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Background: The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) utilizes the Transposase Tn5 to probe open chromatic, which simultaneously reveals multiple transcription factor binding sites (TFBSs) compared to traditional technologies. Deep learning (DL) technology, including convolutional neural networks (CNNs), has successfully found motifs from ATAC-seq data. Due to the limitation of the width of convolutional kernels, the existing models only find motifs with fixed lengths. A Graph neural network (GNN) can work on non-Euclidean data, which has the potential to find ATAC-seq motifs with different lengths. However, the existing GNN models ignored the relationships among ATAC-seq sequences, and their parameter settings should be improved. Results: In this study, we proposed a novel GNN model named GNNMF to find ATAC-seq motifs via GNN and background coexisting probability. Our experiment has been conducted on 200 human datasets and 80 mouse datasets, demonstrated that GNNMF has improved the area of eight metrics radar scores of 4.92% and 6.81% respectively, and found more motifs than did the existing models. Conclusions: In this study, we developed a novel model named GNNMF for finding multiple ATAC-seq motifs. GNNMF built a multi-view heterogeneous graph by using ATAC-seq sequences, and utilized background coexisting probability and the iterloss to find different lengths of ATAC-seq motifs and optimize the parameter sets. Compared to existing models, GNNMF achieved the best performance on TFBS prediction and ATAC-seq motif finding, which demonstrates that our improvement is available for ATAC-seq motif finding. [ABSTRACT FROM AUTHOR]

Previous studies on environmental restorative effects have mainly focused on visual landscapes, and less on the influence of soundscapes on restorative, but soundscapes play a crucial role in restorative environments, especially rural soundscapes, but there is insufficient existing theoretical evidence on the subject. Therefore, this study aims to investigate the influence of Rural Soundscape Perception on Environmental Restoration Perception, and introduces two affective variables, tourism nostalgia and place attachment, to explore the mechanism of Rural Soundscape Perception on Environmental Restoration Perception, as well as the moderating role of the number of trips is also discussed. Based on the theory of restorative environment, this study took the Taohuayuan Scenic Spot in Changde, Hunan Province, China, as the case site, and selected the rural soundscape in the area as the research object; a total of 506 valid data were collected through questionnaire surveys, and structural equation modeling was used to validate the collected data. It was found that rural soundscape perception had a significant positive effect on tourism nostalgia, place attachment, and environmental restoration perception. The results also showed that tourism nostalgia and place attachment mediated the relationship between rural soundscape perception and environmental restoration perception. Additionally, the results revealed that the number of trips did not play a moderating role in the structural relationship between rural soundscape perception and environmental restoration perception. Last, the results of the study shed light on the complex influence path of "rural soundscape perception→tourism nostalgia→place attachment→environmental restoration perception", which provides a new perspective for understanding the mechanism of the rural environment to people's health, and also has a certain guiding significance for the landscape planning of rural tourism sites. [ABSTRACT FROM AUTHOR]

Stable and radiogenic isotopes represent powerful tools for reconstructing ecological and environmental patterns in ancient ecosystems. The Cretaceous of North America preserves a diverse record of fossil vertebrates well‐suited to analysis using these proxies, contained within many well‐sampled and stratigraphically well‐characterized intervals. Multiple hypotheses have been offered to explain the diverse assemblages of megaherbivores that co‐occurred in the relatively restricted available landmass here, including various forms of niche‐partitioning related to habitat preference, dietary specialization and feeding height stratification. Here we analyse the 87Sr/86Sr, δ13C and δ18O of bioapatite samples obtained from a range of herbivores, faunivores and endemic taxa, from a spatiotemporally‐constrained and intensively‐sampled site in the upper Oldman Formation, to test if megaherbivores partitioned their niches based on spatial patterns of occupation and resource‐use. We also compare measured strontium values to regional 87Sr/86Sr data to assess biogeographical range sizes, habitat breadth and migration potential. We find that hadrosaurs had broad ranges, whereas ankylosaurs and ceratopsids were more spatially restricted. The 87Sr/86Sr ranges of hadrosaurs are much wider and do not overlap with those of other ornithischians, potentially related to dietary differences driven by a combination of feeding height‐stratification and habitat breadth differences. Ankylosaurs and ceratopsids overlapped extensively in 87Sr/86Sr, δ13C and δ18O, indicating overlap in the same habitats and intake of similar resources, and suggesting more complex spatiotemporal variation in resource‐use patterns, fine‐scale dietary differences, and/or sufficient resource‐availability to reduce the degree of competition given this theoretical niche overlap. Additional analyses integrating ecomorphological proxies may elucidate these patterns further. [ABSTRACT FROM AUTHOR]

Identifying cis -regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear and systematic assessment, users will struggle to choose the most appropriate tool for their specific studies. In this manuscript, we evaluated 20 DL methods for cis -regulatory motif prediction using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq and 55 RNA CLIP-seq data. Four metrics were investigated, including the accuracy of motif finding, the performance of DNA/RNA sequence classification, algorithm scalability and tool usability. The assessment results demonstrated the high complementarity of the existing DL methods. It was determined that the most suitable model should primarily depend on the data size and type and the method's outputs. [ABSTRACT FROM AUTHOR]

Motivation Transcription factor binding sites (TFBSs) prediction is a crucial step in revealing functions of transcription factors from high-throughput sequencing data. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) provides insight on TFBSs and nucleosome positioning by probing open chromatic, which can simultaneously reveal multiple TFBSs compare to traditional technologies. The existing tools based on convolutional neural network (CNN) only find the fixed length of TFBSs from ATAC-seq data. Graph neural network (GNN) can be considered as the extension of CNN, which has great potential in finding multiple TFBSs with different lengths from ATAC-seq data. Results We develop a motif predictor called MMGraph based on three-layer GNN and coexisting probability of k-mers for finding multiple motifs from ATAC-seq data. The results of the experiment which has been conducted on 88 ATAC-seq datasets indicate that MMGraph has achieved the best performance on area of eight metrics radar score of 2.31 and could find 207 higher-quality multiple motifs than other existing tools. Availability and implementation MMGraph is wrapped in Python package, which is available at https://github.com/zhangsq06/MMGraph.git Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Promoters are DNA non-coding regions around the transcription start site and are responsible for regulating the gene transcription process. Due to their key role in gene function and transcriptional activity, the prediction of promoter sequences and their core elements accurately is a crucial research area in bioinformatics. At present, models based on machine learning and deep learning have been developed for promoter prediction. However, these models cannot mine the deeper biological information of promoter sequences and consider the complex relationship among promoter sequences. In this work, we propose a novel prediction model called PromGER to predict eukaryotic promoter sequences. For a promoter sequence, firstly, PromGER utilizes four types of feature-encoding methods to extract local information within promoter sequences. Secondly, according to the potential relationships among promoter sequences, the whole promoter sequences are constructed as a graph. Furthermore, three different scales of graph-embedding methods are applied for obtaining the global feature information more comprehensively in the graph. Finally, combining local features with global features of sequences, PromGER analyzes and predicts promoter sequences through a tree-based ensemble-learning framework. Compared with seven existing methods, PromGER improved the average specificity of 13%, accuracy of 10%, Matthew's correlation coefficient of 16%, precision of 4%, F1 score of 6%, and AUC of 9%. Specifically, this study interpreted the PromGER by the t-distributed stochastic neighbor embedding (t-SNE) method and SHAPley Additive exPlanations (SHAP) value analysis, which demonstrates the interpretability of the model. [ABSTRACT FROM AUTHOR]

Background: Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths. GNN-based methods has the limitation of using the edge weight information directly, makes it difficult to aggregate the neighboring nodes' information more efficiently when representing node embedding. Results: To address this challenge, we developed a novel graph attention network framework named MMGAT, which employs an attention mechanism to adjust the attention coefficients among different nodes. And then MMGAT finds multiple ATAC-seq motifs based on the attention coefficients of sequence nodes and k-mer nodes as well as the coexisting probability of k-mers. Our approach achieved better performance on the human ATAC-seq datasets compared to existing tools, as evidenced the highest scores on the precision, recall, F1_score, ACC, AUC, and PRC metrics, as well as finding 389 higher quality motifs. To validate the performance of MMGAT in predicting TFBSs and finding motifs on more datasets, we enlarged the number of the human ATAC-seq datasets to 180 and newly integrated 80 mouse ATAC-seq datasets for multi-species experimental validation. Specifically on the mouse ATAC-seq dataset, MMGAT also achieved the highest scores on six metrics and found 356 higher-quality motifs. To facilitate researchers in utilizing MMGAT, we have also developed a user-friendly web server named MMGAT-S that hosts the MMGAT method and ATAC-seq motif finding results. Conclusions: The advanced methodology MMGAT provides a robust tool for finding ATAC-seq motifs, and the comprehensive server MMGAT-S makes a significant contribution to genomics research. The open-source code of MMGAT can be found at https://github.com/xiaotianr/MMGAT, and MMGAT-S is freely available at https://www.mmgraphws.com/MMGAT-S/. [ABSTRACT FROM AUTHOR]

Abstract When, how, and following which paths hominins created the innovations that allowed them to colonize regions of the planet that were not suited to their thermal physiology is still a matter of inquiry. In this paper, we elaborate a theoretical framework to investigate the origin and diversification of bone needles, summarize the evidence for their emergence, create a large database of their morphometric and stylistic characters, and present results of the study of an exceptionally well-preserved collection of needles from Shuidonggou Locality 12 (SDG12), a site located in the Ningxia Hui Autonomous Region, Northern China, dated to ca. 11.2 ka BP. Bone needles are reported from 271 sites and 355 archaeological layers. Revision of the evidence shows they represent an original cultural innovation that emerged in Eurasia between 45–40 ka BP. Size differences between the earliest known specimens, found in Siberia and China, indicate needles may have been invented independently in these two regions. Needles from Eastern Europe may represent either an independent invention or a geographic extension of earlier Siberian and Caucasian sewing traditions. In Western Europe, needles appear during the Solutrean. The wider size range characteristic of Magdalenian specimens supports the idea that needles of different sizes were used in a variety of tasks. In China, the robust sub-circular needles found at sites dated between 35–25 ka BP are followed, between 26–23 ka BP, by small flat needles, which may represent an innovation associated with the microblades/microcores toolkit. At SDG12, technological, functional, and morphometric analyses of finished needles and manufacturing by-products identify two previously undetected reduction sequences for the production of needles of different size and, probably, function. The bone needles found at Paleoindian sites are the smallest and reflect a never previously achieved mastery in the production of such tools. [ABSTRACT FROM AUTHOR]

Liquefaction residue of Heishan bituminous coal (HLR) was subject to two hydroconversion reactions under 5 MPa initial pressure of hydrogen at 300°C for 3 h, without catalyst and with acid supported catalyst (ASC), respectively. The reaction products were analyzed with gas chromatography/mass spectrometer (GC/MS). The results show that 222 organic compounds were detected totally in the products and they can be divided into alkanes, aromatic hydrocarbons (AHCs), phenols, ketones, ethers, and other species (OSs). The yield of hydroconversion over the ASC is much higher than that without catalyst. The most abundant products are aromatic hydrocarbons in the reaction products from both catalytic and noncatalytic reactions of HLR. The yield of aromatic hydrocarbons in the reaction product from hydroconversion with the ACS is considerably higher than that from hydroconversion without a catalyst. [ABSTRACT FROM AUTHOR]

Plants play a very important role in landscape construction. In order to explore whether different living environment will affect people's preference for the structural features of plant organs, this study examined 26 villagers and 33 college students as the participants, and pictures of leaves, flowers and fruits of plants as the stimulus to conduct eye-tracking and EEG detection experiments. We found that eye movement indicators can explain people's visual preferences, but they are unable to find differences in preferences between groups. EEG indicators can make up for this deficiency, which further reveals the difference in psychological and physiological responses between the two groups when viewing stimuli. The final results show that the villagers and the students liked leaves best, preferring aciculiform and leathery leaves; solitary, purple and capitulum flowers; and medium-sized, spathulate, black and pear fruits. In addition, it was found that the overall attention of the villagers when watching stimuli was far lower than that of the students, but the degree of meditation was higher. With regard to eye movement and EEG, the total duration of fixations is highly positively correlated with the number of fixations, and the average pupil size has a weak negative correlation with attention. On the contrary, the average duration of fixations has a weak positive correlation with meditation. Generally speaking, we believe that Photinia×fraseri, Metasequoia glyptostroboides, Photinia serratifolia, Koelreuteria bipinnata and Cunninghamia lanceolata are superior landscape building plants in rural areas and on campuses; Pinus thunbergii, Myrica rubra, Camellia japonica and other plants with obvious features and bright colours are also the first choice in rural landscapes; and Yulania biondii, Cercis chinensis, Hibiscus mutabilis and other plants with simple structures are the first choice in campus landscapes. This study is of great significance for selecting plants for landscape construction and management according to different environments and local conditions. [ABSTRACT FROM AUTHOR]

One of the key factors to improve electrochemical properties is to find exceptional electrode materials. In this work, the nickel-cobalt layered double hydroxide (CNT@CoS/NiCo-LDH) with the structure of a hollow nanocage was prepared by etching CNT@CoS with zeolitic imidazolate framework-67 (ZIF-67) as a template. The results show that the addition of nickel has a great influence on the structure, morphology and chemical properties of materials. The prepared material CNT@CoS/NiCo-LDH-100 (C@CS/NCL-100) inherited the rhombic dodecahedral shape of ZIF-67 well and the CNTs were evenly interspersed among the rhombic dodecahedrons. The presence of CNTs improved the conductivity and surface area of the samples. The C@CS/NCL-100 demonstrates a high specific capacitance of 2794.6 F·g−1 at 1 A·g−1. Furthermore, as an assemble device, the device of C@CS/NCL-100 as a positive electrode exhibits a relatively high-energy density of 35.64 Wh·kg−1 at a power density of 750 W·kg−1 Further, even at the high-power density of 3750 W·kg−1, the energy density can still retain 26.38 Wh·kg−1. Hence, the superior performance of C@CS/NCL-100 can be ascribed to the synergy among CNTs, CoS and NiCo LDH, as well as the excellent three-dimensional structure obtained by used ZIF-67 as a template. [ABSTRACT FROM AUTHOR]

Glioma is the main type of malignant brain tumor in adults, and the status of isocitrate dehydrogenase (IDH) mutation highly affects the diagnosis, treatment, and prognosis of gliomas. Radiographic medical imaging provides a noninvasive platform for sampling both inter and intralesion heterogeneity of gliomas, and previous research has shown that the IDH genotype can be predicted from the fusion of multimodality radiology images. The features of medical images and IDH genotype are vital for medical treatment; however, it still lacks a multitask framework for the segmentation of the lesion areas of gliomas and the prediction of IDH genotype. In this paper, we propose a novel three-dimensional (3D) multitask deep learning model for segmentation and genotype prediction (SGPNet). The residual units are also introduced into the SGPNet that allows the output blocks to extract hierarchical features for different tasks and facilitate the information propagation. Our model reduces 26.6% classification error rates comparing with previous models on the datasets of Multimodal Brain Tumor Segmentation Challenge (BRATS) 2020 and The Cancer Genome Atlas (TCGA) gliomas' databases. Furthermore, we first practically investigate the influence of lesion areas on the performance of IDH genotype prediction by setting different groups of learning targets. The experimental results indicate that the information of lesion areas is more important for the IDH genotype prediction. Our framework is effective and generalizable, which can serve as a highly automated tool to be applied in clinical decision making. [ABSTRACT FROM AUTHOR]

The spin symmetry in the Dirac sea has been investigated with relativistic Brueckner–Hartree–Fock theory using the bare nucleon–nucleon interaction. Taking the nucleus 16 O as an example and comparing the theoretical results with the data, the definition of the single-particle potential in the Dirac sea is studied in detail. It is found that if the single-particle states in the Dirac sea are treated as occupied states, the ground state properties are in better agreement with experimental data. Moreover, in this case, the spin symmetry in the Dirac sea is better conserved and it is more consistent with the findings using phenomenological relativistic density functionals. [ABSTRACT FROM AUTHOR]

A systematic and specific pattern due to the effects of the tensor forces is found in the evolution of spin–orbit splittings in neutron drops. This result is obtained from relativistic Brueckner–Hartree–Fock theory using the bare nucleon–nucleon interaction. It forms an important guide for future microscopic derivations of relativistic and nonrelativistic nuclear energy density functionals. [ABSTRACT FROM AUTHOR]

Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) is a member of the TNF superfamily and plays multifunctional roles in the immune system. In the present study, a homolog of TRAIL from the Mongolian horse (named ecTRAIL) was identified and characterized. The 870-bp open reading frame encodes a polypeptide of 289 amino acid residues with a predicted molecular weight of 33.47 kDa and p I of 8.47. The genomic structure of ecTRAIL shares a five-exon/six-intron arrangement similar to its orthologs. Multiple alignments show that ecTRAIL is a type II transmembrane protein with a typical transmembrane region, three conserved cysteine residues (Cys56, Cys77, Cys238) and a TNF family signature sequence ([LV]-x-[LIVM]-x(3)-G-[LIVMF]-Y-[LIVMFY](2)-x(2)-[QEKHL]-[LIVMGT]-x-[LIVMFY]). Three-dimensional structure prediction based on the same template revealed that the positional arrangement of the key amino acid residues, Cys238 and Cys230 in ecTRAIL and human TRAIL, respectively, is significantly conserved. Evolutionary analysis suggests that ecTRAIL is most closely related to its ortholog from pigs, with an identity of 83.99%. The solubilizing small ubiquitin-related modifier (SUMO) tag fused recombinant protein SUMO-ecsTRAIL was successfully expressed in E. coli and exhibited binding activity and cytotoxicity to HeLa cells in a cross-species manner in vitro . These results provide a better understanding of TRAILs in mammals and indicate that ecTRAIL may play an important role in the immune response in horses. [ABSTRACT FROM AUTHOR]

B-cell activating factor of the TNF family (BAFF) has been documented to act as a critical factor in the development of aggressive B lymphocytes and autoimmune diseases. However, the effect of various cytokines on BAFF-elicited neoplastic B-lymphoid cells is not known. In this study, we exhibited that administration of human soluble BAFF (hsBAFF), IL-2, IL-4, IFN-γ, or TNF-α alone increased cell viability and survival in Raji cells concentration-dependently, yet a more robust viability/survival was seen in the cells co-treatment of IL-2, IL-4, IFN-γ, or TNF-α with hsBAFF, respectively. Further research revealed that both Erk1/2 and S6K1 signaling pathways were essential for IL-2, IL-4, IFN-γ, or TNF-α enhancement of the viability/survival in the hsBAFF-stimulated cells, as inhibition of Erk1/2 with U0126 or down-regulation of Erk1/2, or blockage of S6K1 with rapamycin or silencing S6K1, or silencing S6K1/Erk1/2, respectively, reduced the cell viability/survival in the cells treated with/without hsBAFF ± IL-2, IL-4, IFN-γ, or TNF-α. These findings indicate that IL-2, IL-4, IFN-γ or TNF-α enhances BAFF-stimulated cell viability/survival by activating Erk1/2 and S6K1 signaling in neoplastic B-lymphoid cells. Our data suggest that modulation of IL-2, IL-4, IFN-γ and/or TNF-α levels, or inhibitors of Erk1/2 or S6K1 may be a new approach to prevent BAFF-induced aggressive B-cell malignancies. [ABSTRACT FROM AUTHOR]

B-cell activating factor (BAFF) is involved in not only physiology of normal B cells, but also pathophysiology of aggressive B cells related to malignant and autoimmune diseases. Rapamycin, a lipophilic macrolide antibiotic, has recently shown to be effective in the treatment of human lupus erythematosus. However, how rapamycin inhibits BAFF-stimulated B-cell proliferation and survival has not been fully elucidated. Here, we show that rapamycin inhibited human soluble BAFF (hsBAFF)-induced cell proliferation and survival in normal and B-lymphoid (Raji and Daudi) cells by activation of PP2A and inactivation of Erk1/2. Pretreatment with PD98059, down-regulation of Erk1/2, expression of dominant negative MKK1, or overexpression of wild-type PP2A potentiated rapamycin's suppression of hsBAFF-activated Erk1/2 and B-cell proliferation/viability, whereas expression of constitutively active MKK1, inhibition of PP2A by okadaic acid, or expression of dominant negative PP2A attenuated the inhibitory effects of rapamycin. Furthermore, expression of a rapamycin-resistant and kinase-active mTOR (mTOR-T), but not a rapamycin-resistant and kinase-dead mTOR-T (mTOR-TE), conferred resistance to rapamycin's effects on PP2A, Erk1/2 and B-cell proliferation/viability, implying mTOR-dependent mechanism involved. The findings indicate that rapamycin inhibits BAFF-stimulated cell proliferation/survival by targeting mTOR-mediated PP2A-Erk1/2 signaling pathway in normal and neoplastic B-lymphoid cells. Our data highlight that rapamycin may be exploited for preventing excessive BAFF-induced aggressive B-cell malignancies and autoimmune diseases. [ABSTRACT FROM AUTHOR]

Support vector clustering (SVC) is a boundary-based algorithm, which has several advantages over other clustering methods, including identifying clusters of arbitrary shapes and numbers. Leveraged by the high generalization ability of the large margin distribution machine (LDM) and the optimal margin distribution clustering (ODMC), we propose a new clustering method: minimum distribution for support vector clustering (MDSVC), for improving the robustness of boundary point recognition, which characterizes the optimal hypersphere by the first-order and second-order statistics and tries to minimize the mean and variance simultaneously. In addition, we further prove, theoretically, that our algorithm can obtain better generalization performance. Some instructive insights for adjusting the number of support vector points are gained. For the optimization problem of MDSVC, we propose a double coordinate descent algorithm for small and medium samples. The experimental results on both artificial and real datasets indicate that our MDSVC has a significant improvement in generalization performance compared to SVC. [ABSTRACT FROM AUTHOR]

Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA–protein interactions through experimental methods is expensive and time-consuming, we propose a novel method based on deep learning that combines raw sequence composition features, hand-designed features and structure features, called LGFC-CNN, to predict lncRNA–protein interactions. The two sequence preprocessing methods and CNN modules (GloCNN and LocCNN) are utilized to extract the raw sequence global and local features. Meanwhile, we select hand-designed features by comparing the predictive effect of different lncRNA and protein features combinations. Furthermore, we obtain the structure features and unifying the dimensions through Fourier transform. In the end, the four types of features are integrated to comprehensively predict the lncRNA–protein interactions. Compared with other state-of-the-art methods on three lncRNA–protein interaction datasets, LGFC-CNN achieves the best performance with an accuracy of 94.14%, on RPI21850; an accuracy of 92.94%, on RPI7317; and an accuracy of 98.19% on RPI1847. The results show that our LGFC-CNN can effectively predict the lncRNA–protein interactions by combining raw sequence composition features, hand-designed features and structure features. [ABSTRACT FROM AUTHOR]

It is important to understand the mechanism of toluene absorption by phosphonium ionic liquids (PILs) for volatile organic compound removal with ionic liquids. In this work, the molecular structures of trimethyl(butyl)phosphonium tetrafluoroborate ([P 1114 [BF 4), trimethyl(butyl)phosphonium bis(trifluoromethylsulfonyl)imide ([P 1114 [Tf 2 N]), tributyl(propyl)phosphonium tetrafluoroborate ([P 4443 [BF 4) and tributyl(propyl)phosphonium bis(trifluoromethylsulfonyl)imide ([P 4443 [Tf 2 N]) were optimized by quantum chemical calculations at the GGA/PW91 level with the DNP basis set of density functional theory (DFT). The most stable configurations for toluene absorption by these PILs with multiple sites were also obtained by calculation under the same conditions. The main parameters for the most stable structure, data for the Mulliken charge and frontier molecular orbitals were further determined and discussed. It was found that the interactions of PILs with toluene are mainly composed of hydrogen bonds, C-H···π bond interactions and electrostatic attraction, and most of the calculated frontier molecular orbitals are concentrated on the toluene molecule. Additionally, analysis showed that [BF 4 − is inclined to form hydrogen bonds with cations and the methyl groups of toluene, and [Tf 2 N]− tends to attract toluene via electrostatic attraction. Investigation of the absorption energy with multiple sites indicated that PILs have great performance for toluene absorption. Analysis of the ionic size and geometric structure of the PILs demonstrated that the space available for accommodating toluene is an important condition for toluene absorption and is mainly provided by cations. In particular, PILs with cations having suitable long alkyl side chains show excellent capacity for toluene absorption. Moreover, NBO and AIM analyses confirmed the existence of weak closed-shell interactions (hydrogen bonds and C-H···π bonds) between toluene and PILs. The experimental results obtained for the static toluene absorption by the PILs provide a great supplement and confirmation of the calculations [Display omitted] • Structure of phosphonium cation is conducive for multiple sites absorption. • Space for accommodating toluene is mainly provided by phosphonium cation. • Suitable long alkyl chain is necessary for the high toluene absorption. • Static toluene absorption amount for [P 8883 [BF 4 is as high as 2840 mg/g. [ABSTRACT FROM AUTHOR]

Toluene absorption by the ion pairs of 1-butyl-3-methylimidazolium bis((trifluoromethyl)sulfonyl)imide ([Bmim][TFSI]), 1-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF 4) and tributyl(propyl)phosphonium tetrafluoroborate ([P 4443 [BF 4) is performed at the GGA/PW91 level using a density functional theory (DFT). The ion pairs of [Bmim][TFSI], [Bmim][BF 4 and [P 4443 [BF 4 are stable with five, five and six hydrogen bonds between the anions and cations, respectively. There is no hydrogen bond and just one C–H⋯π bond between toluene and [Bmim][TFSI], while two hydrogen bonds and three C–H⋯π bonds are formed between absorbed toluene and [Bmim][BF 4 or [P 4443 [BF 4. That shows the interaction between the ion pairs of [Bmim][BF 4 and [P 4443 [BF 4 with toluene is stronger than that of [Bmim][TFSI]. Moreover, [P 4443 + has greater effect on the charge transfer of toluene than [Bmim]+ based on the higher electrostatic force with toluene, which indicates that [P 4443 [BF 4 has better advantage on toluene absorption than [Bmim][BF 4. The absorption energy (Eabs, in kJ·mol-1) between toluene and ion pairs of [Bmim][TFSI], [Bmim][BF 4 and [P 4443 [BF 4 is −28.88 kJ mol−1, -34.13 kJ mol−1 and -39.38 kJ mol−1, respectively. The frontier molecular orbital (FMO) analysis indicates that absorption of toluene by ionic liquids is a physical process. The HOMO and LUMO of [P 4443 [BF 4 ·C 7 H 8 are all localized in toluene, but the LUMO of [Bmim][BF 4 ·C 7 H 8 is composed by the atoms of imidazolium ring in cation, which also shows that the interaction between toluene and [P 4443 [BF 4 is stronger. In the simulation study, the molecular insight into mechanism of toluene absorption by imidazolium and phosphonium ionic liquids is provided by the comprehension including hydrogen bond, C–H⋯π bonds, electrostatic force and the frontier molecular orbital, which is fundamental for industrial application of ionic liquids in toluene separation. • [P 4443 + charge transfer to toluene is larger than [Bmim]+. • HOMO and LUMO of [P 4443 [BF 4 ·C 7 H 8 are all concentrated in toluene. • Hydrogen bond and C–H⋯π bond are all present in ILs absorption of toluene. [ABSTRACT FROM AUTHOR]

Adsorption and diffusion of water vapor in phosphonium ionic liquid modified silica gel were studied, aiming to reduce the loading of water vapor in porous materials. The modified silica gel was prepared through a grafting method and characterized by FTIR, thermal gravity analysis and X-ray photoelectron spectroscopy. N2 sorption isotherms at −196 °C and CO2 sorption isotherms at 0 °C were also measured to analyzee the porosity. Water vapor adsorption equilibriums at 25 °C up to 30 mbar were tested. The results indicate that the ionic liquids (ILs) phase acts as a protecting film which decreases water vapor adsorption. The improvement of water-resistant performance is also attributed to the decrease of micro-porosity and silanol groups on the silica surface. Diffusion behavior of water vapor on modified silica was determined on the basis of the adsorption equilibrium. The effective diffusivity of water vapor in modified silica is almost the same as in bare silica and decreases with the increasing of water vapor loading. [ABSTRACT FROM AUTHOR]