Reaction Condition Optimization Research Articles

Efficient synthesis of halobenzothioamides from the hydrosulfuration of halobenzonitriles and mechanism investigation

An efficient method for the synthesis of various halobenzothioamides from the hydrosulfuration of a variety of halobenzonitriles with NaHS under mild conditions was developed by adding auxiliary reagent MgCl2 · 6 H2O. It was found that an excellent yield of benzothioamides is obtained from the hydrosulfuration of halobenzonitrile with inorganic sulfur reagents by adjusting the pH value of the reaction solution. Reaction condition optimization experiments revealed, that high yields of products were obtained in the pH range of 8–10.

Task decomposition strategy based on machine learning for boosting performance and identifying mechanisms in heterogeneous activation of peracetic acid process

Heterogeneous activation of peracetic acid (PAA) process is a promising method for removing organic pollutants from water. Nevertheless, this process is constrained by several complex factors, such as the selection of catalysts, optimization of reaction conditions, and identification of mechanism. In this study, a task decomposition strategy was adopted by combining a catalyst and reaction condition optimization machine learning (CRCO-ML) model and a mechanism identification machine learning (MI-ML) model to address these issues. The Categorical Boosting (CatBoost) model was identified as the best-performing model for the dataset (1024 sets and 7122 data points) in this study, achieving an R2 of 0.92 and an RMSE of 1.28. Catalyst composition, PAA dosage, and catalyst dosage were identified as the three most important features through SHAP analysis in the CRCO-ML model. The HCO3– is considered the most influential water matrix affecting the k value. The errors between all reverse experiment results and the predictions of the CRCO-ML and MI-ML models were <10 % and 15 %, respectively. This interdisciplinary work provides novel insights into the design and application of the heterogeneous activation of PAA process, significantly contributing to the rapid development of this technology.

Anomalous Reaction Pathways to Methane Production in Photocatalytic Ethanol Oxidation.

Photocatalytic reduction reactions occasionally utilize sacrificial agents to scavenge photogenerated holes, thus enhancing the kinetics and efficiency of electron harvesting. However, exploring alternative hole-mediated oxidation reactions and their potential impact on photoredox processes is limited. This study investigates the products resulting from the oxidation of ethanol, a commonly used hole scavenger, and the underlying mechanisms involved. We examine a homogeneous eosin Y photoreaction scheme containing a Cu complex coordinated with an N-heterocyclic carbene, a combination often employed in CO2 conversion. Under visible-light excitation, this photosystem yields methane as an unusual product, alongside acetaldehyde and carbon monoxide. Mechanistic analysis reveals that ethanol undergoes a catalytic cascade involving oxidative processes, C-C bond cleavage, and intermolecular hydrogen atom transfer. Notably, the Lewis-acidic metal center of the Cu complex activates a novel pathway for ethanol oxidation. This work presents the influence of catalyst selection and reaction condition optimization on the emergence of new or unexpected catalytic processes.

A synthesis of styrene-grafted starch accompanied with reaction condition optimization and microstructural and macroscopic property analysis

Styrene grafting is efficient to alter starch macroscopic property. Nevertheless, reaction mechanism, condition and products structure and properties are blurry. A deep exploration is necessary. Herein, K2S2O8 and styrene were chosen for corn starch modification. Reaction conditions involving K2S2O8 and styrene amounts, temperature and period were explored. Concurrently, microstructural and macroscopic properties of modified starches were explored via FT-IR, XPS, 13C NMR, GPC, XRD, SEM, TGA, DSC, CA and water dispersibility analysis. Main results reveal that K2S2O8 and styrene amounts, temperature and period as 3 and 200 wt%, 50 °C and 3 h is an optimized condition for starch grafting styrene, achieving a grafting yield (GY) as 45.8 wt%. GPC, XPS and 13C NMR analysis certified successful grafting and grafting mainly occurred at C-3 site OH within glucose unit. This variation converted starch crystalline shape transformation from A-typed to V-typed accompanied with crystallinity, thermal-stability and swelling characteristic decrease and surface hydrophobicity elevation. With GY increased from 0 to 45.8 wt% crystallinity decreased from 38.7 to 15.1%, thermal-decomposition temperature decreased by 10 °C, gelatinization temperature and CA increased by 50 °C and 70°, respectively. It seems a small amount styrene grafting can alter starch macroscopic property such as hydrophilicity efficiently, readily to manufacture hydrophobic starch. This hydrophobic starch might have a potential use in plastic industry, enlarging starch use application, meaningful.

Ionic liquid modification reshapes the substrate pockets of lipase to boost its stability and activity in vitamin E succinate synthesis.

The relative low stability, reusability and activity of enzymes made the industrial production of vitamin E succinate (VES) can only be performed with complex processes and high cost using chemical methods. To address these issues, in the present study, an ionic liquids (ILs) modification strategy was developed to improve the activity and stability of lipases in VES synthesis. The results showed that the [1-butyl-3-methyl imidazole] [N-acetyl-l-proline] ILs modified Candida rugosa lipase (CRL) has the highest modification degree (48.28%), activity (774 U g-1 ), thermostability and solvent tolerance in three selected modifiers. Additionally, after reaction condition optimization, the highest yield of VES can be improved to 95.18% at 45 °C for 15 h, which was significantly improved compared to some previous studies. In the present study, a high-efficiency VES synthesis strategy was successfully developed via modification of lipase. Moreover, the mechanism by which ILs modification can enhance the activity and stability of lipase was investigated via both experimental and computational-aided methods. Molecular dynamics simulation suggested that ILs modification changed the geometry of Phe344 from flat to upright, which significantly reshaped and enhanced the size of substrate binding pocket of CRL. It is also agreement with our circular dichroism and fluorescence spectroscopy results, which suggested that the modification changed the secondary structure of CRL to a certain extent. The larger pocket also endowed the suitable binding pose of succinate, which made the hydrogen bonds between succinate and active site Ser209 become stronger, and thus improving the yield of VES. © 2023 Society of Chemical Industry.

Unraveling the promoter effect and the roles of counterion exchange in glycosylation reaction.

The stereoselectivity of glycosidic bond formation continues to pose a noteworthy hurdle in synthesizing carbohydrates, primarily due to the simultaneous occurrence of SN1 and SN2 processes during the glycosylation reaction. Here, we applied an in-depth analysis of the glycosylation mechanism by using low-temperature nuclear magnetic resonance and statistical approaches. A pathway driven by counterion exchanges and reaction byproducts was first discovered to outline the stereocontributions of intermediates. Moreover, the relative reactivity values, acceptor nucleophilic constants, and Hammett substituent constants (σ values) provided a general index to indicate the mechanistic pathways. These results could allow building block tailoring and reaction condition optimization in carbohydrate synthesis to be greatly facilitated and simplified.

A synthesis of post‐esterified oxidized starch accompanied with physicochemical property analysis and esterification reaction condition optimization

AbstractEsterification is a normal method for starch modification while reaction condition is not clear. Here, pure corn starch and vinyl acetate were chosen for starch esterification. Reaction conditions involving esterifying agent content, pH, reaction time and temperature were explored. Dess‐Martin periodinane (DMP) pretreatment was adopted to accelerate esterification. Main results reveal that a content of 30 wt% vinyl acetate, pH = 9, time as 1 h and temperature as 45°C is an optimized condition for vinyl acetate esterification and DMP oxidization can enhance it. After modification, variations in microstructure, surface morphology and macroscopic properties such as viscosity, transparency, adhesion property were explored by FT‐IR, XRD, SEM, TGA, UV–Vis, viscosity, and slushing experiments. Main results reveal that after esterification carbonyl groups were introduced, lattice plane occurred expansion, surface morphology was destroyed, water absorption capacity, viscosity, and gelatinization temperature decreased while transparency, thermal‐stability and adhesion property increased. DMP treatment enhanced the previous variations. It seems via a middle‐level DMP pretreatment and vinyl acetate esterification starch solution viscosity and gelatinization temperature decreased greatly while transparency and adhesion force increased greatly, possibly having a potential use in textile industry. For instance, using as slurry its manufacture cost can be decreased with decreased energy consumption, economical.

An Efficient and Cost- Effective Pretreatment of Rice Straw Using Steam Explosion: A Pilot Scale Experience

Surplus availability of rice straw (RS) presents it as a potential feedstock for ethanol production. Steam explosion (SE) is considered as a green approach to extract fermentable sugars at lower cost. The present study deals with the reaction condition optimization for water and dilute acid assisted steam explosion of rice straw at different temperatures and explores the effect of structural properties of solid residue on enzymatic hydrolysis along with mass balance. SE conditions were optimized at pilot scale, raising the temperature from 170 to 200 °C in water assisted SE resulting in an increased glucan conversion from 21.4 to 42.5% at 15% solid loading using 1.5 FPU of cellulases g–1 biomass. Further, it was improved up to 58.7% by increasing the enzyme dosage to 5 FPU, although it might lead to enhanced enzyme cost by threefold. To reduce costs, small amount of dilute acid (DA) was added during SE and lowering of enzyme consumption i.e. 1.5 FPU/g cellulose has been used to achieve 65.5% glucan conversion. Varying temperature and incorporate dilute acid during pretreatment induced structural alterations in biomass evident by compositional analysis, FT-IR and mass balance. Mass balance study revealed that the overall sugar recovery i.e. 58.7 and 38.8% and theoretical yield of ethanol shall be 222 and 186 L ton–1 RS respectively, with and without DA addition.Graphical

Efficient mapping of lignin depolymerization product pools and quantification of specific monomers through rapid GPC-HPLC-UV/VIS analysis

BackgroundGrowing research on lignin depolymerization to functionalized bio-aromatics has necessitated dedicated analysis techniques. However, immense variability in molecular weight and functional groups of the depolymerization products impedes fast analysis of a large number of samples while remaining in-depth enough for catalyst screening or reaction condition optimization. While GPC-HPLC-UV/VIS has been a promising technique, up until now, the information it provides is largely qualitative. By enabling quantification of key monomeric products and through further reduction of overall analysis time, this study aims to increase the potential of GPC-HPLC-UV/VIS for fast and in-depth characterization of lignin depolymerization product pools. ResultsAnalysis of selected samples, isolated from GPC-HPLC-UV/VIS analyses of lignin depolymerization product pools, with gas chromatography (GC) equipped with an Orbitrap high-resolution accurate mass spectrometer (Orbitrap-HR/AM-MS) is successful in identifying the main low monomeric products. Moreover, these identifications are further substantiated through GPC-HPLC-UV/VIS analysis of standards. Furthermore, straight forward quantification of these products directly within GPC-HPLC-UV/VIS is successfully developed with limits of detection ≤0.05 mmol/L, which is at least on par with more complex analysis techniques. Additionally, several different reversed phase columns are assessed to reduce 2nd dimension (2D) time and, hence, overall analysis time while maintaining the possibility for quantification. A reduction in overall analysis time of about 30% as compared to the state-of-the-art is achieved by using a YMC Triart BIO C4 column as 2D. SignificanceThrough the enhancements introduced in this study, GPC-HPLC-UV/VIS emerges as a unique technique for the analysis of lignin depolymerization product pools, which is capable of fast yet sufficiently in-depth analysis of a large volume of samples. This capability is indispensable for catalyst screening and fine-tuning reaction conditions.

Electronic Structures of Nickel(II)-Bis(indanyloxazoline)-dihalide Catalysts: Understanding Ligand Field Contributions That Promote C(sp2)-C(sp3) Cross-Coupling.

NiII(IB) dihalide [IB = (3aR,3a'R,8aS,8a'S)-2,2'-(cyclopropane-1,1-diyl)bis(3a,8a-dihydro-8H-indeno[1,2-d]-oxazole)] complexes are representative of a growing class of first-row transition-metal catalysts for the enantioselective reductive cross-coupling of C(sp2) and C(sp3) electrophiles. Recent mechanistic studies highlight the complexity of these ground-state cross-couplings but also illuminate new reactivity pathways stemming from one-electron redox and their significant sensitivities to reaction conditions. For the first time, a diverse array of spectroscopic methods coupled to electrochemistry have been applied to NiII-based precatalysts to evaluate specific ligand field effects governing key Ni-based redox potentials. We also experimentally demonstrate DMA solvent coordination to catalytically relevant Ni complexes. Coordination is shown to favorably influence key redox-based reaction steps and prevent other deleterious Ni-based equilibria. Combined with electronic structure calculations, we further provide a direct correlation between reaction intermediate frontier molecular orbital energies and cross-coupling yields. Considerations developed herein demonstrate the use of synergic spectroscopic and electrochemical methods to provide concepts for catalyst ligand design and rationalization of reaction condition optimization.

A Chemical Model of a TET Enzyme for Selective Oxidation of Hydroxymethyl Cytosine to Formyl Cytosine.

Methylation/demethylation of cytosines in DNA is central to epigenetics, which plays crucial roles in the regulation of about half of all human genes. Although the methylation mechanism, which downregulates gene expression, has been sufficiently decoded; the demethylation pathway, which upregulates gene expression, still holds questions to be answered. Demethylation of 5-methylcytosine by ten-eleven translocation (TET) enzymes yields understudied but epigenetically relevant intermediates, 5-hydroxymethyl (5-hmC), 5-formyl (5-fC), and 5-carboxyl (5-caC) cytosines. Here we report an iron complex, FeIIITAML (TAML = tetraamido macrocyclic ligand), which can facilitate selective oxidation of 5-hmC to its oxidative derivatives by forming a high-valent Fe-oxo intermediate in the presence of H2O2 under physiologically relevant conditions. Detailed HPLC analyses supported by a wide reaction condition optimization for the 5-hmC → 5-fC oxidation provides us with a chemical model of the TET enzyme. This study shines light on future efforts for a better understanding of the roles of 5-hmC and the TET enzyme mechanism and potentially novel therapeutic methods.

Rh-catalyzed hydroformylation of allyl acetate toward 4-acetoxybutyraldehyde: The crucial step in production of 1,4-butanediol via hydroformylation/hydrogenation sequence

The synthesis protocol for 1,4-butanediol via two-step hydroformylation-hydrogenation sequence using allyl acetate as the starting material has attracted much attention in industry. In this work, the first-step hydroformylation of allyl acetate determining the overall efficiency of this protocol was studied. Based on the phosphine screening and reaction condition optimization allyl acetate, Xantphos-modified Rh(acac)(CO)2 catalytic system proved highly efficient for hydroformylation of allyl acetate, giving the target product of 4-acetoxybutanal in the yield of 80%. This catalytic system is advantageous with excellent regioselectivity to the linear product of 4-acetoxybutanal as well as the recyclability up to 10 runs without activity loss, demonstrating its remarkable stability required for large-scale application. The control experiments were conducted to shed light on the involved reaction pathways for generation of target 4-acetoxybutanal and the related side-products.

The synthesis of highly crystalline covalent organic frameworks via the monomer crystal induction for the photocatalytic asymmetric α‐alkylation of aldehydes

AbstractCovalent organic frameworks (COFs) are ordered porous organic frameworks, which found wide applications in gas sorption and separation, catalysis, sensoring, and many others owing to their permanent porosity and designable structural motifs. To take full advantage of the well‐defined porous structure, COFs need to be synthesized with high crystallinity. However, the synthesis of COFs with high crystalline is a general challenge, which requires dedicated linker design and reaction condition optimization. To achieve this, we developed a monomer crystal‐induced strategy to construct crystalline COFs. The synthesized COFs have higher crystallinity, surface area, and thermal stability than those prepared without monomer crystal induction. Furthermore, the high crystalline COFs exhibit excellent performance in the photocatalytic asymmetric alkylation of aldehydes. The monomer crystal‐induced method not only represents a new route for the synthesis of crystalline COFs but also sheds light on the mechanism of the formation of COFs.

Biosynthesis of Silver Nanoparticles Using Salvia pratensis L. Aerial Part and Root Extracts: Bioactivity, Biocompatibility, and Catalytic Potential.

The aim of this research was the synthesis of silver nanoparticles (SPA- and SPR-AgNPs) using the aqueous extracts of the aerial (SPA) and the root (SPR) parts of the plant Salvia pratensis L., their characterization, reaction condition optimization, and evaluation of their biological and catalytic activity. UV-Vis spectroscopy, X-ray powder diffraction (XRPD), scanning electron microscopy with EDS analysis (SEM/EDS), and dynamic light scattering (DLS) analysis were utilized to characterize the nanoparticles, while Fourier transform infrared (FTIR) spectroscopy was used to detect some functional groups of compounds present in the plant extracts and nanoparticles. The phenolic and flavonoid contents, as well as the antioxidant activity of the extracts, were determined spectrophotometrically. The synthesized nanoparticles showed twice-higher activity in neutralizing 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS+) compared with the respective extracts. SPR-AgNPs exhibited strong antimicrobial activity against almost all of the tested bacteria (<0.0039 mg/mL) and fungal strains, especially against the genus Penicillium (<0.0391 mg/mL). Moreover, they were fully biocompatible on all the tested eukaryotic cells, while the hemolysis of erythrocytes was not observed at the highest tested concentration of 150 µg/mL. The catalytic activity of nanoparticles toward Congo Red and 4-nitrophenol was also demonstrated. The obtained results confirm the possibility of the safe application of the synthesized nanoparticles in medicine and as a catalyst in various processes.

Modeling of Artificial Neural Networks for Hydrogen Production via Water Electrolysis

Artificial neural networks have emerged as a promising tool for estimating hydrogen production process variables for reaction condition optimization. Here we aim to predict complex nonlinear systems that use of artificial neural networks for modeling hydrogen production via water electrolysis and to evaluate the common challenges that arise. To estimate the effect of different electrolyzer systems input parameters such as electrolyte material, electrolyte type, supplied power (voltage and current), temperature, and time on hydrogen production, a predictive model was developed. The percentage contributions of the input parameters to hydrogen production and the best network architecture to minimize computation time and maximize network accuracy were shown. The results show that the hydrogen production parameters from electrolysis and the predicted safety explosive limit are 7% of the average root mean square error. Furthermore, coefficient of determination value was found 0.93. This predicted value is very close to the observed values. The neural network algorithm developed in this study could be used to make critical decisions in the electrolysis process for parameters affecting hydrogen production.

Fabrication and Performance Evaluation of Gelatin/Sodium Alginate Hydrogel-Based Macrophage and MSC Cell-Encapsulated Paracrine System with Potential Application in Wound Healing.

A gelatin/sodium alginate-based hydrogel microsphere has been fabricated after reaction condition optimization. Macrophages (RAW246.7) and adipose mesenchymal stem cells (ADSC) have been subsequently encapsulated in the microsphere in order to construct a 3D paracrine system for wound healing treatment. The synthesized microsphere displayed neglectable cytotoxicity toward both encapsulated cells until 10 days of incubation, indicating promising biocompatibility of the microsphere. A qRT-PCR and ELISA experiment revealed positive regulation of cytokines (Arg-1, IL-6, IL-8, IL-10, bFGF, HGF, VEGF, TLR-1, and CXCL13) expression regarding macrophage phenotype transformation and anti-inflammatory performance both inside the microsphere and in the microenvironment of established in vitro inflammatory model. Additionally, positive tendency of cytokine expression benefit wound healing was more pronounced in a fabricated 3D paracrine system than that of a 2D paracrine system. Furthermore, the 3D paracrine system exhibited more efficiently in the wound healing rate compared to the 2D paracrine system in an in vitro model. These results suggested the current paracrine system could be potentially used as a robust wound healing dressing.

How machine learning can accelerate electrocatalysis discovery and optimization.

Advances in machine learning (ML) provide the means to bypass bottlenecks in the discovery of new electrocatalysts using traditional approaches. In this review, we highlight the currently achieved work in ML-accelerated discovery and optimization of electrocatalysts via a tight collaboration between computational models and experiments. First, the applicability of available methods for constructing machine-learned potentials (MLPs), which provide accurate energies and forces for atomistic simulations, are discussed. Meanwhile, the current challenges for MLPs in the context of electrocatalysis are highlighted. Then, we review the recent progress in predicting catalytic activities using surrogate models, including microkinetic simulations and more global proxies thereof. Several typical applications of using ML to rationalize thermodynamic proxies and predict the adsorption and activation energies are also discussed. Next, recent developments of ML-assisted experiments for catalyst characterization, synthesis optimization and reaction condition optimization are illustrated. In particular, the applications in ML-enhanced spectra analysis and the use of ML to interpret experimental kinetic data are highlighted. Additionally, we also show how robotics are applied to high-throughput synthesis, characterization and testing of electrocatalysts to accelerate the materials exploration process and how this equipment can be assembled into self-driven laboratories.

Black-box optimization algorithms for chemical synthesis and reaction condition optimization

<p indent="0mm">With the development of automated experimental platform technology, self-optimization technology and reaction condition optimization have become a promising trend in chemical synthesis. Self-optimization techniques optimize reaction objective functions, such as yield, by adjusting various reaction condition parameters. Among self-optimization algorithms, black-box optimization algorithms are widely used in the optimization of reaction conditions in synthetic chemistry. This article outlines the application of black-box optimization algorithms in reaction condition optimization, and systematically introduces the encoding of chemical reaction, various black-box optimization algorithms and the existing public data and software.

An efficient multicomponent synthesis, characterization, SAR, In-silico ADME prediction and molecular docking studies of 2-Amino-7-(substituted-phenyl)-3-cyano-4-phenyl-4,5,6,7-tetrahydropyrano[2,3-b] pyrrole-5-carboxylic acid derivatives and their in-vitro antimicrobial activity

An efficient multicomponent reaction (MCR) allows the creation of several chemical bonds in one sequence without labour-consuming reaction condition optimization and work-up procedures, minimizing the waste production and cost, thus multicomponent reactions (MCRs) have proved to be extremely beneficial in organic reactions. The salient features of the present protocol are mild reaction conditions, excellent yields and easy isolation of products. Herein, MCR of 5-Oxo-1-phenylpyrrolidine-3-carboxylic acid, substituted benzaldehydes and malononitrile were designed and the synthesized 2-Amino-7-(substituted- phenyl)-3-cyano-4-phenyl-4,5,6,7-tetrahydropyrano[2,3-b]pyrrole-5-carboxylic acid derivatives were evaluated for their antimicrobial activity. The structures of the synthesized compounds were assigned using different spectroscopic techniques such as FT-IR, 1H NMR, 13C NMR and elemental analyses. Compound 5d containing -NO2 group showed incredible antimicrobial activity against Salmonella typhi (MTCC 537) as compared to reference drug (tetracycline), and also showed excellent activity against Bacillus licheniformis (MTCC 10341), Pseudomonas aeruginosa (MTCC 741) and Staphylococcus aureus (MTCC 10341). Structural activity relationship results displayed that the presence of nitro function or halogens plays an important role in the antimicrobial activities of these tested compounds. Theoretical calculation of ADME properties suggested that synthesized compounds showed good pharmacokinetic profile. The molecular docking study of compound 5d revealed that there were good interactions between the active sites of 7KW6, 5E68, 4EQA and 3IP4 protiens.

Catalytic hydrogenolysis over Cu-based oxide derived from hydrotalcite-like structured materials: Reaction condition optimization and artificial neural network modelling

In this study, the catalytic activity of Cu-derived oxide catalysts in glycerol hydrogenolysis was investigated and the effect on the selectivity of 1,2-propanediol (1,2-PDO) with the different Cu/Al molar ratios was observed. The glycerol conversion/1,2-PDO selectivity was found to be dependent on the metallic sites and surface acidity of the Cu-based catalyst. The surface acidity and metallic sites of Cu nanoparticles were significantly improved with the increase of Cu/Al mole fraction in the catalyst. The efficiency of the Cu-derived oxide catalyst was attributed to the well-dispersed Cu and the presence of acidic sites. As expected, the Cu-based oxide catalyst (3CuAl) showed remarkable catalytic performance for 1,2-PDO synthesis with the highest turnover frequency (TOF) from the hydrogenolysis reaction. It is observed that the Cu-derived oxide catalyst is stable and efficient under strident reaction conditions in the presence of water and high temperature (aqueous glycerol solution (20 wt%), 210 °C, 40 bar). Moreover, the reaction conditions for glycerol hydrogenolysis in the presence of active 3CuAl were optimized to maximize the selectivity of 1,2-PDO with TOF. In addition, overcoming the limitation due to the external mass transfer resistance was investigated. The rate of glycerol consumption was subjected to kinetic analysis, which revealed a zero-order relationship on glycerol concentration, indicating that adsorbed glycerol was the most tightly bonded organic adsorbate. Further, the experimental reaction data was validated through the developed artificial neural network (ANN) model.