Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence.

The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model's estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.

Rethinking the applicability domain analysis in QSAR models.

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

Hierarchical Y Zeolite-Based Catalysts for VGO Cracking: Impact of Carbonaceous Species on Catalyst Acidity and Specific Surface Area.

The performance of catalysts prepared from hierarchical Y zeolites has been studied during the conversion of vacuum gas oil (VGO) into higher-value products. Two different catalysts have been studied: CatY.0.00 was obtained from the standard zeolite (Y-0.00-M: without alkaline treatment) and CatY.0.20 was prepared from the desilicated zeolite (Y-0-20-M: treated with 0.20 M NaOH). The cracking tests were carried out in a microactivity test (MAT) unit with a fixed-bed reactor at 550 °C in the 20-50 s reaction time range, with a catalyst mass of 3 g and a mass flow rate of VGO of 2.0 g/min. The products obtained were grouped according to their boiling point range in dry gas (DG), liquefied petroleum gas (LPG), naphtha, and coke. The results showed a greater conversion and selectivity to gasoline with the CatY.0.20 catalyst, along with improved quality (RON) of the C5-C12 cut. Conversely, the CatY.0.00 catalyst (obtained from the Y-0.00-M zeolite) showed greater selectivity to gases (DG and LPG), attributable to the electronic confinement effect within the microporous channels of the zeolite. The nature of coke has been studied using different analysis techniques and the impact on the catalysts by comparing the properties of the fresh and deactivated catalysts. The coke deposited on the catalyst surfaces was responsible for the loss of activity; however, the CatY.0.20 catalyst showed greater resistance to deactivation by coke, despite showing the highest selectivity. Given that the reaction occurs in the acid sites of the zeolite and not in the matrix, the increased degree of mesoporosity of the zeolite in the CatY.0.20 catalyst facilitated the outward diffusion of products from the zeolitic channels to the matrix, thereby preserving greater activity.

ElectroPredictor: An Application to Predict Mayr's Electrophilicity E through Implementation of an Ensemble Model Based on Machine Learning Algorithms.

Electrophilicity (E) is one of the most important parameters to understand the reactivity of an organic molecule. Although the theoretical electrophilicity index (ω) has been associated with E in a small homologous series, the use of w to predict E in a structurally heterogeneous set of compounds is not a trivial task. In this study, a robust ensemble model is created using Mayr's database of reactivity parameters. A combination of topological and quantum mechanical descriptors and different machine learning algorithms are employed for the model's development. The predictability of the model is assessed using different statistical parameters, and its validation is examined, including a training/test partition, an applicability domain, and a y-scrambling test. The global ensemble model presents a Q5-fold2 of 0.909 and a Qext2 of 0.912, demonstrating an excellent predictability performance of E values and showing that w is not a good descriptor for the prediction of E, especially for the case of neutral compounds. ElectroPredictor, a noncommercial Python application (https://github.com/mmoreno1/ElectroPredictor), is developed to predict E. QM9, a well-known large dataset containing 133885 neutral molecules, is used to perform a virtual screening (94.0% coverage). Finally, the 10 most electrophilic molecules are analyzed as possible new Mayr's electrophiles, which have not yet been experimentally tested. This study confirms the necessity to build an ensemble model using nonlinear machine learning algorithms, topographic descriptors, and separating molecules into charged and neutral compounds to predict E with precision.

Experimental-Density Functional Theory (DFT) Study of the Inhibitory Effect of Furan Residues in the Ziegler-Natta Catalyst during Polypropylene Synthesis.

In this experimental-theoretical study, the effect of furan on Ziegler-Natta catalyst productivity, melt flow index (MFI), and mechanical properties of polypropylene were investigated. Through the analysis of the global and local reactivity of the reagents, it was determined that the furan acts as an electron donor. In contrast, the titanium of the ZN catalyst acts as an electron acceptor. It is postulated that this difference in reactivity could lead to forming a furan-titanium complex, which blocks the catalyst's active sites and reduces its efficiency for propylene polymerization. Theoretical results showed a high adsorption affinity of furan to the active site of the Ti catalyst, indicating that furan tends to bind strongly to the catalyst, thus blocking the active sites and decreasing the availability for propylene polymerization. The experimental data revealed that the presence of furan significantly reduced the productivity of the ZN catalyst by 10, 20, and 41% for concentrations of 6, 12.23, and 25.03 ppm furan, respectively. In addition, a proportional relationship was observed between the furan concentration and the MFI melt index of the polymer, where the higher the furan concentration, the higher the MFI. Likewise, the presence of furan negatively affected the mechanical properties of polypropylene, especially the impact Izod value, with percentage decreases of 9, 18, and 22% for concentrations of 6, 12.23, and 25.03 ppm furan, respectively.

Elucidating the Racemization Mechanism of Aliphatic and Aromatic Amino Acids by In Silico Tools.

The racemization of biomolecules in the active site can reduce the biological activity of drugs, and the mechanism involved in this process is still not fully comprehended. The present study investigates the impact of aromaticity on racemization using advanced theoretical techniques based on density functional theory. Calculations were performed at the ωb97xd/6-311++g(d,p) level of theory. A compelling explanation for the observed aromatic stabilization via resonance is put forward, involving a carbanion intermediate. The analysis, employing Hammett's parameters, convincingly supports the presence of a negative charge within the transition state of aromatic compounds. Moreover, the combined utilization of natural bond orbital (NBO) analysis and intrinsic reaction coordinate (IRC) calculations confirms the pronounced stabilization of electron distribution within the carbanion intermediate. To enhance our understanding of the racemization process, a thorough examination of the evolution of NBO charges and Wiberg bond indices (WBIs) at all points along the IRC profile is performed. This approach offers valuable insights into the synchronicity parameters governing the racemization reactions.

Identifying, Quantifying, and Recovering a Sorbitol-Type Petrochemical Additive in Industrial Wastewater and Its Subsequent Application in a Polymeric Matrix as a Nucleating Agent.

Currently, polypropylene (PP) is highlighted using sorbitol-based clarifying agents since these agents are high quality, low cost, and work as a barrier against moisture, which makes PP ideal for packaging food, beverages, and medical products, among others. The use of analytical methods capable of recovering these additives in wastewater streams and then reusing them in the PP clarification stage represents an innovative methodology that makes a substantial contribution to the circular economy of the PP production industry. In this study, a method of extraction and recovery of the Millad NX 8000 was developed. The additive was recovered using GC-MS and extracted with an activated carbon column plus glass fiber, using an injection molded sample, obtaining a recovery rate greater than 96%. TGA, DSC, and FTIR were used to evaluate the recovered additive's glass transitions and purity. The thermal degradation of the recovered additive was found to be between 340 and 420 °C, with a melting temperature of 246 °C, adopting the same behavior as the pure additive. In FTIR, the characteristic absorption peak of Millad NX 8000 was observed at 1073 cm-1, which indicates the purity of the extracted compound. Therefore, this work develops a new additive recovery methodology with high purity to regulate the crystallization behavior and of PP.

Effect of the Nucleophile's Nature on Chloroacetanilide Herbicides Cleavage Reaction Mechanism. A DFT Study.

In this study, the degradation mechanism of chloroacetanilide herbicides in the presence of four different nucleophiles, namely: Br-, I-, HS-, and S2O3-2, was theoretically evaluated using the dispersion-corrected hybrid functional wB97XD and the DGDZVP as a basis set. The comparison of computed activation energies with experimental data shows an excellent correlation (R2 = 0.98 for alachlor and 0.97 for propachlor). The results suggest that the best nucleophiles are those where a sulfur atom performs the nucleophilic attack, whereas the other species are less reactive. Furthermore, it was observed that the different R groups of chloroacetanilide herbicides have a negligible effect on the activation energy of the process. Further insights into the mechanism show that geometrical changes and electronic rearrangements contribute 60% and 40% of the activation energy, respectively. A deeper analysis of the reaction coordinate was conducted, employing the evolution chemical potential, hardness, and electrophilicity index, as well as the electronic flux. The charge analysis shows that the electron density of chlorine increases as the nucleophilic attack occurs. Finally, NBO analysis indicates that the nucleophilic substitution in chloroacetanilides is an asynchronous process with a late transition state for all models except for the case of the iodide attack, which occurs through an early transition state in the reaction.

In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2.

Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha's test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.

Synthesis, In Vitro, and In Silico Analysis of the Antioxidative Activity of Dapsone Imine Derivatives.

Dapsone (DDS) is an antibacterial drug with well-known antioxidant properties. However, the antioxidant behavior of its derivatives has not been well explored. In the present work, the antioxidant activity of 10 dapsone derivatives 4-substituted was determined by an evaluation in two in vitro models (DPPH radical scavenging assay and ferric reducing antioxidant power). These imine derivatives 1-10 were obtained through condensation between DDS and the corresponding aromatic aldehydes 4-substuited. Three derivatives presented better results than DDS in the determination of DPPH (2, 9, and 10). Likewise, we have three compounds with better reducing activity than dapsone (4, 9, and 10). In order to be more insight, the redox process, a conceptual DFT analysis was carried out. Molecular descriptors such as electronic distribution, the total charge accepting/donating capacity (I/A), and the partial charge accepting/donating capacity (ω+/ω-) were calculated to analyze the relative donor-acceptor capacity through employing a donor acceptor map (DAM). The DFT calculation allowed us to establish a relationship between GAPHOMO-LUMO and DAM with the observed antioxidant effects. According to the results, we concluded that compounds 2 and 3 have the lowest Ra values, representing a good antioxidant behavior observed experimentally in DPPH radical capturing. On the other hand, derivatives 4, 9, and 10 display the best reducing capacity activity with the highest ω- and Rd values. Consequently, we propose these compounds as the best antireductants in our DDS imine derivative series.

Modeling the Antileukemia Activity of Ellipticine-Related Compounds: QSAR and Molecular Docking Study.

The antileukemia cancer activity of organic compounds analogous to ellipticine representes a critical endpoint in the understanding of this dramatic disease. A molecular modeling simulation on a dataset of 23 compounds, all of which comply with Lipinski's rules and have a structure analogous to ellipticine, was performed using the quantitative structure activity relationship (QSAR) technique, followed by a detailed docking study on three different proteins significantly involved in this disease (PDB IDs: SYK, PI3K and BTK). As a result, a model with only four descriptors (HOMO, softness, AC1RABAMBID, and TS1KFABMID) was found to be robust enough for prediction of the antileukemia activity of the compounds studied in this work, with an R2 of 0.899 and Q2 of 0.730. A favorable interaction between the compounds and their target proteins was found in all cases; in particular, compounds 9 and 22 showed high activity and binding free energy values of around -10 kcal/mol. Theses compounds were evaluated in detail based on their molecular structure, and some modifications are suggested herein to enhance their biological activity. In particular, compounds 22_1, 22_2, 9_1, and 9_2 are indicated as possible new, potent ellipticine derivatives to be synthesized and biologically tested.

New Insight into the Chloroacetanilide Herbicide Degradation Mechanism through a Nucleophilic Attack of Hydrogen Sulfide.

The nucleophilic attack of hydrogen sulfide (HS-) on six different chloroacetanilide herbicides was evaluated theoretically using the dispersion-corrected hybrid functional wB97XD and the 6-311++G(2d,2p) Pople basis sets. The six evaluated substrates were propachlor (A), alachlor (B), metolachlor (C), tioacetanilide (D), ß-anilide (E), and methylene (F). Three possible mechanisms were considered: (a) bimolecular nucleophilic substitution (SN²) reaction mechanism, (b) oxygen assistance, and (c) nitrogen assistance. Mechanisms based on O- and N-assistance were discarded due to a very high activation barrier in comparison with the corresponding SN² mechanism, with the exception of compound F. The N-assistance mechanism for compound F had a free activation energy of 23.52 kcal/mol, which was close to the value for the corresponding SN² mechanism (23.94 kcal/mol), as these two mechanisms could occur in parallel reactions with almost 50% of each one. In compounds A to D, an important electron-withdrawing effect of the C=O and C=S groups was seen, and consequently, the activation free energies in these SN² reactions were smaller, with a value of approximately 18 kcal/mol. Instead, compounds E and F, which have a CH2 group in the ß-position, presented a higher activation free energy (≈22 kcal/mol). Good agreement was found between experimental and theoretical values for all cases, and a reaction force analysis was performed on the intrinsic reaction coordinate profile in order to gain more details about the reaction mechanism. Finally, from the natural bond orbital (NBO) analysis, it was possible to evaluate the electronic reorganization through the reaction pathway where all the transition states were early in nature in the reaction coordinate (δBav < 50%); the transition states corresponding to compounds A to D turned out to be more synchronous than those for compounds E and F.

Classical QSAR and Docking Simulation of 4-Pyridone Derivatives for Their Antimalarial Activity.

In this work, the minimum energy structures of 22 4-pyridone derivatives have been optimized at Density Functional Theory level, and several quantum molecular, including electronic and thermodynamic descriptors, were computed for these substrates in order to obtain a statistical and meaningful QSAR equation. In this sense, by using multiple linear regressions, five mathematical models have been obtained. The best model with only four descriptors (r² = 0.86, Q² = 0.92, S.E.P = 0.38) was validated by the leave-one-out cross-validation method. The antimalarial activity can be explained by the combination of the four mentioned descriptors e.g., electronic potential, dipolar momentum, partition coefficient and molar refractivity. The statistical parameters of this model suggest that it is robust enough to predict the antimalarial activity of new possible compounds; consequently, three small chemical modifications into the structural core of these compounds were performed specifically on the most active compound of the series (compound 13). These three new suggested compounds were leveled as 13A, 13B and 13C, and the predicted biological antimalarial activity is 0.02 µM, 0.03 µM, and 0.07 µM, respectively. In order to complement these results focused on the possible action mechanism of the substrates, a docking simulation was included for these new structures as well as for the compound 13 and the docking scores (binding affinity) obtained for the interaction of these substrates with the cytochrome bc1, were -7.5, -7.2, -6.9 and -7.5 kcal/mol for 13A, 13B, 13C and compound 13, respectively, which suggests that these compounds are good candidates for its biological application in this illness.

Study of the Chemical Activities of Carbon Monoxide, Carbon Dioxide, and Oxygen Traces as Critical Inhibitors of Polypropylene Synthesis.

This study outlines the investigation into how the compounds CO2, CO, and O2 interact with the active center of titanium (Ti) on the surface of MgCl2 and how these interactions impact the productivity of the Ziegler-Natta catalyst, ultimately influencing the thermal stability of the produced polypropylene. The calculations revealed that the adsorption energies of Ti-CO2-CO and O2 were -9.6, -12.5, and -2.32 Kcal/mol, respectively. Using the density functional theory in quantum calculations, the impacts of electronic properties and molecular structure on the adsorption of CO, O2, and CO2 on the Ziegler-Natta catalyst were thoroughly explored. Additionally, the Gibbs free energy and enthalpy of adsorption were examined. It was discovered that strong adsorption and a significant energy release (-16.2 kcal/mol) during CO adsorption could explain why this gas caused the most substantial reductions in the ZN catalyst productivity. These findings are supported by experimental tests showing that carbon monoxide has the most significant impact on the ZN catalyst productivity, followed by carbon dioxide, while oxygen exerts a less pronounced inhibitory effect.

Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging.

Sorbitol derivatives and other additives are commonly used in various products, such as packaging or food packaging, to improve their mechanical, physical, and optical properties. To accurately and precisely evaluate the efficacy of adding sorbitol-type nucleating agents to these articles, their quantitative determination is essential. This study systematically investigated the quantification of sorbitol-type nucleating agents in food packaging made from impact copolymers of polypropylene (PP) and polyethylene (PE) using attenuated total reflectance infrared spectroscopy (ATR-FTIR) together with analysis of principal components (PCA) and machine learning algorithms. The absorption spectra revealed characteristic bands corresponding to the C-O-C bond and hydroxyl groups attached to the cyclohexane ring of the molecular structure of sorbitol, providing crucial information for identifying and quantifying sorbitol derivatives. PCA analysis showed that with the selected FTIR spectrum range and only the first two components, 99.5% of the variance could be explained. The resulting score plot showed a clear pattern distinguishing different concentrations of the nucleating agent, affirming the predictability of concentrations based on an impact copolymer. The study then employed machine learning algorithms (NN, SVR) to establish prediction models, evaluating their quality using metrics such as RMSE, R2, and RMSECV. Hyperparameter optimization was performed, and SVR showed superior performance, achieving near-perfect predictions (R2 = 0.9999) with an RMSE of 0.100 for both calibration and prediction. The chosen SVR model features two hidden layers with 15 neurons each and uses the Adam algorithm, balanced precision, and computational efficiency. The innovative ATR-FTIR coupled SVR model presented a novel and rapid approach to accurately quantify sorbitol-type nucleating agents in polymer production processes for polymer research and in the analysis of nucleating agent derivatives. The analytical performance of this method surpassed traditional methods (PCR, NN).

Molecular Recognition between Carbon Dioxide and Biodegradable Hydrogel Models: A Density Functional Theory (DFT) Investigation.

In this research, we explore the potential of employing density functional theory (DFT) for the design of biodegradable hydrogels aimed at capturing carbon dioxide (CO2) and mitigating greenhouse gas emissions. We employed biodegradable hydrogel models, including polyethylene glycol, polyvinylpyrrolidone, chitosan, and poly-2-hydroxymethacrylate. The complexation process between the hydrogel and CO2 was thoroughly investigated at the ωB97X-D/6-311G(2d,p) theoretical level. Our findings reveal a strong affinity between the hydrogel models and CO2, with binding energies ranging from -4.5 to -6.5 kcal/mol, indicative of physisorption processes. The absorption order observed was as follows: chitosan > PVP > HEAC > PEG. Additionally, thermodynamic parameters substantiated this sequence and even suggested that these complexes remain stable up to 160 °C. Consequently, these polymers present a promising avenue for crafting novel materials for CO2 capture applications. Nonetheless, further research is warranted to optimize the design of these materials and assess their performance across various environmental conditions.

Computational approaches for lead compound discovery in dipeptidyl peptidase-4 inhibition using machine learning and molecular dynamics techniques.

The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Mellitus (T2DM). The inhibition of dipeptidyl peptidase-4 (DPP-4) has been one of the most explored strategies to develop potential drugs against this condition. A diverse dataset of molecules with known experimental inhibitory activity against DPP-4 was constructed and used to develop predictive models using different machine-learning algorithms. Model M36 is the most promising one based on the internal and external performance showing values of Q2CV = 0.813, and Q2EXT = 0.803. The applicability domain evaluation and Tropsha's analysis were conducted to validate M36, indicating its robustness and accuracy in predicting pIC50 values for organic molecules within the established domain. The physicochemical properties of the ligands, including electronegativity, polarizability, and van der Waals volume were relevant to predict the inhibition process. The model was then employed in the virtual screening of potential DPP4 inhibitors, finding 448 compounds from the DrugBank and 9 from DiaNat with potential inhibitory activity. Molecular docking and molecular dynamics simulations were used to get insight into the ligand-protein interaction. From the screening and the favorable molecular dynamic results, several compounds including Skimmin (pIC50 = 3.54, Binding energy = -8.86 kcal/mol), bergenin (pIC50 = 2.69, Binding energy = -13.90 kcal/mol), and DB07272 (pIC50 = 3.97, Binding energy = -25.28 kcal/mol) seem to be promising hits to be tested and optimized in the treatment of T2DM. This results imply a important reduction in cost and time on the application of this drugs because all the information about the its metabolism is already available.

Synthesis, Photophysical Properties, Theoretical Studies, and Living Cancer Cell Imaging Applications of New 7-(Diethylamino)quinolone Chalcones.

In this article, three unsymmetrical 7-(diethylamino)quinolone chalcones with D-π-A-D and D-π-A-π-D type push-pull molecular arrangements were synthesized via a Claisen-Schmidt reaction. Using 7-(diethylamino)quinolone and vanillin as electron donor (D) moieties, these were linked together through the α,ß-unsaturated carbonyl system acting as a linker and an electron acceptor (A). The photophysical properties were studied, revealing significant Stokes shifts and strong solvatofluorochromism caused by the ICT and TICT behavior produced by the push-pull effect. Moreover, quenching caused by the population of the TICT state in THF-H2O mixtures was observed, and the emission in the solid state evidenced a red shift compared to the emission in solution. These findings were corroborated by density functional theory (DFT) calculations employing the wb97xd/6-311G(d,p) method. The cytotoxic activity of the synthesized compounds was assessed on BHK-21, PC3, and LNCaP cell lines, revealing moderate activity across all compounds. Notably, compound 5b exhibited the highest activity against LNCaP cells, with an LC50 value of 10.89 µM. Furthermore, the compounds were evaluated for their potential as imaging agents in living prostate cells. The results demonstrated their favorable cell permeability and strong emission at 488 nm, positioning them as promising candidates for cancer cell imaging applications.

Furan as Impurity in Green Ethylene and Its Effects on the Productivity of Random Ethylene-Propylene Copolymer Synthesis and Its Thermal and Mechanical Properties.

The presence of impurities such as H2S, thiols, ketones, and permanent gases in propylene of fossil origin and their use in the polypropylene production process affect the efficiency of the synthesis and the mechanical properties of the polymer and generate millions of losses worldwide. This creates an urgent need to know the families of inhibitors and their concentration levels. This article uses ethylene green to synthesize an ethylene-propylene copolymer. It describes the impact of trace impurities of furan in ethylene green and how this furan influences the loss of properties such as thermal and mechanical properties of the random copolymer. For the development of the investigation, 12 runs were carried out, each in triplicate. The results show an evident influence of furan on the productivity of the Ziegler-Natta catalyst (ZN); productivity losses of 10, 20, and 41% were obtained for the copolymers synthesized with ethylene rich in 6, 12, and 25 ppm of furan, respectively. PP0 (without furan) did not present losses. Likewise, as the concentration of furan increased, it was observed that the melt flow index (MFI), thermal (TGA), and mechanical properties (tensile, bending, and impact) decreased significantly. Therefore, it can be affirmed that furan should be a substance to be controlled in the purification processes of green ethylene.

A comprehensive study of product distributions and coke deposition during catalytic cracking of vacuum gas oil over hierarchical zeolites.

In this study, zeolites (Z) were used as catalysts in the cracking of a Colombian vacuum gas oil (VGO), with a focus on product distribution and coke deposition. The catalytic tests were carried out in a MAT-type reactor under typical conditions. The zeolites were subjected to alkaline treatment with NaOH at concentrations ranging from 0.05 to 0.4 mol/L, resulting in the creation of several samples (Z-0.05, Z-0.10, Z-0.20, Z-0.30 and Z-0.40) that were then hydrothermally stabilized (Z-0.05-M, Z-0.10-M, Z-0.20-M, Z-0.30-M and Z-0.40-M) to increase mesoporosity and reduced crystallinity. The increase in mesoporosity was accompanied by an improvement in acidity. Despite Z-0.30-M having higher acidity, Z-0.00-M and Z-0.10-M exhibited the highest activity due to their high crystallinity and microporosity, yielding the highest gas yields. Gasoline was the main product, with maximum yields exceeding 30%. Z-0.20-M produced more aromatic and olefin compounds than the others, resulting in higher quality gasoline. Coke formation followed the trend: Z-0.00-M < Z-0.10-M < Z-0.20-M < Z-0.30-M. The higher intracrystalline mesoporosity in the zeolites favored the formation of a more condensed coke.