Using predictive models unravel the potential of titanium oxide-loaded activated carbon for the removal of leachate ammoniacal nitrogen.

The TiO2 nanocomposite efficiency was determined under optimized conditions with activated carbon to remove ammoniacal nitrogen (NH3-N) from the leachate sample. In this work, the facile impregnation and pyrolysis synthesis method was employed to prepare the nanocomposite, and their formation was confirmed using the FESEM, FTIR, XRD, and Raman studies. In contrast, Raman phonon mode intensity ratio ID/IG increases from 2.094 to 2.311, indicating the increase of electronic conductivity and defects with the loading of TiO2 nanoparticles. The experimental optimal conditions for achieving maximum NH3-N removal of 75.8% were found to be a pH of 7, an adsorbent mass of 1.75 mg/L, and a temperature of 30 °C, with a corresponding time of 160 min. The experimental data were effectively fitted with several isotherms (Freundlich, Hill, Khan, Redlich-Peterson, Toth, and Koble-Corrigan). The notably elevated R2 value of 0.99 and a lower ARE % of 14.61 strongly support the assertion that the pseudo-second-order model compromises a superior depiction of the NH3-N reduction process. Furthermore, an effective central composite design (CCD) of response surface methodology (RSM) was employed, and the lower RMSE value, precisely 0.45, demonstrated minimal disparity between the experimentally determined NH3-N removal percentages and those predicted by the model. The subsequent utilization of the desirability function allowed us to attain actual variable experimental conditions.

Comparison of numerical calculations and ALOHA modeling in consequence assessment of chlorine gas emissions from ethylene dichloride reactors.

Incidents involving chemical storage tanks in the petrochemical industry are significant events with severe consequences. Within the petrochemical industry, EDC is a sector that produces ethylene dichloride through the reaction of chlorine and ethylene. The present research was conducted to evaluate the consequences of chlorine gas released from the EDC reactor in a petrochemical industry in southern Iran. Data regarding reactor specifications were obtained from the factory's technical office, while climatic data was acquired from the Meteorological Organization. The consequences of chlorine gas release from the reactor were assessed in four predefined scenarios using numerical calculation methods and modeling with the ALOHA software. The numerical calculation method involved thermodynamic fluid path analysis, discharge coefficient calculations, and wind speed impact analysis. The hazard radius was determined based on the ERPG1-2-3 index. Results showed that in the scenario of chlorine gas release from EDC reactors, according to the ALOHA model, an increase in wind speed from 3 to 7 m/h led to an expanded dispersion radius. At a radius of 700 m from the reactor, the maximum outdoor concentration reached 3.12 ppm, decreasing to 2.27 ppm at 800 m and further to 1.53 ppm at 1000 m. The comparison of numerical calculations and modeling using the ALOHA software indicates the desirable conformity of the results with each other. The R2 coefficient for evaluating the conformity of the results was 0.9964, indicating the desired efficiency of the model in evaluating the consequences of the release of toxic gasses from the EDC tank. The results of this research can be useful in designing the site and emergency response plan.

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.

Characteristics and adsorption behavior of typical microplastics in long-term accelerated weathering simulation.

Microplastics can function as carriers in the environment, absorbing various toxins and spreading to diverse ecosystems. Toxins accumulated in microplastics have the potential to be re-released, posing a threat. In this study, two typical plastics, namely polyethylene (PE) and polystyrene (PS), along with the degradable plastic poly(butylene adipate-co-terephthalate) (PBAT), were subjected to a long-term ultraviolet alternating weathering experiment. The study investigated the variations in the weathering process and pollutant adsorption of microplastics of different particle sizes. Furthermore, the adsorption capacity of microplastics for various pollutants was assessed. The findings indicate that particle size significantly influences weathering, leading to variations in adsorption capacity. The weathered PE displays a higher adsorption capacity for azo dyes. Additionally, the adsorption capacity of PBAT for neutral red is double that of antibiotics. Importantly, the maximum adsorption capacity of PBAT for pollutants after aging is approximately 10 times greater than that of PE. Consequently, degradable plastics undergoing weathering in the natural environment may pose a higher ecological risk than traditional plastics.

Pore-blocking steric mass-action model for adsorption of bioparticles.

The steric mass-action (SMA) model has been widely reported to describe the adsorption of proteins in different types of chromatographic adsorbents. Here in the present work, a pore-blocking steric mass-action model (PB-SMA) was developed for the adsorption of large-size bioparticles, which usually exhibit the unique pore-blocking characteristic on the adsorbent and thus lead to a fraction of ligands in the deep channels physically inaccessible to bioparticles adsorption, instead of being shielded due to steric hindrance by adsorbed bioparticles. This unique phenomenon was taken into account by introducing an additional parameter, Lin, which is defined as the inaccessible ligand densities in the physically blocked pore area, into the PB-SMA model. This fraction of ligand densities (Lin) will be deducted from the total ligand (Lt) for model development, thus the steric factor (σ) in the proposed PB-SMA will reflect the steric shielding effect on binding sites by adsorbed bioparticles more accurately than the conventional SMA model, which assumes that all ligands on the adsorbent have the same accessibility to the bioparticles. Based on a series of model assumptions, a PB-SMA model was firstly developed for inactivated foot-and-mouth disease virus (iFMDV) adsorption on immobilized metal affinity chromatography (IMAC) adsorbents. Model parameters for static adsorption including equilibrium constant (K), characteristic number of binding sites (n), and steric factor (σ) were determined. Compared with those derived from the conventional SMA model, the σ values derived from the PB-SMA model were dozens of times smaller and much closer to the theoretical maximum number of ligands shielded by a single adsorbed iFMDV, indicating the modified model was more accurate for bioparticles adsorption. The applicability of the PB-SMA model was further validated by the adsorption of hepatitis B surface antigen virus-like particles (HBsAg VLPs) on an ion exchange adsorbent with reasonably improved accuracy. Thus, it is considered that the PB-SMA model would be more accurate in describing the adsorption of bioparticles on different types of chromatographic adsorbents.

Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) approaches.

Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from an aqueous medium. An adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward artificial neural network (ANN) were used in this study. Four operational factors were chosen to examine their influence on the adsorption study: pH, contact duration, initial Cu (II) ions concentration, and adsorbent dosage. Using sawdust from wood, prediction models of copper (II) ions adsorption were optimized, created, and developed using the ANN and ANFIS models for tests. The result indicates that the determination coefficient for copper (II) metal ions in the training dataset was 0.987. Additionally, the ANFIS model's R2 value for both pollutants was 0.992. The findings demonstrate that the models presented a promising predictive approach that can be applied to successfully and accurately anticipate the simultaneous elimination of copper (II) and dye from the aqueous solution.

Effect of temperature on the dissolution of the lead (II) carbonate hydrocerussite for varying pH and dissolved inorganic carbon conditions.

The effect of temperature on the solubility of lead-bearing solid phases in water distribution systems for different water chemistry conditions remains unclear although lead concentrations are known to vary seasonally. The study objective is to explore the effect of temperature on the solubility of the lead(II) carbonate hydrocerussite under varying pH and DIC conditions. This is achieved through batch dissolution experiments conducted at multiple pHs (6-10) and DIC concentrations (20-200 mg CL-1) at temperatures ranging from 5 to 40 °C. A thermodynamic model was also applied to evaluate the model's ability to predict temperature effects on lead(II) carbonate solubility including solid phase transformations. In general, increasing temperature increased total dissolved lead at high pHs and the effect of temperature was greater for high DIC conditions, particularly for pH > 8. Temperature also influenced the pH at which the dominant lead(II) solid phase switched from hydrocerussite to cerussite (occurred between pH 7.25 to 10). Finally, the model was able to capture the overall trends observed despite thermodynamic data limitations. While this study focuses on a simple lead solid-aqueous system, findings provide important insights regarding the way in which temperature and water chemistry interact to affect lead concentrations.

A Refined Thin-Film Model for Drug Dissolution Considering Radial Diffusion - Simulating Powder Dissolution.

PURPOSE: We aim to present a refined thin-film model describing the drug particle dissolution considering radial diffusion in spherical boundary layer, and to demonstrate the ability of the model to describe the dissolution behavior of bulk drug powders. METHODS: The dissolution model introduced in this study was refined from a radial diffusion-based model previously published by our laboratory (So et al. in Pharm Res. 39:907-17, 2022). The refined model was created to simulate the dissolution of bulk powders, and to account for the evolution of particle size and diffusion layer thickness during dissolution. In vitro dissolution testing, using fractionated hydrochlorothiazide powders, was employed to assess the performance of the model. RESULTS: Overall, there was a good agreement between the experimental dissolution data and the predicted dissolution profiles using the proposed model across all size fractions of hydrochlorothiazide. The model over-predicted the dissolution rate when the particles became smaller. Notably, the classic Nernst-Brunner formalism led to an under-estimation of the dissolution rate. Additionally, calculation based on the equivalent particle size derived from the specific surface area substantially over-predicted the dissolution rate. CONCLUSION: The study demonstrated the potential of the radial diffusion-based model to describe dissolution of drug powders. In contrast, the classic Nernst-Brunner equation could under-estimate drug dissolution rate, largely due to the underlying assumption of translational diffusion. Moreover, the study indicated that not all surfaces on a drug particle contribute to dissolution. Therefore, relying on the experimentally-determined specific surface area for predicting drug dissolution is not advisable.

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.

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.

Novel kinetic modeling of photocatalytic degradation of ethanol and acetaldehyde in air by commercial and reduced ZnO: Effect of oxygen vacancies and humidity.

A comprehensive kinetic model has been developed to address the factors and processes governing the photocatalytic removal of gaseous ethanol by using ZnO loaded in a prototype air purifier. This model simultaneously tracks the concentrations of ethanol and acetaldehyde (as its primary oxidation product) in both gas phase and on the catalyst surface. It accounts for reversible adsorption of both compounds to assign kinetic reaction parameters for different degradation pathways. The effects of oxygen vacancies on the catalyst have been validated through the comparative assessment on the catalytic performance of commercial ZnO before and after the reduction pre-treatment (10% H2/Ar gas at 500 °C). The influence of humidity has also been assessed by partitioning the concentrations of water molecules across the gas phase and catalyst surface interface. Given the significant impact of adsorption on photocatalytic processes, the beginning phases of all experiments (15 min in the dark) are integrated into the model. Results showcase a notable decrease in the adsorption removal of ethanol and acetaldehyde with an increase in relative humidity from 5% to 75%. The estimated number of active sites, as determined by the model, increases from 7.34 10-6 in commercial ZnO to 8.86 10-6 mol gcat-1 in reduced ZnO. Furthermore, the model predicts that the reaction occurs predominantly on the catalyst surface while only 14% in the gas phase. By using quantum yield calculations, the optimal humidity level for photocatalytic degradation is identified as 25% with the highest quantum yield of 6.98 10-3 (commercial ZnO) and 10.41 10-3 molecules photon-1 (reduced ZnO) catalysts.

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.

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.

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.

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.

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.

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.

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.

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.