The Michaelis-Menten Reaction at Low Substrate Concentrations: Pseudo-First-Order Kinetics and Conditions for Timescale Separation.

We demonstrate that the Michaelis-Menten reaction mechanism can be accurately approximated by a linear system when the initial substrate concentration is low. This leads to pseudo-first-order kinetics, simplifying mathematical calculations and experimental analysis. Our proof utilizes a monotonicity property of the system and Kamke's comparison theorem. This linear approximation yields a closed-form solution, enabling accurate modeling and estimation of reaction rate constants even without timescale separation. Building on prior work, we establish that the sufficient condition for the validity of this approximation is s 0 ≪ K , where K = k 2 / k 1 is the Van Slyke-Cullen constant. This condition is independent of the initial enzyme concentration. Further, we investigate timescale separation within the linear system, identifying necessary and sufficient conditions and deriving the corresponding reduced one-dimensional equations.

Eliminating Imaginary Vibrational Frequencies in Quantum-Chemical Cluster Models of Enzymatic Active Sites.

In constructing finite models of enzyme active sites for quantum-chemical calculations, atoms at the periphery of the model must be constrained to prevent unphysical rearrangements during geometry relaxation. A simple fixed-atom or "coordinate-lock" approach is commonly employed but leads to undesirable artifacts in the form of small imaginary frequencies. These preclude evaluation of finite-temperature free-energy corrections, limiting thermochemical calculations to enthalpies only. Full-dimensional vibrational frequency calculations are possible by replacing the fixed-atom constraints with harmonic confining potentials. Here, we compare that approach to an alternative strategy in which fixed-atom contributions to the Hessian are simply omitted. While the latter strategy does eliminate imaginary frequencies, it tends to underestimate both the zero-point energy and the vibrational entropy while introducing artificial rigidity. Harmonic confining potentials eliminate imaginary frequencies and provide a flexible means to construct active-site models that can be used in unconstrained geometry relaxations, affording better convergence of reaction energies and barrier heights with respect to the model size, as compared to models with fixed-atom constraints.

Adsorption-desorption of 241Am(â ¢) on montmorillonite colloids and quartz sand: Effects of pH, ionic strength, colloid concentration and grain size.

Clay colloids in the subsurface environment have a strong adsorption capacity for radionuclides, and the mobile colloids will carry the nuclides for migration, which would promote the movability of radionuclides in the groundwater environment and pose a threat to the ecosphere. The investigations of the adsorption/desorption behaviors of radionuclides in colloids and porous media are significant for the evaluation of the geological disposal of radioactive wastes. To illustrate the adsorption/desorption behaviors of 241Am(Ⅲ) in Na-montmorillonite colloid and/or quartz sand systems at different pH (5, 7 and 9), ionic strengths (0, 0.1 and 5 mM), colloid concentrations (300 and 900 mg/L), nuclide concentrations (500, 800, 1100 and 1400 Bq/mL) and grain sizes (40 and 60 mesh), a series of batch sorption-desorption experiments were conducted. Combining the analysis of the physical and chemical properties of Na-montmorillonite with the Freundlich model, the influencing mechanism of different controlling factors is discussed. The experimental results show that the adsorption/desorption behaviors of 241Am(Ⅲ) in Na-montmorillonite colloid and/or quartz sand strongly are influenced by the pH value and ionic strength of a solution, the colloid concentration as well as quartz sand grain size. The adsorption and desorption isotherms within all the experimental conditions could be well-fitted by the Freundlich model and the correlation coefficients (R2) are bigger than 0.9. With the increase in pH, the adsorption partition coefficient (Kd) at 241Am(Ⅲ)-Na-montmorillonite colloid two-phase system and 241Am(Ⅲ)-Na-montmorillonite colloid-quartz sand three-phase system presents a trend which increases firstly followed by decreasing, due to the changes in the morphology of Am with pH. The Kd of 241Am(Ⅲ) adsorption on montmorillonite colloid and quartz sand decreases with increasing in ionic strength, which is mainly attributed to the competitive adsorption, surface complexation and the reduction of surface zeta potential. Additionally, the Kd increases with increasing colloid concentrations because of the increase in adsorption sites. When the mean grain diameter changes from 0.45 to 0.3 mm, the adsorption variation trends of 241Am(Ⅲ) remain basically unchanged. The research results obtained in this work are meaningful and helpful in understanding the migration behaviors of radionuclides in the underground environment.

Microscopic Mechanism for Further NO Heterogeneous Reduction by Potassium-Doped Biochar: A DFT Study.

Biomass reburning is an efficient and low-cost way to control nitric oxide (NO), and the abundant potassium (K) element in biomass affects the heterogeneous reaction between NO and biochar. Due to the incomplete simulation of the NO heterogeneous reduction reaction pathway at the molecular level and the unclear catalytic effect of K element in biochar, further research is needed on the possible next reaction and the influencing mechanism of the element. After the products of the existing reaction pathways are referenced, two reasonably simplified biochar structural models are selected as the basic reactants to study the microscopic mechanism for further NO heterogeneous reduction on the biochar surface before and after doping with the K atom based on density functional theory. In studying the two further NO heterogeneous reduction reaction pathways, we find that the carbon monoxide (CO) molecule fragment protrudes from the surface of biochar models with the desorption of N2 at the TS4 transition state, and the two edge types of biochar product models obtained by simulation calculation are Klein edge and ac56 edge observed in the experiment. In studying the catalytic effect of potassium in biochar, we find that the presence of K increases the heat release of adsorption of NO molecules, reduces the energy barrier of the rate-determining step in the nitrogen (N2) generation and desorption process (by 50.88 and 69.97%), and hinders the CO molecule from desorbing from the biochar model surface. Thermodynamic and kinetic analyses also confirm its influence. The study proves that the heterogeneous reduction reaction of four NO molecules on the surface of biochar completes the whole reaction process and provides a basic theoretical basis for the emission of nitrogen oxides (NOx) during biomass reburning.

Model-based design of gradient elution in liquid-liquid chromatography: Application to the separation of cannabinoids.

Liquid-liquid chromatography (LLC) is a separation technique that utilizes a biphasic solvent system as the mobile and stationary phases. The components are separated solely due to their different distributions between the two liquid phases. Gradient change in the mobile phase composition during the chromatographic process is a powerful method for improving the resolution of separation or shortening the process time. Gradient elution readily applies to LLC with biphasic solvent systems in which the stationary phase composition remains nearly constant when the mobile phase composition changes. This work proposes a model-based approach to optimize gradients in LLC and circumvent tedious trial-and-error experiments. The solutes' distribution constant depends on the mobile phase composition. Thus, the distribution constants were described as a function of the content of one of the solvents (= modifier) in the mobile phase. The dispersive and mass-transfer effects in the tubing and the column are modeled with a stage model. Only a few experiments are required to determine the model parameters. After the validation of the model and its parameters, the model can be used for LLC gradient optimization. The proposed approach was demonstrated for a gradient LLC separation of a mixture of four cannabinoids. Two different gradient shapes, one-step and linear gradient, were considered. For a pre-selected minimal purity requirement, the gradient was optimized for maximum process efficiency, defined as the product of productivity and yield. An experiment conducted with the optimized gradient conditions was in good agreement with the simulation, showing the potential of the proposed method.

Flexible modelling of the dissolution performance of directly compressed tablets.

In this study, a compartmental disintegration and dissolution model is proposed for the prediction and evaluation of the dissolution performance of directly compressed tablets. This dissolution model uses three compartments (Bound, Disintegrated, and Dissolved) to describe the state of each particle of active pharmaceutical ingredient. The disintegration of the tablet is captured by three fitting parameters. Two disintegration parameters, ß0 and ßt,0, describe the initial disintegration rate and the change in disintegration rate, respectively. A third parameter, α, describes the effect of the volume of dissolved drug on the disintegration process. As the tablet disintegrates, particles become available for dissolution. The dissolution rate is determined by the Nernst-Brunner equation, whilst taking into account the hydrodynamic effects within the vessel of a USP II (paddle) apparatus. This model uses the raw material properties of the active pharmaceutical ingredient (solubility, particle size distribution, true density), lending it towards early development activities during which time the amount of drug substance available may be limited. Additionally, the strong correlations between the fitting parameters and the tablet porosity indicate the potential to isolate the manufacturing effects and thus implement the model as part of a real-time release testing strategy for a continuous direct compression line.

Do Chemformers Dream of Organic Matter? Evaluating a Transformer Model for Multistep Retrosynthesis.

Synthesis planning of new pharmaceutical compounds is a well-known bottleneck in modern drug design. Template-free methods, such as transformers, have recently been proposed as an alternative to template-based methods for single-step retrosynthetic predictions. Here, we trained and evaluated a transformer model, called the Chemformer, for retrosynthesis predictions within drug discovery. The proprietary data set used for training comprised ∼18 M reactions from literature, patents, and electronic lab notebooks. Chemformer was evaluated for the purpose of both single-step and multistep retrosynthesis. We found that the single-step performance of Chemformer was especially good on reaction classes common in drug discovery, with most reaction classes showing a top-10 round-trip accuracy above 0.97. Moreover, Chemformer reached a higher round-trip accuracy compared to that of a template-based model. By analyzing multistep retrosynthesis experiments, we observed that Chemformer found synthetic routes, leading to commercial starting materials for 95% of the target compounds, an increase of more than 20% compared to the template-based model on a proprietary compound data set. In addition to this, we discovered that Chemformer suggested novel disconnections corresponding to reaction templates, which are not included in the template-based model. These findings were further supported by a publicly available ChEMBL compound data set. The conclusions drawn from this work allow for the design of a synthesis planning tool where template-based and template-free models work in harmony to optimize retrosynthetic recommendations.

Low-temperature NH3 abatement via selective oxidation over a supported copper catalyst with high Cu+ abundance.

Selective catalytic NH3-to-N2 oxidation (NH3-SCO) is highly promising for abating NH3 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 N2 selectivity. Here, using defect-rich nitrogen-doped carbon nanotubes (NCNT-AW) as the support, we developed a highly active and durable copper-based NH3-SCO catalyst with a high abundance of cuprous (Cu+) sites. The obtained Cu/NCNT-AW catalyst demonstrated outstanding activity with a T50 (i.e. the temperature to reach 50% NH3 conversion) of 174°C in the NH3-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 NH3 as a probe molecule revealed that the NH3-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 NH3-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 NH3 emissions via selective oxidation.

Improving benzene catalytic oxidation on Ag/Co3O4 by regulating the chemical states of Co and Ag.

Silver (9 wt.%) was loaded on Co3O4-nanofiber using reduction and impregnation methods, respectively. Due to the stronger electronegativity of silver, the ratios of surface Co3+/Co2+ on Ag/Co3O4 were higher than on Co3O4, 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 Co3O4. Hence the Ag/Co3O4 showed better catalytic performance than Co3O4 in benzene oxidation. Compared with the Ag/Co3O4 synthesized via impregnation method, the one prepared using reduction method (named as AgCo-R) exhibited higher contents of surface Co3+ and adsorbed oxygen species, stronger reducibility, as well as more active surface lattice oxygen species. Consequently, AgCo-R showed lowest T90 value of 183°C, admirable catalytic stability, largest normalized reaction rate of 1.36 × 10-4 mol/(h·m2) (150°C), and lowest apparent activation energy (Ea) 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 CO2 and water on the surface of AgCo-R. At last, potential reaction pathways including five detailed steps were proposed.

Improving photochemical indicators for attributing ozone sensitivities in source apportionment analysis.

Comprehensive Air Quality Model with extensions (CAMx)-Decoupled Direct Method (DDM) simulations of first-order ozone (O3) sensitivity to nitrogen oxides (NOx) and volatile organic compounds (VOCs) emissions were performed and combined with modelled [Formula: see text] ratios to obtain a range of thresholds for determining O3-sensitivity regimes for different areas of China. Utilising the new threshold ranges for photochemical indicators, the method for determining O3 formation in the Ozone Source Apportionment Technology (OSAT) module within CAMx was improved by a dynamically varied threshold of [Formula: see text] ratio. The O3 concentration contributions in the newly added transition regime were apportioned to NOx and VOCs emissions in proportion to the relationship between the [Formula: see text] ratio and first-order O3 sensitivity. The source contributions of O3 concentrations from different emission sectors from June to September 2019 were compared using the original and improved CAMx-OSAT. The results showed that the O3 concentration contributions changed significantly in the NOx-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/m3 in the transition regime. The modified OSAT module enabled a more sophisticated attribution of O3 to precursor emissions and may have far-reaching implications for informing O3 pollution control policy.

Degradation of o-dichlorobenzene by DBD-NTP co-modified titanium gel catalyst.

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 CuMnTiOx catalyst, had better o-DCB degradation performance compared to plasma alone. The combination of the CuMnTiOx catalyst with NTP can achieve a degradation efficiency of up to 97.2% for o-DCB; the selectivity of CO and CO2 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 O2-TPD results, the conversion of CO to CO2 follows the M-v-K mechanism; thus, the active species on the catalyst surface play an important role. Moreover, the CuMnTiOx 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.

Cotransport of aged biochar colloids and thallium(I) in water-saturated porous media: Impact of the ionic strength, pH and aging degree.

Biochar colloids entering the soil undergo aging over time and exhibit strong capabilities in adsorbing and transporting pollutants. Therefore, investigating the cotransport of aged biochar colloids and thallium (Tl(I)) in quartz sand media is crucial for understanding Tl(I) migration in underground environments. This study investigated the migration of biochar colloids with two different aging degrees and Tl(I) in quartz sand media at various pH and ionic strengths (ISs). The results revealed that under all ISs and pH, 30%AWB (biochar aged with 30 % (w/w) HNO3) inhibited Tl(I) migration in media. This inhibition primarily arose from the introduction of hydroxyl and carboxyl groups during aging, which significantly enhanced colloid adsorption onto Tl(I). At lower ISs, 30%AWB colloids exhibited greater inhibition of Tl(I) migration due to their increased adsorption capacity. Additionally, aging promoted the migration of biochar colloids in the media. Greater biochar aging notably enhanced this promotion, potentially owing to reduced colloidal particle size and the formation of biochar derivatives. Moreover, 50%AWB (biochar aged with 50 % (w/w) HNO3) inhibited Tl(I) migration under low ISs but had almost no impact under high ISs. Nonetheless, at high pH, 50%AWB colloids facilitated Tl(I) migration. This phenomenon might be attributed to the inhibitory effect of aged biochar colloids on Tl(I) adsorption onto media at a high pH, as well as the stable binding between Tl(I) and aged biochar colloids. This study discusses the cotransport of biochar with various degrees of aging and Tl(I) in media, providing insights into remediating soils contaminated with Tl.

The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES.

Simulation of the physicochemical and biochemical behavior of nanomaterials has its own specifics. However, the main goal of modeling for both traditional substances and nanomaterials is the same. This is an ecologic risk assessment. The universal indicator of toxicity is the n-octanol/water partition coefficient. Mutagenicity indicates the possibility of future undesirable environmental effects, possibly greater than toxicity. Models have been proposed for the octanol/water distribution coefficient of gold nanoparticles and the mutagenicity of silver nanoparticles. Unlike the previous studies, here the models are built using an updated scheme, which includes two improvements. Firstly, the computing involves a new criterion for prediction potential, the so-called coefficient of conformism of a correlative prediction (CCCP); secondly, the Las Vegas algorithm is used to select the potentially most promising models from a group of models obtained by the Monte Carlo algorithm. Apparently, CCCP is a measure of the predictive potential (not only correlation). This can give an advantage in developing a model in comparison to using the classic determination coefficient. Likely, CCCP can be more informative than the classical determination coefficient. The Las Vegas algorithm is able to improve the model obtained by the Monte Carlo method.

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

De novo drug design as GPT language modeling: large chemistry models with supervised and reinforcement learning.

In recent years, generative machine learning algorithms have been successful in designing innovative drug-like molecules. SMILES is a sequence-like language used in most effective drug design models. Due to data's sequential structure, models such as recurrent neural networks and transformers can design pharmacological compounds with optimized efficacy. Large language models have advanced recently, but their implications on drug design have not yet been explored. Although one study successfully pre-trained a large chemistry model (LCM), its application to specific tasks in drug discovery is unknown. In this study, the drug design task is modeled as a causal language modeling problem. Thus, the procedure of reward modeling, supervised fine-tuning, and proximal policy optimization was used to transfer the LCM to drug design, similar to Open AI's ChatGPT and InstructGPT procedures. By combining the SMILES sequence with chemical descriptors, the novel efficacy evaluation model exceeded its performance compared to previous studies. After proximal policy optimization, the drug design model generated molecules with 99.2% having efficacy pIC50 > 7 towards the amyloid precursor protein, with 100% of the generated molecules being valid and novel. This demonstrated the applicability of LCMs in drug discovery, with benefits including less data consumption while fine-tuning. The applicability of LCMs to drug discovery opens the door for larger studies involving reinforcement-learning with human feedback, where chemists provide feedback to LCMs and generate higher-quality molecules. LCMs' ability to design similar molecules from datasets paves the way for more accessible, non-patented alternatives to drug molecules.

Effect of calcium and phosphorus on ammonium and nitrate nitrogen adsorption onto iron (hydr)oxides surfaces: CD-MUSIC model and DFT computation.

Calcium (Ca2+) and phosphorous (PO43-) significantly influence the form and effectiveness of nitrogen (N), however, the precise mechanisms governing the adsorption of ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3--N) are still lacking. This study employed batch adsorption experiments, charge distribution and multi-site complexation (CD-MUSIC) models and density functional theory (DFT) calculations to elucidate the mechanism by which Ca2+ and PO43- affect the adsorption of NH4+-N and NO3--N on the goethite (GT) surface. The results showed that the adsorption of NH4+-N on the GT exhibited an initial increase followed by a decrease as pH increased, peaking at a pH of 8.5. Conversely, the adsorption of NO3--N decreased with rising pH. According to the CD-MUSIC model, Ca2+ minimally affected the NH4+-N adsorption on the GT but enhanced NO3--N adsorption via electrostatic interaction, promoting the adsorption of ≡FeOH-NO3- and ≡Fe3O-NO3- species. Similarly, PO43- inhibited the adsorption of ≡FeOH-NO3- and ≡Fe3O-NO3- species. However, PO43- boosted NH4+-N adsorption by facilitating the formation of ≡Fe3O-NH4+ via electrostatic interaction and site competition. DFT calculations indicates that although bidentate phosphate (BP) was beneficial to stabilize NH4+-N than monodentate phosphate (SP), SP-NH4+ was the main adsorption configuration at pH 5.5-9.5 owing the prevalence of SP on the GT surface under site competition of NH4+-N. The results of CD-MUSIC model and DFT calculation were verified mutually, and provide novel insights into the mechanisms underlying N fixation and migration in soil.

Modeling the degradation of polypropylene and polystyrene under shock compression and mechanical cleaving using the ReaxFF force field.

Polypropylene (PP) and polystyrene (PS) underwent a comprehensive investigation into their mechanical and chemical degradation through reactive molecular dynamics simulations. The simulations utilized the ReaxFF force field for CHO (carbon-hydrogen-oxygen) systems in the combustion branch. The study included equilibrium simulations to determine densities and melting temperatures, non-equilibrium simulations for stress-strain and Young moduli determination, mechanical cleaving to identify surface species resulting from material fragmentation, and shock compression simulations to elucidate chemical reactions activated by some external energy sources. The results indicate that material properties such as densities, phase transition temperatures, and Young moduli are accurately reproduced by the ReaxFF-CHO force field. The reactive dynamics analysis yielded crucial insights into the surface composition of fragmented polymers. Both polymers exhibited backbone breakage, leaving -CH2· and -CH·- radicals as terminals. PP demonstrated substantial fragmentation, while PS showed a tendency to develop crosslinks. A detailed analysis of chemical reactions resulting from increasing activation due to increasing value of compression pressure is presented and discussed.

Insights into the hydrolysis/alcoholysis/ammonolysis mechanisms of ethylene naphthalate dimer using density functional theory (DFT) method.

Hydrolysis, alcoholysis and ammonolysis are viable routes for the efficient degradation and recycling of polyethylene naphthalate (PEN) plastic waste. Various possible hydrolysis/alcoholysis/ammonolysis reaction pathways for the degradation mechanism of the ethylene naphthalate dimer were investigated using the density functional theory (DFT) B3P86/6-31++G(d,p). To determine the thermodynamic and kinetic parameters, geometric structure optimization and frequency calculation were performed on a range of intermediates, transition states, and products associated with the reaction. The calculation results show that the highest energy barrier of the main element reaction step in hydrolysis is about 169.0 kJ/mol, the lowest is about 151.0 kJ/mol for ammonolysis, and the second is about 155.0 kJ/mol for alcoholysis. The main hydrolysis products of the ethylene naphthalate dimer are 2,6-naphthalenedicarboxylic acid and ethylene glycol; the main products of alcoholysis are dimethyl naphthalene-2,6-dicarboxylate and ethylene glycol, and the main products of ammonolysis are naphthalene-2,6-dicarboxamide and ethylene glycol. Furthermore, in the process of ethylene naphthalate dimer hydrolysis/alcoholysis/ammonolysis, the decomposition reaction in the NH3 atmosphere is better than that in methanol, and the reaction in CH3OH is better than that in the H2O molecular environment, and the increase in reaction temperature can increase its spontaneity. Our study presents the molecular mechanism of PEN hydrolysis/alcoholysis/ammonolysis and provides a reference for studying the degradation of other plastic wastes.

Prediction of critical micelle concentration for per- and polyfluoroalkyl substances.

In this study, we focus on the development of Quantitative Structure-Property Relationship (QSPR) models to predict the critical micelle concentration (CMC) for per- and polyfluoroalkyl substances (PFASs). Experimental CMC values for both fluorinated and non-fluorinated compounds were meticulously compiled from existing literature sources. Our approach involved constructing two distinct types of models based on Support Vector Machine (SVM) algorithms applied to the dataset. Type (I) models were trained exclusively on CMC values for fluorinated compounds, while Type (II) models were developed utilizing the entire dataset, incorporating both fluorinated and non-fluorinated compounds. Comparative analyses were conducted against reference data, as well as between the two model types. Encouragingly, both types of models exhibited robust predictive capabilities and demonstrated high reliability. Subsequently, the model having the broadest applicability domain was selected to complement the existing experimental data, thereby enhancing our understanding of PFAS behaviour.

Photochemically induced aging of polystyrene nanoplastics and its impact on norfloxacin adsorption behavior.

The co-occurrence of nanoplastics (NPs) and antibiotics in the environment is a growing concern for ecological safety. As NPs age in natural environments, their surface properties and morphology may change, potentially affecting their interactions with co-contaminants such as antibiotics. It is crucial to understand the effect of aging on NPs adsorption of antibiotics, but detailed studies on this topic are still scarce. The study utilized the photo-Fenton-like reaction to hasten the aging of polystyrene nanoplastics (PS-NPs). The impact of aging on the adsorption behavior of norfloxacin (NOR) was then systematically examined. The results showed a time-dependent rise in surface oxygen content and functional groups in aged PS-NPs. These modifications led to noticeable physical changes, including increased surface roughness, decreased particle size, and improved specific surface area. The physicochemical changes significantly increased the adsorption capacity of aged PS-NPs for norfloxacin. Aged PS-NPs showed 5.03 times higher adsorption compared to virgin PS-NPs. The adsorption mechanism analysis revealed that in addition to the electrostatic interactions, van der Waals force, hydrogen bonding, π-π* interactions and hydrophobic interactions observed with virgin PS-NPs, aged PS-NPs played a significant role in polar interactions and pore-filling mechanisms. The study highlights the potential for aging to worsen antibiotic risk in contaminated environments. This study not only enhances the comprehension of the environmental behavior of aged NPs but also provides a valuable basis for developing risk management strategies for contaminated areas.