Tailoring parameters for QM/MM simulations: accurate modeling of adsorption and catalysis in zirconium-based metal-organic frameworks.

Quantum mechanics/molecular mechanics (QM/MM) simulations offer an efficient way to model reactions occurring in complex environments. This study introduces a specialized set of charge and Lennard-Jones parameters tailored for electrostatically embedded QM/MM calculations, aiming to accurately model both adsorption processes and catalytic reactions in zirconium-based metal-organic frameworks (Zr-MOFs). To validate our approach, we compare adsorption energies derived from QM/MM simulations against experimental results and Monte Carlo simulation outcomes. The developed parameters showcase the ability of QM/MM simulations to represent long-range electrostatic and van der Waals interactions faithfully. This capability is evidenced by the prediction of adsorption energies with a low root mean square error of 1.1 kcal mol-1 across a wide range of adsorbates. The practical applicability of our QM/MM model is further illustrated through the study of glucose isomerization and epimerization reactions catalyzed by two structurally distinct Zr-MOF catalysts, UiO-66 and MOF-808. Our QM/MM calculations closely align with experimental activation energies. Importantly, the parameter set introduced here is compatible with the widely used universal force field (UFF). Moreover, we thoroughly explore how the size of the cluster model and the choice of density functional theory (DFT) methodologies influence the simulation outcomes. This work provides an accurate and computationally efficient framework for modeling complex catalytic reactions within Zr-MOFs, contributing valuable insights into their mechanistic behaviors and facilitating further advancements in this dynamic area of research.

AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry.

This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry. Recent advances in artificial intelligence have expanded the application of machine learning in chemistry, particularly in yield prediction, retrosynthesis, and reaction condition prediction. However, the effectiveness of these models hinges on the integrity of chemical reaction datasets, which are often plagued by inconsistencies like missing reactants, incorrect atom mappings, and outright erroneous reactions. AutoTemplate introduces a two-stage approach to refine these datasets. The first stage involves extracting meaningful reaction transformation rules and formulating generic reaction templates using a simplified SMARTS representation. This simplification broadens the applicability of templates across various chemical reactions. The second stage is template-guided reaction curation, where these templates are systematically applied to validate and correct the reaction data. This process effectively amends missing reactant information, rectifies atom-mapping errors, and eliminates incorrect data entries. A standout feature of AutoTemplate is its capability to concurrently identify and correct false chemical reactions. It operates on the premise that most reactions in datasets are accurate, using these as templates to guide the correction of flawed entries. The protocol demonstrates its efficacy across a range of chemical reactions, significantly enhancing dataset quality. This advancement provides a more robust foundation for developing reliable machine learning models in chemistry, thereby improving the accuracy of forward and retrosynthetic predictions. AutoTemplate marks a significant progression in the preprocessing of chemical reaction datasets, bridging a vital gap and facilitating more precise and efficient machine learning applications in organic synthesis. SCIENTIFIC CONTRIBUTION: The proposed automated preprocessing tool for chemical reaction data aims to identify errors within chemical databases. Specifically, if the errors involve atom mapping or the absence of reactant types, corrections can be systematically applied using reaction templates, ultimately elevating the overall quality of the database.

Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions.

In the field of chemical synthesis planning, the accurate recommendation of reaction conditions is essential for achieving successful outcomes. This work introduces an innovative deep learning approach designed to address the complex task of predicting appropriate reagents, solvents, and reaction temperatures for chemical reactions. Our proposed methodology combines a multi-label classification model with a ranking model to offer tailored reaction condition recommendations based on relevance scores derived from anticipated product yields. To tackle the challenge of limited data for unfavorable reaction contexts, we employed the technique of hard negative sampling to generate reaction conditions that might be mistakenly classified as suitable, forcing the model to refine its decision boundaries, especially in challenging cases. Our developed model excels in proposing conditions where an exact match to the recorded solvents and reagents is found within the top-10 predictions 73% of the time. It also predicts temperatures within ± 20 [Formula: see text] of the recorded temperature in 89% of test cases. Notably, the model demonstrates its capacity to recommend multiple viable reaction conditions, with accuracy varying based on the availability of condition records associated with each reaction. What sets this model apart is its ability to suggest alternative reaction conditions beyond the constraints of the dataset. This underscores its potential to inspire innovative approaches in chemical research, presenting a compelling opportunity for advancing chemical synthesis planning and elevating the field of reaction engineering. Scientific contribution: The combination of multi-label classification and ranking models provides tailored recommendations for reaction conditions based on the reaction yields. A novel approach is presented to address the issue of data scarcity in negative reaction conditions through data augmentation.

Integrating Chemical Information into Reinforcement Learning for Enhanced Molecular Geometry Optimization.

Geometry optimization is a crucial step in computational chemistry, and the efficiency of optimization algorithms plays a pivotal role in reducing computational costs. In this study, we introduce a novel reinforcement-learning-based optimizer that surpasses traditional methods in terms of efficiency. What sets our model apart is its ability to incorporate chemical information into the optimization process. By exploring different state representations that integrate gradients, displacements, primitive type labels, and additional chemical information from the SchNet model, our reinforcement learning optimizer achieves exceptional results. It demonstrates an average reduction of about 50% or more in optimization steps compared to the conventional optimization algorithms that we examined when dealing with challenging initial geometries. Moreover, the reinforcement learning optimizer exhibits promising transferability across various levels of theory, emphasizing its versatility and potential for enhancing molecular geometry optimization. This research highlights the significance of leveraging reinforcement learning algorithms to harness chemical knowledge, paving the way for future advancements in computational chemistry.

Pathogens in PICU before and during the SARS-CoV-2 pandemic in China: a multicenter retrospective study.

BACKGROUND: Nonpharmacological interventions for COVID-19 could reduce the incidence of children hospitalized in pediatric intensive care units (PICU) and the incidence of children with bacterial infections. This study aimed to evaluate changes in the bacterial profile of children in PICU before and during the COVID-19 pandemics. METHODS: This is a retrospective study, involving clinical data of children with positive bacterial cultures admitted to the PICU respectively in 2019 and 2021. RESULTS: In total 652 children were included in this study. The total number of hospitalized patients and the incidence of bacteria-positive children in 2021 were lower than those in 2019. There were no significant differences in the ratio of Gram-positive bacterial infection, Gram-negative bacteria infection or fungi infection between the two years. The rate of Streptococcus pneumoniae in 2021 was higher than that in 2019(p = 0.127). The incidence of Haemophilus influenzae in hospitalized patients decreased with a downward trend(p = 0.002). The distribution of previous underlying diseases in children admitted to PICU with different outcomes of bacterial infection between the two years were homogeneous (p > 0.05). CONCLUSION: After the implementation of COVID-19 isolation, prevention and control measures, the number of hospitalizations and bacterial infections in PICU decreased, which may be due to changes in population's behavior patterns. Meanwhile, the incidence of Haemophilus influenzae in hospitalized patients decreased with a downward trend.

Fast Water Transport in UTSA-280 via a Knock-Off Mechanism.

Water and other small molecules frequently coordinate within metal-organic frameworks (MOFs). These coordinated molecules may actively engage in mass transfer, moving together with the transport molecules, but this phenomenon has yet to be examined. In this study, we explore a unique water transfer mechanism in UTSA-280, where an incoming water molecule can displace a coordinated molecule for mass transfer. We refer to this process as the "knock-off" mechanism. Despite UTSA-280 possessing one-dimensional channels, the knock-off transport enables water movement along the other two axes, effectively simulating a pseudo-three-dimensional mass transfer. Even with a relatively narrow pore width, the knock-off mechanism enables a high water flux in the UTSA-280 membrane. The knock-off mechanism also renders UTSA-280 superior water/ethanol diffusion selectivity for pervaporation. To validate this unique mechanism, we conducted 1 H and 2 H solid-state NMR on UTSA-280 after the adsorption of deuterated water. We also derived potential energy diagrams from the density functional theory to gain atomic-level insight into the knock-off and the direct-hopping mechanisms. The simulation findings reveal that the energy barrier of the knock-off mechanism is marginally lower than the direct-hopping pathway, implying its potential role in enhancing water diffusion in UTSA-280.

Mixed-linker strategy for suppressing structural flexibility of metal-organic framework membranes for gas separation.

Structural flexibility is a critical issue that limits the application of metal-organic framework (MOF) membranes for gas separation. Herein we propose a mixed-linker approach to suppress the structural flexibility of the CAU-10-based (CAU = Christian-Albrechts-University) membranes. Specifically, pure CAU-10-PDC membranes display high separation performance but at the same time are highly unstable for the separation of CO2/CH4. A partial substitution (30 mol.%) of the linker PDC with BDC significantly improves its stability. Such an approach also allows for decreasing the aperture size of MOFs. The optimized CAU-10-PDC-H (70/30) membrane possesses a high separation performance for CO2/CH4 (separation factor of 74.2 and CO2 permeability of 1,111.1 Barrer under 2 bar of feed pressure at 35°C). A combination of in situ characterization with X-ray diffraction (XRD) and diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy, as well as periodic density functional theory (DFT) calculations, unveils the origin of the mixed-linker approach to enhancing the structural stability of the mixed-linker CAU-10-based membranes during the gas permeation tests.

[Predictive value of Pv-aCO2/Ca-vO2 combined blood lactate concentration for primary peritonitis-related septic shock in children].

OBJECTIVE: To investigate the prognostic value of the ratio of veno-arterial carbon dioxide partial pressure difference to arterio-venous oxygen content difference (Pv-aCO2/Ca-vO2) in children with primary peritonitis-related septic shock. METHODS: A retrospective study was conducted. Sixty-three children with primary peritonitis-related septic shock admitted to department of intensive care unit of the Children's Hospital Affiliated to Xi'an Jiaotong University from December 2016 to December 2021 were enrolled. The 28-day all-cause mortality was the primary endpoint event. The children were divided into survival group and death group according to the prognosis. The baseline data, blood gas analysis, blood routine, coagulation, inflammatory status, critical score and other related clinical data of the two groups were statistics. The factors affecting the prognosis were analyzed by binary Logistic regression, and the predictability of risk factors were tested by the receiver operator characteristic curve (ROC curve). The risk factors were stratified according to the cut-off, Kaplan-Meier survival curve analysis compared the prognostic differences between the groups. RESULTS: A total of 63 children were enrolled, including 30 males and 33 females, the average age (5.6±4.0) years old, 16 cases died in 28 days, with mortality was 25.4%. There were no significant differences in gender, age, body weight and pathogen distribution between the two groups. The proportion of mechanical ventilation, surgical intervention, vasoactive drug application, and procalcitonin, C-reactive protein, activated partial thromboplastin time, serum lactate (Lac), Pv-aCO2/Ca-vO2, pediatric sequential organ failure assessment, pediatric risk of mortality III in the death group were higher than those in the survival group. Platelet count, fibrinogen, mean arterial pressure were lower than those in the survival group, and the differences were statistically significant. Binary Logistic regression analysis showed that Lac and Pv-aCO2/Ca-vO2 were independent risk factors affecting the prognosis of children [odds ratio (OR) and 95% confidence interval (95%CI) were 2.01 (1.15-3.21), 2.37 (1.41-3.22), respectively, both P < 0.01]. ROC curve analysis showed that the area under curve (AUC) of Lac, Pv-aCO2/Ca-vO2 and their combination were 0.745, 0.876 and 0.923, the sensitivity were 75%, 85% and 88%, and the specificity were 71%, 87% and 91%, respectively. Risk factors were stratified according to cut-off, and Kaplan-Meier survival curve analysis showed that the 28-day cumulative probability of survival of Lac ≥ 4 mmol/L group was lower than that in Lac < 4 mmol/L group [64.29% (18/28) vs. 82.86% (29/35), P < 0.05]. Pv-aCO2/Ca-vO2 ≥ 1.6 group 28-day cumulative probability of survival was less than Pv-aCO2/Ca-vO2 < 1.6 group [62.07% (18/29) vs. 85.29% (29/34), P < 0.01]. After a hierarchical combination of the two sets of indicator variables, the 28-day cumulative probability of survival of Pv-aCO2/Ca-vO2 ≥ 1.6 and Lac ≥ 4 mmol/L group significantly lower than that of the other three groups (Log-rank test, χ2 = 7.910, P = 0.017). CONCLUSIONS: Pv-aCO2/Ca-vO2 combined with Lac has a good predictive value for the prognosis of children with peritonitis-related septic shock.

Explainable uncertainty quantifications for deep learning-based molecular property prediction.

Quantifying uncertainty in machine learning is important in new research areas with scarce high-quality data. In this work, we develop an explainable uncertainty quantification method for deep learning-based molecular property prediction. This method can capture aleatoric and epistemic uncertainties separately and attribute the uncertainties to atoms present in the molecule. The atom-based uncertainty method provides an extra layer of chemical insight to the estimated uncertainties, i.e., one can analyze individual atomic uncertainty values to diagnose the chemical component that introduces uncertainty to the prediction. Our experiments suggest that atomic uncertainty can detect unseen chemical structures and identify chemical species whose data are potentially associated with significant noise. Furthermore, we propose a post-hoc calibration method to refine the uncertainty quantified by ensemble models for better confidence interval estimates. This work improves uncertainty calibration and provides a framework for assessing whether and why a prediction should be considered unreliable.

Comparative Analysis of Uncoupled Mode Approximations for Molecular Thermochemistry and Kinetics.

The accurate prediction of thermochemistry and kinetic parameters is an important task for reaction modeling. Unfortunately, the commonly used harmonic oscillator model is often not accurate enough due to the absence of anharmonic effects. In this work, we improve the representation of an anharmonic potential energy surface (PES) using uncoupled mode (UM) approximations, which model the full-dimensional PES as a sum of one-dimensional potentials of each mode. We systematically analyze different PES sampling schemes and coordinate systems for constructing the one-dimensional potentials, and benchmark the performance of UM methods on data sets of molecular thermochemistry and kinetic properties. The results show that the accuracy of the UM approach strongly depends on how the one-dimensional potentials are defined. If one-dimensional potentials are constructed by sampling along normal mode directions (UM-N) or along the directions that minimize intermode coupling (E- and E'-optimized), the accuracies of the predicted properties are not significantly improved compared to the harmonic oscillator model. However, significant improvements can be achieved by sampling the torsional modes separately from the vibrational modes (UM-T and UM-VT). In this work, three types of coordinate systems are examined, including redundant internal coordinates (RIC), hybrid internal coordinates (HIC), and translation-rotation-internal coordinates (TRIC). The HIC and TRIC coordinate systems can outperform RIC since transition state species may contain large-amplitude interfragmentary motions that regular internal coordinates can not describe adequately. Among all the methods we examined, the activation energies and pre-exponential factors calculated using UM-VT with either TRIC or HIC best agree with the reference values. Since UM-VT requires only a number of additional single point energy calculations for each independent mode, the scaling of computational costs of UM-VT is the same as that of the standard harmonic oscillator model, making UM-VT an appealing way of calculating the thermochemistry and kinetic properties for large-size systems.

Deep Learning-Based Increment Theory for Formation Enthalpy Predictions.

Machine learning predictions of molecular thermochemistry, such as formation enthalpy, have been limited for large and complicated species because of the lack of available training data. Such predictions would be important in the prediction of reaction thermodynamics and the construction of kinetic models. Herein, we introduce a graph-based deep learning approach that can separately learn the enthalpy contribution of each atom in its local environment with the effect of the overall molecular structure taken into account. Because this approach follows the additivity scheme of increment theory, it can be generalized to larger and more complicated species not present in the training data. By training the model on molecules with up to 11 heavy atoms, it can predict the formation enthalpy of testing molecules with up to 42 heavy atoms with a mean absolute error of 2 kcal/mol, which is less than half of the error of the conventional increment theory. We expect that this approach will also enable rapid prediction of other extensive properties of large molecules that are difficult to derive from experiments or ab initio calculation.

Transcriptome Analysis of Low-Temperature-Treated Tetraploid Yellow Actinidia chinensis Planch. Tissue Culture Plantlets.

The cold-resistant mechanism of yellow kiwifruit associated with gene regulation is poorly investigated. In this study, to provide insight into the causes of differences in low-temperature tolerance and to better understand cold-adaptive mechanisms, we treated yellow tetraploid kiwifruit 'SWFU03' tissue culture plantlets at low temperatures, used these plantlets for transcriptome analysis, and validated the expression levels of ten selected genes by real-time quantitative polymerase chain reaction (RT-qPCR) analysis. A number of 1630 differentially expressed genes (DEGs) were identified, of which 619 pathway genes were up-regulated, and 1011 were down-regulated in the cold treatment group. The DEGs enriched in the cold tolerance-related pathways mainly included the plant hormone signal transduction and the starch and sucrose metabolism pathway. RT-qPCR analysis confirmed the expression levels of eight up-regulated genes in these pathways in the cold-resistant mutants. In this study, cold tolerance-related pathways (the plant hormone signal transduction and starch and sucrose metabolism pathway) and genes, e.g., CEY00_Acc03316 (abscisic acid receptor PYL), CEY00_Acc13130 (bZIP transcription factor), CEY00_Acc33627 (TIFY protein), CEY00_Acc26744 (alpha-trehalose-phosphate synthase), CEY00_Acc28966 (beta-amylase), CEY00_Acc16756 (trehalose phosphatase), and CEY00_Acc08918 (beta-amylase 4) were found.

Epigenetic axis of SNHG19/miR-137/TNFAIP1 modulates amyloid beta peptide 25-35-induced SH-SY5Y cytotoxicity.

Aims: In this study, the authors hypothesized that, in an in vitro Alzheimer's disease model, the epigenetic axis of SNHG19/hsa-miR-137 functionally regulates amyloid beta peptide 25-35 (Aß25-35)-induced SH-SY5Y cytotoxicity. Methods: Dual luciferase activity assay demonstrated that SNHG19 could directly bind hsa-miR-137. In Aß25-35-treated SH-SY5Y cells, SNHG19 was upregulated and hsa-miR-137 downregulated. Results: SNHG19 knockdown ameliorated Aß25-35-induced SH-SY5Y cytotoxicity, then reversed by secondary hsa-miR-137 downregulation. TNFAIP1 was dynamically regulated by Aß25-35 and gene modifications in SH-SY5Y cells. Finally, upregulation of TNFAIP1 reversed the protective effect of SNHG19 knockdown on Aß25-35-induced cytotoxicity. Conclusions: The authors concluded that the epigenetic axis of SNHG19/hsa-miR-137/TNFAIP1 may functionally regulate Aß25-35-induced SH-SY5Y cytotoxicity, thus making it a potential molecular target for Alzheimer's disease treatment.

Proton-Conductive Cerium-Based Metal-Organic Frameworks.

In this study, proton-conducting behaviors of a cerium-based metal-organic framework (MOF), Ce-MOF-808, its zirconium-based isostructural MOF, and bimetallic MOFs with various Zr-to-Ce ratios are investigated. The significantly increased proton conductivity (σ) and decreased activation energy (Ea) are obtained by substituting Zr with Ce in the nodes of MOF-808. Ce-MOF-808 achieves a σ of 4.4 × 10-3 S/cm at 25 °C under 99% relative humidity and an Ea of 0.14 eV; this value is among the lowest-reported Ea of proton-conductive MOFs. Density functional theory calculations are utilized to probe the proton affinities of these MOFs. As the first study reporting the proton conduction in cerium-based MOFs, the finding here suggests that cerium-based MOFs should be a better platform for the design of proton conductors compared to the commonly reported zirconium-based MOFs in future studies on energy-related applications.

Permeable reactive barrier of waste sludge from wine processing utilized to block a metallic mixture plume in a simulated aquifer.

Heavy metal contamination in underground water commonly occurs in industrial areas in Taiwan. Wine-processing waste sludge (WPWS) can adsorb and remove several toxic metals from aqueous solutions. In this study, WPWS particles were used to construct a permeable reactive barrier (PRB) for the remediation of a contaminant plume comprising HCrO4-, Cu2+, Zn2+, Ni2+, Cd2+, and AsO33- in a simulated aquifer. This PRB effectively prevented the dispersals of Cu2+, Zn2+, and HCrO4-, and their concentrations in the pore water behind the barrier declined below the control standard levels. However, the PRB failed to prevent the diffusion of Ni2+, Cd2+, and AsO33-, and their concentrations were occasionally higher than the control standard levels. However, 18% to 45% of As, 84% to 93% of Cd, and 16% to 77% of Ni were removed by the barrier. Ni ions showed less adsorption on the fine sand layer because of the layer's ineffectiveness in multiple competitive adsorptions. Therefore, the ions infiltrated the barrier at a high concentration, which increased the loading for the barrier blocking. The blocking efficiency was related to the degree of adsorption of heavy metals in the sand layer and the results of their competitive adsorption.

Characterization of genes encoding ω-6 desaturase PoFAD2 and PoFAD6, and ω-3 desaturase PoFAD3 for ALA accumulation in developing seeds of oil crop Paeonia ostii var. lishizhenii.

Paeonia ostii var. lishizhenii has emerged as a valuable oil-producing crop with splendid characteristic of high α-linolenic acid (C18:3, ALA) content in its seed oil for healthy food supplement, but the molecular mechanism for seed ALA accumulation remains enigmatic. In our previous report, a PoSAD gene encoding stearoyl-ACP desaturase had been cloned and functional charactered for the first desaturation procedure involved in ALA biosynthesis pathway in P. ostii var. lishizhenii endosperms, while other participants have not been identified to date. In this study, full-length cDNAs of PoFAD2 (1489 bp), PoFAD6 (1638 bp), and PoFAD3 (1709 bp) were isolated based on our recent transcriptome sequencing data. Bioinformatic analyses revealed that the PoFADs were closest to their counterparts from Paeoniaceae species P. ludlowii, P. rockii, and P. suffruticosa in phylogenetic tree, which shared highly conserved histidine boxes (HXXXH, HXXHH, and HXXHH), exhibiting typical characters of membrane-bound desaturases in higher plants. Additionally, the PoFAD2 and PoFAD3 were specifically expressed and highly associated with LA and ALA accumulation in developing endosperms, whereas PoFAD6 expression has no significantly difference during whole seed developing stages. The catalytic function of these PoFADs were further analyzed by heterologous expression in Saccharomyces cerevisiae and Arabidopsis thaliana. The results showed that PoFAD2 and PoFAD6 could catalyze linoleic acid (C18:2) synthesis, while PoFAD3 had ability to produce ALA. This study functional identified three PoFAD genes, which indicates their critical roles in ALA biosynthesis pathway in P. ostii var. lishizhenii, and is of great theoretical and practical meaning on breeding and cultivating new tree peony varieties to promote human health and nutrition supplement.

Quantum Mechanical Calculations for Biomass Valorization over Metal-Organic Frameworks (MOFs).

Metal-organic framework (MOF) in biomass valorization is a promising technology developed in recent decades. By tailoring both the metal nodes and organic ligands, MOFs exhibit multiple functionalities, which not only extend their applicability in biomass conversion but also increase the complexity of material designs. To address this issue, quantum mechanical simulations have been used to provide mechanistic insights into the catalysis of biomass-derived molecules, which could potentially facilitate the development of novel MOF-based materials for biomass valorization. The aim of this review is to survey recent quantum mechanical simulations on biomass reactions occurring in MOF catalysts, with the emphasis on the studies of the catalytic activity of active sites and the effects of organic ligand and porous structures on the kinetics. Moreover, different model systems and computational methods used for MOF simulations are also surveyed and discussed in this review.

Learning to Optimize Molecular Geometries Using Reinforcement Learning.

Though quasi-Newton methods have been widely adopted in computational chemistry software for molecular geometry optimization, it is well known that these methods might not perform well for initial guess geometries far away from the local minima, where the quadratic approximation might be inaccurate. We propose a reinforcement learning approach to develop a model that produces a correction term for the quasi-Newton step calculated with the BFGS algorithm to improve the overall optimization performance. Our model is able to complete the optimization in about 30% fewer steps than pure BFGS for molecules starting from perturbed geometries. The new method has similar convergence to BFGS when complemented with a line search procedure, but it is much faster since it avoids the multiple gradient evaluations associated with line searches.

Nitrogen-rich covalent triazine frameworks for high-efficient removal of anion dyes and the synergistic adsorption of cationic dyes.

Efficient adsorption of organic dyes from effluent has great importance for ecological and environmental protection. Herein, covalent triazine frameworks (CTFs) were constructed via the polycondensation of melamine and cyanuric chloride directly. Due to the numerous basic nitrogen atoms as high as 58.98 wt%, high BET surface area (670.2 m g-1), and hierarchical pore structure, CTFs demonstrated selective adsorption of anionic dyes in high capacity (e.g., a maximum adsorption capacity of 1581 mg g-1 for Congo red at 30 °C). The mechanism of the outstanding adsorption performance was carefully verified and ascribed to the electrostatic attraction and hydrogen bonding between CTFs and anionic dyes. The amine groups linking two adjacent triazine rings have primary responsibility for the superior performance. Unexpectedly, CTFs expressed a tuning synergetic effect for removing cationic dyes in aqueous solution coexisting with anionic dyes, exhibiting a great superiority in the specific and comprehensive treatment of organic dyes contaminated water. Furthermore, CTFs were stable and had long-periodic availability for more than 6 times, ensuring the adsorption rate higher than 90%. For better operation, hybrid monolithic aerogels were constructed by incorporating CTFs into polyvinylidene fluoride then casting in melamine resin foams. The obtained aerogels expressed high-efficient removal of anionic dyes coupled with convenient operation. This well-established metal-free porous material is a promising adsorbent candidate for anionic dyes selectively and even synergetic adsorption of cationic dyes in water remediation.

Diels-Alder Conversion of Acrylic Acid and 2,5-Dimethylfuran to para-Xylene Over Heterogeneous Bi-BTC Metal-Organic Framework Catalysts Under Mild Conditions.

The heterogeneous metal-organic framework Bi-BTC successfully catalyzed the synthesis of para-xylene from bio-based 2,5-dimethylfuran and acrylic acid in a promising yield (92 %), under relatively mild conditions (160 °C, 10 bar), and with a low reaction-energy barrier (47.3 kJ mol-1 ). The proposed reaction strategy also demonstrates a remarkable versatility for furan derivatives such as furan and 2-methylfuran.