sensitive to PFAAs in all sub-compartments, which are essential indexes for exploring promising remediation pathways for lacustrine PFAA contamination based on the fugacity model simulation., 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.)

Knowledge of antibiotic desorption from high-temperature biochar is essential for assessing their environmental risks, and for the successful application of biochar to remove antibiotics. In previous studies, irreversible pore deformation, formation of charge-assisted hydrogen bonds or amide bonds were individually proposed to explain the desorption hysteresis of antibiotics on biochars, leading to a debate on hysteresis mechanism. In this study, desorption of sulfamethoxazole (SMX), ciprofloxacin (CFX) and tetracycline (TET) on a wood chip biochar produced at 700 °C (WBC700) and its oxidized product (O-WBC700) was investigated to explore the underlying hysteresis mechanism. Significant desorption hysteresis was observed for SMX, CFX and TET on WBC700 and O-WBC700. Hysteresis index (HI) of each antibiotic was higher on O-WBC700 with more oxygen-containing groups than WBC700, and was higher at lower equilibrium concentration. HI of antibiotics on WBC700 (or O-WBC700) increased in the order of SMX < CFX < TET. The calculated adsorption enthalpy of each antibiotic on WBC700 was positive, indicating an endothermic process. These phenomena together with FTIR, XPS spectra confirmed that the desorption hysteresis mechanism of antibiotics on high-temperature biochar is the formation of amide bonds by amidation reaction, but not the pore deformation or the hydrogen bond. Moreover, antibiotic can form amide bonds with WBC700 only if the amine group with pK a  > 4.0, and the HI values were positively correlated with their pK a values. Amine group of antibiotics with higher pK a value show more nucleophilicity and could form stronger amide bonds with carboxyl group of biochar. The obtained results could help to solve the debate on desorption hysteresis mechanism of antibiotics on high-temperature biochars, and provide a new insight into the role of amine groups and amidation reaction on the hysteresis., 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.)

In mining regions, flotation reagents can interact with heavy metals, thereby increasing the complexity of their migration. However, most current studies solely focus on the migration of heavy metals, neglecting the influence of flotation reagents in their models concerning mining area pollution. This study developed the reactive transport model, Multisurface Speciation Model (MSM), which integrated the reaction processes of the three main soil components (iron oxides, organic matter, clay minerals) and ethyl xanthate (EX), a typical flotation reagent, with cadmium (Cd²⁺) to investigate the effects of EX on the transport and retention of Cd²⁺ in natural porous media under varying pH conditions. The study revealed that EX formed new adsorption sites for Cd²⁺, enhancing its retention and inhibiting transport with increased EX loading (0 to 2.5 mmol·L -1 ), while higher pH levels (ranging from 4 to 8) further strengthened the retention capability of Cd²⁺. The MSM further predicted the solid-phase concentration distribution of Cd²⁺ among various components. With increasing EX-loaded concentrations, xanthate became the dominant adsorbing component, accounting for 48.93 % to 95.31 % of adsorption, and competitively interacted with other components. Xanthate retention was lower under acidic conditions compared to neutral and alkaline environments. Sensitivity analysis highlighted the concentrations of iron oxide adsorption sites (SurfaOH, SurfbOH) as critical parameters in the models, underscoring the need for precise determination of soil physicochemical indicators. This study stressed the crucial role of flotation reagents and pH conditions in controlling heavy metal mobility, offering important insights for environmental management in mining regions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bowen Luo Kouping Chen Jichun Wu Ping Li reports financial support was provided by National Key Research and Development Program of China. Bowen Luo Kouping Chen Jichun Wu reports financial support was provided by National Natural Science Foundation of China. 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 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Hydroponics combined with fugacity model was employed to investigate the kinetics of uptake, accumulation, and metabolism of organophosphate esters (OPEs) by japonica rice. The time-dependent process for uptake and accumulation of 5 OPEs and their diester-metabolites in both rice root and shoot fitted well with the pseudo-first-order kinetic model. The peak OPE accumulations in rice root and shoot were significantly positively or negatively correlated with their octanol-water partition coefficient (logK ow ) respectively, but not for their apparent accumulation rates. Root concentration factors (RCFs) and root-to-shoot translocation factors (TFs) of OPEs were found to be positively and negatively correlated with their logK ow , respectively. Triphenyl phosphate with benzene ring substituents showed the highest RCF, but the lowest TF, because of its high potential for root adsorption due to the π electron-rich structures. Sterilized root exudates can hinder the root adsorption and absorption of OPEs from solution probably through competitive adsorption of OPEs with root surface. The first-hand transport and metabolism rates were also obtained by generating these rates to fit the dynamic fugacity model with the measurement values. The simulation indicated that the kinetics of OPE accumulation in rice plants may be controlled by multiple processes and physicochemical properties besides K ow ., 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.)

Microplastics and antibiotics are receiving increasing attention as two emerging pollutants in the aquatic ecosystem. The absorption of antibiotics by microplastics can potentially intensify their impact on marine organisms and human health. However, the detailed mechanisms underlying this interaction remain to be elucidated. Through molecular dynamics (MD) simulations and density functional theory (DFT) calculations, this study investigated the adsorption of cefradine (CED) onto three typical microplastics (MPs)-polyethylene (PE), polypropylene (PP), and polyamide (PA). The results of the molecular dynamics simulations showed that the interaction energy between CED and microplastics followed the order of PA-CED > PP-CED > PE-CED, indicating that PA microplastics had the highest adsorption capacity for CED antibiotics. The total energy contribution of the microplastics-cefradine (MPs-CED) systems suggested that the van der Waals and electrostatic interactions were the two primary mechanisms for the adsorption of CED by these three microplastics. In DFT calculations, the adsorption of CED on PA was found to be significantly influenced by both electrostatic and van der Waals effects, while the main driving force in the adsorption of PE and PP is van der Waals effect. In addition, IGMH analysis and AIM topological analysis confirmed that the adsorption of CED on PA relied heavily on the synergistic effect of hydrogen bonding and the van der Waals effect. The findings of this study validate the results obtained from molecular dynamics simulations, laying a foundation for a comprehensive exploration of the interaction mechanisms between microplastics and organic pollutants by integrating MD simulations and DFT calculations., Competing Interests: Declaration of competing interest All authors disclosed no relevant relationships., (Copyright © 2024. Published by Elsevier B.V.)

The addition of active ingredients such as antibacterial agent and non-active ingredients such as plastic microspheres (MPs) in personal care products (PCPs) are the common pollutants in the aquatic environment, and their coexistence poses potential threat to the aquatic ecosystem. As a substitute for the traditional antibacterial ingredients triclosan and triclocarban, the usage of parachlormetaxylenol (PCMX) is on the rise and is widely used in PCPs. In this study, the adsorption and desorption behaviors of PCMX were investigated with two typical MPs, polyvinyl chloride (PVC) and polyethylene (PE), and the effects of different aging modes and molecular mechanisms were explored through batch experiments and density functional theory calculation. Both laboratory aging and field aging resulted in surface wrinkles of MPs, along with an increased proportion of oxygen-containing functional groups (CO, -OH). At the same aging time, the degree of laboratory aging was stronger than that of field aging, and the aging degree of PVC was greater that of PE. The aging process enhanced the adsorption capacity of MPs for PCMX. The equilibrium adsorption capacity of PVC increased from 3.713 mg/g (virgin) to 3.823 mg/g (field aging) and 3.969 mg/g (laboratory aging), while that of PE increased from 3.509 mg/g to 3.879 mg/g and 4.109 mg/g, respectively. Meanwhile, aging also resulted in an increase in the desorption capacity of PCMX from PVC and PE. Oxygen-containing functional groups in aged MPs could serve as adsorption sites for PCMX and improved the electrostatic adsorption capacity. Oxygen-containing groups generated on the surface of aged MPs formed hydrogen bonding with the phenolic hydroxyl groups of PCMX, which became the main driving force for adsorption. Our results reveal the potential impact and mechanism of aging on the adsorption of PCMX by MPs, which provides new insights for the interaction mechanism between environmental MPs and associated contaminants., 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.)

This study investigates the formation of atmospheric molecular clusters containing ammonia (NH 3 , A), methylamine (CH 3 NH 2 , MA), or dimethylamine (CH 3 NHCH 3 , DMA) with nitric acid (HNO 3 , NA) using quantum mechanics. The Atmospheric Cluster Dynamic Code (ACDC) was employed to simulate the total evaporation rate, formation rate, and growth pathways of three types of clusters under dry and hydrated conditions. This study evaluates the enhancing potential of A/MA/DMA for NA-based new particle formation (NPF) at parts per trillion (ppt) levels. The results indicate that A/MA/DMA can enhance NA-based NPF at high nitric acid concentrations and low temperatures in the atmosphere. The enhancing potential of MA is weaker than that of DMA but stronger than that of A. Cluster growth predominantly follows the lowest free energy pathways on the acid-base grid, with the formation of initial acid-base dimers (NA)(A), (NA)(MA), and (NA)(DMA) being crucial. Hydration influences the evaporation rate and formation rate of clusters, especially for initial clusters. When the humidity is at 100%, the formation rate for NA-A, NA-MA, and NA-DMA clusters can increase by approximately 10 9 , 10 7 , and 10 4 -fold compared to the corresponding unhydrated clusters, respectively. These results highlight the significance of nitric acid nucleation in NPF events in low-temperature, high-humidity atmospheres, particularly in regions like China with significant automobile exhaust pollution.

Mercury (Hg), a ubiquitous atmospheric trace metal posing serious health risks, originates from natural and anthropogenic sources. India, the world's second-largest Hg emitter and a signatory to the Minamata Convention, is committed to reducing these emissions. However, critical gaps exist in our understanding of the spatial and temporal distribution of Hg across the vast Indian subcontinent due to limited observational data. This study addresses this gap by employing the GEOS-Chem model with various emission inventories (UNEP2010, WHET, EDGAR, STREETS, and UNEP2015) to simulate Hg variability across the Asian domain, with a specific focus on India from 2013 to 2017. Model performance was evaluated using ground-based GMOS observations and available literature data. Emission inventory performance varied across different observational stations. Hence, we employed ensemble results from all inventories. The maximum relative bias for Total Gaseous Mercury (TGM) and Gaseous Elemental Mercury (GEM; Hg 0 ) concentrations is about ±20%, indicating simulations with sufficient accuracy. Total Hg wet deposition fluxes are highest over the Western Ghats and the Himalayan foothills due to higher rainfall. During the monsoon, the Hg wet deposition flux is about 65.4% of the annual wet deposition flux. Moreover, westerly winds cause higher wet deposition in summer over Northern and Eastern India. Total Hg dry deposition flux accounts for 72-74% of total deposition over India. Hg 0 dry deposition fluxes are higher over Eastern India, which correlates strongly with the leaf area index. Excluding Indian anthropogenic emissions from the model simulations resulted in a substantial decrease (21.9% and 33.5%) in wet and total Hg deposition fluxes, highlighting the dominant role of human activities in Hg pollution in India.

Per- and polyfluoroalkyl substances (PFAS) are chemicals of high concern and are undergoing hazard and risk assessment worldwide. Reliable physicochemical property (PCP) data are fundamental to assessments. However, experimental PCP data for PFAS are limited and property prediction tools such as quantitative structure-property relationships (QSPRs) therefore have poor predictive power for PFAS. New experimental data from Endo 2023 are used to improve QSPRs for predicting poly-parameter linear free energy relationship (PPLFER) descriptors for calculating water solubility ( S W ), vapor pressure (VP) and the octanol-water ( K OW ), octanol-air ( K OA ) and air-water ( K AW ) partition ratios. The new experimental data are only for neutral PFAS, and the QSPRs are only applicable to neutral chemicals. A key PPLFER descriptor for PFAS is the molar volume and this work compares different versions and makes recommendations for obtaining the best PCP predictions. The new models are included in the freely available IFSQSAR package (version 1.1.1), and property predictions are compared to those from the previous IFSQSAR (version 1.1.0) and from QSPRs in the US EPA's EPI Suite (version 4.11) and OPERA (version 2.9) models. The results from the new IFSQSAR models show improvements for predicting PFAS PCPs. The root mean squared error (RMSE) for predicting log  K OW versus expected values from quantum chemical calculations was reduced by approximately 1 log unit whereas the RMSE for predicting log  K AW and log  K OA was reduced by 0.2 log units. IFSQSAR v.1.1.1 has an RMSE one or more log units lower than predictions from OPERA and EPI Suite when compared to expected values of log  K OW , log  K AW and log  K OA for PFAS, except for EPI Suite predictions for log K OW which have a comparable RMSE. Recommendations for future experimental work for PPLFER descriptors for PFAS and future research to improve PCP predictions for PFAS are presented.

Analytical solution to ideal chromatography model has been established for binary Langmuir isotherm and rectangular injections. However, retention time of the less adsorbed species, which is of great theoretical and practical significance, cannot be given in a closed form and is conventionally solved by numerical integration with a floating boundary. A simplified approach is provided in this article. A 4th order algebraic equation was derived and used to solve the maximum concentration that can be further used to explicitly calculate retention time. Under most practical conditions, reliable initial guess can be easily acquired, allowing for the application of Newton-Raphson method for rapid determination of the root of the 4th order equation. In addition, derivatives of retention time with respective to isotherm parameters can be given in analytical forms., 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.)

Slalom chromatography (SC) re-emerged in 2024 due to the availability of low adsorption ultra-high pressure liquid chromatography (UHPLC) packed columns/instruments and large modalities being investigated in the context of cell and gene therapies. The physico-chemical principles of SC retention combined with hydrodynamic chromatography (HDC) exclusion have been recently reported. In SC, DNA macromolecules are retarded because: (1) they can be stretched to lengths comparable to the particle diameter, and (2) their elastic relaxation time is long enough to maintain them in non-equilibrium extended conformations under regular UHPLC shear flow conditions. Here, a quantitative HDC-SC retention model is consolidated. A general plate height model accounting for the band broadening of long DNA biopolymers along packed beds is also derived for supporting method development and predicting speed-resolution performance in SC. For illustration, the chromatographic speed-resolution properties in SC are predicted for the separation of specific critical pairs (4.0/4.5, 10/11, and 25/27 kbp) of linear dsDNA polymers. The calculations are performed for two available custom-made particle sizes, d p = 1.7 and 2.5μm, at a constant pressure of 10,000 psi. The predictions are directly validated from experimental data acquired using low adsorption MaxPeak TM 4.6 mm i.d. Columns packed with 1.7μm BEH TM 45 Å (15 cm long column) and 2.5μm BEH 125 Å (30 cm long column) Particles, and by injecting six linear dsDNAs (λ DNA-Hind III Digest). The LC system is very low dispersion ACQUITY TM UPLC TM I-class PLUS System, and the mobile phase is a 100 mM phosphate buffer at pH 8. Maximum resolution is always achieved when the average extended lengths of linear dsDNAs are equal to a critical length, which is proportional to the particle diameter and to the square root of the applied shear rate. Most advantageously, the experimental results reveal that the relaxation times of linear dsDNAs observed under shear flow conditions are two orders of magnitude shorter than those expected in the absence of flow: this enables the detection of the longest linear dsDNAs up to 25 kbp without irremediable loss in column performance. Finally, the retention-efficiency model elaborated in this work can be used to rapidly anticipate and develop methods (selection of particle size, column length, and operating pressure) for any targeted DNA and time-resolution constraints., 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. Published by Elsevier B.V. All rights reserved.)

Axial dispersion in chromatographic columns is responsible for a reduced separation efficiency. In the present research macrotransport theory is used to predict the phenomenological constants related to axial dispersion. We evaluate the efficacy of lateral flow induced by alternating current (AC) in the presence of retaining walls on the separation resolution. Results show that lateral flows induced by laterally applied potentials as low as 0.3 V reduce C-term dispersion by a factor of 5.0 for unretained conditions (k = 0) and 2.7 for retained (k = 5) conditions, with a diffusion coefficient (D m ) of 10 -11 m 2 /s. The present paper further contributes to the understanding of the use of secondary lateral flows for dispersion reduction and offers practical guidance for designing future vortex chromatographic columns. It appears that a maximal performance gain is attained at low aspect ratios (AR=1), with the gain reduced from a factor of 5 to 1.6 for AR=4 for unretained conditions, and from 2.7 to 1.4 for retained conditions (k = 5)., 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.)

Oligonucleotides (ONs) are acquiring clinical relevance and their demand is expected to grow. However, the ON production capacity is currently limited by high manufacturing costs. Since the purification of the target ON sequence from molecularly similar variants represents a major bottleneck, this work presents a resource-effective strategy for the optimization of their preparative reversed-phase chromatographic purification. First, a model based on the equilibrium-dispersive theory was introduced to describe the chromatographic operation. Considering a deoxyribose nucleic acid with 20 nucleobases as case study, a genetic algorithm was developed to efficiently determine the adsorption isotherm and mass transfer parameters for the target ON and impurities. After the estimation of these parameters, a strategy for the in-silico optimization of the operation was established. The product collection window, gradient duration, and resin loading were considered as process variables and their influence on yield and productivity was investigated after setting a purity specification of 99.0%. The optimal process parameters identified through this analysis were experimentally verified, confirming the reliability of the model, calibrated with only 5 experimental runs. In addition, this optimal setpoint was exploited to design the multicolumn countercurrent solvent gradient purification (MCSGP) of this ON mixture, which allowed to boost the yield of the process and to work at cyclic steady state, while respecting the purity constraint. This study confirmed the potential of this in-silico optimization strategy in both improving the performance of the traditional single-column operations and in the rapid development of multicolumn processes., 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.)

Iron-bearing smectite clay minerals can act as electron sources and sinks in the environment. Previous studies using mediated electrochemical analyses to determine the reduction potential ( E H ) values of smectites observed that the relationship between the structural Fe 2+ (s) /Fe Total ratio in the smectite and E H varied based on the redox history of the smectite. We hypothesize that this behavior, referred to as redox hysteresis, results from the smectite particles not equilibrating with the applied E H over the course of the experiment (∼30 min). To test this hypothesis, we developed a model incorporating interfacial electron transfer kinetics and charge redistribution within the particle to simulate the mediated electrochemical experiments from previous studies. The simulated redox curves accurately matched the previously reported experimental redox curves of the smectite SWa-1, demonstrating that longer equilibration periods led to a decrease in redox hysteresis. We validated this experimentally by measuring the redox curve of SWa-1 after an equilibration period of at least 12 h. Furthermore, we extended the simulations to three other smectites (NAu-1, NAu-2, and SWy-2) and extracted their respective thermodynamic and kinetic parameters. This work offers a framework for interpreting and modeling redox reactions on clay surfaces, along with key parameters for four commonly studied smectites.

The formation of energetically favorable and metastable mineral phases within the Fe-H 2 O system controls the long-term mobility of iron complexes in natural aquifers and other environmentally and industrially relevant systems. The fundamental mechanism controlling the formation of these phases has remained enigmatic. We develop a general partial equilibrium model, leveraging recent synchrotron-based data on the time evolution of solid Fe(III) hydroxides along with aqueous complexes. We combine thermodynamic considerations and particle-morphology-dependent kinetic rate equations under full consideration of the aqueous phase in disequilibrium with one or more of the forming minerals. The new model predicts the rate of amorphous 2-line ferrihydrite precipitation, dissolution, and overall transformation to crystalline goethite. It is found that the precipitation of goethite (i) occurs from solution and (ii) is limited by the comparatively slow dissolution of the first forming amorphous phase 2-line ferrihydrite. A generalized transformation mechanism further illustrates that differences in the kinetics of Fe(III) precipitation are controlled by the coordination environment of the predominant Fe(III) hydrolysis product. The framework allows modeling of other iron(bearing) phases across a broad range of aqueous phase compositions.

Protein folding is an intrinsically multitimescale problem. While it is accepted that non-Markovian effects are present on short timescales, it is unclear whether memory-dependent friction influences long-timescale protein folding reaction kinetics. We combine friction memory-kernel extraction techniques with recently published extensive all-atom simulations of the α3D protein under neutral and reduced pH conditions, and we show that the pH reduction modifies the friction acting on the folding protein by dramatically decreasing the friction memory decay time. This switches α3D folding reaction kinetics from the pronounced non-Markovian regime, where memory significantly accelerates folding, to the Markovian regime, where memory does not influence the folding time. We explore salt-bridge interactions, which are eliminated under pH reduction, as a key microscopic origin of non-Markovian friction in α3D.

The assumption of local equilibrium, especially in test standards for assessing the leaching of hazardous substances from materials, is crucial for the use of test results and the robustness of testing. However, previous studies of contact time conditions in percolation test standard have evaluated equilibrium and robustness separately. Therefore, this study tests the assumption of local equilibrium in the up-flow percolation test, standardized as ISO 21268-3 in 2019, and discusses the relationship between the similarity of test results and degree of equilibrium. Thus, we conducted approximately 6000 numerical simulations in total with varying leaching parameters to determine breakthrough curves (BTCs) for the substances investigated by the test standard. The results showed that the two BTCs for the longest and shortest contact time conditions within the standard test were identical over a wide range of parameters, supporting the robustness of the standard test. Interestingly, identical BTCs occur in equilibrium or near-equilibrium and nonequilibrium leaching. This finding indicates the need to reconsider the conventional interpretation that equilibrium is reached when test results with different contact time conditions appear identical and encourages efforts to develop procedures to verify equilibrium leaching., 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.)

Column-leaching tests are a common approach for assessing the leaching behavior and resulting environmental risks of contaminated soils and waste materials, which are frequently reused for various construction purposes. The observed breakthrough curves of the contaminants are influenced by the complex dynamics of solute transport and kinetic inter-phase mass transfer. Disentangling these interactions necessitates numerical models. However, inverse modeling and sensitivity analysis can be time-consuming, especially when sorption kinetics are explicitly described by intraparticle diffusion, which requires discretizing the domain both in the flow direction along the column axis and inside the grains. To circumvent the need for such computationally intensive models, we have developed two different ensemble surrogate models. These models employ two separate ensemble methods: random forest stacking and inverse-distance weighted interpolation. Each method is applied to base surrogate models that cover different parts of the parameter space. The base surrogate models use the method of Extremely randomized Trees (ExtraTrees). The defined parameter range is based on the German standard for column-leaching tests. To optimize the base surrogate models, we utilized adaptive-sampling methods based on three distinct infill criteria: maximizing the expected improvement, staying within a certain Mahalanobis distance to the best estimate (both for exploitation), and maximizing the standard deviation (for exploration). The ensemble surrogate model demonstrates excellent performance in emulating the behavior of the original numerical model, with a relative root mean squared error of 0.09. We applied our proposed ensemble surrogate model to estimate the complete posterior parameter distribution using Simulation-Based Inference, specifically Neural Posterior Estimation, to determine the full parameter distribution conditioned on copper-leaching data from two different soils. Samples drawn from the posterior distribution align perfectly with the observed data for both the surrogate and original models., Competing Interests: Declaration of competing interest The authors declare no competing financial interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The transport of per- and polyfluoroalkyl substances (PFASs) through unsaturated source-zone soils is a critical yet poorly understood aspect of their environmental behavior. To date, most experimental studies have only focused on the equilibrium or non-equilibrium partitioning of PFASs to the air-water interface, or solid-phase based equilibrium or non-equilibrium transport. Currently, there are discrepancies between air-water interfacial partitioning (K ia ) results measured using a drainage-based column method (which supports a Langmuir isotherm) when compared to measurements from alternative experimental methods (which support a Freundlich isotherm). We hypothesize that this discrepancy is the result of non-Fickian transport conditions developing during column tests using the drainage method, which reduces the magnitude of the apparent K ia (K ia,app ) when estimated using the retardation factor correlation from breakthrough curve experiments. To test the validity of this hypothesis, the drainage method was implemented using PFOS in a sand column and compared with prior data collected using a quasi-saturated column method. Results demonstrate that the apparent K ia was reduced by 3 to 123-fold, resulting in up to 123-fold faster breakthrough of PFOS than predicted with the assumption of equilibrium adsorption to the air-water interface. A novel mobile-immobile model (MIM) of PFAS fate and transport was developed, incorporating a term for anomalously adsorbed solute in the mobile zone to explain highly anomalous data. The modelling results using a modified HYDRUS-1D software show that anomalous air-water interfacial adsorption and/or flowpath channelization are plausible mechanisms for accelerated transport of PFOS and support the application of a Freundlich isotherm for PFOS. Overall, non-Fickian transport mechanisms demonstrate the potential to accelerate PFOS transport through the vadose zone by up to a factor of 123 under specific circumstances. This work demonstrates the assumption of equilibrium adsorption to air-water interfaces, even for homogeneous laboratory experiments, is not necessarily valid., Competing Interests: Declaration of competing interest John Forrest Stults reports financial support was provided by Strategic Environmental Research and Development Program. 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 Elsevier B.V. All rights reserved.)

Zeolites are a promising support for Pd catalysts in lean methane (CH 4 ) combustion. Herein, three types of zeolites (H-MOR, H-ZSM-5 and H-Y) were selected to estimate their structural effects and deactivation mechanisms in CH 4 combustion. We show that variations in zeolite structure and surface acidity led to distinct changes in Pd states. Pd/H-MOR with external high-dispersing Pd nanoparticles exhibited the best apparent activity, with activation energy (E a ) at 73 kJ/mol, while Pd/H-ZSM-5 displayed the highest turnover frequency (TOF) at 19.6 × 10 -3 sec -1 , presumably owing to its large particles with more step sites providing active sites in one particle for CH 4 activation. Pd/H-Y with dispersed PdO within pore channels and/or Pd 2+ ions on ion-exchange sites yielded the lowest apparent activity and TOF. Furthermore, Pd/H-MOR and Pd/H-ZSM-5 were both stable under a dry condition, but introducing 3 vol.% H 2 O caused the CH 4 conversion rate on Pd/H-MOR drop from 100% to 63% and that on Pd/H-ZSM-5 decreased remarkably from 82% to 36%. The former was shown to originate from zeolite structural dealumination, and the latter principally owed to Pd aggregation and the loss of active PdO., 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.)

High energy consumption has seriously hindered the development of Fenton-like reactions for the removal of refractory organic pollutants in water. To solve this problem, we designed a novel Fenton-like catalyst (Cu-PAN 3 ) by coprecipitation and carbon thermal reduction. The catalyst exhibits excellent Fenton-like catalytic activity and stability for the degradation of various pollutants with low H 2 O 2 consumption. The experimental results indicate that the dual reaction centers (DRCs) are composed of Cu-N-C and Cu-O-C bridges between copper and graphene-like carbon, which form electron-poor/rich centers on the catalyst surface. H 2 O 2 is mainly reduced at electron-rich Cu centers to free radicals for pollutant degradation. Meanwhile, pollutants can be oxidized by donating electrons to the electron-poor C centers of the catalyst, which inhibits the ineffective decomposition of H 2 O 2 at the electron-poor centers. This therefore significantly reduces the consumption of H 2 O 2 and reduces energy consumption., 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.)

An eco-friendly adsorbent was prepared by reverse suspension crosslinking method to remove multiple pollutants from aqueous solution. Both raw materials, derived from humus (HS) and chitosan (CS), are biodegradable and low-cost natural biopolymers. After combining HS with CS, the adsorption capacity was significantly improved due to compensation effects between the two components. HS/CS exhibited the features of amphoteric adsorption through pH adjustment, enabling it to adsorb not only anionic pollutants (Methyl Orange (MO) and Cr(VI)), but also cationic ones (Methylene Blue (MB) and Pb(II)). The adsorption capacities were approximately 242 mg/g, 69 mg/g, 188 mg/g and 57 mg/g for MO, Cr(VI), MB and Pb(II), respectively. HS/CS showed a slight preference for MO in MO/Cr(VI) co-adsorption system, whereas strong selectivity for MB over Pb(II) in MB/Pb(II) system under acidic condition (pH<3.0). This selective behavior would allow for potential applications in separating MB/Pb(II) effluents and selectively recycling Pb(II) in acidic environment. The isothermal and kinetic adsorption behaviors followed Langmuir model and pseudo-second-order model, respectively. The density functional theory (DFT) confirmed that the interaction between metal ions and adsorbents was primarily attributed to chelation and electrostatic adsorption, owing to nitric and oxygenic functional groups. Whereas, the adsorption mechanisms for dyes were involved in electrostatic attraction, H-bond and π-π bond, due to available hydrogen, oxygen, nitrogen atoms and aromatic groups on the surface of adsorbent and adsorbates. The adsorbent could be efficiently regenerated and retained over 90% of its adsorption capacity after five cycles, which has a potential for practical applications in water treatment., 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.)

Due to the imperative global energy transition crisis, hydrogen storage and adsorption technologies are becoming popular with the growing hydrogen economy. Recently, complex hydrides have been one of the most reliable materials for storing and transporting hydrogen gas to various fuel cells to generate clean energy with zero carbon emissions. With the ever-increasing carbon emissions, it is necessary to substitute the current energy sources with green hydrogen-based efficient energy-integrated systems. Herein, we propose an input-output model that comprehends complex hydrides such as lithium and magnesium alanates, amides and borohydrides to predict, estimate, and directly analyse hydrogen storage and adsorption. A critical and thorough comparative analysis of the respective complex hydrides for hydrogen adsorption and storage is discussed, elucidating the storage applications in water bodies. Several industrial scale-up processes, economic analysis, and plant design of hydrogen storage and adsorption approaches are suggested through volumetric and gravimetric calculations., 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 Inc. All rights reserved.)

Initial volatile concentration (C s0 ) is a crucial parameter for the migration and diffusion of volatile organic pollutants (VOCs) from the soil to the atmosphere. The acquisition of C s0 is, however, time-consuming and labor-intensive. This study developed a prediction model for C s0 based on theoretical analysis and experimental simulations. The model was established by correlating the molecular kinetic and sorption potential energy. The pore structure and pore size distribution of the soil were analyzed based on the fractal theory of porous media, followed by calculating the sorption potential energy corresponding to each pore size. It was observed that the pore size distribution of soil influenced BTEX (benzene, toluene, ethylbenzene, and xylene) volatilization by impacting sorption potential energy. The soil parameters, such as organic matter and soil moisture content, and the initial concentration and physical properties of BTEX were coupled to the prediction model to ensure its practicability. Red soil was finally used to verify the accuracy and applicability of the model. The experimental and predicted values' maximum relative and root-mean-square errors were determined to be 24.2% and 11.7%, respectively. The model provides a simple, rapid, and accurate assessment of soil vapor emission content due to BTEX contamination. This study offers an economical and practical method for quantifying the amount of volatile BTEX in contaminated sites, providing a reference for its monitoring, control, and subsequent remediation., 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.)

Monosodium methanearsonate (MSMA), a sodium salt of monomethylarsonic acid (MMA), is a selective contact herbicide used for the control of a broad spectrum of weeds. In water, MSMA dissociates to ions of sodium (Na + ) and monomethylarsonate (MMA - ) that is stable and does not transform abiotically. In soils characteristic of MSMA use, several simultaneous processes can occur: (1) microbial methylation of MMA to dimethylarsinic acid (DMA), (2) microbial demethylation of MMA to inorganic arsenic (iAs), (3) methylation of iAs to MMA, and (4) sorption and sequestration of MMA and its metabolites to soil minerals. Sequestered residues are residues that cannot be desorbed from soil in environmental conditions. Sequestration is rapid in the initial several days after MSMA application and continues at a progressively slower rate over time. Once sequestered, MMA and its metabolites are inaccessible to soil microorganisms and cannot be transformed. The rate and extent of the sorption and sequestration as well as the mobility of MMA and its metabolites depend on the local edaphic conditions. In typical MSMA use areas, the variability of the edaphic conditions is constrained. The goal of this research was to estimate the amount of iAs potentially added to drinking water as a result of the use of MSMA, with models and scenarios developed by the US Environmental Protection Agency for pesticide risk assessment. In this project, the estimated drinking water concentrations (EDWCs) for iAs were assessed as the average concentration in the reservoir over a 30-year simulation with annual applications of MSMA at maximum label rates. When the total area of suitable land was assumed to be treated, EDWCs ranged from <0.001 to 0.12 µg/L. When high estimates of actually treated acreage are considered, the EDWCs are below 0.06 µg/L across all scenarios. Integr Environ Assess Manag 2024;20:2076-2087. © 2024 The Author(s). Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)., (© 2024 The Author(s). Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).)

In this work, a novel nitrogen-doped biochar bentonite composite was synthesized by a single-pot co-pyrolysis method. Batch studies were conducted to evaluate the performance of the developed composite in eliminating synthetic dyes from the aqueous matrix. Energy dispersive X-ray spectroscopy analysis and field emission scanning electron microscopy imaging confirmed the N doping and bentonite impregnation into biochar. X-ray photoelectron spectroscopy analysis revealed that the N atoms were doped interstitially into the carbon matrix of biochar in the form of pyridinic and pyrrolic nitrogen. Simultaneous heteroatom doping and bentonite impregnation reduced the specific surface area to 41.721 m 2  g -1 but improved the adsorption capacity of biochar for dye adsorption. Further experimental studies depicted that simultaneous bentonite impregnation and N doping into the biochar matrix is beneficial for direct blue-6 (DB-6) and methylene blue (MB) removal and maximum adsorption capacities of 53.17 mg. g -1 and 41.33 mg. g -1 were obtained for MB and DB-6, respectively, at varying conditions. Adsorption energetics of the dyes with the developed composite portrayed the spontaneity of the process through negative ΔG values. The Langmuir and Freundlich isotherm models fitted the best for MB and DB-6 adsorption. The monolayer adsorption capacity and favourability factor for MB and DB-6 adsorption were calculated to be 54.15 mg. g -1 and 0.217, respectively from the best-fitted isotherms. Based on density functional theory calculations and spectroscopic studies, major interactions governing the adsorption were predicted to be charge-based interactions, π-π interactions, H-bonding, and Lewis acid-base interactions., 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 Inc. All rights reserved.)

Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 14 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC 50 ) of 1.9 μM. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field., (© 2024. The Author(s).)

Ultralarge virtual chemical spaces have emerged as a valuable resource for drug discovery, providing access to billions of make-on-demand compounds with high synthetic success rates. Chemical language models can potentially accelerate the exploration of these vast spaces through direct compound generation. However, existing models are not designed to navigate specific virtual chemical spaces and often overlook synthetic accessibility. To address this gap, we introduce product-of-experts (PoE) chemical language models, a modular and scalable approach to navigating ultralarge virtual chemical spaces. This method allows for controlled compound generation within a desired chemical space by combining a prior model pretrained on the target space with expert and anti-expert models fine-tuned using external property-specific data sets. We demonstrate that the PoE chemical language model can generate compounds with desirable properties, such as those that favorably dock to dopamine receptor D2 (DRD2) and are predicted to cross the blood-brain barrier (BBB), while ensuring that the majority of generated compounds are present within the target chemical space. Our results highlight the potential of chemical language models for navigating ultralarge virtual chemical spaces, and we anticipate that this study will motivate further research in this direction. The source code and data are freely available at https://github.com/shuyana/poeclm.

The Soret effect is a significant factor in various scenarios, with thermodiffusion in binary systems serving as a common method for the study. Most research focuses rarely on the distribution characteristics of components in diffusion systems; and Soret coefficients in the porous media could not be obtained by typical methods based on the thermodiffusion column, which are particularly important in the field of oil and gas development. Moreover, experiments on ground conditions have struggled to determine the Soret coefficient accurately due to the convective effect caused by gravity differentiation. The thermodiffusion behavior of n-pentane (C5) and n-heptane (C7) binary mixtures in both bulk and porous media conditions have been investigated, aiming to provide corrected coefficients that mitigate the influence of gravity using theoretical derivation. A new method was proposed to calculate the Soret coefficients in this work by establishing a model based on gas chromatography technology. Dynamic variation of component concentration along the path was studied, and the corresponding Soret coefficients were calculated and analyzed in parallel. The results indicate that the concentration and temperature exhibit a logarithmic relationship with the distance from the heat source. The Soret coefficient values obtained from measurements in porous media are closer to the theoretically corrected values, which do not account for gravity effects. Additionally, as the permeability decreases, the counteracting effect of porous media on convection becomes more pronounced. Therefore, it presents a novel method for accurately measuring the Soret coefficient that ignores convection to some extent., 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 pharmaceutical industry is increasingly drawn to the research of innovative drug delivery systems through the use of supercritical CO 2 (scCO 2 )-based techniques. Measuring the solubility of drugs in scCO 2 at varying conditions is a crucial parameter in this context. In this research, the supercritical solubility of two pharmaceutical ingredients, namely Febuxostat and Chlorpromazine, has been assessed theoretically using various thermodynamic approaches, including PR, SRK, UNIQUAC, and Wilson models. Additionally, hybrid machine learning models of PO-GPR, and PO-KNN were applied to anticipate the supercritical solubility of these medicines. Verification of the accuracy of each model for each pharmaceutical substance is conducted against previously reported experimental solubility data. In the comparison between the SRK and PR models, it is observed that the SRK model displays greater precision in correlating the solubility of both drugs. It consistently achieves a mean R adj value of 0.995 across all cases and mean AARD% values of 14.47 and 9.30 for Febuxostat and Chlorpromazine, respectively. Furthermore, the findings indicate that the UNIQUAC model surpasses the Wilson model in precisely representing the solubility of both medicines. It consistently achieves a mean R adj value higher than 0.985 across both cases and mean AARD% values of 11.39 and 7.08 for Febuxostat and Chlorpromazine, respectively. Additionally, the performance of both hybrid machine learning models proved to be excellent in anticipating the supercritical solubility of both compounds., 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.)

Over the past decade, continuous spin freeze-drying technology has emerged as a promising alternative to conventional batch freeze-drying, effectively addressing many of the latter's inherent disadvantages. Much of the focus during this period has been on controlling and optimizing the primary drying phase of this process. However, optimizing the secondary drying step is equally critical for the overall efficiency of the process. The primary aim of this study was to develop a comprehensive semi-mechanistic model for the secondary drying phase in continuous spin freeze-drying, accounting for the effects of process settings such as freezing rate and product temperature on desorption kinetics. Additionally, the study aimed to address discrepancies between conventional desorption models, typically applied in batch freeze-drying, and the observed data in this research. To achieve this, a residual moisture-dependent activation energy was introduced to improve the accuracy of the desorption model. Using NIR spectroscopy and IR-thermography, unknown model parameters could reliably be estimated using a simple and fast procedure. The calibrated model successfully predicted the final moisture content with an accuracy within 0.11% of the measured value under previously untested process conditions. Ultimately, the proposed semi-mechanistic model demonstrated its reliability in predicting the impact of new process conditions on both product temperature and residual moisture over time, enabling the development of a practical design space., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Thomas De Beer reports a relationship with RheaVita that includes: board membership, employment, and equity or stocks. Laurens Leys reports a relationship with RheaVita 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 Elsevier B.V. All rights reserved.)

Life sciences research and experimentation are resource-intensive, requiring extensive trials and considerable time. Often, experiments do not achieve their intended objectives, but progress is made through trial and error, eventually leading to breakthroughs. Machine learning is transforming this traditional approach, providing methods to expedite processes and accelerate discoveries. Deep Learning is becoming increasingly prominent in chemistry, with Convolutional Graph Networks (CGN) being a key focus, though other approaches also show significant potential. This research explores the application of Natural Language Processing (NLP) to evaluate the effectiveness of chemical language representations, specifically SMILES and SELFIES, using tokenization methods such as Byte Pair Encoding (BPE) and a novel approach developed in this study, Atom Pair Encoding (APE), in BERT-based models. The primary objective is to assess how these tokenization techniques influence the performance of chemical language models in biophysics and physiology classification tasks. The findings reveal that APE, particularly when used with SMILES representations, significantly outperforms BPE by preserving the integrity and contextual relationships among chemical elements, thereby enhancing classification accuracy. Performance was evaluated in downstream classification tasks using three distinct datasets for HIV, toxicology, and blood-brain barrier penetration, with ROC-AUC serving as the evaluation metric. This study highlights the critical role of tokenization in processing chemical language and suggests that refining these techniques could lead to significant advancements in drug discovery and material science., (© 2024. The Author(s).)

The modulation of autophagy plays a dual role in tumor cells, with the potential to both promote and suppress tumor proliferation. In order to gain a deeper understanding of the nature of autophagy, we have developed a chemical reaction kinetic model of autophagy and apoptosis based on the mass action kinetic models that have been previously described in the literature. It is regrettable that the authors did not provide all of the information necessary to reconstruct their model, which made their simulation results irreproducible. In this study, based on an extensive literature review, we have identified concentrations for each species in the stress-free, homeostatic state. These ranges were randomly sampled to generate sets of initial concentrations, from which the simulations were run. In every case, abnormal behavior was observed, with apoptosis and autophagy being activated, even in the absence of stress. Consequently, the model failed to reproduce even the basal conditions. Detailed examination of the model revealed erroneous reactions, which were corrected. The influential kinetic parameters of the corrected model were identified and optimized using the Optima++ code. The model is now capable of simulating homeostatic states, and provides a suitable basis for further model development to describe cell response to various stresses.

This study investigates the adsorption of methylene blue (MB) and methyl orange (MO) dyes from aqueous solutions using purified Moroccan bentonite, being mainly composed of silica and alumina, in the form of quartz and cristobalite. The temperature controls the adsorption capacity for the kinetics, increasing 5.08% (from 295.1 to 310.1 mg/g) for the MB and 55.47% (from 86.8 to 134.9 mg/g) for the MO. It was discovered that the pseudo-second-order model, with a low Bayesian criterion indicator of 12.72 and R 2 adj > 0.996, was the best suitable for explaining both systems. The adsorption isotherm, experimental data indicate that both systems follow the Langmuir isotherm. At lower temperatures, 298.15 K 1.22 molecules are adsorbed per site. However, at a higher temperature of 328.15 K, the number of molecules is less than a unit of 0.68. As for MO, the number of molecules remains above 1.4 per site for all the temperatures studied. The endothermic nature of the system is indicated by the observation that the adsorption energy tends to grow for both systems: for the MB, it increases from 18.85 to 21.26 kJ/mol, and for the MO, it increases from 14.83 to 19.01 kJ/mol. Last, thermodynamic functions indicate that maximum entropy is reached around the half-concentration saturation at 25 and 124 mg/L, which is the maximum energetic concentration of the system. The same results were obtained for Gibbs free energy, where the maximum energy found was - 5.39 × 10 -18 kJ/mol for the MB and - 1.99 × 10 -18 kJ/mol for the MO at 328.15 K., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Biocatalysis is becoming a data science. High-throughput experimentation generates a rapidly increasing stream of biocatalytic data, which is the raw material for mechanistic and novel data-driven modeling approaches for the predictive design of improved biocatalysts and novel bioprocesses. The holistic and molecular understanding of enzymatic reaction systems will enable us to identify and overcome kinetic bottlenecks and shift the thermodynamics of a reaction. The full characterization and modeling of reaction systems is a community effort; therefore, published methods and results should be findable, accessible, interoperable, and reusable (FAIR), which is achieved by developing standardized data exchange formats, by a complete and reproducible documentation of experimentation, by collaborative platforms for developing sustainable software and for analyzing data, and by repositories for publishing results together with raw data. The FAIRification of biocatalysis is a prerequisite to developing highly automated laboratory infrastructures that improve the reproducibility of scientific results and reduce the time and costs required to develop novel synthesis routes.

Decabromodiphenyl ethane (DBDPE), as one of the important new brominated flame retardants, is widely utilized in a variety of plastic products. However, the pyrolysis mechanism of DBDPE remains uncertain. In this article, the evolution behavior of the main products during the thermal decomposition of DBDPE is investigated using density functional theory at the theoretical level of M06-2X/6-311++G(2df,p)//M06-2X/6-311+G(d). The results show that the initial reaction starts with the cleavage of the ethane bridge bond, with an absorbed heat value of 298 kJ/mol, and the cleavage of the C aromatic -Br bond generates bromine radical, which is the main competitive reaction, with a heat absorption of 317 kJ/mol. The initial degradation of DBDPE generates a large number of pentabromobenzyl radicals and bromine radicals, which facilitate the secondary pyrolysis of DBDPE to a certain extent, resulting in the formation of possible products such as pentabromobenzyl bromide, pentabromobenzene, pentabromotoluene, hexabromobenzene, pentabromostyrene, and hydrogen bromide. In the pyrolysis system of DBDPE with hydrogen radicals, the reactions are classified into two types: extraction reaction and addition reaction. It can be known that the addition reaction plays a dominant role in the degradation process, with a branching ratio of 89.8% at 1600 K. The degradation of DBDPE with hydrogen radicals is mainly characterized by debromination, and the main products are hydrogen bromide, low-brominated diphenyl ethanes, brominated phenanthrenes, and brominated monoaromatic compounds. In addition, the lowest reaction energy barrier (18 kJ/mol) is required for the addition of hydrogen radical to the ipso-C site of DBDPE. DBDPE is dangerous for the environment and humans since its fate includes bioaccumulation, biomagnification, and toxicity via hormones and endocrine disruptors., 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 recent years, it has become evident that human activities have significantly disrupted the nitrogen cycle surpassing acceptable environmental thresholds. In this study, chemical and isotopic tracers were combined with a mathematical mass balance model (EMMA), PHREEQC inverse mixing model, and statistical analyses to evaluate groundwater quality, across an area experiencing substantial human activities, with a specific focus on tracing the origin of nitrate (NO 3 - ) with potential water mixing processes. This multi-technique approach was applied to an unconfined aquifer underlying an agricultural area setting in an inter-mountain depression (i.e., the "Pampa de Pocho Plain" in Argentina). Here, the primary identified geochemical processes occurring in the investigated groundwater system include the dissolution of carbonate salts, cation exchange, and hydrolysis of alumino-silicates along with incorporating ions from precipitation. It was observed that the chemistry of groundwater, predominantly of sodium bicarbonate with sulfate water types, is controlled by the area's geology, recharge from precipitation, and stream water infiltration originating from the surrounding hills. Chemical results reveal that 60% of groundwater samples have NO 3 - concentrations exceeding the regional natural background level, confirming the impact of human activities on groundwater quality. The dual plot of δ 15 N NO3 versus δ 18 O NO3 values indicates that groundwater is affected by NO 3 - sources overlapping manure/sewage with organic-rich soil. The mathematical EMMA model and PHREEQC inverse modeling, suggest organic-rich soil as an important source of nitrogen in the aquifer. Here, 64 % of samples exhibit a main mixture of organic-rich soil with manure, whereas 36 % of samples are affected mainly by a mixture of manure and fertilizer. This study demonstrates the utility of combining isotope tracers with mathematical modeling and statistical analyses for a better understanding of groundwater quality deterioration in situations where isotopic signatures of contamination sources overlap., 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 Inc. All rights reserved.)

Aquaponic systems differ from hydroponics by a higher pH and higher concentrations of dissolved organic matter (DOM). This study assessed whether plant nutrient deficiencies in aquaponics are caused by lacking input of the deficient nutrients or their chemical saturation. Nine scenarios with nutrient concentrations based on Hoagland's solution and different pH (5.5, 6.5, 7.5) and DOM concentrations (0 mg L -1 , 20 mg L -1 ) were constructed, representing theoretical hydroponic and aquaponic systems. Eventually, nutrient concentrations at equilibrium were calculated. In addition, a meta-analysis was conducted to assess whether nutrient concentrations reported in aquaponic studies could be predicted by equilibrium calculations. Theoretical results indicate that solubility thresholds cause deficiencies of P, Ca, Fe, and Cu at equilibrium due to the higher pH in aquaponics compared with hydroponics. Deficiencies in K and other plant nutrients are, meanwhile, likely caused by lacking supply through nutrient inputs at equilibrium. The presence of DOM can increase Fe and Cu solubility. However, equilibrium calculations could not predict nutrient concentrations found in literature. P was present at higher concentrations (max. 0.3 mmol L -1 ) than predicted (10 -3 -10 -6 mmol L -1 ), indicating chemical equilibrium was not reached in the assessed systems (average hydraulic retention time = 17 d). Future studies should consider reaction rates. Furthermore, considering the low concentrations of dissolved P in all studies, a system scaling based on P instead of N might be considered., 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 Ltd.)

There are large masses of coal tar asphalt present in old roads, containing high concentrations of polycyclic aromatic hydrocarbons (PAHs). Uncertainty surrounding the risk they pose causes problems during road reconstruction and for the reuse of the asphalt present. To help elucidate potential risks, a parsimonious linear equilibrium partitioning model for the bioavailability of PAHs in soils contaminated by tar asphalt particles was developed. Furthermore, a set of partitioning coefficients for PAHs between sampled coal tar binders and water were determined experimentally, as well as measurements of freely dissolved concentrations using polyoxymethylene samplers in batch tests and column recirculation experiments with various mixtures of different soils (peat and sandy loam) and tar asphalts. The model predictions of freely dissolved concentrations were conservative and within an order of magnitude of measurements in both batch and column tests. The model presented here only relies on soil organic carbon content and the fraction coal tar binder in the soil to model PAH partitioning. This model could be used for more realistic. Low tier risk assessments towards rational prioritization of sensitive areas for risk reduction efforts., 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 Ltd.)

Ground ozone (O 3 ) pollution has emerged as a prominent environmental concern in eastern cities of China, particularly during the summer and autumn seasons. However, a comprehensive investigation into the three-dimensional (3-D) evolution characteristics of O 3 within complicated urban environments, especially in lake-land environment, is notably scarce. To enhance our understanding of the mechanisms underlying elevated O 3 concentrations within a 3-D scale, this study employed an ozone lidar to delineate vertical ozone profiles in Changzhou, a typical city in China with complicated anthropogenic and biogenic emissions and complex land cover. The process analysis tool integrated into the Weather Research and Forecasting with Chemistry (WRF-Chem) model was further utilized to analyze the formation processes of O 3 . The results unveil a persistent O 3 pollution episode lasting over 15 days in Changzhou during the study period, with multiple peaks exceeding 200 μg m⁻³. Notably, O 3 predominantly accumulated within the boundary layer, confined below 1.2 km. Both ground and vertical contributions to this pollution were mainly due to local chemical reactions, with a maximum near-surface contribution reaching 19 ppb h -1 and a vertical contribution of 10 ppb h -1 at the height of 900 ± 200 m. Furthermore, episodes of the enhanced O 3 concentrations on August 9 and August 26, 2021, were influenced by external advection process. Our study also found that local circulation plays an important role in the accumulation of surface O 3 during certain periods. There was a temperature difference between the surface of Lake Tai and the adjacent land, resulting in the formation of lake-land breezes that facilitate the transport of O 3 from the lake surface to the terrestrial environment during pollution events. Our study emphasizes the necessity of reducing local pollutant emissions and implementing joint emission controls as the primary strategies for mitigating O 3 pollution in Changzhou and the surrounding region., 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.)

High entropy alloys and amorphous metallic alloys represent two distinct classes of advanced alloy materials, each with unique structural characteristics. Their emergence has garnered considerable interest across the materials science and engineering communities, driven by their promising properties, including exceptional strength. However, their extensive compositional diversity poses substantial challenges for systematic exploration, as traditional experimental approaches and high-throughput calculations struggle to efficiently navigate this vast space. While the recent development in data-driven materials discovery could potentially help, such efforts are hindered by the scarcity of comprehensive data and the lack of robust predictive tools that can effectively link alloy composition with specific properties. To address these challenges, we have deployed a machine-learning-based workflow for feature selection and statistical analysis to afford predictive models that accelerate the data-driven discovery and optimization of these advanced materials. Our methodology is validated through two case studies: (i) a regression analysis of the bulk modulus, and (ii) a classification analysis based on glass-forming ability. The Bayesian-optimized regression model trained for the prediction of bulk modulus achieved an R 2 of 0.969, an mean absolute error (MAE) of 3.958 GPa, and an root mean square error (RMSE) of 5.411 GPa, while our classification model for predicting glass-forming ability achieved an F1-score of 0.91, an area-under-the-curve of the receiver-operating-characteristic curve of 0.98, and an accuracy of 0.91. Furthermore, by leveraging a wide array of chemical data from diverse literature sources, we have successfully predicted a broad range of properties. This success underscores the efficacy of our modeling approach and emphasizes the importance of a comprehensive feature analysis and judicious feature selection strategy over a mere reliance on complex modeling techniques.

Protonation is the most frequent adduct found in positive electrospray ionization collision-induced mass spectra (CID-MS/MS). In a parallel report Lee, J. J. Chem. Inf. Model. 2024, 10.1021/acs.jcim.4c00760, we developed a quantum chemistry framework to predict mass spectra by collision-induced dissociation molecular dynamics (CIDMD). As different protonation sites affect fragmentation pathways of a given molecule, the accuracy of predicting tandem mass spectra by CIDMD ultimately depends on the choice of its protomers. To investigate the impact of molecular protonation sites on MS/MS spectra, we compared CIDMD-predicted spectra to all available experimental MS/MS spectra by similarity matching. We probed 10 molecules with a total of 43 protomers, the largest study to date, including organic acids (sorbic acid, citramalic acid, itaconic acid, mesaconic acid, citraconic acid, and taurine) as well as aromatic amines including uracil, aniline, bufotenine, and psilocin. We demonstrated how different protomers can converge different fragmentation pathways to the same fragment ions but also may explain the presence of different fragment ions in experimental MS/MS spectra. For the first time, we used in silico MS/MS predictions to test the impact of solvents on proton affinities, comparing the gas phase and a mixture of acetonitrile/water (1:1). We also extended applications of in silico MS/MS predictions to investigate the impact of protonation sites on the energy barriers of isomerization between protomers via proton transfer. Despite our initial hypothesis that the thermodynamically most stable protomer should give the best match to the experiment, we found only weak inverse relationships between the calculated proton affinities and corresponding entropy similarities of experimental and CIDMD-predicted MS/MS spectra. CIDMD-predicted mechanistic details of fragmentation reaction pathways revealed a clear preference for specific protomer forms for several molecules. Overall, however, proton affinity was not a good predictor corresponding to the predicted CIDMD spectra. For example, for uracil, only one protomer predicted all experimental MS/MS fragment ions, but this protomer had neither the highest proton affinity nor the best MS/MS match score. Instead of proton affinity, the transfer of protons during the electrospray process from the initial protonation site (i.e., mobile proton model) better explains the differences between the thermodynamic rationale and experimental data. Protomers that undergo fragmentation with lower energy barriers have greater contributions to experimental MS/MS spectra than their thermodynamic Boltzmann populations would suggest. Hence, in silico predictions still need to calculate MS/MS spectra for multiple protomers, as the extent of distributions cannot be readily predicted.

In this study, we propose a model for the simulation of the pH-dependent separation of dicarboxylic acids from aqueous solutions using strongly hydrophobic adsorbents. Building upon results of our previous study, where we experimentally investigated the pH-dependent adsorption behavior of the individual acid species of itaconic acid (IA) on a strongly hydrophobic adsorbent using in-line Raman spectroscopy, we utilize a transport-dispersive model as the basis for our simulation model. Instead of considering IA as a single component in our model, we simulated each acid species of IA individually. For this purpose, we expanded the transport-dispersive model with reaction terms in all aqueous phases. The reaction terms include all dissociation reactions of all involved components for each time step and spatial discretization. This model enables the time and spatial dependent simulation of the pH value in the chromatographic column and thus the time and spatial dependent knowledge of each acid species concentration. The consideration of activity coefficients due to high local ionic strength is achieved using the Truesdell-Jones (TdJ) model. The simulation model is successfully validated using experimental data from our previous study and used in a simulation study that demonstrates the potential of the model approach for analyzing associated separation tasks., 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.)

The ability of double-stranded DNA or RNA to locally melt and form kinks leads to strong nonlinear elasticity effects that qualitatively affect their packing in confined spaces. Using analytical theory and numerical simulation we show that kink formation entails a mixed spool-nematic ordering of double-stranded DNA or RNA in spherical capsids, consisting of an outer spool domain and an inner, twisted nematic domain. These findings explain the experimentally observed nematic domains in viral capsids and imply that nonlinear elasticity must be considered to predict the configurations and dynamics of double-stranded genomes in viruses, bacterial nucleoids or gene-delivery vehicles. The nonlinear elastic theory suggests that spool-nematic ordering is a general feature of strongly confined kinkable polymers.

Soil Cd contamination has become increasingly prominent in karst regions. Studies have generally elucidated the natural sources of Cd in high-background areas and analyzed their migration and enrichment mechanisms. This study comprehensively analyzed the total content and speciation of Cd in high-background areas using the delayed geochemical hazard (DGH) model to identify the sources of Cd in the region. The results indicated that Cd in the research area followed a pattern of gradual geochemical disasters. In Quaternary soil, brick-red soil, and submergenic paddy soil with hydromorphic characteristics, 32%, 7.69%, and 30% of soil Cd samples exceeded the critical threshold of the releasable total amount, respectively. Based on the DGH model, it was concluded that Cd in this region was mainly influenced by human activities. Field investigations corroborated this conclusion and aligned with the findings. Compared with the traditional source apportionment receptor models (mainly PCA and PMF), the DGH model not only saved considerable time and cost, but also avoided uncertainty associated with the results and complex and varied data processing and computational analysis processes. Moreover, the DGH model was able to identify the factors having the greatest impact on the ecological risk of Cd in the research area, thus facilitating targeted prevention and management planning based on the characteristics or chemical properties of their elements., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Sulfur dioxide (SO 2 ), produced mainly from the combustion of coal, is the most important cause of acidic rain, skin diseases, and environmental issues. To overcome the environmental problems, SO 2 must be captured on an industrial scale before it is released into the air. In chemical industries, organic solvents are used for partial absorption of SO 2 . However, those organic solvents have negative environmental effects. Thus, proposing environmentally friendly and green solvents for SO 2 absorption is vital for industries. Recently, increased attention has been paid to capturing SO 2 using Deep Eutectic Solvents (DESs) as the most recently introduced category of green solvents. This study performed a comprehensive screening study on the investigation of the performance of various simple and complicated models for SO 2 solubilities in a wide range of different nature DESs. For this purpose, the most updated and largest SO 2 solubility data bank in DESs involving 976 data points for 63 different nature DESs over wide temperature and pressure ranges has been gathered from open literature. For model screening, for the physical absorption models, the performances of SRK and CPA as the simple cubic and complicated sophisticated equations of state, NRTL and UNIQUAC as the well-known activity coefficient models, and for the chemical absorption models, RETM were investigated and compared. For physical absorption models, coupling an equation of state with the UNIQUAC activity coefficient model i.e. CPA-UNIQUAC, SRK-UNIQUAC, and also using simple SRK-SRK models led to the best performances. Compared to all investigated models, RETM as the chemical absorption model showed the best performance with the AARD% value of 12.95. This shows the importance of considering the chemical absorption mechanism for SO 2 absorption by DESs. Finally, general guidelines for using different modeling approaches were proposed to be considered by the researchers., 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.)

Motivation: Molecular representation learning (MRL) models molecules with low-dimensional vectors to support biological and chemical applications. Current methods primarily rely on intrinsic molecular information to learn molecular representations, but they often overlook effectively integrating domain knowledge into MRL., Results: In this article, we develop a reaction-enhanced graph learning (RXGL) framework for MRL, utilizing chemical reactions as domain knowledge. RXGL introduces dual graph learning modules to model molecule representation. One module employs graph convolutions on molecular graphs to capture molecule structures. The other module constructs a reaction-aware graph from chemical reactions and designs a novel graph attention network on this graph to integrate reaction-level relations into molecular modeling. To refine molecule representations, we design a reaction-based relation learning task, which considers the relations between the reactant and product sides in reactions. In addition, we introduce a cross-view contrastive task to strengthen the cooperative associations between molecular and reaction-aware graph learning. Experiment results show that our RXGL achieves strong performance in various downstream tasks, including product prediction, reaction classification, and molecular property prediction., Availability and Implementation: The code is publicly available at https://github.com/coder-ACAC/RLM., (© The Author(s) 2024. Published by Oxford University Press.)

Nanoscale zero-valent iron (NZVI), which has the advantages of small particle size, large specific surface area, and high reactivity, is often injected into contaminated aquifers in the form of slurry. However, the prone to passivation and agglomeration as well as poor stability and mobility of NZVI limit the further application of this technology in fields. Therefore, sulfided NZVI loaded on reduced graphene oxide (S-NZVI/rGO) and guar gum (GG) with shear-thinning properties as stabilizers were used to synthesize S-NZVI/rGO@GG slurries. SEM, TEM, and FT-IR confirmed that the dispersion and anti-passivation of NZVI were optimized in the coupled system. The stability and mobility of the slurry were improved by increasing the GG concentration, enhancing the pH, and decreasing the ionic strength and the presence of Ca 2+ ions, respectively. A modified advection-dispersion equation (ADE) was used to simulate the transport experiments considering the strain and physicochemical deposition/release. Meanwhile, colloidal filtration theory (CFT) demonstrated that Brownian motion plays a dominant role in the migration of S-NZVI/rGO@GG slurry, and the maximum migration distance can be increased by appropriately increasing the injection rate. Extended-Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory showed that the excellent stability and migration of S-NZVI/rGO@GG slurry mainly came from the GG spatial forces. This study has important implications for the field injection of S-NZVI/rGO@GG slurry. According to the injection parameters, the injection range of S-NZVI/rGO@GG slurry is effectively controlled, which lays the foundation for the promotion of application in actual fields., 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.)

Dissolved organic matter (DOM) is a heterogeneous pool of compounds and exhibits diverse adsorption characteristics with or without phosphorous (P) competition. The impacts of these factors on the burial and mobilization of organic carbon and P in aquatic ecosystems remain uncertain. In this study, an algae-derived DOM (ADOM) and a commercially available humic acid (HA) with distinct compositions were assessed for their adsorption behaviors onto iron (oxy)hydroxides (FeOx), both in the absence and presence of phosphate. ADOM contained less aromatics but more protein-like and highly unsaturated structures with oxygen compounds (HUSO) than HA. The adsorption capacity of FeOx was significantly greater for ADOM than for HA. Protein-like and HUSO compounds in ADOM and humic-like compounds and macromolecular aromatics in HA were preferentially adsorbed by FeOx. Moreover, ADOM demonstrated a stronger inhibitory effect on phosphate adsorption than HA. This observation suggests that the substantial release of autochthonous ADOM by algae could elevate internal P loading and pose challenges for the restoration of restore eutrophic lakes. The presence of phosphate suppressed the adsorption of protein-like compounds in ADOM onto FeOx, resulting in an increase in the relative abundance of protein-like compounds and a decrease in the relative abundance of humic-like compounds in post-adsorption ADOM. In contrast, phosphate exhibited no discernible impact on the compositional fractionation of HA. Collectively, our results show the source-composition characters of DOM influence the immobilization of both DOM and P in aquatic ecosystems through adsorption processes. The preferential adsorption of proteinaceous compounds within ADOM and aromatics within HA highlights the potential for the attachment with FeOx to diminish the original source-specific signatures of DOM, thereby contributing to the shared DOM characteristics observed across diverse aquatic environments., 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.)

Atrazine causes concern due to its resistant to biodegradation and could be accumulated in aquatic organisms, causing pollution in lakes. This study measured the concentration of atrazine in ice and the water under ice through a simulated icing experiment and calculated the distribution coefficient K to characterize its migration ability in the freezing process. Furthermore, density functional theory (DFT) calculations were employed to expatiate the migration law of atrazine during icing process. According to the results, it could release more energy into the environment when atrazine staying in water phase (-15.077 kcal/mol) than staying in ice phase (-14.388 kcal/mol), therefore it was beneficial for the migration of atrazine from ice to water. This explains that during the freezing process, the concentration of atrazine in the ice was lower than that in the water. Thermodynamic calculations indicated that when the temperature decreases from 268 to 248 K, the internal energy contribution of the compound of atrazine and ice molecule (water cluster) decreases at the same vibrational frequency, resulting in an increase in the free energy difference of the compound from -167.946 to -165.390 kcal/mol. This demonstrated the diminished migratory capacity of atrazine. This study revealed the environmental behavior of atrazine during lake freezing, which was beneficial for the management of atrazine and other pollutants during freezing and environmental protection., 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.)